My main responsibilities were to 1) characterize a quantum representation of hidden Markov models (HMMs) for improved parameter estimation, 2) help implement a neural network architecture in Caffe for fast segment-based object tracking, and 3) derive a faster, updateable Gaussian process regression (GPR)-based model that incorporates random feature selection and recursive Bayesian linear regression (BLR). I was supported by student stipends (the President's Undergraduate Research Award) awarded based on written proposals. Object Tracking Final Report QHMM Proposal QHMMs Final Report

I was advised by Dr. Jennifer Welch and supported by an NSF REU grant. My work consisted of giving correct, fast relaxed data structure implementations in partially synchronous message-passing systems, as well as proving upper and lower bounds on their performance. Relaxed Stacks Presentation

I wrote part of a web application that processes and standardizes order information from various online sales channels and automatically handles RESTful communications with different APIs.

Our research in Dr. Mujica-Parodi's group focused on modeling human pattern recognition and shifts in mental rules under conditions of varying ambiguity. I programmed an engaging graphical pattern detection task for the purpose of testing these models. NCRC 2015 Poster Presentation.pptx