I also thank ODC for giving me an opportunity to talk in this. I hope I'm audible, right? Okay, maybe I can't hear you. That's why. Sorry for that. I'm Venkat Ramanjay. I'm a senior software engineer in Metapack. I'm also pursuing my masters in the University of London in the area of NLP and machine learning. The presentation today is about detection and classification of fake news using convolutional neural networks. If I'm fast or something in the middle, please raise your hands. Okay, I'll slow down. The outline is the section one, we talk why fake news is more important now, what are the implications. And the section two, so this is my own work. So I've done end to end, so this is my own masters project actually. So I'll go through the network architecture I used, the deep learning approaches to combat fake news. And in the section three, we'll have the results summary. And if you have time, we'll take some questions. Okay, what is fake news? This is a definition from Wikipedia, nothing new actually.  Now, there are different reasons why people create fake news. It could be financially or it could be business wise. There are multiple reasons. It's quite hot topic at the moment in the deep learning world. Companies like Google and Facebook are trying to productize the fake news identification or the classification models. It's because the online media is more important for those companies to get advertisement revenues. And if people don't trust in the online media, the advertisement revenues for Facebook and Google will go down. So it's very important for those companies to productize these models. Just a general thing about this social media to be blamed for the spread of fake news. Because in social media, people actually like and share whatever their friends share. They don't care actually if it's trusted or anything. They just share because sharing is quite easy and you like. And the more you share and the more you like, Facebook ranks the page higher, article higher. So if it's a fake news and if you like and share, Facebook actually will put it on the top of news feed. So when I did my research, actually I found out in 2010, Morgan Freeman was declared dead. According to some tweets and it's published in CNN actually. Later CNN had to report it's a fake news. And I posted a site that's from snopes.com. They are a fact-checking sites actually. They fact-checked that and then they informed CNN that Morgan Freeman wasn't dead. In fact, even Jackie Chan was declared dead. A data set or corpus exploration. So in the work I did, it's very challenging to get the... Sorry. Oh, it's gone. Sorry. So the data set is very challenging. It's because even humans find it difficult to identify what is fake and what is not fake. And if you want to treat a machine to identify what is fake, you need a human intervention to annotate the data. So the labeling of the data was done manually in my case. But I also used a Chrome plugin called a Bullshit Detector. I'm not sure if you guys heard actually.  Don't just read. It's a nice plugin if you guys want to use actually. It's part of the Chrome. So I also took some data sets published on Kaggle and GitHub. Kaggle is a data size competition site. I mean it's good to trust because it's bought over by Google. So there's been a curation of the data sets published in Kaggle. A lot of PhD guys have published the data sets on GitHub. If you guys want to start identifying some things, you can use GitHub. Most important, I think 80% of the data scientists spend their time in data set cleaning and preparation. And they keep mourning about, okay, I have to do all the cleaning and everything. So as I said, I also spend most amount of time in preparing the data for the learning algorithm. I used a wide variety of NLP techniques. One is the stopwatch removal and then padding the documents to be of the same length, drop missing columns. I forgot to mention actually, I also used some entity extraction, which is even used in the previous one. So you can do clustering, you can do whatever you want. Once you have the documents, you can do clustering and then take only those documents according to some labels. I manually took all the documents. Okay, deep learning approaches. Now, I model the fake news identification as a binary classification problem, which means it's a function. It's defined as function of E where one means if the piece of news is a fake news, zero is otherwise. Now, the question is why deep learning? Because the baseline classifiers in the text classification world, the native base and support vector machines have done extremely well on the stance classification, sentimental analysis, and the text classification techniques. But I personally found if you want to use these baseline classifiers, you have to use a bag of words model, which is just a one-hot vector encoding as possible, or you'll use a TFIDF. Well, then you have to do the feature engineering, the dimensionality reduction, as well as the feature extraction. So I went ahead with some deep learning approaches and traditional models does not capture semantics in text. When I said traditional model means the bag of words model and the TFIDF scheme, it doesn't capture the semantics in text, which means words with similar meaning appear together in a similar concept and must have the same representation. This is very key, right? If you have a word which could mean differently in a different context, but if they appear together, that means they are slightly similar to each other. That's what the embeddings come to. That's called the word embeddings, and that was actually done by Thomas Milkoff from Google. He was the first guy who came up with this algorithm, and he called it as word-to-vec. Word-to-vec is an algorithm which will actually vectorize a word in terms of similarity, which is the words which are very similar to each other. They are placed in a high-dimensional space very similarly. If you have a document like, I am Venkat, and if Venkat is even in another document, the Venkat in both the documents will have similar representations. That's the n-gram models, and n-gram model is nothing but the language models, actually. We have been hearing about language models since morning. That's nothing but the n-gram model, and we have by-gram and trigrams. In the keynote, actually, he did mention about by-gram model and trigram models. They are all probabilistic language models. They are all trained on a neural network with a two-layer neural network, and they are all probabilistic language models. They are actually used in the vectorization. Now, the key difference between a baseline classifier and a deep learning approach is this word embeddings. If we don't capture words which are similar to each other with the same representation, then we are missing the semantics of the text, because in text documents, semantics matters. Where the word occurs in the document is extremely important than just assigning a unique integer to a word in the document. Facebook, actually, two years back, they released a model called Fast Text, which is, again, developed by Thomas Milko after he moved to Facebook from Google. But the difference between Fast Text and Word2Vag is that the Fast Text operates on a character-level n-grams, so you can have a two-level n-gram, which is like a two-character level n-gram. The glow vectors is from guys from Carnegie Mellon and Stanford University. You can use any of the vectorization mechanisms, but I personally choose Fast Text in this case. Convolutional neural networks in text classifications. We all know convolutional neural networks. They are the state out of the art in the computer vision area. They have proven really well in the video analytics and the image recognition. Two years or three years back, there was a guy called Kim Yoon. I still have the paper. Actually, he published a paper on convolutional neural networks for stance classification, and that triggered the convolutional neural nets for text processing. Until then, even, you know, we heard in the keynote that recurrent neural networks are best for text. Until that paper came out, actually, people never thought CNN would be fit for text processing. After that, even actually, as the keynote speaker mentioned, even Google, actually, they're productizing CNN for their text classifications now. Well, I hope actually everybody has a fair understanding of convolutional neural networks. It's got a convoluted layers, pooling layers, and fully connected layers. This is very different from a multi-layer perceptron, which is a feed-forward neural network. How does CNN fit for text and NLP? The key thing in computer vision is that the images have a spatial structure. That's the key thing which CNN tries to maintain. So even text, they have some spatial structure and orientation, right? It's not that the words just appear together. They appear because they have some context, and you need to capture that context. That's the key thing. That's why the CNN, the convoluted layers, they try to maintain the spatial structure, which is one-dimensional in case of text. If you're running computer vision, it's actually two-dimensional. In computer vision, for the spatial structure, it's made in two-dimensional, but in a text, it's one-dimensional. Feature extraction from text is effective using convoluted layers. This is the key difference for deep learning, right? The feature engineering is done automatically by the neural networks. You don't need a feature engineering team. Of course, you need to do some kind of feature engineering, like annotating labels and things, but you don't need to explicitly tell the algorithm which feature you need to take it. The convoluted neural network layers will learn, actually, from the data what are the best features to be used for the classification. And also, I'm sure you must be knowing that neural networks, actually, they are good for identifying the global maxima, because even in any data set, you want to find what's the global maxima rather than a global minima. They actually minimize an objective function, and they are used for finding... So the global feature extraction using global max pooling ID function will help you to find out what's the maximum best feature out of your data set features. And through this case, actually, you reduce the dimensionality, you convert the word to vectors using fast text, and then you apply these convoluted layers, and you try to reduce the dimensionality as much as possible so that the training time is pretty less. In my case, actually, I trained on a gigabyte of data, but people actually, in production, they're trained on a terabyte of data, actually. They just train huge, huge amount of data size. Okay. This is the network architecture which I built for my project. I mean, this is not nothing new, actually. It's a convolutional neural network. The input layer is a document vector that, of course, everybody knows like the neural networks take integers as inputs. It doesn't take words. So the document vector for each document has to be of same length. Otherwise, the neural network will not work properly. I use the pre-trained embeddings. Now, pre-trained embeddings in the machine learning world is called transfer learning. Why is it called transfer learning? That's because Google and Facebook, they have actually built a model on a separate set of data. I think Google built it on their Google news corpus site and Facebook built it on Wiki and you transfer the knowledge from that pre-trained one onto your data set. Assume, if not for this, I'll be just creating the training, the embeddings for hours together or maybe days together, right? Now, those companies actually have open-source these models. It helps us to do the transfer learning. So the pre-trained is the key there. The next layer in the neural network is a convolutional 1D layer. Well, that's where you start the feature extraction. It's just mentioned windows and filters. It's not so straightforward because you do a lot of iterations and then find out what's the best optimal window size and the filter size you have to use in your problem. In my case, actually, I started with 128 as the window size and then 5 actually as the filter size. And then the next layer in the neural network is the max pooling layer. The max pooling layer actually will try to, within the small window size, it will try to find out what's the best feature to extract. So convolutional neural networks work that way. It takes a square of images like from your data set and it tries to find out what's the best feature you can extract. You can actually keep adding layers and layers and that