aaron dharna
Machine Learning Researcher at New York University
ph.d. student focusing reinforcement learning
data science excites represents bleeding edge computer science mathematics merge empower informed decision making .
New York University
https://www.linkedin.com/company/new-york-university
Machine Learning Researcher
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
https://media-exp1.licdn.com/dms/image/C4D0BAQF6TmzLkch0dQ/company-logo_100_100/0/1561566354660?e=1655942400&v=beta&t=r124YsAgZw3h7dsF0vOzexg0zy8oSXN8XNsK3Wyu_KI
Fordham University
https://www.linkedin.com/company/fordham-university
Computational Research Intern
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
https://media-exp1.licdn.com/dms/image/C4D0BAQFB_jf2aPklgQ/company-logo_100_100/0/1614796566213?e=1655942400&v=beta&t=hAdujTozYsDm0g3pEIZHL1piXFi0ucfM7PlIXuSMQjg
Instrumental Inc.
https://www.linkedin.com/company/instrumental-inc.
Machine Learning Engineer Intern
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
https://media-exp1.licdn.com/dms/image/C560BAQHlH63fSfYGhg/company-logo_100_100/0/1625608982183?e=1655942400&v=beta&t=NaHk0LAMBhCsRYlyfZBMPLXNp4yl-7J5ZjHY37ExMhY
nTopology inc.
https://www.linkedin.com/company/ntopology
Software Engineering Intern
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
https://media-exp1.licdn.com/dms/image/C4D0BAQGylfBREPLmcw/company-logo_100_100/0/1643313019201?e=1655942400&v=beta&t=eXqA8hug4f7E3RM_5QtmC5-E8ZpeeC-a4uVV1hfMKUc
School District of Clayton
https://www.linkedin.com/company/liberty-public-schools
Substitute Teacher
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
https://media-exp1.licdn.com/dms/image/C4E0BAQEUU6v_54vkiw/company-logo_100_100/0/1519888289479?e=1655942400&v=beta&t=Qg9aJYmxjbIE-UJem1fQvVCKm4xulC7ux9gAMYVGfSg
Fordham University
https://www.linkedin.com/company/fordham-university
Robotics Research Assistant
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
https://media-exp1.licdn.com/dms/image/C4D0BAQFB_jf2aPklgQ/company-logo_100_100/0/1614796566213?e=1655942400&v=beta&t=hAdujTozYsDm0g3pEIZHL1piXFi0ucfM7PlIXuSMQjg
Fordham University
https://www.linkedin.com/company/fordham-university
Mathematics Tutor
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.
https://media-exp1.licdn.com/dms/image/C4D0BAQFB_jf2aPklgQ/company-logo_100_100/0/1614796566213?e=1655942400&v=beta&t=hAdujTozYsDm0g3pEIZHL1piXFi0ucfM7PlIXuSMQjg
Machine Learning
Doctor of Philosophy - PhD
New Jersey Institute of Technology
https://www.linkedin.com/school/njit/
https://media-exp1.licdn.com/dms/image/C4E0BAQGeM2xL_Ibjhw/company-logo_100_100/0/1564427229540?e=1655942400&v=beta&t=iOsMVa64fF9eCgQ-E20F0koKZ66XyGSOWVIlRmCvUsc
Data Analytics
Master's degree
Fordham University Graduate School of Arts and Sciences
https://www.linkedin.com/school/fordham-university-graduate-school-of-arts-and-sciences/
https://media-exp1.licdn.com/dms/image/C4D0BAQF7zXGJURhz1w/company-logo_100_100/0/1519922079093?e=1655942400&v=beta&t=u1DLOba9I6nzNlNvLr9QK5aivj8kOzbqfeME8PemIRE
Mathematics and Computer Science
Bachelor of Science (B.S.)
Fordham University
https://www.linkedin.com/school/fordham-university/
https://media-exp1.licdn.com/dms/image/C4D0BAQFB_jf2aPklgQ/company-logo_100_100/0/1614796566213?e=1655942400&v=beta&t=hAdujTozYsDm0g3pEIZHL1piXFi0ucfM7PlIXuSMQjg
Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?
ACS
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.
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
Predicting Victory in Online Team-Based Zero-Sum Games
● 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
https://github.com/aadharna/Destiny
Image Classification
● 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 .
https://github.com/aadharna/CIFAR-10_ConvNet
Training a smartcab to Drive
● 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
https://github.com/aadharna/smartcab
Creating Customer Segments
● 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
https://github.com/aadharna/customer_segments
Building Python Bayesian Networks
● 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
https://github.com/aadharna/PythonBayesNet
Machine Learning Engineer NanoDegree
Udacity
https://www.linkedin.com/in/anamaria-alexandrescu-ph-d-7a663866
Anamaria Alexandrescu, Ph.D.
