Research

Areas

• Human-computer interaction and computing education
• Generative AI in the learning and teaching of computing
• Training teaching assistants and tutors as educators
• Earlier background: computational neuroscience and human memory

Lab affiliations

CS Education Lab, UC San Diego

PI: Leo Porter · 2023–present

Studying LLM-assisted learning in introductory CS courses, and helping develop a novel intro CS course with GenAI as a core member of both the teaching and research teams. Led and presented a study on novice use of GenAI for open-ended programming tasks at the Microsoft AI Economy Institute summit.

Design Lab, UC San Diego

PIs: Steven Dow, Philip Guo · 2022–2023

Studied dynamic interventions for computer science learners in classroom and tutoring settings. (Joined UCSD as a PhD student in Cognitive Science before switching to CSE.)

Parallel Distributed Processing Lab, Stanford University

PI: Jay McClelland · 2021–2022

Studied learners' formulation, explanation, and use of abstract principles while completing a novel task. Designed and implemented a sequence of simplified Sudoku puzzles in Python and JavaScript for online deployment.

Abbott Lab, Columbia University

PI: Larry Abbott · 2020–2021

Studied biologically plausible models of memory using neural network models in PyTorch. Trained a custom network with the covariance matrix adaptation evolutionary strategy (CMA-ES) and compared its performance with stochastic gradient descent.

Visual Thinking Lab, Johns Hopkins University

PI: Jonathan Flombaum · 2016–2017

Studied the effect of visual working memory encoding on long-term object representation. Ran human-subject behavioral experiments using everyday object images partially obscured by random visual noise to test effects on recognition and familiarity.

Full history and dates are on my CV.