becomes a really, really big, deep neural network. If you just add more and more layers, if you add more convolutional 1D layer after this, if you keep order after this, then it becomes really, really big. I think DeepMind, I'm not sure if you guys know actually, they did something on the medical image things. They used to identify medical documents actually. I think they had some hundreds and thousands of layers with actually millions of neurons in each layer. They trained on a GPU. That's something brought over by Google now. So in my case, then I again apply the input to a convolutional 1D layer where again I tune the windows and filters and then the global max pooling layer is used. Now this is where the global maxima is identified in your data set. It's very important to identify it because that's where you deduct what's important in your data. In case of a computer vision, you deduct if you have a computer vision, in case of a computer vision, you deduct if you want to identify Venkat, does he have a round eyes? Does he have a whatever nose? That's important actually. So the global max pooling layer will identify the best features. Also bear in mind, you need to keep iterating again and again because they're iterative algorithms. You need to go again and again to allow the network to learn the best representation of your data. If you just try it one time, it may not work. Then if you try it again, then the network will learn, okay, this is the best representation of the text. And finally, the last layer is a softmax, which is a probabilistic output layer, which is called a cross entropy. It's also called the log likelihood actually in the machine learning world. If you guys know about logistic regression, you do a probability, right, which is a log likelihood. So it's modeled as a probabilistic layer where probability of article equal to fake given a new input document. That's the probabilistic classifier. So the softmax layer will give an output between 0 and 1, and the sum of that will be 1. It gives a probability, nice probability distribution. We expect a Gaussian distribution, but you don't get that actually. You get a different distribution, but the sum of the probabilities will equal to 1. Now this is just the model. You train on it. Now how you do the inference? How you save the model, and how you load the model into the main memory, and then how do you question the model? It's a different topic totally. This is just the model actually where you are trained. And in your label to training data set, you explicitly say that, okay, for this sequence of inputs, this is the label actually which you attach. It is a fake or a not fake. It's up to how your data set is created, right? Yes. No, the softmax will give that. The softmax in the output layer, you actually tell how much output you expect, the output dimensionality, which will be 2. So the softmax layer will do that. And the output layer actually, that's where the categorical, so I use the algorithm RMS prop and the categorical cross-entropic. That's that actually. If it's a feed-forward neural network, they need to flatten it out here. Because if it's a feed-forward neural network, they need a flattened layer. Okay. This is a result summary actually. You know, the model was trained using Keras, which is a TensorFlow background. It was trained on my own laptop. This is a 16-gig ram with a heavy CPU. It's a Mac. It's a horsepower Mac. The data set I trained was about 2.5 gig. I think if it's more than that, I have to go to cloud. Amazon has got nice solutions in the cloud. Epox I trained is about 150. I'm sure what Epox is actually the number of iterations a neural network has to go through on the data. It's because in your first iteration, the network won't learn the representations. Keep repeating iteratively again and again. Eventually, after some Epox actually, you will learn the best representation. The batch size I trained is about 256. And I trained on a CPU. I did not train on a GPU yet. This is the comparison thing which I want to, you know, give on the model accuracy part. The metrics I've shown is the accuracy. But you can have different metrics in a neural network. You can have a mean squared error, which is a different metric actually given in case of a neural network. I found 99.8% accuracy using a CNN. I did the same using SVN. And SVM gave me 90%.  That's why SVM was giving a low accuracy. But this is what I got actually. I got a naive-based classifier, which is a probabilistic classifier. The model accuracy was 85%. So this is my project, actually. The source code is with me, and I'm publishing a paper right after the, you know, ODSC. And if anyone wants to know the source code, I think I'll make it public after my project is approved. Until then, the college doesn't allow me to publish this. Okay. The data set actually was taken from a trusted news media. Like if you take in, I did for UK. In UK, you've got BBC, Guardian, Telegraph. They are trusted because people do trust it. And when you download data from those sites, you can assume that it's real because the fact-checking is already done before a reporter publishes it on a BBC.


It's Hansen once again off the inside, Kim Nielsen coming through, then momentarily in the second plus, down the outside, here comes Lambert, Lambert round the outside in the second place. Now in the middle of the track, Hansen's very vulnerable, Kim Nielsen off the inside. Robert Lambert firing himself to the front, but Hansen remarkably how an earthy hunger, Kim Nielsen surges through in the second place with a stunning move on the opening lap from Robert Lambert. You've got to be out in the dirt or you've got to have your front wheel there across the curve. And there again, Dodgums as they leave turn two on lap three, get so, so close, look at that. And you've got to say, Hats off to Mount Hansen, he's been in the league of the action all night, scoring points every race, eight points, one race win, great chance of being in the semi-finals. Yeah, looking really good.


William Celeste Watkins Hayes, the Interim Dean of the Gerald R. Ford School of Public Policy. Thank you. It is my great honor to welcome you here this afternoon on behalf of the entire Ford School community as we honor and celebrate our 2023 graduates. Oh, graduates, you did it. Through your hard work and perseverance, you earned a degree in public policy from the University of Michigan. You made it through courses, policy simulations, and internships. You've made us all so proud, and did you know you're the first group of Forties ever to celebrate commencement in this gorgeous Hill Auditorium? So I'm thinking we should take a picture. I brought my phone, and we are going to take a picture. Let me start with some introductions now that we've gotten the selfie out of the way. First, our wonderful keynote speaker, social entrepreneurship leader, and two-time University of Michigan graduate, Ms. Annie Maxwell. Annie, we are so grateful to have you here and look forward to your address. Also here on stage with me are members of the Ford School faculty. I want to extend my heartfelt thanks to our faculty for their brilliance, care, and dedication to our students and the mission of our school. Please join me in thanking all the folks who have taught and mentored our graduates, the amazing faculty of the Ford School. I also want to sing the praises of our terrific team of staff who serve and deliver on our mission. Many are here today having organized our celebration with true Ford School collaboration. Please join me in thanking our staff for their hard work and creativity and for all they do for our community. I'd like to take the time to introduce a few folks on stage by name. Our outstanding associate deans are here. First, Luke Schaefer, who also directs our Poverty Solutions Center. And John Chichori, who directs our Wiser Diplomacy Center and our International Policy Center. John will be reading the names of our graduates as they cross the stage. Luke and John, it has been an honor and joy serving alongside you this year. Leading our academic programs are economists Catherine Dominguez, who directs our PhD program. Political Sciences John Hansen, who directs our MPP and MPA programs. And social demographer Paula Lance, who directs our BA program. Also in the front row are several faculty who will be proudly hooding our PhD graduates today. Alan Dierdorf, Jeffrey Mornoff, Fabian Pfeffer, Al Young, and Sandy Danziger. My colleague and organizational psychologist, Morella Hernandez, was elected by the students to deliver remarks on behalf of the faculty. Morella is the director and driving force behind our growing leadership initiative, and we look forward to hearing from her soon. Finally, elected by their classmates to provide the student commencement addresses, are soon to be Ford MPP graduate, Issa Lawan Wanderama, and BA graduate, Joe Timmer. So my dear graduates, colleagues and friends and families of the Ford School. We've gathered here today in this beautiful space to recognize and celebrate the accomplishments of our 189 outstanding graduates. Smart, resilient, public-minded people who will be leading and more important, serving our communities. Serving our communities. We'll hear several speeches to this afternoon and I expect the theme of service will reverberate loud and clear across our remarks. Service, after all, is the beating heart of the Ford School. There's a quote I think of often. It's been credited to Shirley Chisholm, Muhammad Ali, Marion Wright-Elleman, among others. I first heard it stated by my mentor, Dr. Janetta B. Cole. It's a call to action that reminds each of us that we can and must use our gifts, talents, skills, and platforms to create positive change. The quote is this, service to others is the rent we pay for living on this earth. Service is in our school's mission statement. The Ford School at the University of Michigan, we say and believe, is a community dedicated to the public good. We inspire and prepare diverse leaders grounded in service, conduct transformational research, and collaborate on evidence-based policymaking to take on our communities and our world's most pressing challenges. Service, too, is the core of the legacy of our namesake, the 38th President of the United States and one of the University of Michigan's most distinguished graduates. History remembers Gerald R. Ford as a man of integrity and courage, a man who devoted his life to public service. We proudly call ourselves 40s. Now I know the families and friends gathered here today are proud, too. They're proud of your hard work and determination, and like me, they're proud of the decision each of you have made to live a life of service. But I wonder if I may ask the family members gathered today with a show of hands, please raise your hand if you're not 100% sure just what a school of public policy is. What do we do here exactly? Fair question, fair question. Let me take a few minutes to share how I think about the urgent work of a great policy school and how exactly we've prepared your graduates to be of service. Policy schools, I believe, serve three very important interconnected functions, a three-legged stool, if you will. Policy analysis, policy debate, and policy advocacy. First, we teach our students to conduct sharp policy analysis. We look at the evidence, the facts, and we call balls and strikes. Our faculty are renowned for their rigorous and timely research. They've taught today's graduates how to analyze complicated data sets, leveraging quantitative and qualitative tools to evaluate costs and benefits and to interrogate policy prescriptions according to their impacts on different communities and stakeholder groups. Second, we create a rich intellectual environment that spurs policy debate. That is, we inspire and inform that-based dialogue about how we should tackle pressing issues based on the evidence we have available to us and on our values and ethics. Our faculty speak out in policy circles and publications and in the media, injecting much-needed facts into public dialogue on a diverse set of issues, including national security, climate change, poverty, and health. They teach our students powerful tools of communication, including how to speak and write clearly and persuasively. Policy advocacy is the third and final leg of the stool. We are advocates, and we teach our students how to advocate for our communities, how to be a strong, persistent, and informed voice for what they believe should be done, how to move the right levers, and how to best lead from where they are. At the Ford School, we work to create an environment where all three of these