nicholas johnson

AI Researcher @ MIT, Princeton, Oxford - Professional Speaker


Nicholas André G. Johnson has engaged in optimization and machine learning research at MIT, Princeton University, Oxford University and the Montreal Institute of Learning Algorithms. He is currently a PhD student in Operations Research, the study of how to make good decisions with limited information in uncertain environments, at MIT where he is working towards developing a more unified theory of optimization and machine learning.

Nicholas holds an undergraduate degree with highest honours in operations research and financial engineering with minors in computer science, statistics and machine learning, and applied and computational mathematics from Princeton University. He was the Valedictorian of Princeton’s Class of 2020 and is the first Black Valedictorian in the University’s 274 year history. His undergraduate thesis focused on developing high performance, efficient algorithms to solve a network based optimization problem that models a community based preventative health intervention designed to curb the prevalence of obesity in Canada.

Nicholas has interned as a software engineer at Google and as a quantitative developer at the D. E. Shaw Investment Group, and has conducted and presented international sustainable engineering projects at the United Nations Headquarters. He is a member of the Phi Beta Kappa, Tau Beta Pi and Sigma Xi honor societies. Nicholas has previously been featured by the New York Times, CNN, ABC News, Time and BET.


As a professional speaker, Nicholas is an advocate for educational attainment in marginalized communities and increased representation in STEM industries. Through his consulting work, Nicholas helps corporations leverage state of the art technology to optimize their business operations.

Computer Programming


Nicholas’ current research is focused on making methodological and algorithmic contributions to discrete optimization and leveraging modern advances in discrete optimization to solve central machine learning problems exactly at scale without using heuristics. He has a particular interest in working on applied problems in healthcare and finance. Nicholas is advised by Professor Dimitris Bertsimas at MIT. Hover over the boxes below to learn more about some of his past research work!

Sequential Stochastic Network Structure Optimization with Applications to Addressing Canada's Obesity Epidemic
Generating Privacy Preserving Synthetic Datasets 
General purpose adaptive optimization engine in R.
Desk Computer


Predicting BIXI Montreal Bikeshare Hourly Volumes
Approximation Theorem For ReLU based Neural Networks

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NICHOLAS A. G. Johnson

Ph.D. Candidate - Operations Research

Massachusetts Institute of Technology

B.S.E. Operations Research

& Financial Engineering

Princeton University

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