Aaditi Vivek Saoji
Big Data Developer at Tata Consultancy Services
Big Data Developer at Tata Consultancy Services | Research Assistant at Biomedical Engineering, University of Michigan | M.S. ECE at University of Michigan, Ann Arbor
I am passionate about learning and working in field of machine learning, deep learning, data analysis, computer vision, artificial intelligence, and ready to take up new challenges. I am currently working as a Big Data Developer at Tata Consultancy Services. I completed my M.S. from University of Michigan, Ann Arbor in Electrical and Computer Engineering with specialization in Signal Image Processing and Machine Learning. I 
worked as a Software Developer Intern at Invia Medical Imaging Solutions where my work is mainly focused upon adding new functionalities to the databases. I excel in academics, have good problem solving, decision making, team management, interpersonal and communication skills. I have also been part of number of social outreach activities like Clean India Mission and health awareness drives. Coming from Pune, India, I am an enthusiastic, smart, hardworking and dynamic girl. I like to cook, travel, play tennis, read books and listen music.
Tata Consultancy Services
https://www.linkedin.com/company/tata-consultancy-services
United States
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Big Data Developer
University of Michigan
https://www.linkedin.com/company/university-of-michigan
- I am working on a project that uses k-Top Scoring Pairs algorithm in combination with other machine learning algorithms to improve inter-study prediction of genomic profiles. Tumor subtype can be determined using machine learning models and gene expression profiles.
- The goal of the analysis was to use the histone markers to classify CCLE metabolite data and analyze the strong relationships that are preserved between CCLE histone global chromatin profiles and metabolomics data with matching cancer cell lines.
- The k-TSP model was executed for all the metabolites to see the performance of each of the metabolite in order to identify the ones which can be most effectively used for classification.
- To ensure that these classification methods are solid enough for clinical use, I also performed inter-study prediction as well as validation on a synthetic dataset. 
- The models were also tested using Logistic Regression and Random Forests by keeping the test settings same to determine computational and time efficiency.
Ann Arbor, Michigan, United States
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Research Assistant
INVIA Medical Imaging Solutions
https://www.linkedin.com/company/invia-medical-imaging-solutions
I worked as a Software Developer Intern at Invia, a leading Ann Arbor based medical imaging software development company.
I analyzed their databases and created three standalone console utility applications which accomplished the following tasks:
- To give count of specific types of studies from patient records as per the stakeholder needs.
- To chronologically sort the data for every patient in order to find the closest study date from a date specified by the stakeholder.
- To read DICOM files and store the values for specified tags from them in the database.
Complex SQL queries and data manipulation techniques to identify key patterns and actionable trends in the data were performed for the console applications.
The existing approach to these problems were manual intervention - it was time consuming and error-prone process, but the console applications greatly boosted the computational efficiency. 
These developments were also added to the User Interface (UI) of Invia’s core offering 4DM. It laid out all the information to the client in a single glance.
Ann Arbor, Michigan, United States
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Software Developer Intern
eQ Technologic
https://www.linkedin.com/company/eq-technologic
At eQ Technologic, an IT company in data migration and updation, I successfully accomplished hands-on training on SQL queries and learnt to handle and manipulate large datasets. 
I performed manual and automation testing using Selenium on eQube-TM, a proprietary software for data mapping and transformation. 
I was also accountable for the complete deployment and installation of the product in various application servers, which involved refining, migrating and updating data from one system to another. 
I also wrote json and xml scripts for automation testing which influenced the business metrics.
Pune, Maharashtra, India
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Quality Assurance Engineer
Master's degree
I joined University of Michigan, Ann Arbor as Master's student in Electrical and Computer Engineering with specialization in Signal Image Processing and Machine Learning in August 2019. 
The courses which I have completed are:
Fall 2019: Probability and Random Processes, Matrix Methods for Signal Processing, Data Analysis and Machine Learning and Biomedical Imaging Systems
Winter 2020: Foundations of Computer Vision, Artificial Intelligence in Biomedical Applications, Project Management and Consultation
Fall 2020: Machine Learning, Introduction to Innovation, Directed Research
Winter 2021: Directed Research
Electrical and Computer Engineering
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University of Michigan - Rackham Graduate School
https://www.linkedin.com/school/university-of-michigan---rackham-graduate-school/
Bachelor of Engineering
I completed my B.E. in Electronics and Telecommunications from University of Pune affiliated college,  Pune Institute of Computer Technology, one of the leading engineering colleges in the state. I was amongst the top 5 rank holders during all 4 years of college with a CGPA of 3.92/4 (9.80/10)
The courses which I completed during that period are:
Digital Image Processing, Digital Signal Processing, Signals and Systems, Biomedical Signal Processing, Computer Networks, Artificial Intelligence, Mobile Communication, System Programming and Operating Systems, Object Oriented Programming, Control Systems, VLSI Design and Technology
Electrical and Electronics Engineering
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Pune Institute of Computer Technology
https://www.linkedin.com/school/pune-institute-of-computer-technology/
Bachelor of Engineering - BE
Electronics and Telecommunications
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Savitribai Phule Pune University
https://www.linkedin.com/school/university-of-pune/
Science
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Fergusson College
https://www.linkedin.com/school/fergusson-college/
Abhinava Vidyalaya School
The project of "Obstructive Sleep Apnea Detector" was amongst the top 15 ideas from over 15000 ideas all over the country for IICDC 2017. We received a government fund of Rs. 5,00,000 towards its research and product development.
Texas Instruments and Department of Science and Technology, Government of India
Finalist of India Innovation Challenge Design Contest 2017-18 organised by Texas Instruments and Department of Science and Technology, Government of India
The proposal of "Obstructive Sleep Apnea Detector" was presented at the Design Contest in IEEE International Conference on VLSI and Embedded Systems in January 2018. Our proposed idea won the 2nd Runner Up award from 15 shortlisted ideas.
We won cash prize of Rs.10000 and ARM Cortex-M Prototyping System for further development of this idea.
Institute of Electrical and Electronics Engineers (IEEE)
2nd Runner Up in Design Contest at IEEE International Conference on VLSI and Embedded Systems 2018
Antenna and Wave Propagation
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Audio Video Engineering
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Computer Networks
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Control Systems
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Data Strcutures and Algorithms
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Digital Electronics
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Electronic Devices and Circuits
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Information Theory and Coding Techniques
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Object Oriented Programming
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System Programming and Operating Systems
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VLSI Design and Technology
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Artificial Intelligence
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Biomedical Signal Processing
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Digital Image Processing
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Digital Signal Processing
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Signals and Systems
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Artificial Intelligence in Biomedical Applications
BIOMEDE 499-060
Introduction to Innovation
ENTR 500
Machine Learning
EECS 545
Project Management and Consultation
ENTR 560
Project Management and Consulting
ENTR 560
Biomedical Imaging Systems
EECS 516
Foundations of Computer Vision
EECS 504
Matrix Methods for Signal Processing, Data Analysis and Machine Learning
EECS 551
Probability and Random Processes
EECS 501
- The goal of the analysis was to use the histone markers to classify CCLE metabolite data. The training dataset consisted of 51 features for 879 cell lines and CCLE metabolite dataset of 136 metabolites was used for classification.
- The metabolites were normalized and binarized by setting threshold as 0 to train the k-TSP classifier. Scores for all possible feature pairs were computed and ‘K’ number of top pairs to be used in the final classifier were identified. A 10-fold cross validation was performed and average value for each performance parameter was calculated. The same process was repeated for all 136 metabolites, to perform a comparative analysis. 
- Similar analysis was performed by changing the threshold for binarization to -1, -0.5, 0.5 and 1. 
- Common histone pairs for a metabolite across different thresholds were identified for robustness.
Analyzing the relation between histone markers and metabolite data using k-Top Scoring Pairs algorithm
https://github.com/aaditis/KTSP
7) Object Detection
Implemented a single stage object detector based on YOLO v1 and v2. ModelNetv2, pretrained on ImageNet classification was used as backbone network for image extraction. 
At each point in the 7 x 7 feature map from the backbone network, anchor boxes of varying shapes and sizes were considered. A prediction network was implemented for each anchor box to classify the object contained in the region. 

