Aaron Dharna
Machine Learning Researcher at New York University
Ph.D. student focusing on reinforcement learning
Data Science excites me because it represents the bleeding edge of where computer science and mathematics merge to empower informed decision making.
New York University
https://www.linkedin.com/company/new-york-university
Researching into endlessly generate new paired learning environments and agents such that agents easily adapt to new environments by combining evolutionary algorithms, neural networks, optimization, and reinforcement learning. 

Peer-reviewed paper: https://arxiv.org/abs/2007.08497

Supervisor: Dr. Julian Togelius
Greater New York City Area
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Machine Learning Researcher
Fordham University
https://www.linkedin.com/company/fordham-university
Full-Stack development work on DARPA-SD2E - ESCALATE: Pseudo-Random sampling of chemical experiments and automated materials science research platform.

Integrated machine learning models on ESCALATE to aid the materials discovery process and predict chemical-reaction outcomes in perovskites with Dr. Joshua Schrier.
Greater New York City Area
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Computational Research Intern
Instrumental Inc.
https://www.linkedin.com/company/instrumental-inc.
Created and adapted metrics around anomaly detection in image processing
Researched machine learning algorithm improvements by finding, reading, and implementing academic papers
Researched, found, tested, and integrated a new algorithm into Instrumental’s ML Stack that improved accuracy
San Francisco Bay Area
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Machine Learning Engineer Intern
nTopology inc.
https://www.linkedin.com/company/ntopology
Port, clean-up, improve, and optimize C++ code from Element 1.0

Create new internal facing debug and external facing design tools used by other engineers to speed up testing and asset creation

Helped develop data-driven design and functional modeling tools on the front and back-end

Interface between the mathematical kernel team and the design team
Greater New York City Area
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Software Engineering Intern
School District of Clayton
https://www.linkedin.com/company/liberty-public-schools
Refer to the lesson plan left by lead teacher and ensure that appropriate measures are taken to follow it

Manage classroom order by encouraging task adherence and appropriate classroom behavior

Ensure that classroom instruction is handled according to the school’s policies, goals and objectives
Clayton, MO
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Substitute Teacher
Fordham University
https://www.linkedin.com/company/fordham-university
Robotics research to develop more robust version of previously-developed class-project prototype

Add sensory fusion software to the robot—based on notion that if the robot has a camera and sonar sensors, it should be able to take a picture of its surroundings and then attach distances to those objects in the photo. 

Successfully managed to get the robot to orient itself towards incoming objects and track them.
Greater New York City Area
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Robotics Research Assistant
Fordham University
https://www.linkedin.com/company/fordham-university
My responsibilities include making sure that my tutees understand the material they're learning so that they can do those types of problems without and others on their own later. 