important functions can thrive, analysis, debate, and advocacy. Different students arrive to the Ford School for different purposes in terms of what they want to learn and experience, of course. Those who come to learn policy analysis, we hope absorb the lessons from those who see their primary function as policy advocates. And we hope that those who come to learn about policy advocacy also sharpen their policy analysis skills. That exchange, that symbiotic relationship, is crucial, and it speaks to the power of policy schools in general and the Ford School in particular. Our best work happens when we use the whole stool. I'll give you just one example. When Detroit's 2020 census count came in much lower than the city had expected, city leaders asked the Ford School researchers to find out why. Professors Jeffrey Morinoff and Ren Farley, alumnus Pat Cooney, and others knew how to approach the analysis. They conducted extensive visual housing audits of over 114,000 addresses located within over 4,000 census blocks, where the city of Detroit was challenging the housing count. Their evidence strongly suggested that Michigan's largest city, with an overwhelming majority of black and Latinx residents, was seemingly undercounted by as much as 8%, resulting in the loss of up to $25 million in state and federal funds. Our researchers made their findings known. They talked about what they learned in the press and shared it with researchers in other American cities that may have been similarly undercounted. And they advocated, testifying before a Senate field hearing and helping as Detroit filed a dispute with the U.S. Census Bureau. They continue to help as the matter makes its way to federal court. That is the three-legged stool. Now let me tell you a bit about each of our graduating classes. Four outstanding students have earned PhDs in pioneering, in our pioneering joint doctoral program, three in public policy and sociology and one in public policy and political science. They've each done groundbreaking research here and are each headed towards amazing first jobs where they'll in turn teach and speak out for the public good. To our doctoral students, congratulations. You've completed a Herculean feat in your expertise in intellectual contributions in and beyond the professoriate are much needed. We celebrate 107 students who have earned master's degrees in public affairs and public policy. They hail from 16 different countries and speak dozens of languages. Some of them are parents. Many of them are working while pursuing their degrees. This is a group that has had to be adaptable. They led the way as we move back to in-person classes and activities in 2021, coping with constantly shifting expectations and safety plans for classes, internships, international student trips and more. Through two years of the pandemic, campus discord and political strife, they've lifted each other up. They've supported and care for each other as classmates, as colleagues and as friends. Most of them completed their required professional internships last summer and they were the first 40s to be offered leadership coaching. Now they're entering careers and fields that will let them serve, let them change the world for the better. They're heading for the mayor's office in the city of Detroit, the city of Emsilani, the California Department of Finance. They're headed to the government accounting office, the congressional budget office, Congress, the State Department, USID, the Department of Commerce, the Environmental Protection Agency and the Department of Treasury. And they're going worldwide, working with the governments of Japan, Thailand and Bangladesh. They are going to work for for-profit firms that provide valuable consulting services to government agencies and they are heading to non-profit organizations, both multinational and grassroots. Please join me in congratulating our new master's alumni. You all did the thing, didn't you? 78 students have earned Bachelor of Arts in Public Policy. Those students have completed just over one semester at, completed just over one semester at the University of Michigan when the pandemic emerged. They spent their sophomore year, second year, taking courses over Zoom. Some scattered around the US. Some maybe even sharing an internet connection at home with some of the families seated here today. For some, that was a gift of family connection. For others, it was a challenge that tested fortitude. Perhaps it was both of those things for many in the audience today. When our undergraduate students arrived at the Ford School as third year students as juniors, they jumped in with tremendous heart and effort. They spoke up and advocated when they saw challenges working constructively with school leadership to improve university policy. And they made our third floor reading room one of the coolest spots to hang out in, in Wild Hall. When they returned to campus, they also witnessed two straight years of Michigan football winning Big Ten championships and beating Ohio State, not bad at all. And a good number of them have jobs lined up already. Some will go into consulting, including public sector consulting. Others will go on to work for nonprofits such as Color of Change, Teach for America, the Institute for the Study of War, and American public media. Some will serve in government in Senator Stabenow's office and the Congressional Budget Office, Treasury and State. Several will go on to graduate programs in law, education, and policy. A large number of our BA graduates are moving to DC and I predict that in between making the world better, they'll have a lot of fun. Undergraduate 40s, well done and congratulations.  Taken together, the classes of 2023 are resilient. They care about each other and about the world and they care about service. After all, service to others is the rent we pay for living on this earth. Graduates, we are grateful to have served you and we know that you'll continue to serve. You will make the world better. You'll pay your dues, you'll serve your communities. You're off to a great start. You've already made the Ford School better and the University of Michigan as well and we thank you.  Graduates of the class of 2023, never forget that the Ford School is your home in Ann Arbor.  And now I'm delighted to introduce our keynote commencement speaker, Annie Maxwell. Sure, of course, of course. Especially after you hear this bio. Annie earned her undergraduate degree at Michigan in 2000 and served as captain of the volleyball team. She earned her master's degree in public policy at the Ford School in 2002. Annie has remained closely connected with Michigan throughout her successful career in global health, humanitarian aid, and philanthropy. She is the executive vice president of the Social Change Venture Amidiar Network, a trustee of the German Martial Fund and a lifetime member of the Council on Foreign Relations. Annie previously served as the president of the Skoll Global Threats Fund and is the chief operating officer for the Humanitarian Aid Relief Organization, Direct Relief. Early in her career, Annie was a White House fellow in the office of Vice President Biden. Over the years, Annie has mentored Ford School students, developed internships for a number of our graduates, hired our students, and served on our alumni board. She currently serves on the Ford School's advisory committee. A side note to our graduates, some day soon, you'll be in a position to pay it forward for our students as Annie has so wonderfully done. I hope you do. It is my great honor, Annie, to welcome you back to the University of Michigan. We have to kick the box out of the way. Thank you, Dean, for that warm introduction. And it is truly an honor to be here today. And may I have my hearty congratulations to the graduating class of 2023. Just over 20 years ago, I sat in your seat as a graduate and coming back, of course, has me reflecting on my time here and my decision to come to the Ford School. When I applied in the spring of 2000, the world was a very different place and in many ways, a much more stable place. The U.S. was an established global superpower and we were a nation at peace. Our electoral system, well, not without its longstanding concerns around access and rights, was fairly trusted and established. And while their cracks beginning to show in Washington with the impeachment of President Clinton, there was still such thing as bipartisan legislation. And of course, Michigan football and Michigan hockey had just won national championships. So the order of the world was reasonably clear and the pace of change fairly predictable. Personally, I was looking to make the world a better place and a public policy degree felt like a pretty surefire way to do so. And while the world had its real challenges, there's a general sense that together, you know, we could do hard stuff. It was in the fall of my second year that some real cracks in the facade started to show. I vividly remember sitting on the bus coming up State Street early in the morning and yes, like any good student before 9 a.m. seemed really early to me. I was on my way to Professor Cohen's ed policy class and once I got there, the room was full of students but no professor and we waited and we waited and then someone finally got the call that the Twin Towers had fallen. In that moment, the world started to make a dramatic turn and over the next 20 years, U.S. foreign policy and the role of America and the world evolved and there was not only a rapid shift in foreign policy but also in our institutions here at home. And there have been dynamic changes in technology with the rise of social media, dramatic shifts in the climate with a rapidly warming planet and the pace of change just seems to be accelerating in almost every facet of our lives. Now, I mentioned all this because I decided on a public policy degree and about as optimistic and naive a frame as you could have. But you as a class, you've made a very different decision. The world's a much more dynamic, dare I say, chaotic place than it was 20 years ago. While 9-11 happened while I was in school, COVID, January 6th, that all happened before you guys applied. And so you knew there were fundamental questions about how government and democracy works. You'd seen the impact of misinformation fueled by social media.   You had a lot of different paths and choices. And one of the many things that COVID has done is forced us to think about our choices in life. Where do we wanna spend our time? How do we wanna spend our time? And I find myself looking at a group of individuals who looked at this broken and fragmented world and said, we can do hard stuff. I can tell you it's not the call a lot of people have made and it makes me optimistic for the future to be with this group. But the question now is, what do you do next? And I'm sure your parents have asked you. When I left the Ford School, I had a rough sketch of my next step. I was gonna go to Washington, I was gonna learn the ropes in Capitol Hill, spend some time in a think tank, research advocate for the policies I thought were needed. And I thought a lot about where I was gonna work, the institutions I was gonna influence, the policies I wanted to change, but very little about the broader trends in the world.  