Portfolio
Background
I graduated from Stanford with my M.S. with Research Distinction in Computer Science. Before that, I graduated with my B.S. with Honors in Symbolic Systems and minor in Math.
Inspired by my interdisciplinary coursework, I am drawn to research leveraging cognitive science for robot learning and visual understanding. I aim to better understand human cognitive processes, such as multimodal perception, curiosity, and interactive learning, to develop human-inspired learning algorithms for robotics.
In my downtime, I enjoy playing tennis, snowboarding, ashtanga yoga, science fiction, and brush calligraphy.
Thesis Papers
Scaling Robot Learning without Scaling Human Effort [thesis]
Master’s Thesis
Topics: Robotics, Autonomous Reinforcement Learning, Vision-Language Models, Sim-to-Real Reinforcement Learning, Dexterous Manipulation, Learning from Human Demonstration
Leveraging Affordance Representations for Robot Learning [thesis] [publication]
Undergraduate Honors Thesis
Topics: Robotics, Online Reinforcement Learning, Vision-Language Models, Video Pre-training, Affordance Theory
Teaching
Summer 2024: CS 229 Machine Learning
Taught by Prof. Jehangir Amjad
Topics: Machine Learning, Supervised Learning, Unsupervised Learning
Winter 2024, Spring 2024: CS 224N Natural Language Processsing with Deep Learning
Taught by Prof. Tatsunori Hashimoto & Prof. Diyi Yang (Winter 2024) and Prof. Christopher Manning (Spring 2024)
Topics: Natural Language Processing, Machine Learning, Deep Learning
Fall 2023, Fall 2024: CS 157 Computational Logic
Taught by Prof. Michael Genesereth
Topics: Propositional Logic, Relational Logic, Functional Logic
Computational Projects
Today Years Old: Adapting Language Models to Word Shifts [paper] [poster] [code]
Final report, poster, and code for CS 224N: Natural Language Processing with Deep Learning (Winter 2023)
Finetuned GPT-2 and RoBERTa to predict word embeddings for novel lexical items from Urban Dictionary.
Topics: Natural Language Processing, Machine Learning, Supervised Learning, Domain Adaptation
A Shot in the Dark: Modeling Improved Zero-Shot and Few-Shot Transfer Learning with Self-Supervised Models for Sentiment Classification [paper] [poster]
Final report and poster for CS 229: Machine Learning (Spring 2022)
Transfer learning with self-supervised embeddings can improve model performance on sentiment classification tasks.
Topics: Natural Language Processing, Machine Learning, Self-Supervised Learning, Transfer Learning
Model Predictive Curiosity [paper] [poster]
Final report and poster for PSYCH 240A: Curiosity in Artificial Intelligence (Spring 2022)
Model Predictive Curiosity (MPCu) optimizes for high-curiosity action values and enriches multi-object interactions in a Box2D environment.
Topics: Curiosity-Based Models, Model-Based Reinforcement Learning, Representation Learning, Self-Supervised Learning
Philosophy Papers
The Missing Piece: Dispelling the Mystery of Introspective Illusion [paper]
Final paper for PHIL 186: Philosophy of Mind (Spring 2023)
Topics: Consciousness, Illusionism, Higher-Order Thought Theory
Consciousness, Phenomenality, and the Representational Layer [paper]
Final paper for SYMSYS 202: Theories of Consciousness (Winter 2023)
Topics: Consciousness, Representationalism, Phenomenality, Higher-Order Thought Theory, Global Workspace Theory
Philosophy of Mind: Wittgenstein, The Unconscious Mind, and Self-Knowledge [paper]
Collection of essays for OSPOXFRD 199: Philosophy of Mind (Fall 2022)
Topics: Philosophy of Mind, Philosophy of Psychology, Wittgenstein, Private Language Argument, Consciousness, Self-Knowledge
Predictive Processing: Efficiently processing high-dimensional, multimodal inputs [paper]
Final paper for SYMSYS 205: The Philosophy and Science of Perception (Spring 2022)
Topics: Multimodal Perception, Perceptual Cognition, Cognitive Processing
The Future of Human-Machine Interaction: Keeping Humans in the Loop [paper]
Final paper for OSPOXFRD 29: Artificial Intelligence & Society (Fall 2022)
Topics: Human-AI Interaction, AI Safety, Human-In-The-Loop Development, Decision-Making
Math Papers
Asymmetric Processes [paper]
Research report for MATH 101: Math Discovery Lab (Winter 2024)
Studies the properties of probability distributions of particle configurations at equilibrium for asymmetric Markov models, specifically irreducibility, aperiodicity, and double stochasticity.
Topics: Markov Processes, Markov Chains, Probability Theory
Infinite Coin Tosses [paper]
Research report for MATH 101: Math Discovery Lab (Winter 2024)
Studies pathological behavior of cumulative distribution functions for infinite coin tosses in terms of continuity, differentiability, and arc length.
Topics: Probability Theory, Continuous Random Variables
Hilbert’s 10th Problem [paper]
Research report for PHIL 152: Computability and Logic (Spring 2023)
A proof of Hilbert’s 10th Problem: determining the solvability of Diophantine equations over integers.
Topics: Computability Theory