This past summer, I interned at the Georgia Tech Research Institute where I developed a machine learning platform called EMADE (Evolutionary Multi-Objective Algorithm Design Engine) using genetic programming to automatically create combinations of machine learning algorithms (e.g., deep neural networks, gradient boosted decision trees, SVMs, and clustering algorithms) that achieve better performance than any individual algorithm on multiple objectives (e.g., F1 score, mean squared error, and complexity of GP tree). Now I'm working part-time at GTRI during the fall to apply this framework to detect underwater targets with bathymetric LIDAR 3D point clouds. In the spring of 2017, I was a technical business analyst intern at Interface, Inc. where I performed data analytics and web scraping to create a consolidated data source and mobile application for the marketing and sales team. During the summer of 2016, I was a software developer at Molina Healthcare where I did data analytics, specifically extract, transform, and load (ETL) between databases and automatic report generation.
Under Dr. Charles Isbell and Himanshu Sahni, I'm doing research on AI decision making through hierarchical deep reinforcement learning with state space decomposition, action space hierarchy, and object-centric attention mechanism. I'm continuing what I've learned in my summer internship through the Automated Algorithm Design Vertically Integrated Project where I'm applying the EMADE to Kaggle data science competitions and continuing to improve the framework.
"Computer science is no more about computers than astronomy is about telescopes."
- Edsger Dijkstra
Created video game AI using Asynchronous Advantage Actor-Critic (A3C) model and Deep Q Networks for OpenAI Gym reinforcement learning environments with Python and TensorFlow for neural networks. Only done with CartPole problem but hope to scale to more complex problems (like Atari) when I get a stronger computer.
Competed in Kaggle data science competitions with EMADE machine learning framework developed at GTRI for house price prediction and image recognition competitions.
Winner of HackDuke Best Use of Facebook API prize! Personal carbon footprint tracker that enables the ordinary individual to take action against climate change. Android app in which I wrote much of core Java that integrated the various XML layouts and scoring algorithms. I also made the app interactive. Click here for Devpost link. Also, click here for GitHub link.
Developed Android application to track the quality of water through crowdsourced information and used Google Maps API. Exceeded expectations for project with Firebase to store user data and additional user tracking features. Also created a web app with Express and Node to accompany app for more experience.
HackGT project that helps potential job applicants form better relations with companies, creating richer dialogue between recruiters and individuals. Website created using Indeed's API, Wikipedia's API, Python and Flask. First hackathon project but somewhat slow. Click image for link.