Postdoctoral Teaching Fellow at New York University
New York, NY
https://www.linkedin.com/in/berta-bartoli
Berta Bartoli
PhD Student in Psychology and Social Intervention at NYU
Brooklyn, NY
https://www.linkedin.com/in/marcelle-james-866496a
Marcelle James
Versatile Individual who is easily adaptable to any environment
New York City Metropolitan Area
https://www.linkedin.com/in/mikhala-stein-kotlyar
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
Fair Lawn, NJ
https://www.linkedin.com/in/nick-buehler-nyu
Nicholas Buehler
NYU '25; Phillips Academy '21; Founder and President of Students Against the Virus Entrepreneurship (SAVE) Program
New York, NY
https://www.linkedin.com/in/lasya-damaraju-511204169
Lasya Damaraju
Philosophy & Biochemistry major at New York University
New York, NY
https://www.linkedin.com/in/karin-elizabeth-lehr-lcsw-lmft-p-a-68522225
Karin Elizabeth Lehr, LCSW, LMFT, P.A.
Adjunct Lecturer at New York University
New York City Metropolitan Area
https://www.linkedin.com/in/lawrence-seldin-ms-pmp-cmc-cpc-sfc-and-fema-certified-90852b12
Lawrence Seldin, MS, PMP, CMC, CPC, SFC and FEMA Certified
Enterprise Risk Management, Audit, Compliance, QA, Policy & Security Business Leader
New York, NY
https://www.linkedin.com/in/joshua-meisel-665556158
Joshua Meisel
Teaching Assistant at New York University
New York, NY
https://www.linkedin.com/in/shelly-j-smith-5288881a
Shelly J. Smith
LaGuardia Studio at New York University
New York, NY
https://kz.linkedin.com/in/aiym-mazhenova-51953b80
Aiym Mazhenova
In permanent beta: learning, improving, evolving
Kazakhstan
https://www.linkedin.com/in/alexandra-forrester-787b0173
Alexandra Forrester
Senior Program Director, Office of Academic Program Review and Assessment at New York University
New York, NY
https://www.linkedin.com/in/erica-kotta
Erica Kotta
PhD Candidate, Experimental Condensed Matter at NYU
Brooklyn, NY
https://www.linkedin.com/in/varunbl
Varun Bharadhwaj Lakshminarasimhan
Machine Learning Researcher at Apple | Carnegie Mellon University
San Francisco Bay Area
https://www.linkedin.com/in/nidhivyas
Nidhi Vyas
Founding Team Member @ Galileo | ex-Apple | ex-CMU SCS | ML Research (We are hiring!)
Cupertino, CA
https://www.linkedin.com/in/misraaa
Aashish Misraa
ML Research @ Adobe
San Francisco Bay Area
https://www.linkedin.com/in/aditya-rawal-15678b4
Aditya Rawal
Machine Learning Researcher at AWS AI Labs, Previously: Uber AI Labs, PhD@UT Austin
San Francisco Bay Area
https://www.linkedin.com/in/kunalkhadilkar
Kunal Khadilkar
Data Scientist @ Adobe Photoshop | TEDx Speaker | Carnegie Mellon Grad | AI for Social Impact
Seattle, WA
https://www.linkedin.com/in/alimousavi1988
Ali Mousavi
Senior Machine Learning Researcher at Apple
Kirkland, WA
An old friend of mine, Kamalesh Rao, has been working on a project developing improved polymers matrices for injectable, time-released…
https://www.linkedin.com/signup/cold-join
Liked by Aaron Dharna
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…
https://www.linkedin.com/signup/cold-join
Liked by Aaron Dharna
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…
https://www.linkedin.com/signup/cold-join
Liked by Aaron Dharna
https://media-exp1.licdn.com/dms/image/C4E07AQFhCrLxFhm7nA/group-logo_image-shrink_92x92/0/1631000459077?e=1647691200&v=beta&t=eYZvPWOTkF0pVNSSbVQArnbEWxuUe6nDr9Zcc6MYG0M
Fordham University M.S. Data Science Alumni
https://www.linkedin.com/groups/12371103
https://media-exp1.licdn.com/dms/image/C4E07AQEhJiQ1hajo4Q/group-logo_image-shrink_92x92/0/1630998694818?e=1647691200&v=beta&t=neZQ6FXsXZ4hXmxt39IVe4_-xSuCDwX3DJR9rJdiHlk
The Do Work Project
https://www.linkedin.com/groups/1839561
https://media-exp1.licdn.com/dms/image/C4D07AQFrHdSUESfBZw/group-logo_image-shrink_200x200/0/1631358236061?e=1647691200&v=beta&t=KQ-BEdomcgxuAu1f2ge4Ca13LHce60T2jrPVvJgva3c
Udacity
https://www.linkedin.com/groups/4360157