I am a Ph.D. student in Computer Science at the University of Maryland, College Park, where I am fortunate to be advised by Prof. John Dickerson. Before that, I obtained my Bachelor’s degree in Computer Science and Mathematics from Bryn Mawr College. My research focuses on understanding and enriching the reasoning capabilities of current deep learning models. I believe having the ability to reason is an important and necessary step to achieving general intelligence.
Across my Ph.D., I have interned at Microsoft Research under the guidance of Adith Swaminathan on designing better hindsight learning schemes for combinatorial optimization problems. I have also interned at DeepMind under the supervision of Dr. Petar Veličković, working on how to re-use the learned knowledge and skills in reinforcement learning. In addition, I have worked closely with Prof. Jimmy Ba and Prof. Taiji Suziki when doing research internships at Vector Institute and RIKEN AIP. During my research, I also received great guidance from Prof. Don Perlis, Prof. Furong Huang, and Prof. Justin Brody.
|Aug 26, 2022||I finished my summer internship at Microsoft Research, and I am continuing working on designing better RL algorithms for combintorial optimization problems.|
|Jan 21, 2022||I passed my preliminary exam, and I am now a Ph.D. candidate!|
|Sep 28, 2021||Our work on How Does a Neural Network’s Architecture Impact its Robustness to Noisy Labels has been accepted to NeurIPS 2021.|
|Sep 28, 2021||Our work on VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization has been accepted to NeurIPS 2021.|
|May 24, 2021||Started internship at DeepMind.|
NeuripsHow Does a Neural Network’s Architecture Impact Its Robustness to Noisy Labels?In Advances in Neural Information Processing Systems, 2021
ICLRHow Neural Networks Extrapolate: From Feedforward to Graph Neural NetworksIn International Conference on Learning Representations (Oral) 2021
ICLRWhat Can Neural Networks Reason About?In International Conference on Learning Representations (Spotlight) 2020
AISTATSUnderstanding Generalization in Deep Learning via Tensor MethodsIn International Conference on Artificial Intelligence and Statistics 2020