I foster their mathematical skills and hone their thinking so that they can approach problems in the future without needing help from others.
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Mathematics Tutor
Doctor of Philosophy - PhD
Machine Learning
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New Jersey Institute of Technology
https://www.linkedin.com/school/njit/
Master's degree
Data Analytics
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Fordham University Graduate School of Arts and Sciences
https://www.linkedin.com/school/fordham-university-graduate-school-of-arts-and-sciences/
Bachelor of Science (B.S.)
Mathematics and Computer Science
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Fordham University
https://www.linkedin.com/school/fordham-university/
Discovery of new perovskite materials is motivated by a broad range of materials applications and accelerated by recent advances in machine learning. We herein report dataset augmentation, benchmarking, and interrogation for an ongoing experimental campaign consisting of 9483 halide perovskite synthesis experiments. To address limitations in previous work, we developed an improved description of the reactant concentrations in the experiments (validated against experimental observations) and performed experiments quantifying the excess volume of mixing of γ-butyrolactone/formic acid mixtures used in the perovskite syntheses. Combining this improved description of reactant concentration with other physicochemical features of the reactants, we constructed 1108 ML models to elucidate the roles of the algorithm (k-nearest neighbors, linear support-vector machine, and gradient boosted tree), feature set (12 in total), preprocessing regime (e.g., standardization), and training data holdout scheme on ML predictive ability. ML comparisons illustrated that the chemical accuracy of less sophisticated physical models in a dataset do not hinder interpolative model performance. Analysis of feature contributions showed how ML models “learn” competitive representations for concentration using raw experimental descriptions. Interrogation of the most performant models indicated that the numerical values of physicochemical features were not important, rather these features were being used to identify and interpolate within a particular reactant set. ML models were shown to be capable of making rudimentary extrapolations to untrained chemical systems when compared against basic benchmarks, and models which included the newly developed chemical features were shown to be more reliable than models trained without. These results illustrate how a stepwise comparative approach to machine learning can provide insight into what and how much models are “learning” for a given prediction task.
Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?
ACS
https://pubs.acs.org/doi/abs/10.1021/acs.jpcc.0c01726
Artificial Intelligence
6525
Complex Analysis
3004
Data Mining
6049
Data Structures
2001
Differential Equations
3000
Differential Geometry
4001
Linear Algebra
3001
MultiVariable Calculus
2005
Probability
3000
Real Analysis
3003
● Identified a relevant real-world problem that can be solved using machine learning, and modeled it using techniques learned throughout the Nanodegree. 
● Built a matches data set using Bungie’s REST API. 
● Designed and implemented an experiment using behavioral data and performance metrics to predict the outcome of a game between two teams of players. 
● Built and trained: a neural network using Tensorflow and Keras and a Support Vector Machine using SkLearn. 
● Achieved a 94.8% accuracy on my neural network’s prediction on the testing data set. 
● Laid the groundwork for future research into how to use player-play-styles to inform matchmaking
Predicting Victory in Online Team-Based Zero-Sum Games
https://github.com/aadharna/Destiny
● Preprocessed and classified images from the CIFAR-10 dataset.
● Built and Trained a convolutional neural network using Tensorflow.
● Achieved a 71.6% accuracy on my neural network's predictions on the sample images .
Image Classification
https://github.com/aadharna/CIFAR-10_ConvNet
● Applied reinforcement learning to build a simulated vehicle navigation agent.
● Modeled a complex control problem in terms of limited available data.
● Used Q Learning to design a scheme to automatically learn an optimal driving strategy based on rewards and penalties
Training a smartcab to Drive
https://github.com/aadharna/smartcab
● Reviewed unstructured data to understand the patterns and natural categories that the data fits into.
● Used multiple algorithms; both empirically and theoretically compared and contrasted their results.
● Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis
Creating Customer Segments
https://github.com/aadharna/customer_segments
● Coded a scalable Bayesian Network in  Python (and originally in C++)
● Performs Bayesian Inference on the network and returns the Maximum A Posteriori (most likely) class, if applicable
Building Python Bayesian Networks
https://github.com/aadharna/PythonBayesNet
Udacity
Machine Learning Engineer NanoDegree
https://www.linkedin.com/in/anamaria-alexandrescu-ph-d-7a663866
New York, NY
Anamaria Alexandrescu, Ph.D.
Postdoctoral Teaching Fellow at New York University
https://www.linkedin.com/in/berta-bartoli
Brooklyn, NY
Berta Bartoli
PhD Student in Psychology and Social Intervention at NYU
https://www.linkedin.com/in/marcelle-james-866496a
New York City Metropolitan Area
Marcelle James
Versatile Individual who is easily adaptable to any environment
https://www.linkedin.com/in/mikhala-stein-kotlyar
Fair Lawn, NJ
Mikhala Stein Kotlyar
Resourceful and mission-driven manager with extensive experience in operations and program development; strategic communication; donor stewardship and grant management; budget and financial management
https://www.linkedin.com/in/nick-buehler-nyu
New York, NY
Nicholas Buehler
NYU '25; Phillips Academy '21; Founder and President of Students Against the Virus Entrepreneurship (SAVE) Program
https://www.linkedin.com/in/lasya-damaraju-511204169
New York, NY
Lasya Damaraju
Philosophy & Biochemistry major at New York University
https://www.linkedin.com/in/karin-elizabeth-lehr-lcsw-lmft-p-a-68522225
New York City Metropolitan Area
Karin Elizabeth Lehr, LCSW, LMFT, P.A.
Adjunct Lecturer at New York University
https://www.linkedin.com/in/lawrence-seldin-ms-pmp-cmc-cpc-sfc-and-fema-certified-90852b12
New York, NY
Lawrence Seldin, MS, PMP, CMC, CPC, SFC and FEMA Certified
Enterprise Risk Management, Audit, Compliance, QA, Policy & Security Business Leader
https://www.linkedin.com/in/joshua-meisel-665556158
New York, NY
Joshua Meisel
Teaching Assistant at New York University
https://www.linkedin.com/in/shelly-j-smith-5288881a
New York, NY
Shelly J. Smith
LaGuardia Studio at New York University
https://kz.linkedin.com/in/aiym-mazhenova-51953b80
Kazakhstan
Aiym Mazhenova
In permanent beta: learning, improving, evolving
https://www.linkedin.com/in/alexandra-forrester-787b0173
New York, NY
Alexandra Forrester
Senior Program Director, Office of Academic Program Review and Assessment at New York University
https://www.linkedin.com/in/erica-kotta
Brooklyn, NY
Erica Kotta
PhD Candidate, Experimental Condensed Matter at NYU
https://www.linkedin.com/in/varunbl
San Francisco Bay Area
Varun Bharadhwaj Lakshminarasimhan
Machine Learning Researcher at Apple | Carnegie Mellon University
https://www.linkedin.com/in/nidhivyas
Cupertino, CA
Nidhi Vyas
Founding Team Member @ Galileo | ex-Apple | ex-CMU SCS | ML Research (We are hiring!)
https://www.linkedin.com/in/misraaa
San Francisco Bay Area
Aashish Misraa
ML Research @ Adobe
https://www.linkedin.com/in/aditya-rawal-15678b4
San Francisco Bay Area
Aditya Rawal
Machine Learning Researcher at AWS AI Labs, Previously: Uber AI Labs, PhD@UT Austin
https://www.linkedin.com/in/kunalkhadilkar
Seattle, WA
Kunal Khadilkar
Data Scientist @ Adobe Photoshop | TEDx Speaker | Carnegie Mellon Grad | AI for Social Impact
https://www.linkedin.com/in/alimousavi1988
Kirkland, WA
Ali Mousavi
Senior Machine Learning Researcher at Apple
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An old friend of mine, Kamalesh Rao, has been working on a project developing improved polymers matrices for injectable, time-released…
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Hot Release! Now when you look at images of your units, you can see a heat map of where our Discover AI thinks there might be issues. I was an…
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I might be biased, but this company sounds awesome!If this sounds like something you'd like to help us make even better – we're growing our Product…
Fordham University M.S. Data Science Alumni
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The Do Work Project
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Udacity
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