I am a research asistant at The Chinese University of Hong Kong (CUHK). I am interested in physic-informed machine learning, trustworthy AI, and reinforcement learning (sometimes plays with distributed system). My goal is to build a reliable AI system that can be impact the real world massively.
I received Bachelor's degree in Computer Science from National Tsing Hua University (NTHU). Before graduate, I was also a research assistant at DataLab and advised by Prof. Shan-Hung Wu. It's my pleasure to work with Dr. Pin-Yu Chen and Prof. Tsung-Yi Ho after graduate.
Cornell University Incomming Ph.D. in CS Sep. 23 - Present
Carnegie Mellon University Safe AI Lab, Research Internship Jun. 23 - Oct. 23
The Chinese University Hong Kong Research Assistant Jul. 22 - Aug. 24
National Tsing Hua University B.S. in CS Sep. 17 - Jun. 22
Proposed a new backdoor attack on the diffusion models. We found that the attacker can embed a backdoor into the diffusion models with 5% poison rate and 50 epoch fine-tuning.
Proposed a new generative model that reduce the bilevel optimization objective into single-level one while keeping the spirit of adversarial learning. Our method takes NTK-GP as the surrogate of the discriminator and generate comparable images with SOTA GANs.
Projects
EfficientDet
An EfficientDet implementation in TF2.0 based on the paper EfficientDet: Scalable and Efficient Object Detection on CVPR’20.
DRL Collection
A collection of implements of classical DRL algorithms. It contain modular implementations of A3C, A2C, DDQN, and REINFORCE (naive) with Tensorflow2.0.
ML Collection
Implemetation and derivation of ML algorithms, including SVM and VBGMM in Python.
Implementation of 2V2PL
Implemented the 2V2PL concurrency protocol on VanillaDB with Java and improved the throughput at most 5 times than S2PL in TPCC workload
