I am a senior double-majoring in
Computer Science (AI
concentration) & Psychology.
I am currently doing research in unsupervised segmentation for my
undergraduate honors thesis with
Professor Erik Learned-Miller
in the Computer Vision Lab.
I do freelance development as Imaginary Number and hack on
personal and open source projects on
github.
Interests
My interests are computational & biological vision, the generation of perception from sensation, and more broadly unsupervised machine learning. I’m most interested in the process of learning to see and the kinds of representations that underlie vision. I’m curious about how observations of the world are captured by the brain and how these encodings are transmuted into models.
Research
I am working on or have worked on:
- Information-theoretic visual segmentation
- Character alignment for scene text recognition
- Medical image registration by maximization of mutual information
- Creating a data set for estimating the 3D pose of faces
Coming soon: real details!
Coursework
In Progress
Graphical Models: Representation, Inference, and Learning (Graduate)
Mathematical Models of Behavior (Graduate)
Capstone Research
AI & Machine Learning
Computer Vision (Graduate)
Artificial Intelligence
Knowledge Discovery & Data Mining
Reasoning Under Uncertainty: combinatorics, probability, bayesian
reasoning,
markov models, game theory, intro to coding theory
Research in Genetic AI and Genetic Programming
Core CS & Mathematics
Discrete Mathematics: logic, intro set theory, intro graph theory,
grammars, automata, intro theory of computation
Linear Algebra
Calculus I & II
Algorithms
Applied Information Theory (Graduate)
Statistics I
Psychology
NeuroCognition and Perception Lab Research Practicum
Perceptual Neuroscience (Independent Study)
Behavioral Neuroscience (Honors)
Behavioral Decision Making
Cognitive Psychology – at Université Denis–Diderot, Paris
Social Psychology – at Université Denis–Diderot, Paris
Research & Statistical Methods of Psychology
Linguistics
Syntax (Honors)
Introduction to Linguistic Theory