Biography
I am an MEng student at MIT EECS, fortunate to be advised by Caroline Uhler and mentored by Aldo Pacchiano.
My research broadly lies in the field of advancing the theoretical understanding of AI-driven decision making, and I actively work in the subjects of causal representation learning and reinforcement learning.
I previously graduated from MIT in 2024 with a Bachelor’s in Mathematics (18) and Artificial Intelligence and Decision Making (6-4). During my undergrad, I had the pleasure of working with Caroline Uhler on causal representation learning as a SuperUROP Scholar and Eric and Wendy Schmidt Center Innovation Scholar.
Publications & Projects
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
Ryan Welch*, Jiaqi Zhang*, Caroline Uhler
Published in NeurIPS 2024 and featured in MIT News
Automatic BioNER Data Annotation with Large Language Models
Joint work with Jenny Cai, Victoria Gao and Isabella Struckman
Joint work with Sarah Bentley and Isabella Struckman
Professional Experience
Broad Institute of MIT and Harvard
Researcher
Quantitative Research Intern
Quantitative Research Intern
Data Science Intern
Fall 2023 – Present
Summer 2023
Summer 2022
Summer 2021
Teaching Experience
Quantitative Methods for NLP (6.8610)
Teaching Assistant (TA)
Design and Analysis of Algorithms (6.1220)
Teaching Assistant (TA)
Design and Analysis of Algorithms (6.1220)
Grader
Fall 2024
Spring 2023
Fall 2023
Campus Activities
Risk Manager
House Manager, Rush Chairman
Member
Winter 2022 – Winter 2023
Spring 2022 – Fall 2023
Fall 2020 – Winter 2022
Coursework
Algorithms for Inference (6.7810)
Reinforcement Learning: Foundations and Models (6.7920)
Quantitative Methods for Natural Language Processing (6.8610)
Optimization methods (6.7200)
Computational Cognitive Science (9.660)
Networks (6.3260)
Seminar in Undergraduate Advanced Research (6.UAR)
Special Topics in Causality (6.S091)
Design and Analysis of Algorithms (6.1220)
Computability and Complexity Theory (6.1400)
Introduction to Machine Learning (6.3900)
Introduction to Algorithms (6.1210)
Fundamentals of Programming (6.1010)
Mathematics for Computer Science (6.1200)
Introduction to Computational Thinking and Data Science (6.100B)
Introduction to Computer Science (6.100A)
Seminar in Discrete Mathematics (18.204)
Real Analysis (18.100P)
Combinatorial Analysis (18.211)
Computability and Complexity Theory (18.400)
Fundamentals of Statistics (18.650)
Linear Algebra (18.06)
Probability and Random Variables (18.600)
Differential Equations (18.03)
Multivariables Calculus (18.02)
Economic Data Science (14.32)
Networks (14.15)
Psychology and Economics (14.13)
Principles of Macroeconomics (14.02)
Principles of Microeconomics (14.01)
Managerial Finance (15.401)
Physics II (8.02)
Physics I (8.01)
Introduction to Solid-State Chemistry (3.091)
Introductory Biology (7.014)
Hacking from the South (21A.511)
Technology and Culture (STS.075)
Introduction to World Music (21M.030)
Minds and Machines (24.09)