Your world is defined as much by the institutions and their policies, and it is by the global trends that are gonna shape them. Climate change, AI, shifting global order. The world we live in now won't be the same in five to 10 years, and many of the institutions and policies to address our challenges are just not known yet. For me, this is the starkest in the world of AI and particularly generative AI. We're on the precipice of a generational shift in how work gets done, and not just some people's work, but like everyone's work. You know, GPT-4, one of the most well-known large-language models. Words, by the way, I wouldn't have even uttered a year ago, large-language model. GPT-4 just passed the bar exam with a score in the 90th percentile. This large-language model wasn't given the multiple choice, wasn't just given the multiple choice section, but also the essay section, and crushed it. This is far from the most remarkable thing it's done, but it shows the implications of technology on the way we work. And let's just say I'm glad I'm not speaking at the law school graduation. So, as you think about your career, in addition to thinking about the issues and places you want to work, I'd like to add another layer. Three things that I'd like you to keep in mind as you navigate these dynamic times. First, try being bad at something. Two, get good at people. And three, say thank you. And if you indulge me, I'd like to walk through these three. First, try being bad at something. People often advise you that you should try new things, but inherent in that advice is that you try it and then you're great at it, right? Like, try Spanish and then become fluent, right? Learn the piano and then become that person on TikTok who sits down at a random piano in the mall and wows everyone with Bach. And I don't know if I'm the only one who gets those videos in their feed. But what I'm suggesting is quite different, right? I'm telling you to try something and be bad at it. And let me give you an example for my own life that one or two of you may relate to. When I was here at the Ford School, I took econometrics. And I don't know if I even need to continue this story. I took econometrics and I did not get it. And in my defense, I wasn't alone. John Donardo was my professor and he was brilliant, but perhaps maybe too brilliant to be teaching an intro class. In fact, there was a woman in my class from the School of Public Health who had already taken econometrics but was taking it again so she could have Donardo. He was that brilliant and was a class. I distinctly remember her turning around in her desk to look at me. Like she could sense the panic that was radiating off of me. And she said, it's okay, I've taken this before and I don't even know what's happening. So I think I got 50% right on the final and passed and I'm not sure that happened, but thank God for the curve. So for years, I kept my econometrics book on my bookshelf at home, just like a frat boy would keep some empty vodka bottle as like a reminder of this mind-bending, nausea-inducing time in my life I somehow survived. But I can tell you, I've had two job interviews. Two, where econometrics have come up. Two interviews where I got the job. And while I may not have understood what a do-file is, I can talk about regression analysis, I know who Cardin Kruger are. I meant to get 50% right on the test, the rest was blank. It turns out that trying stuff and being bad at it is light years ahead of not trying. Because here's the deal, trying something and being bad at it is helpful in two ways. First, it tells you what you're not good at. And let me tell you, much of your career is finding out not only what you love, but also what you hate or just are not good at. And it turns out, despite my strength and quantitative work, I am not gonna be an economist or an econometrician if that's even a thing. Cross that one just off the career list. Second, and most importantly, trying stuff and then being okay with your mediocrity lowers the bar to entry.  You don't have to get an advanced degree in everything you do, right?  People who do stuff and are great at it, and I'm sure your parents can all tell me about it, right? But please don't let that be the standard, right? Because our future is gonna be defined by stuff that no one understands right now. Like, everyone is gonna be figuring it out. And most people are gonna be really bad at it for quite some time. Do you think people really understand how generative AI works? Like, even the people designing these large-angle models can't explain it. There's almost a sense of mystery to some of the outputs. And the other one's making it, right? The computer does something you ask like, hey, why did it do that? And they're really like, I don't know. I mean, that's a little bit scary, right? So let's talk about generative AI for a moment. I have no question that it will shape the world we live in. When you ask folks for analogies or metaphors about this moment, they say that AI will be as ubiquitous as electricity, powerful as nuclear. And some are predicting we're in our own version of the new industrial revolution. This is an area that needs public policy experts. That's all you guys, to engage on this, right? Because if not, the rules are gonna be written by technologists. And just as I don't want this group writing code, I don't want coders writing policy. Because while the technology is complex and it's hard to get, so is policy, centering equity, incorporating diverse voices, thinking through the ethics. That's also hard. So this work has to be done in partnership. So get your hands messy, try something new. Play around with these new tools. Chat, GPT, stable diffusion, listen to podcasts. Get comfortable with the language. You don't need to be perfect at it. You just need to get engaged. Innovation happens not in the center of fields, but on the boundaries. By folks who are not necessarily the expert in the work, but those who are able to cross different disciplines, fields, sectors, geographies. And that work at the boundaries means being okay with not knowing everything. At trying something new, being bad at it, but soaking in all that you can. It's where creativity lies and innovation lies. And it's where I believe the future lies. So if you wanna work in interesting world changing stuff in the future, get good at being bad at stuff and being okay with it. Okay, now that you're okay with being bad at some things, my next advice is to get good at people. Let's continue with AI. Do you know what the benchmark is for true intelligence and AI? It's not being able to pass the bar or write a commencement address. Just kidding, I'd never do that. Or cure, can you feel the dean behind me? Or cure cancer, right? The benchmark for truly intelligent AI is when it can be mistaken for a human. Does that tell you something about how complex we humans are? So your job then is to get at something that AI can't do very well right now. Get good at humans. And that means the rich diversity of humans all around you. We're facing some wicked problems in the world. And at the end of the day, things are only gonna get solved by diverse groups. And working in diverse groups may be getting used to discomfort. And even starting to see discomfort as a sign of growth, not harm. Because diverse groups talk more. They argue more. Diverse groups are hard, right? Anyone been in a group assignment when you're working with someone really differently than you? Different than you? It's not always very fun, right? We've all thought at some point, I'm looking at everyone in the room right now. This work would be so easy except for all the people involved. My push for you is to seek out folks who look different than you, think differently than you and have different life experiences. It's hard.  But let's be honest, that's not how we're gonna address these issues. And here you have a leg coming from such an, leg up coming from such an incredible university as the University of Michigan and in particular the Ford School. This place is not just about the great diversity of the state that's represented here but also the world. Because these wicked problems, the problems that will be generated as we unleash AI on the world as we work to address the impacts of climate change will not be addressed by the US alone. It'll only get done in partnership between Americans, Chinese, Europeans, the entire global community. So nurture those relationships you've built here today because you may come to rely on them more than you anticipated in the coming years. So that leads me to my final point. It's a messy world and if you're lucky you'll be working in challenging and emerging issues with a diverse set of people. And if you're trying new things and working with folks different from you, you're gonna fall on your face a lot. So be thankful for those along the journey who are making their way through this mess with you. Practice some gratitude and not in some meditative kind of way though that's good too. But I'm talking about in the proactive do it today kind of way. Daniel Pink recently wrote a book on regrets and he said that the majority of things that people regret are not things that they did but things that they didn't do. And I have to say the main regrets I have in my life are not thanking someone or telling them how much they meant to me.  I regret that I never thanked Becky Blank, the former Dean of the school for bringing her young daughter to class sometimes when she taught. It just didn't happen 20 years ago. And as the mother of two children that was an example that stayed with me. Becky was a model of strength who I looked to when I had my second child in April of 2020. My son has literally grown up in front of my team's eyes at Zoom but if Becky could lead a lecture with her daughter at her feet, I could lead a staff meeting with my son in my lap. Now putting myself out there to say thank you is particularly hard for me because I'm an introvert and I'm happiest at home reading a book, not putting myself out there with people. In fact, when I got the call from the Dean for this commencement address, I literally looked at my bookshelf and thought, oh, sure there's some book I should be reading that weekend, maybe that econometrics textbook. And so you remember, may you wonder why this house cat took the risk to get on a plane for this speech. It's because I saw a chance to say thank you. I'm just 20 years out of school and I'm surprised that many of the folks who mentored me are gone, including David Cohen, John DiNardo, and Becky Blank, the professors I've mentioned. People come in and out of your life either permanently or temporarily all the time. Maybe it's someone from your first year here who helped you through a tough class but now is in a different friend circle. Or a professor where you're one of the many in a large lecture, but the work really hit home. Practicing gratitude may feel like something you can put off, but I can tell you, you may not always be able to do it. Don't wait, thanks someone for how they've impacted you. It may feel awkward or clumsy, but it's worth it. And now it's my turn to say thank you. Thank you to my parents and not an uncle who are here and my husband and kids at home for a list of things too long to mention but mostly for all their love. John Chamberlain for introducing me to values and ethics. Ann Lynn for opening my eyes to the complexity of immigration. David Thatcher for exposing me to the injustices in our criminal sentencing guidelines. Jennifer Nigermeyer and Sue Johnson for just putting up with me in general. The past three deans, Susan Collins, Michael Barr, Celeste Watkenhays for continuously believing me and thinking I was someone to keep investing in. We all have a list of people we know we should thank. I encourage you to thank someone today, not in a text, I'll take it, but in a face-to-face, real way. You probably won't remember this speech in 10 years, but that person you connect with will likely remember your kind words for a long time. So in closing, let me say thank you to this community, a community that's let me be bad at things, centered people in diversity and has created space for this gratitude. Best of luck to all of you in your journey. Can't wait to see what you do and of course, go blue. Annie, thank you so much. That was phenomenal. Thank you so much. Wow. We've got a special musical treat coming up next. Our music this afternoon is being provided by the talented Ann Arbor Community High School Jazz Band. Please join me in thanking the band. And we wanna welcome to the stage the band's tenor saxophonist high school senior, Cole Oswald, to perform a Michigan classic. Take it away, Cole. How much, Cole, in the community high band members, you all did a fantastic job. So each year, the four schools graduating students are asked to elect people to play key roles in commencement. One faculty member is chosen to speak to the class and our BAs and masters graduating classes choose a representative student speaker as well. As the faculty speaker, the classes of 2023 elected Professor Morella Hernandez. Morella Hernandez is the Leah Ramirez Day Reynolds, collegiate professor of public policy at the Ford School and holds an appointment as well at the Ross School of Business. Hernandez is an organizational psychologist whose expertise is at the intersection of leadership and diversity. Hernandez is widely published in academic journals including Academy of Management Review, Journal of Applied Psychology, and Psychological Science. Her work has also appeared in media outlets such as Time Magazine, The New York Times, The Washington Post, and National Public Radio. At the Ford School, Hernandez teaches courses on leadership and diversity and is the faculty director of the school's leadership initiative. She received her PhD from the Fuqua School of Business at Duke University. I'm delighted to welcome her now to speak on behalf of our faculty. Morella. Hey guys. Congratulations. I'm so excited for you. This is an amazing day and it's a great honor to represent the faculty at this commencement ceremony. Today, as you begin a new phase of your personal and professional lives, our hope as Ford School faculty is that you continue tackling our biggest challenges. That's not that you're not discouraged by the obstacles you encounter but rather you are inspired to approach difficult situations with an open heart and determination. So to that end, let me frame a mindset for you today. A mindset that will help you navigate these difficult situations. And a good starting point is how you view failure. So Annie got us started and I'm gonna continue on that theme. During your time at Ford, were you ever part of a group, a team project that was a colossal failure? Oh, of course not but hypothetically speaking, this might have been the group project that could be identified as the one in which maybe only one or two of your group members ever showed up to meetings or the one in which the writing would not have gotten done had you not stepped up or the one in which you went to your professor to ask for more time only to actually turn it in late anyway.  