About Me
My background is in data science and applied machine learning research — including deep learning, reinforcement learning, convex optimization, and recommender systems / personalization. My primary interest is in the design of robust, scalable decision making systems. In my free time, I enjoy coding ML algorithms and web apps in Python and Julia.
I obtained my PhD in Computational Marketing from Columbia Business School (2022), co-advised by Prof. Asim Ansari and Prof. Kamel Jedidi. I hold an MS in Operations Research (MS&E) from Stanford University (2016) and a BS in International Business from Taras Shevchenko University (2012). While at Stanford, I worked as a research assistant at Stanford Intelligent Systems Laboratory (SISL) of Aeronautics and Astronautics Department, under Prof. Mykel J. Kochenderfer, doing research on deep reinforcement learning. Prior to that, in 2013-2014, I spent a year working for BlackRock Global Client Insights team.
Teaching
Papers
Position: Enforced Amnesia as a Way to Mitigate the Potential Risk of Silent Suffering in the Conscious AI
ICML | OpenReview | Project Page | .pdf
This piece explores a philosophical hypothetical. It generated some heated discussion on Hacker News.
A Megastudy on the Predictability of Personal Information From Facial Images: Disentangling Demographic and Non-Demographic Signals
Supplementary Information | Code & Data
(Older version is available on SSRN, titled "What Personal Information Can a Consumer Facial Image Reveal?")
This project, at its various stages, has received energized book and blog coverage.
Pet Projects
Proof-of-Concept Web App for Matching People for Any Purpose
Side-project put together over a couple of weeks in Jan 2024.
Books
Cases
Chapters
Tutorials
Intro to R Programming
As prepared for edX Marketing Analytics course taught by Prof. Asim Ansari and Prof. Kamel Jedidi.
Logistic Regression for Churn Modeling and CLV Optimization
As prepared for edX Marketing Analytics course taught by Prof. Asim Ansari and Prof. Kamel Jedidi.
Other Writing
Misc.
The Car and the Pedestrian Experiment by Fons Trompenaars pdf
Stanford Marshmallow Experiment by Walter Mischel wiki pdf
Responsibility-for-Plant Experiment by Ellen J. Langer and Judith Rodin pdf
Basic Econometrics Lectures by Arne Hallam site
Gaussian Components by Peter Orbanz pdf
CS231n Convolutional Neural Networks Course Notes by Andrej Karpathy site
Information Cascades by David Easley and Jon Kleinberg pdf
Decision Making Under Uncertainty by Mykel J. Kochenderfer book
Philosophy of Civilization by Albert Schweitzer book
A Theory of Human Motivation by Abraham H. Maslow text
Eros and Civilization by Herbert Marcuse wiki book
Atlas Shrugged by Ayn Rand wiki book
The Book of Ecclesiastes wiki text
List of Action Verbs for Resumes pdf
Convex Optimization by Stephen Boyd and Lieven Vandenberghe pdf
Discrete Choice Methods with Simulation by Kenneth Train pdf
Harbingers of Failure by Eric Anderson et al. text
Big Five Inventory pdf
Reference Notes From Gyan's Research wiki
Free Will Experiments by Benjamin Libet pdf
The Mind's Best Trick by Daniel M. Wegner pdf
Conscious Mind by David Chalmers pdf
Breakdown of the Bicameral Mind by Julian Jaynes book
CS168: The Modern Algorithmic Toolbox site
Compressed Sensing Illustration in Python site
20 Simple Distinct Colors site
Power and Sample Size Analysis Notes by Charles DiMaggio site
Big Maс Index Generator by The Economist code
Top 50 matplotlib Visualizations site
Humans May Sense Earth's Magnetic Field by Kelly Servick pdf
A Classroom Demonstration of Gullibility by Bertram R. Forer pdf
Intuitive Explanation of Discrete Fourier Transform as Correlation Analysis site
Make Matplotlib Plot Fonts Look Good With LaTeX site
Hoeffding’s D Explainer site
Concentration bounds via optimization site
PyTorch internals site