8) Image Translation
Implemented image-to-image translation method based on pix2pix. The model was trained to generate pictures of facades from label maps using CMP Facade Database using GAN architecture and Adam optimizer.

9) Representation Learning
Implemented self-supervised learning method - autoencoder by training it on the  STL-10 dataset. A linear classifier was trained on the autoencoder representations and compared with another linear classifier trained on randomly initialized encoder. The project confirmed that unsupervised pretraining improves classification accuracy. 
Implemented contrastive learning method - Contrastive Multiview Coding (CMC). Designed CMC loss functions and trained two autoencoders and a linear classifier which showed significant improvement compared to autoencoder representation. 

10) Panoramic Stitching 
Developed an algorithm for stitching a panorama from overlapping photos by computing and matching ORB features in both images. RANSAC method for estimating parametric model from noisy observations was used to detect keypoints and represent descriptors using ORB. 

11) Optical Flow
Implemented Lucas-Kanade optical flow algorithm for estimating dense motion between a pair of images. The algorithm is applied in a course-to-fine manner as some of the motions are too large for Taylor approximation.
Computer Vision Projects - EECS 504
https://github.com/aaditis/EECS-504-Computer-Vision-Projects
1) Image Filtering and Edge Detection
Designed an edge detector by implementing convolution of image with horizontal and vertical gradient filters without using black box filtering functions. The performance was improved by first blurring the image with a Gaussian filter and then computing the gradient filter. 
Further the edge detector was modified to calculate gradients at an angle, from just the horizontal and vertical gradients. 

2) Image Blending
Designed the architecture for laplacian and gaussian pyramids with 6 levels for blending two images of an apple and orange. The difference between the blended images at each level was observed and noted to understand how the results change with more pyramid levels. 
 
3) Motion Magnification
Investigated motion magnification in videos by converting them to frequency domain and examining the corresponding phase shifts. Gaussian filters to mask small windows of the image were used and magnification was performed on each window independently. A moving average of the Fourier transform phases was kept and compared with each new new frame's DFT phase 

4) Texture Synthesis
Implemented Efros and Leung algorithm for texture synthesis - method for generating new textures from an initial sample texture. The new texture was built pixel-by-pixel by taking a window and performing exhaustive search over source texture to find the closest window. Sum of squared differences was used as a distance metric. 

5) Multi-layer Perceptron
Trained a two-layer neural network to classify images from the CIFAR-10 dataset with a softmax layer to perform classification without using any deep learning library.

