Bachelor of Science, Computer Science, 2018
University of Virginia, Charlottesville, VA
|Programming Languages||Python, C++, Java, Ruby, C#, C, (Ba)sh|
|Machine Learning Framework||Tensorflow/Keras, Pytorch|
Co-location and Co-equipment Convolutional Neural Network
2018 - present
Trained a Convolutional Neural Network with Triplet Loss Architecture to extract features from raw sensory data, creating embeddings where sensors of the same group are closer than those of different group. In the embedding space, I calculated the pairwise distances and with a Genetic Algorithm clustered sensors under the contraints of number of each type of sensor in each group. The algorithm has achieved over 85% accuracy on both co-location (belonging to the same room) and co-equipment (belonging to the same control unit) dataset.
Automatically Co-locate Raw Sensory data of Building Sensors
Extracts features from raw sensory data with Canny Edge detector
Encodes signal that maximizes the Pearson Correlation Coefficient difference between in-group and out-group sensors, with a CNN-based Siamese network trained by Triplet loss
A genetic algorithm that clusters sensor to maximize the mean intra-group correlation coefficients, which is faster than Integer Linear Programming (linear vs. exponential complexity), and more accurate than approximation by Linear Programming and simulated annealing (both proposed by previous work).
Student Intern (Deep Reinforcement Learning) at Happy Elements
Assisted in building, improving, and accelerating an AI player in python with Tensorflow for a modern mobile game, achieving better winning rate (from ~23% to ~70%, higher than average human), and 4 – 5 times better performance, by changing the training program and model:
- Distributed work across servers with asynchronous workers and backpropagations;
- Implemented Gradients broadcast without centralized parameter server;
- Refactored the architecture to fully convolutional, with less overfitting, earlier convergence, and better score.
Teaching Assistant for CS 2102 (Discrete Math) at University of Virginia
2015 Spring – 2016 Spring
Assisted in the teaching of multiple semesters of CS 2102 (Discrete Mathematics) by grading examinations, hosting office hours, and pre-viewing some assignment questions.
Wu, H., Jin, H., Sun, Y., Wang, Y., Ge, M., Chen, Y., & Chi, Y. (2016). Evaluating stereoacuity with 3D shutter glasses technology. BMC Ophthalmology BMC Ophthalmol, 16(1). doi:10.1186/s12886-016-0223-3
Ticket-To-Ride AI Player
Created an AI player (Java) for Ticket-To-Ride that won the first place in the class tournament, by modeling the game with Markov Decision Process.
Created a desktop application that helps generating stereo-images for ophthalmology tests with Python and Qt. It draws stereo symbols with fully customizable size, color, stereo depth, orientation, and places. It generates two formats of stereoimage: plain PNGs and randot images (filled with randomly placed pixels).
Interpreter for Classroom Object Oriented Language
Created a lexer, a parser, a type system, and interpreter for COOL. It efficiently evaluated a COOL program, could produce annotated AST, and had some debugging capability. The core of the program was written in OCaml.
Wrote a simple shell that can evaluate user input and execute proper program in a separate process. The program was written in C++ on Linux, using POSIX interface for system calls.