about

I'm a computer science PhD student at Carnegie Mellon University.

My research interests include artificial intelligence, machine learning, neural networks, reinforcement learning, and game theory.

education

International Baccalaureate Diploma Programme. Mulgrave School. 2014–05.
Bachelor of Science in computer science, minor in applied math. Columbia University. 2019–05. Magna cum laude.
Master of Science in computer science. Carnegie Mellon University. 2021–05.
Doctor of Philosophy in computer science. Carnegie Mellon University. Ongoing.

publications

Predicting the large-scale evolution of tag systems. Complex Systems. 2016. Volume 25. Issue 2. Pages 79–107.
Efficient exploration of zero-sum stochastic games. AAAI Conference on Artificial Intelligence Workshop on Reinforcement Learning in Games. 2021–02.
Bayesian multiagent inverse reinforcement learning for policy recommendation. AAAI Conference on Artificial Intelligence Workshop on Reinforcement Learning in Games. 2021–02.
Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks. International Joint Conference on Artificial Intelligence (IJCAI). 2023-08. Pages 2844–2852.
Model-free preference elicitation. International Joint Conference on Artificial Intelligence (IJCAI). 2024–08. Pages 3493–3503.
ApproxED: Approximate exploitability descent via learned best responses. International Conference on Autonomous Agents and Multiagent Systems (AAMAS). 2025.
AlphaZeroES: Direct score maximization outperforms planning loss minimization. Extended abstract. International Conference on Autonomous Agents and Multiagent Systems (AAMAS). 2025.
Joint-perturbation simultaneous pseudo-gradient. International Joint Conference on Artificial Intelligence (IJCAI). 2025. To appear.

preprints

selected honors

teaching

Graduate artificial intelligence. Teaching assistant. Carnegie Mellon University. Spring 2021.
Artificial intelligence: Representation and problem solving. Teaching assistant. Carnegie Mellon University. Spring 2024.

work experience

TRIUMF National Lab for Particle and Nuclear Physics. Researcher. 2014–06 to 2014–08.
Researched laser ion sources and resonance ionization spectroscopy.
Wolfram Research. Summer researcher. 2015–06 to 2015–07.
Researched large-scale dynamics of cellular automata and tag systems.
Columbia Lightwave Research Lab. Researcher. 2015–06 to 2015–08.
Researched parallel computing architectures and algorithms for photonic networks.
Wolfram Research. Research intern. 2016–01 to 2016–05.
Created step-by-step educational problem-solving software for Mathematica.
Goldman Sachs. Summer analyst. 2016–06 to 2016–08.
Created automated information retrieval and information extraction system.
Columbia Robotics Lab. Researcher. 2018–05 to 2018–08.
Developed reinforcement learning algorithms for robots that use EEG signals.
Columbia Center for Theoretical Neuroscience. Researcher. 2018–09 to 2019–05.
Researched mean-field variational Bayesian inference with adaptive priors.
Columbia Computer Vision Lab. Researcher. 2018–09 to 2019–05.
Researched one-shot image recognition through spatiochromatic deformations.
Google Research. Student researcher. 2023–06 to 2023–09.
Researched model-free preference elicitation algorithms for recommender systems.

volunteering

Columbia Organization of Rising Entrepreneurs. Software developer. 2015–09 to 2015–12.
Developed website using Python Flask, Bootstrap, Sass, Material Design.
Columbia Application Development Initiative. Software developer. 2015–09 to 2015–12.
Created Bayesian online changepoint detection system for stream processor.
Columbia Data Science Society. Board member. 2015–10 to 2017–05.
Organized data science and machine learning workshops, lectures, and hackathons.
Wolfram Research. Student ambassador. 2015–12 to 2018–05.
Organized workshops and seminars on Mathematica and Wolfram Research.
Open-source contributions, including to JAX and Optax.
Synax, a neural network library for JAX. Under development.

skills

Programming languages: Python, JavaScript, Wolfram Language, Haskell, C++, C#, Java.
Natural languages: English (fluent), Spanish (fluent), Mandarin Chinese (elementary).
Courses taken: artificial intelligence, machine learning, natural language processing, computer vision, statistics, graphical models, numerical methods, optimization, ordinary differential equations, partial differential equations, graph theory, abstract algebra, real analysis, complex analysis, thermodynamics, classical mechanics, data structures, analysis of algorithms, computational game solving.