I study brains, minds, and machines at Harvard and MIT.
I concentrate in Computer Science with a focus in Mind, Brain, and Behavior, a highly interdisciplinary program of study that draws on ideas and methods from computer science, cognitive science, neuroscience, psychology, linguistics, and philosophy.
Massachusetts Institute of Technology
The Fedorenko Lab uses neuroimaging and behavioral methods to construct computational models for language processing in the brain. As a research assistant at the lab, I am currently working on a project in distributional semantics. We aim to discover interesting representational structures within word-based vector models like GloVe and Word2Vec.
Harvard Center for Brain Science
The Cox Lab seeks to understand the computational underpinnings of vision cognition. I currently work as a research assistant on a project that aims to improve the performance of machine learning algorithms for image recognition and face detection using human behavioral data.
Center for Mind/Brain Sciences
University of Trento, Italy
I studied cognitive neuropsychology and social cognitive neuroscience at CIMeC during summer 2015 as part of the Harvard MBB study abroad program. Under the supervision of Paul Downing and Marius Peelen, I helped to design and execute an fMRI experiment that found differential activation of face (FFA/OFA) and body (EBA) regions when subjects viewed images of humans vs. wax figures of those same individuals.
Harvard Society for Mind, Brain, and Behavior (HSMBB)
HSMBB is an undergraduate organization that regularly hosts events, talks, and symposia on a wide range of topics related to cognitive science. As chair of the HSMBB board, I coordinate with faculty and students, plan programming, promote events, and manage the group's budget.
Structure discovery with semantic vectors
Distributional semantic models represent word meanings in a high-dimensional vector space.
However, because these vector spaces typically encompass several hundred dimensions, their structure remains an active area of research.
In this project, undertaken at the Fedorenko Lab, we use unsupervised machine learning algorithm to automatically map and discover various category-based substructures within the GloVe vector space.
Perceptual annotation through eyetracking
Despite recent breakthroughs in deep learning, humans remain significantly better than machines for many problems in computer vision -- for instance, in identifying partially-obscured faces in images.
The goal of perceptual annotation research, undertaken at the Cox Lab, is to measure human performance on various computer vision problems, and to use this psychophysical data to improve the performance of machine learning algorithms.
By tracking participants' eye movements as they view images, we can better understand which features of images are most salient across different image identification tasks.
Transfer learning in convolutional neural networks
One key component of human perceptual learning is the ability to rapidly generalize acquired knowledge across categorical domains.
In contrast, modern neural networks require thousands of training iterations on vast datasets.
In this project, undertaken at Harvard, we conducted a systematic study of convolutional neural nets’ ability to generalize perceptual knowledge from one object classification task to another.
Imitating celebrity tweets with A.I.
We developed a software system that learns over a Twitter user's timeline to generate novel tweets in their style. Our goal was to explore the viability of using a K-dimensional Markov model to generate reasonable tweets.
Hyper-local social network for Harvard dining
Berg aims to create a better sense of community for the 1,700+ freshmen at Harvard by connecting students with their friends at mealtimes. Berg is available as an iOS and mobile web app.
Live crisis break feed for Model UN
Model UN crisis committees simulate international relations through fast-paced "crisis breaks."
I developed an open-source Live Crisis Tool, based on the meteor.js framework, that allows directors to display crisis updates in real time to their committee members via a Twitter-style UI.
- CS20: Discrete Mathematics for Computer Science
- CS50: Introduction to Computer Science I
- CS51: Introduction to Computer Science II
- CS108: Intelligent Machines: Design and Ethical Challenges
- CS121: Introduction to the Theory of Computation
- CS182: Artificial Intelligence*
- Math 21a: Multivariate Calculus
- Math 21b: Linear Algebra
- Stat 110: Probability Theory*
- FRSEMR 26K: Transformative Ideas in Brain Science and Neuroscience
- MBB S-96: The Social Brain
- MBB S-101: Windows into the Structure of the Mind and Brain
- MCB81: Fundamentals of Neuroscience
- PSY1401: Computational Cognitive Neuroscience
- SLS20: Psychological Science
* denotes courses I am currently taking as of fall 2016
Mandarin Chinese (basic)