If you ever experienced something like this, you might have thought your members were, your group members were total jerks, obstacles to getting the job done. You might have thought they were the problem. Okay, well let me pivot and echo Annie and ask, does anyone hear like being right? Yes, I do. I do. We all do because we work hard and we wanna be seen but the mindset I've just put you into won't get you where you need to go. It's a mindset where what matters is winning and what matters most is being right. To effectively navigate difficult situations, I wanna get you into a different mindset and for that I want you to think about a time when others, perhaps your teammates, brought out the best in you. When you experienced curiosity and learning that made you joyful and excited, these experiences might not have been defined by the highest grade in the class but instead the connections you made with your classmates. This mindset focuses your attention not on yourself but on the broader context around you.  So let me give you an example. Again, from a personal example. And it involves airlines and airline travel and I see you wincing. Airline travel is not fun. Last year I took my family on our first post-pandemic trip to France. After more than two years of essentially house arrest, we found that we needed a little bit of travel. So off we went and on our return trip, I found myself in the Paris airport, Charles de Gaulle, with my two teenage kids. Our vacation was over. We had had multiple COVID scares. We were tired of eating out and we missed our cats and our dogs, okay? We were ready to go home. So we're in line to check in and my kids get their boarding passes but I'm told that there's an issue with mine. Okay, I think to myself, airline computers are slow. They require a thousand keystrokes to generate any sort of satisfactory answer. So we wait. 10 minutes of standing, later the airline representative tells me she cannot find my ticket home. I have no return ticket to the United States. My kids look at her and look at me and I swear to you at least one of them was like I'm just gonna go on without you, peace. But they don't leave me there just yet. And we're sent to a partner airline which apparently might be responsible for having canceled my return ticket. So we walk about the 30 miles from one end of the airport to the other to stand in yet another line. By this time, my kids have completely processed their shock and outrage at what's happening and have begun horsing around as though they were two very rambunctious five year olds. Okay, the French are giving them dagger eyes but I choose to ignore the kid craziness and focus on the task at hand, get back to the United States. Now, this is where my choice of mindset comes in. I have every reason to be upset, angry, even vengeful. But in approaching the airline counter, I make the decision to approach the woman in front of me as a person. Someone who has a pretty tough job is probably tired of standing on her feet all day and probably also feels drained from dealing with very angry customers. I tell her about my situation and her first reaction is to tell me that she doesn't know why and how weird that I bought a round trip fare but that I have no return ticket. As she tries to brush me off and go to the next unhappy customer behind me, I pause and I hold her gaze, okay? Inside of me, I try as hard as I can to keep an open heart and I tell her, please, I'm traveling with my children and I need to get home. Now, this is not new information for her since because she's talking to me and looking straight behind me, she has already seen that my kids are engaging in some sort of ninja battle with high air kicks, rolling suitcases and screeching while then breaking down in uncontrollable giggles and laughter. Again, they're not making friends with the French. But in that moment, the dynamic between us changed. She seemed willing to stay present. She seemed to see me not as another complaint but as a human under duress. She seemed to actually step into my experience of the situation just as I try to see things from her perspective. 40 minutes later and many creative keystrokes later, she resolved the issue. She might have even called in a favor but she got me on that flight and when she gave me that boarding ticket, I gave her a hug. Minutes later, as my children and I were running down the terminal at a full sprint trying to get to the gate before it closed, they yelled out, how did you do it? And the truth is I really didn't do anything but choose how I want it to approach the situation. I chose my own mindset. In that situation, as is the case with many interactions in our daily lives, we cannot change the behavior of others. They might be operating under a cage of distrust, exhaustion or worry. They might be paralyzed by their own entrenched ideas, positions, their own goals and interests. A critical step toward resolving stressful, even absurd problems is to choose how you want to approach the situation, to view reality from the perspective of others so that your own needs and interests are not brushed aside so that you're seen. What brought me to the Ford School is my aspiration and desire to help each of you become agents of positive change in the world.  When you're under pressure, when you feel like things aren't working or they aren't going your way, how will you approach that situation? Will you try to resolve problems by convincing others that you are right? Or will you approach with curiosity and openness to others' perspectives? A mindset where others are the problem won't get you very far. Only the choice to maintain an open heart in the face of stress and absurdity will get you home. Dear graduates, each of you today has something very special to offer the world, to contribute to your jobs, families and communities in unique and precious ways. Echoing the Dean's sentiment, I believe in you, the Ford School faculty believes in you.  Thank you, Mirella, that was wonderful. Now we'll hear from our student elected to speak from the bachelor's class of 2023, Joe Timmer. Joe was raised in Portland, Michigan. He earned a BA in public policy with a focus on policy framing and communications and has graduated with honors. Joe served on the leadership of the Ford School Undergraduate Council. During his time at Michigan, he worked full-time as a cook and completed an internship at the Michigan State Legislature assisting a state representative with pandemic-related and constituent services. Joe has great public policy skills and he's also a natural leader with an innate ability to connect in some way with everyone he meets. During his two years at the Ford School, Joe not only persevered during some difficult times, he's done so while making a tremendously positive impact on his classmates and leaving a lasting mark on the Ford School. I am so pleased to welcome Joe Timmer to the podium. Okay, I think I do need the box. Thank you so much for that nice introduction. I'm gonna move this up here too, there we go. A good afternoon to the Ford School faculty, families, friends, loved ones, and fellow students. Thank you for allowing me to offer a few words on such an important day. It is my honor and privilege to be a small part of celebrating this exceptional group of students. In the few years that I was able to share with you here at Ford, I had the chance to join in the community of some of the most passionate, bright and kind people that I've ever met. Without giving, because we've had a few really good ones living in unprecedented time speech today, it's safe to say that our time here at Michigan was not necessarily what we had planned it to be. I'll never forget that weird feeling of seeing some of your faces for the first time without a mask, like months after meeting you, kind of that like jump scare feeling, you remember that, pit of your stomach. You all looked great, don't get me wrong, you did, I just, it took some time to get used to it. But in all seriousness, beyond the upheaval and loss of the pandemic, every one of us had our own unprecedented times while we were here at Michigan. It's hard to escape life without them. For me, that was serious struggles with my mental health that at times made school or life responsibilities difficult to complete. And at the end of the first year at the Ford School, it was the loss of my brother and best friends to suicide. This community, the faculty, my classmates and friends supported me through those difficult times and empowered me during the joyful ones. Our graduation is a big deal, worthy of the pomp and circumstance of this ceremony and all the celebrations with family and friends that might happen after. But the ceremony isn't really about anything that we actually did today. For me, I woke up and ate cereal and called my mom like five times and walked to Hill Auditorium. Every day leading up to this day is the reason that we got here. I hope that today can be a celebration beyond just your diploma. I hope that it can be a celebration of the person who walked in your shoes every day leading up to it. Whatever they started and whatever they looked like as they lived through every moment that led you here. Let today honor all the wins and the good days, the past classes, perfected personal goals, nice weather, birthdays, friendships, the times you laughed, the times you felt proud of yourself, the times that it was easy to give and receive love, those days fueled you and the person who lived them brought you here. But let today also celebrate the painful days when giving and receiving that love seemed far out of reach. The loss you faced of friendships or loved ones or dreams that you strove for that didn't come to be. These days, the days you couldn't get out of bed, the days you didn't like yourself or thought that you weren't good enough, the times you didn't do what you think is right, the times you failed to meet responsibilities or things didn't go as you had planned them to or you made mistakes. I don't think that I've spent enough time measuring this success and achievement in the details of the winding and unplanned path that led me to this day. But every single day has led me here. Every single day has made me the person that stands before you, the good days and the uncomfortable ones. Your life is a map of success that isn't just composed of a few singular points where you achieve the end goal like today. It's the failings and the growth and the kindness and the resilience and the beauty of the person that you are in every moment of your life. You have so much to be proud of and celebrate as you exit this university and move forward to the next chapter of your lives. You've worked hard. You have grown and changed and fallen down and gotten up and you should walk forward with pride and with self-compassion for the resilient person that you have become and as a University of Michigan graduate. When you do, remember those days in between. I know every one of you is capable of achieving your biggest dreams and changing the communities and the world in which we live. And hindsight on this commencement day, we can choose to celebrate all that led us to this diploma and I hope that you do. Sorry, I lost my place. But with this sentiment in mind, don't stop celebrating it. Walk forward and remember that every step that you take is worth celebrating, regardless of the direction in which it's pointed because all of them will lead you where you're going.  