6) Scene Recognition
Trained a CNN to solve scene recognition using neural networks. Data preprocessing included normalization, resizing and converting numpy arrays to PyTorch tensors. Adam optimizer was used to update the parameters and top-1 and top-5 accuracy was calculated on the test set for the networks on MiniPlaces dataset.
Computer Vision Projects - EECS 504
https://github.com/aaditis/EECS-504-Computer-Vision-Projects
- The goal of the study is to classify people suffering from PD from healthy people by using speech data.
- It has been scientifically proven that approximately 90% of patients with PD exhibit some form of vocal impairment, which is one of the earliest indicators. Thus, voice measurement to detect and track the progression of symptoms of PD has drawn significant attention.
- Multiple data visualization and statistical analysis techniques like histograms, t-test, heatmap, boxplots, scatterplots etc. were performed along with unsupervised algorithms like PCA, clustergram and k-means to reduce the feature space. 
- A comparative study between different ML algorithms like logistic regression, neural networks, support vector machines, random forests and decision trees was carried out based on their performance parameters. SVM and Random Forests achieved highest accuracy of 88%.
- The project was executed in MATLAB.
Diagnosis of Parkinson's Disease using Dysphonia Measurements
https://github.com/aaditis/Diagnosis-of-Parkinsons-Disease-using-Dysphonia-Measurements
- This project uses Disco GANs for mapping cross domain relationships for gender conversion and successfully transfers the style, pattern and specific attributes from one domain to another
(male to female and vice versa).
- It preserves key attributes such as face identity, expressions, hair color, and orientation while doing so.
- The model couples two GANs, that uses two generators for converting image from domain A to domain B, two discriminators for comparing the original images in domain A to images in domain B and two generators to reconstruct the image from domain B to match the original image in domain A.
- A binary cross-entropy (BCE) loss function was used to compute both generator and discriminator loss.
- The model was also evaluated against our own images, which gave satisfactory gender converted and reconstructed images.
Discovering cross-domain relations for unpaired image-to-image translation using generative adversarial networks
https://github.com/aaditis/Discovering-Cross-Domain-Relations-for-Unpaired-Image-To-Image-Translation-Using-GANs
• My team and I had participated in “India Innovation Challenge Design Contest 2017” supported by Department of Science and Technology, Government of India and Texas Instruments for coming up with innovative solutions for commonly faced problems in the society. 
• We developed a device to diagnose “obstructive sleep apnea (OSA)”, a sleeping disorder. 
• We collected data of 100 patients suffering from OSA and 100 normal patients for extracting features like heart rate and R-R interval. It was classified using Support Vector Machine.
• Real time ECG signal was then recorded and processed using Fast Fourier Transformation to get the significant peaks and windowing method was used to identify R-peaks and to calculate the R-R interval. 
• This algorithm was implemented using ECG Sensor ADS1298 ECG, MSP430f5529 boards and CC3100 WiFi module for wireless transfer of final result obtained from Texas Instruments.
• We developed a prototype for the proposed idea which was shortlisted in top 15 projects from 15000+ submissions and also won the 2nd Runner Up at IEEE International Conference on VLSI and Embedded Systems Design Contest.
Sleep Apnea Detector
- We developed an interactive learning module to test the mathematical ability of speech and hearing impaired students from Std. I to Std. V by presenting them various questions covering their entire syllabus in form of signs and gestures. The modules were scored by converting the gestures shown by the students were converted to text.
- The motivation was to develop a system which can act as a translator between the sign language and spoken language and can make the communication efficient and effective.
- The project used Image Processing techniques like dilation, erosion and calculating mean, median to separate the hand gesture from the image. 
- The selected features for training the model were peaks (detected using Canny Edge Detectors and k-curvature algorithm) and histogram of oriented gradients (HOG). Support vector machines were used for classification which gave an accuracy of 86.7%. 
- The platform used was MATLAB. The module was deployed on test run to schools in Pune to facilitate learning process for specially abled children.
Indian Sign Language Conversion to solve Mathematical Problems
- The aim of the project was to identify the speaker in real time by extracting features of voice which are unique to every person. 
- Preprocessing of the input audio signal was performed in four steps - Truncation, Framing, Windowing and Short Time Fourier Transform (STFT)
