Kevin (YuShan) Zhang

Kevin (YuShan) Zhang

Undergraduate student at Purdue University, aspiring PhD student

Current Works

Foundational LLMs for Decompiled Code

Advisors: Professor Lin Tan, PhD students Nan Jiang and Danning Xie.

In this project, we aim to develop a foundational model for use in refinement of the output of decompilers. Previous state-of-the-art approach LLM4Decompile is trained only on output of the Ghidra SRE tool, and we hope to build on the generalization capabilities of the model for use with output of the Hex-Rays decompiler accessed through IDA Pro. I am thus aiming to explore efficient self-supervised methods and architectural adjustments for finetuning of their released model. Gathering and decompiling code from Github repositories has been a major bottleneck in this project, combined with slow training of very large models, further indicating to me the importance of model and data efficient approaches.

Ensemble and Metapolicy Approaches to Robotic Behavior Cloning

Advisor: Professor Zachary Kingston

In this project, I hope to extend the generalization properties of the NeuralMP approach. My goal is to make use of an ensemble Mixture of Experts approach to optimize a metapolicy such that the combination of experts may generalize better to unseen environments. Through my current progress, I have been able to implement modified versions of previous works’ approaches in training and architecture, and improve the process by which I udnerstand the content of related work.

ML@Purdue Project: Deep Reinforcement Learning for Pokemon Battling

Here, I am leading a project to teach new members about reinforcement learning in a novel environment- Pokemon Battling. I hope to tackle an extension to my previous report, and support a team in developing multiple methods for training of an agent for competitive Pokemon battling. In particular, I hope to focus efforts on development of effective methods in few-shot learning or imitation learning. Current progress is focused on training members in RL techniques and familarizing them with PyTorch and OpenAI Gym; as well as associated other libraries used to simulate and connect Pokemon Battling to these systems. The final aim is to host a competition for the final trained agents to showcase the team’s efforts.