Thank you and again, please join me in celebrating the Gerald R. Ford School of Public Policy, class of 2023. Thank you so much, Joe. That was outstanding. The MPP, MPA class of 2023, elected Issa Luan Wanderama to speak on their behalf. Originally from Niger, Issa earned her BA in international affairs from Eastern Michigan University with a certificate in African Studies and a BBA in Management and Finance from Cleary University. Here at Michigan as a graduate student, Issa is a recipient of two top honors as both a Rack of Merit Fellow and a CEW Plus Scholar. She's on the board of our Students of Color and Public Policy Group and serves as well on the CEW Plus Student Parent Advisory Board. She's participated in the Ross Leaders Academy and has recorded a story with the Story Lab at the Sanger Leadership Center. She's done all of that and excelled at the Ford School while caring for her two young children. Issa speaks five languages and has extensive experience as a humanitarian and development worker in Niger and the DRC. Some members of Issa's large and loving family are here in the auditorium. Others are tuned in right now live from Copenhagen, Paris, and Niame. Greetings to all of you and thank you for sharing Issa with us these past two years. Issa, it is an honor to welcome you to the podium. I'm on heels, so I don't think I need this too. Dear fellow graduates, faculty, family, honored guests, today I stand before you and honor to represent this talented, dedicated and inspiring group of students. As classmates, we've laughed together, cried together and celebrated each other's achievements. We are modern classmates. We are a community of scholars, activists, and advocates from Afghanistan, Australia, Bangladesh, China, Colombia, Germany, Japan, Myanmar, Mexico, Niger, Pakistan, Peru, Republic of Korea, Taiwan, Thailand, and the United States. 12 years ago, I left the academic world behind to pursue a career as a humanitarian worker in Niger and the Democratic Republic of Congo, and to start a family. Passionate about the beauty of education and pushing myself outside of my comfort zone, I decided to join the 2023 cohorts, bringing with me my one-year-old daughter, Ramla, and my six-year-old son, Amir. He's correcting me. He's seven now, and turning 18 June, right? As I reflect on my time here, I am filled with a sense of pride and accomplishment, both for the personal growth and the learning experiences I've gained here. I recognize that this moment will not be possible without overcoming the challenges that I face as a mother, a woman of color, an African, a Muslim, and an immigrant. Balancing the demands of grad school with the responsibilities of being a full-time parent was no easy feat. For in Nama al-Usri Yusra, in Nama al-Usri Yusra, verily with hardship comes ease, verily with hardship comes ease. These words from the Quran have resonated with me throughout my journey towards earning an MPP. This verse is a reminder that even in the midst of difficult times, there is always hope and relief to be found. But I didn't overcome these challenges alone. I want to take a moment to express my gratitude to my amazing family, my mother, my mother, the iron lady, my fellow classmates, my friends, RMFCW and my professors who have supported me every step of the way. Without your unwavering support, I would not be standing here today. Looking back, what was most meaningful about my time here at Ford is the relationships I've built. I've learned from my classmates, my professors and mentors, and their perspectives and experiences have broadened my own. I've been challenged to think critically and to approach problems of empathy and compassion. I've grown as a scholar, a leader, and a person. In fact, all of this learning and growth has emboldened me enough to presume that I might have some valuable advice for you all. My dear fellow graduates, as we leave this place, first, never lose sight of your perseverance and determination. You've already shown that anything is possible if you work hard and believe in yourself. Second, embrace change. Don't be afraid to take risks and try new things. Change is inevitable. And it can be an opportunity to grow and learn. Third, remember that you're not alone. You have a community of classmates, faculty, and alumni who will always be there to support you. Reach out to them when you need help and advice and be there for them as well when they need you. Fourth, find balance between your personal and professional life. This is essential. Take care of yourself and make time to do the things that matter most to you. Finally, be grateful of the opportunities and blessings in your life. Thank your family, friends, and mentor who have supported you along the way. Remember that success is not just about what you achieve, but also the relationships that you built and the lives that you touch. Today, we stand here at the culmination of our hard work and dedication, ready to embark on the next chapter of our lives. I look for many of us, my journey was not easy. And our journey was not easy, but we persevered and we made it to this moment.  I am committed to using my education and skills to make a positive impact in the world and to empower and marginalize communities as well. As a passionate advocate for education, I believe firmly that education is the key to unlocking the potential of individuals and communities. And it can also be a catalyst for a positive change. I am excited to see where all of our paths will take us and how we will each use our education differently to make a difference in our communities and beyond. It won't be easy. As we look into the future, we face many challenges. We live in a world that is increasingly complex and interconnected with problems that are seemingly unsurmountable. But remember, for in Nama al-Usriusra, verily with hardship comes ease, verily with hardship comes ease. I am confident that all the skills, the knowledge, the passion to make a difference, we are public policy experts and we have the power to create change in our communities and in our world. So let us go forth from this place with our heads held high, with a deep sense of purpose, with the courage to face whatever challenges comes our way. Let us remember that we are not alone. We have each other and we have the power to create a brighter future for ourselves and for the future generations. Congratulations to all of you. Go Blue. We are now at the moment that family and friends have been looking forward to all afternoon. Our graduates are ready to come to the stage to receive official congratulations on a job well done. So please try to hold your applause until the end and families, please do remain seated to keep the aisles open for our graduates and staff. You'll be able to order a professional photograph of your graduate after the ceremony. So this year the names will be read as I stated by John Chachari, who is a professor of public policy and the faculty director of our International Policy Center and the Wiser Diplomacy Center. He teaches courses on politics, political institutions and post-conflict law and transition. John has an undergraduate and law degree from Harvard, along with a master's degree and a D-Phil in international relations from the University of Oxford. I'm so pleased to introduce John to call the names of our graduating students. Thank you, Celeste, and so wonderful to be able to celebrate this special day with all of you. I'd like to begin by inviting our doctoral students to stand and move toward the stage and to invite my colleague, Alan Dierdorf, to step forward to assist with hooding our first graduate. Jian Li is earning her Doctor of Philosophy in Public Policy and Political Science. Jian's dissertation title is Political Activities of Foreign Multinational Corporations in the United States, and Jian is an assistant professor in the Department of Political Science at the University of Buffalo. I'd now like to invite Sandra Danziger and Alfred Young, Jr., and our next PhD candidate to the front of the stage. Matthew W. Alemu is earning a Doctor of Philosophy in Public Policy and Sociology. Matthew's dissertation title is Better Than My Father or Better Than His Absence, an exploration of the influence of absentee fatherhood on the minds and lives of disadvantaged black men. Matthew's currently a postdoctoral researcher at the University of Chicago Health Lab, and after he completes his postdoc, he'll become an assistant professor at Northeastern University. I now invite Fabianne Pfeffer and our next PhD candidate to the front of the stage. Elizabeth Berlin is earning a Doctor of Philosophy in Public Policy and Sociology. Elizabeth's dissertation is entitled Stratification in Educational Decision Making, How Policy Interventions in Student Context Shape Educational Inequality. Elizabeth will be an assistant research professor at the University of Connecticut School of Public Policy starting in August. And now I'd like to invite my colleague, Jeff Morinoff, forward as well as our next PhD candidate to the front stage. And a special guest. Lydia Wilden is earning her Doctor of Philosophy in Public Policy and Sociology. Her dissertation title is Misperceiving the Metropolis, the Correlates and Consequences of Imperfect Neighborhood Knowledge. Lydia's a postdoctoral scholar at the Monsueto Institute for Urban Innovation at the University of Chicago. I'll now introduce our Masters of Public Affairs and Masters of Public Policy graduates. And I invite my colleague, John Hanson, to come up across the stage from me. John will present our graduates with their class photos as they come across the stage. Muhammad Ayub, John Boothroyd, Aaron East, Catherine Jones, Benjamin Kelman, Kira Ruman Moore, Sandy Su, Caroline Sweeney, Johanna Youngins Baker, Carmen Altez, Naira Amin, Radhika Arora, Huda Bashir, Heather Berg, Stefan H. Binion, Cara Bradley, Alayna Brash, Alexia Carillo Villalobos, Kaitlyn Catino, Sharon Seron Espinoza, Aya Corrin, Dominic Cachino, Stephen Colbertson, Elena David, Krista December, Samir Deshpande, Sarah DeStefano, Justine DeSousa, Ariel Freed, Maria Alejandra Fuentes-Diestra, Jonathan Guerin, Donovan Gales, Chelsea Gaylord, Jan Gueh, Sarah Godek, Lucas Hagen, Kanice Verwahader, Tania Harris, Michael D. Houser, Frederick R. Hernandez, Tyler D.  And now for our candidates for the Bachelor of Arts in Public Policy. I'll invite my colleague Paula Lance to come forward across me from the stage to present our graduates with a class photo. Isabella Boardwin, Mira Bader, Olivia L. Braddish, Adam Lewis-Bressler, Josh Brodsky, Michael Vincent Cardimoni, Madeleine Chow, Kylie Claxton, Jake Cohen, Sophia Dara, Michael Dieter, Yulia Dobrin, Alyssa Donovan, Sharifa Duty, Alexander Dumont, Addison Egan, Haley S. Espinoza, Julia Fadinelli, Parker Fedrick, Julia Forrest, Sophie Greenberg, Rachel Colleen Greenspan, Adam Grimes, Karenna Holmstrom, Alexis W. Howard, Olivia Howard, Sarah Isaacson, Lily Abigail Israel, Mark Kava, Sarah Elizabeth Kent, Yunjin Kim, Allison Elise Kolpak, Shruti Lakshmanan, Kenneth Larson, Leah Leshinsky, Aaron Lev, Jordan Maish, Payton R. McSherry, Aaron Meyer, Joe Myers, Amelia Navins, Thomas Ohini, Iodella Arebola Ojo, Sophia Olgado, Irving Peña, Lauren Peng, Elizabeth Peppercorn, Divya Periyakarpin, Talia Potters, Hugo Quintana, Zane Rabney, Shana Raspberry Greenspan, Maya Richards, Georgia Richardson Smaller, Jess Robinson, Isabel J. Schindler, Megan Shofi, Kailin Simmons, Kelly Marcella Skulicharis, Cameron Slavkin, Yasmine Slimani, Rebecca Stachel, Jason R. Steiger, Noah Stein, Lydia Storella, Ethan Storey, Kirstie Swanson, Jacqueline Taylor, Sophia Eleni Terencio, Joe Timmer, Emma Eubelor, Brooke Van Horn, Grace Chamberlain Watson, Teagan Weckworth, and Christopher Wong. Thank you, John. Thank you all for coming today. We'll close with a song in a minute. Please stay in your seats until all graduates have possessed out of the auditorium. And then I hope you'll join us just outside on the plaza to continue this celebration of our graduates over dessert. And now, graduates, please stand if you are able and turn to face the crowd. Have achieved something wonderful. But as we see, you didn't get to this moment by yourself. Will everyone please rise if you are able, parents, grandparents, aunts, aunties, uncles, spouses, partners, sisters, brothers, friends, faculty, staff, neighbors, and everyone else here offering their support in this community. Graduates, please show your people some love. MBA students, at this time, please move the tassel on your mortarboard from the right to the left. And now, I am so proud and honored to present to you the Gerald R. Ford School of Public Policy's Classes of 2023.