- Audio features like Pitch and Mel Frequency Cepstrum Coefficients (MFCC) were used. Pitch was calculated using autocorrelation of the audio signal and MFCC was calculated from the Mel Filterbank which was completely designed from scratch by us in MATLAB. 
- Euclidean distance was used to classify the MFCC coefficients and pitch. 
- The project also implemented by loading the MATLAB and C code in Code Composer Studio with the use of DSP Processor TMS320C6713.
Real Time Speaker Recognition
Poverty Alleviation
Pune Institute of Computer Technology
https://www.linkedin.com/company/pune-institute-of-computer-technology
• I was a member of college magazine committee ‘Pictoreal’ for 2 years and worked in the editing team.
• I was the head coordinator of Money Donation Event organised by Pictoreal in 2016. We successfully collected an amount of Rs.58690. It was donated for a noble cause of helping education of children of drought hit farmers.
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Head of Money Donation Event
IEEE
https://www.linkedin.com/company/ieee
• I was an IEEE member from 2014-2016 and in the organising committee of our college technical event “Credenz” (2015-2016). I volunteered for various sub-events under Credenz. It’s the largest technical event in Western Maharashtra which received a footfall of 5000+ students.
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Member
Social Services
Janseva Organisation
I worked with Janseva Foundation, a charitable trust where I spent time with the elderly people. I used to visit them on every weekend for 3 months in 2016.
Volunteer
Social Services
Rotary Club, Belagavi
I had participated in “Pulse Polio Campaign” organised by Rotary Club, Belagavi in September 2018.
Volunteer
Social Services
Bharati Vidyapeeth
https://www.linkedin.com/company/bharatividyapeeth
I participated in various community outreach activities like Clean India (Swaccha Bharat), Health Awareness Rallies (HIV, Tobacco, etc.) and helped in health check up camps in urban slums organised by neighbouring medical college.
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Volunteer
MIT OpenCourseWare
Artificial Intelligence (6.034)
LinkedIn Learning
AcPBjlgaPYWxgsq6eIcqPsng61y3
Machine Learning and AI Foundations: Decision Trees
LinkedIn Learning ⋅ Course Certificate
AcA8gexe0czT2RFxysc7E7XIBgXa
Statistics Foundations 1
LinkedIn Learning ⋅ Course Certificate
Aeh8S9gLtGm_ING3xE5lNUtpMA35
Statistics Foundations 2
HackerRank
www.hackerrank.com
SQL
https://www.hackerrank.com/certificates/df060e445363
Harvard University
Harvard Data Science - Probability
Harvard University
Harvard Data Science - Visualization
Harvard University
Harvard Data Science - R Basics
Coursera Course Certificates
Neural Networks and Deep Learning
Coursera Course Certificates
Python Programming
Texas Instruments
Finalist in DST & Texas Instruments India Innovation Challenge Design Contest 2017
https://www.linkedin.com/in/kunal-kasbekar
Ann Arbor, MI
Kunal Kasbekar
Student at University of Michigan, Ann Arbor in ECE ICVLSI | B.E. in ECE at MITCOE Pune
https://www.linkedin.com/in/akashganesan
Detroit Metropolitan Area
Akash Ganesan
Graduate Student Research Assistant at University of Michigan
https://www.linkedin.com/in/hongjunliu
Ann Arbor, MI
Hongjun Liu
Incoming SDE Intern @ Doordash | Umich Graduate Student
https://www.linkedin.com/in/namasaxe
Ann Arbor, MI
Naman Saxena
M.S. student at University of Michigan, Ann Arbor
https://www.linkedin.com/in/nealsanna
Ann Arbor, MI
Neal Sanna
Actively seeking full time position in Biomedical Engineering - Graduate student at the University of Michigan
https://www.linkedin.com/in/thakkarshrey
Ann Arbor, MI
Shrey Thakker
Graduate Student at the University of Michigan
https://cn.linkedin.com/in/can-jiang-8159a5193
Pudong
Can Jiang
University of Michigan - Rackham Graduate School学生
https://www.linkedin.com/in/rohansequeira
Ann Arbor, MI
Rohan Sequeira
Graduate Student Instructor at University of Michigan
https://www.linkedin.com/in/benjamin-klapper
United States
Benjamin Klapper
MS Candidate in Biomedical Engineering at Northwestern University
https://www.linkedin.com/in/anjali-pillay-370975192
Ann Arbor, MI
Anjali Pillay
Graduate student at the University of Michigan - Ann Arbor
https://www.linkedin.com/in/amogh-rane
Hillsboro, OR
Amogh Rane
Software Engineer @ Intel | Ex-SDE/DS Intern @ Intel | MS ECE UMich, Ann Arbor
https://www.linkedin.com/in/kht861130
Ann Arbor, MI
Ke-Haur Taur
MS ECE @ University of Michigan | Seeking for full-time Design Verification positions
https://www.linkedin.com/in/gshikhar02
Ann Arbor, MI
Shikhar Gupta
Graduate Student @ University of Michigan | Cisco | IIT Guwahati
https://www.linkedin.com/in/ameyatudavekar
New York, NY
Ameya Tudavekar
Electrical Engineering Graduate Student at Columbia University
https://www.linkedin.com/in/anshuman-maan
Ann Arbor, MI
Anshuman Maan
Incoming Intern @NVIDIA | MS ECE Graduate Student at University of Michigan
https://www.linkedin.com/in/anand-wataney
San Jose, CA
Anand Wataney
System Engineer at Qualcomm
https://www.linkedin.com/in/ruchaa-apte
San Jose, CA
Rucha Apte
Machine Learning Engineer @ Velodyne Lidar | Data Science Intern @ Altair Engineering | Software Engineering Intern @ DeepMap, Inc | Signal Image Processing Machine Learning @ University of Michigan, Ann Arbor
Doug Markey
      
