Yegor Tkachenko

Yegor Tkachenko

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

Python Programming for Data Science

Adjunct Assistant Professor of Business

Columbia Business School

Lecturing

Papers

Position: Enforced Amnesia as a Way to Mitigate the Potential Risk of Silent Suffering in the Conscious AI

Yegor Tkachenko. ICML 2024

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

Yegor Tkachenko, Kamel Jedidi. Scientific Reports by Nature 2023

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.

Pixel luminosity effect across variables

Reining in Long Consumer Questionnaires with Self-Supervised Deep Reinforcement Learning

Yegor Tkachenko, Kamel Jedidi, Asim Ansari. Working Paper 2022

Question ranking optimization objective

Customer Simulation for Direct Marketing Experiments

Yegor Tkachenko, Mykel J. Kochenderfer, Krzysztof Kluza. IEEE DSAA 2016

Code

Bayes net for initial data snapshot generation

Autonomous CRM Control via CLV Approximation with Deep Reinforcement Learning

Yegor Tkachenko. arXiv.org 2015   (technical report)

Optimal Allocation of Digital Marketing Budget: the Empirical Bayes Approach

Yegor Tkachenko.Journal of Marketing Analytics 2014

Bayes net for initial data snapshot generation

Evaluation of Marketing Effectiveness: Modern Approaches and Methods

Yegor Tkachenko. Journal of Marketing in Ukraine 2014

Pet Projects

Proof-of-Concept Web App for Matching People for Any Purpose

www.willowisp.us

Side-project put together over a couple of weeks in Jan 2024.

Books

Entrepreneur's Bad Advice: How to Get Rich

Yegor Tkachenko, Margo Poda, Vasiliy Kondyrev. Amazon 2019

www.entrepreneursbadadvice.com

SoftBank

Cases

All Nutrition A: Focus Group Research for Market Segmentation

Kamel Jedidi, Robert J. Morais, Yegor Tkachenko. Columbia CaseWorks 2019

All Nutrition B: Quantitative Research for Market Segmentation

Kamel Jedidi, Robert J. Morais, Yegor Tkachenko. Columbia CaseWorks 2020

Teaching Note. All Nutrition (A&B)

Kamel Jedidi, Robert J. Morais, Yegor Tkachenko. Columbia CaseWorks 2020

Chapters

Sample Size Determination

Yegor Tkachenko. 2023

Tutorials

Intro to R Programming

Yegor Tkachenko. 2018

Code

As prepared for edX Marketing Analytics course taught by Prof. Asim Ansari and Prof. Kamel Jedidi.

Logistic Regression for Churn Modeling and CLV Optimization

Yegor Tkachenko. 2018

Code | Data

As prepared for edX Marketing Analytics course taught by Prof. Asim Ansari and Prof. Kamel Jedidi.

Other Writing

Loophole in GDPR Regulation

Yegor Tkachenko. 2019

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