This started out as a four lane but has been reduced due to funding. So what we have done, there are three sections of the roadway that are currently two lane and so we're going to do some work on those. The first section being closer to Whitesburg and which runs from Whitesburg up to Ermine and that will be three lanes and four lanes from the school on up to Ermine. In the middle section at May King it will be three lanes and there will be a whole new route at Jenkins.


What's good, YouTube? This is what you were all back to y'all again with another video. If you're new to the channel, go ahead and hit that like button. Come and subscribe, make sure you click post notifications so you will be notified anytime you budge out some heat. In today's video, we're going to be going inside a second in charge for a little bit. The store I forgot to post some footage in the last video of me going inside that store. So we're going to go inside there and check out some things right now. And then we're going to get back to work inside of Action Air. You know what I'm saying? You did what I'm doing. But other than that, y'all are jumping to this video, man. And another thing I wanted to say too was... No, it's going to be hard for y'all to hear this, man. About a little, y'all. Let's go. Let's go. Man, hold up. Let me pet my little... Pet my little... I pet, got my goddamn phone, and I'm almost out of things. Baby, pet my goddamn. It looked like it's about to rain, but your boy don't care nothing about the rain.  I just left the car. Cut the car open, man.   Boy, look at the sky, boy. It looked like it's about to rain. Make sure I got my damn water. Make sure I lock my Ferrari, because, you know, you can't be driving Ferraris and junk and not locking it dope. You got folks around here, you know what I'm saying? Try to get your stuff out your car, man. Look at that world of the... World of the bad. Oh, why? Why? All right, before we get to it, we're going to go inside the... the second in charge, man. You're fierce, miss. Oh, this is dope. I want to get me a 360 and get all the old games on the... PlayStation 2. I don't mean that as the PlayStation 2.  All this junk, man. All this... I'm telling you, I'm going to have a game... a game in room, and it's just going to have so much stuff in it, bro. Now, this is what I want.  I don't know if I'm getting it, man. I got so much... so much crazy, crazy, dope stuff. You feel me?  Your boy Predator.  Oh, a snap. They got it? I didn't know they made a it over. Bro. Look at this junk, bro. Let me get the focus right, though. Got a little focus on it. Got this crazy. This don't look dead on, too. Got the it, dawg. Got my boyfriend. Friday the 13th. And this is all crazy. Zoop, zoop, zoop, zoop. I mean, they got some dope stuff in there, bro. Bro. And then they be selling some dope comments for so cheap, bro. Look at this. Dollar 25. Zoop, zoop, zoop. Zoop, zoop, zoop. Let's go look at the games. Let's go look at the game, man. I got to get me a down 360 just to get all these old games. This used to be my favorite game on 360. Left 4 Dead. I love playing in Shadow of War. Shadow of Murder. And Shadow of War. This used to be dope. This used to be dope. Another game we used to love to play. Prince of Persia was fire. But the one that was on the PlayStation 2 was real crazy.   They just got a lot of dope stuff in this store, man. They're like, hey, she gonna get back to work, bro. See, bro, you got to go back and get back to work, bro. You tripping, bro. You been over there too long just showing random stuff, bro. You need to go back and draw something, bro. Yo, bro, this video is jumping in for you, bro. You need to find something to do with your time, bro. But you really... You just really out here showing these folks random stuff they can see by themselves, bro.  Like, you about to get a dislike and an unfollow, bro. Boop, boop, boop, boop, boop. Oh, this shit's so pretty. These type of paintings I want to do is that classy-looking stuff. But I want to have them dressed up in designer and stuff like that. These good guys so far.  I ain't talking to myself, man. I'm talking to my game. Talking to the creative game, man. They probably looking at me like, I'm crazy.  There you go. Little. Little. I'm gonna teach y'all how to... There's a lot. You supposed to grab me a little nail. I was a planner. After dough log, that mean don't go inside of it. Don't be going and nobody... stuff. Damn, I just heard somebody say something. What's up? You know that picture I showed you yesterday? Which one? The thing we're building. Those are 18 feet. They're seven and a half. They're two by eights. So I'm painting all that this weekend. But I'm just gonna paint it... Should I just do... Do you want a solid or I was gonna do stripes? Or what do you want? I could do purple and orange on it. You probably can. I'm painting orange and I just gonna put the artwork on top of it. Okay, that works for you. And then the post you can do some kind of little thing of it. Maybe I paint the post gray. I don't know. Keep it in the same... You can paint it the same theme that's going on in there. If I have some of this paint right here, I'll paint the gray on the column. And then I'll do the... Just do stripes. I'll do... So yeah, I should be able to do purple, orange, purple, orange. Yeah, keep that pattern going down. But yeah, man... Another thing too, man. I want to just tell y'all guys I appreciate y'all following me because you didn't have to come in here with me, but y'all so loyal. Y'all like, you know what? I don't want you to be bored today. So I'm just gonna come and help them. So thank you guys for coming to help me out today. You have to feel me. I will always... remember that. All y'all came out... and supported to boy. Creative gang, y'all so terrible, bro. Y'all ain't tellin' me what I got down. Tee for a yellow, bruh. Y'all gotta do better, bruh. Man, Tee... Must be dead, I am. Man... And I've been smilein' in y'all face all night. Aw, bruh, that's just this owl. The long look, though. What is she lookin'? D'oh! I had this junk all on my wall. What you think the whole wall made of this? Aw, it's so easy. Y'all lookin' good, man. It's comin' together, you feel me? Everything comin' together slowly but surely. Now, this is my favorite. This snake is my favorite, bruh. Everything comin' together. Is that too hard? Is that too loose? Yeah. That's too hard.


I've never seen anything like this. The car just exploded. Is somebody trying to put it out or? I don't know. I don't know. It's just spreading by itself? Our car is right there. My car is right on the side. Oh my goodness. It's spreading. What happened? I have no idea. Could that be That is my car right there. The gray one? Yeah, the gray one. I just hope it hasn't spread. What could have happened? Jesus. Jesus. That's crazy. Never seen anything like this. Look at their colonels. This car just exploded. What's your answer?