          



            
              
        
              
  

    
        I have enjoyed working with Aaditi for the past year while she was an intern at our company. She was always pleasant and easy to work with.  She communicated well not hesitating to ask questions to get the requirement well defined. She could worked well independently and as part of the team, delivering her features as described. She always documented her implementations and was flexible when we requested adjustments. I can recommend her as a great team player in any organization. Rev. Linda Seatts-Ogletree, M.A., M.A.D.R.
      
          



            
              
        
              
  

    
        Good Afternoon,
My name is Linda Seatts-Ogletree, M.A., M.A.D.R., President & CEO of Release & Refresh Women's Empowerment Series, Inc., a 501(c)(3) Charity located in metropolitan Detroit. 

I am delighted to recommend Aaditi Vivek Saoji for a position with your company.   I had the pleasure of meeting Aaditi in November 2019.  She was one of the Consultants selected to help grow our organization through philanthropy, marketing, social media, and volunteer recruitment.  

A partnership with the Michigan Non-Profit Association and the University of Michigan offered Student Consultants to qualified non-profit organizations.  I was so impressed with Ms. Saoji.  She is a rising star! Her insight, intellect, and personable skills was refreshing and welcomed.  She is a professional, bright team-player that would benefit your company.  

She was instrumental in writing an impressive and professional  Proposal, Statement of Work, Business Model Canvas, and a comprehensive Recommendation.  I strongly recommend  Ms. Saoji! She will be a refreshing talent!

Please feel free to contact me should you require additional information.  I can be reached at releaserefreshwes@gmail.com or you can call me at (248) 469-3505.

Kind Regards,
Linda Seatts-Ogletree, M.A., M.A.D.R., Release & Refresh Women’s Empowerment Series, Inc.                                                                 

PICT Alumni Forum
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