Thank you.  It doesn't quite do well at cocktail parties. Let me start out by telling you how I got interested, and then I'm going to go into what it's all about. When I was at Dell, there was a project at the Arizona State University, and it was called TGEN. And TGEN was basically doing molecular analysis of genomics in order to be able to cure or at least identify some of the problems with pediatric cancer. There's nothing that is more important to open networking than to be able to enable this kind of application. Everybody in this room has cancer.  But somewhere along the line, the killer cell either goes to sleep or basically cannot see the cancer. Cancer is cloaking it. What happens is we are going to be talking about precision medicine. When we look back at this year in medicine, we're going to look at the way we practice oncology as the way almost like bloodletting. And I'm not saying that negatively. We do not understand, up until just recently, how to work on solving the problem in our lifetime that's going to help us. We want to improve patient outcomes. I don't have to read it for you. But what's important is what I'm going to show you in the next couple of slides, how it works. And it requires a network in order to basically create enough data to give the oncologist a view and let the cancer tumors speak to us and tell us what it needs. And eventually, we'll be able to deal with the information. But the problem is no one, I know LinkedIn was just here, realizes that genomic data is the second biggest big data issue that we all have. Number one is YouTube's largest. Genomics is number two. But what we need to do is take this thing up here and you can see it. That's the genome. You can look at it. You can see the helix. You see the ACGTs, the way that it's created. That's really a blueprint that generates proteins. And from that helix and the 23 chromosomes, thus the name 23andme, you're able to determine what a person is like. Everybody in this room, male and female, we have 99.9% of our genomic data is identical.  But it also, what we need to do is take a look at it because if you look at it, it's 3.3 billion pairs of nucleotide pairs. I'm going to show you how much and it's coding 500,000 proteins. What this does is this generates a lot of data. And from any one of these proteins, you can rebuild the human being that came out of that DNA. And the way it gets down there is something called RNA or nucleotide acids.  But what's important is we need to give the oncologist through all our technology the best evidence-based information available. And what we found is pharma genetics companies need to accelerate the drugs. Nat Health, one of my heroes, Dr. Patrick Soon Shion, has just gotten into phase one with a cancer vaccine. We never thought we'd see that. What that is done, he's able to build that because he can look at the unique biology and the specific health conditions of a patient. And it enables the patient to also participate in the curing of their disease. We have to speed up the analysis. And that means we have to take all this data and move it through the network extremely fast. And one of the things that you just saw from LinkedIn which is being used is Kafka. And what that is is basically a producer-broker-consumer model where you can create virtual speeds that we never thought would be in the network. And you've got to be able to do the data flows right. And I'll talk about that in a second. So one of the things that is used is NiFi. And people look at NiFi and say, well, it dupes running around talking about it. But what it does is it enables you to do data flows. Anybody who thinks of the network as a plane and not doing data flows has to start rethinking some of these applications. Cassandra is in the mix for key databases. Spark Apache is there so you can do in-memory manipulation of the data. And one of the things we want to do is once we get down to that 0.01% which we will call the variant, we want to analyze it up against something of a healthy gene which is called a germline. And when you see the differences, you start to see where the discrepancy is. So what happens is you have sequencer machines. As you know, we've gone from $10,000 to sequence back 10 years ago a DNA sequence to under $100. You have Illumina all the way down to Oxford Nanopore that just came out. But take a look at the sample size for a 50x. And what that means is you take the DNA and you basically cut it into 50 slices. And the reason you're doing that is so that you can start the variant analysis and I'm not going to go through how that happens. But it's 150 gigs of data per person sitting in this room. The BAM file is another output and it's 150 gigabytes. And then you have a 90 cram which takes you down to 94 gigabytes and it has something called lousy compression. And why it's lousy compression is there can be many errors. So what we do is we don't want to use cram files. We rather get it down to a more usable mechanism that we can move across the network. And we want to do it in a pull model, not a push model. We want to then take it and do a mapping alignment. So once you get these 50 pieces, the first thing you do is called a hidden Markovian matrix and put it all back together. And when you do, what you want to look at is the variance in the DNA that is not identical. And then if you look at it, this says 3.3 billion, of which only 3 million are variants. So the thing you're fishing for is the variant. But once you find the variant, you also want to look at sequencing the RNA, which is the acids that move down to the proteins. And you want to basically look for what is going on. And you also want to use images of the tumor. And you want to take biometric data, such as electronic medical records. And all this stuff has to move at the speed of real time. We don't have time to wait. The sequencing used to take three days. We're lucky if we can get it down to three hours. Some of the new processes get it down under an hour. And then once you get it, you've got to get it in line with the radiology, with the imaging, with the germ lines, and put this all together. And it's moving over a big network. This is not just an HPC problem. This is an open networking problem. And the one thing about open source, and you heard Arpit say, it came from the days of OSDL when Linux first came out. The open source community can work hand in hand to making this a reality. So what you need is you need a data pipeline. You heard of, and you see Kafka and Nifine. You want to basically transmit this sections to a server. And then what you need to do is you need to get, and why we went to Nifine, it's lossless. You can't get lousy reception.  And then you want to deal with data provance because you're moving all kinds of different types of data as fast as you can. The key is parallelism. We always knew it. But we didn't know what applications would have to drive it. So one of the companies is using an FPGA at a Go genome down in San Diego. Has a chip called the Dragon. And it takes the four hours down to under an hour. And then one of the things you've got to do is you can't afford hours, so you've got to use Smith-Waterman to basically do quality control. And at the same time, you want to look at the IBM just announced the first commercial quantum computer. Cubits are beautiful things, because the parallelism in the quantum entanglement really takes away the fabric, which you just saw. And by doing that and being able to create all this parallelism, all of a sudden you're speeding up to no end. When Ginny Rometti, the CEO of IBM announced the IBM Q, which is a quantum computing, one of the things they talked about, which is using it for genomics, we actually believe it's great needs. The networking needs to be distributed. And one of the things that we're starting to look at, we haven't, is blockchain fabricating so that you get more security. NIFI gives you great security with encryption. But you need speed, and that's where you guys come in. The open networking committees have to look at it not just as moving around general ledgers or any other accounting data. You've got to look at your moving valuable data that saves people's lives. I can go through the molecular profiling. I'm sure this is going to be available to you. DNA is 3 billion base pairs, 20,000 genes. By the way, 14 million people in the United States today have cancer. And we get 7 million every month. So you've got to ask, is growing that exponentially? No, we're losing people. So we've got to win this war. The recent publication is the Genome Atlas, and I'm not going to go through that. So here's the problem. 20,000 genes and 3 billion DNA base pairs. Until recently, the scientists have focused only on 2%. And incorporating something called a priory assumption captures the most common genes, we have to get everyone in the country sequenced. Right now, we have about 32 million people sequenced, countries of over 350. So you can imagine what kind of data we're going to get and what kind of network needs we're going to have. So it's really also increasing because we got to take the radiology, the imaging, and the electronical and medical records, put them all together. We got to create something called logitutal record access across multiple cases. And we have to know the outcomes. So when you get a variant, you can see whether or not it affects people. And a classic example is the guy who invented the concept of sequencing was a young guy. And when he did it, he found that when he did his own sequence, he had a variant that really matched a lot of people who had prostate cancer. So he went to his doctor. And he said, I want to be checked for prostate cancer. And the guy says, we don't do PSA tests unless you're over 35 or 40 years old. This is the old medicine. Go to the prescription. Go to the concept. And believe it, oncologic doctors get very uptight when you want to use a new protocol. So all of a sudden, by looking at this, he got his PSA backing. Guess what? He had a very aggressive cancer. And he had to get operated on, but it was caught very early. That is the beauty in the magic. This is not magic. This is a lot of computations, a lot of cognitive and machine learning, a lot of different sources bringing it together. There's a couple of software companies say, hey, we can build our databases. Databases are worthless unless they're proactive. So what are the technology issues? Transport, messaging and data graphing, and real-time transport and security. As of today, and you never know in this country, HIPAA is still in effect. And we basically want to make sure that patient confidentiality is there. The SDN is required for aggregating the bandwidth and also dynamically routing and bringing up more bandwidth when it's needed through multiple ports and dynamic adjustments. Cognitive systems are inadequate today for analysis. I know IBM is going around saying Watson can do it. And Cassandra is the fastest mechanism we found for data storage. Problems with the existing data? We've been collecting data for years. 4%, 6% of the data did not provide the proper DNA RNA to be accurate without a germline, which is a cancer-free concept. We have 69% false positives because the data we're getting smarter and smarter in letting the tumors speak to us. We had 26% false negatives. If I took this to any one of my old bosses and said, I'm going to be wrong 69% of the time. And 26% of the times, I'm going to tell you it's not there. It's there. I'd be shot. So we've got to create a bridge, and that's where you guys come in. We've got to basically take the open source technology to be able to deal with the traffic demands of this amount of data. I looked at NFV, block chaining is fascinating. If we can get it performing properly, open SDN is absolutely important. What's happening is we're getting closer and closer to developing a cancer vaccine. It is in phase one trial at FDA right now, and it needs FDA approval to go through all four phases. But unless we get the technology to move the data, and Dr. Shun says I got it, that's great. But you have 26 cancer centers in the United States, all getting samples, all getting real time data. We need this group to really think about open sourcing things, not just for the businesses and trying to make networks better, but actually solving a major problem in this world. Cancer 2020, if you don't know what that is, that is the National Immunotherapy Coalition started by Joe Biden because his son Bo died of cancer. And we're seeking to accelerate the potential of combination immunotherapy where most of this data comes from.  My old friend Steve Jobs had a saying, those people who think they can change the world really do. Dr. Sheehan is doing that in cancer. And I think this group working together, not just the carriers, but everybody else, can change the world. So I could have gone very deep. I could have explained the problems with payers and pharmacies and doctors and patients. But I'd rather just show you what the problem world facing is. And with that, I want to thank you for your time unless you have a question. If you want to talk about immunotherapy, we'll be glad to talk about that. But we'll take one or two questions. Well, thanks very much for this inspiring presentation. I think curing cancer is something that's really exciting. Perhaps motivates people more than maybe, I don't know, help the phone company save money. Although if you're the phone company, saving money is great. And cheaper phone calls and more services, good too. But I have a couple of questions for you. A couple of things that I find are very interesting and kind of genomics. One of them is that often there are at least two flavors of humans, XX and XY humans. There are differences at a cellular level. And actually male humans are maybe closer genetically to chimps than to female humans. And that's sort of question number one. Question two is this idea of privacy of data. As you know, whenever you send something out over the network, well, the cat's out of the bag. You no longer control it. And so do you think that in the networking, open networking and open source can help improve the situation in both of these areas? First is considering all the different types, both types and other types of human cells and possibly their differences, which we don't normally necessarily do enough of. And the other is dealing with this data privacy issue. Data privacy is important. The reason you see NIFI splattered all over data flows is you know that came from the NSA. They donated it to the Apache. We can't unmask who donated it. But we can tell you that it was donated by the NSA. That's a joke, by the way. I'm not. Before I end up on TV. The one thing that we do know is networking and privacy is going to be a big hot topic, especially in this. As far as looking at chromosomes, proteins, RNAs, and DNA, and looking at the differences, your germline, if you're a female, is totally different than if you're a germline of a male. But believe it or not, you still have 99.9% similarities in the way your genome is formed. And by the way, the genome is literally determined at point of conception, not at time of birth. What you're going to be and what you're going to grow to be was determined when the egg was fertilized. So I don't know how open source can help with that, but I know that can help with privacy. Thank you. OK. Thank you.