I am a tenure-track assistant professor of Computer Science and UMIACS at University of Maryland, College Park. My research interests are in machine learning, optimization, and natural language processing. I have published ~90 papers at NeurIPS, ICML, ICLR, AISTATS, ECML/PKDD, ACL, EMNLP, NAACL, COLING, CVPR, ICCV, KDD, ICDM, AAAI, IJCAI, ISIT, Machine Learning (Springer), IEEE TPAMI/TIP/TNNLS/TKDE, etc. I am the recipient of the Best Student Paper Award at ICDM 2013 and the 2020 IEEE TCSC Most Influential Paper Award.

Our recent works study (1) How, why, and when to transfer human learning (e.g., curriculum, retention, sub-tasking, curiosity, exemplar selection, collaboration, etc.) to improve machine learning in the wild (e.g., with unlabeled, biased, noisy, redundant or distributed data, unseen tasks/environments, distribution shift); (2) Controllable AI in both training and inference/adaptation; and (3) Human-AI alignment and AI personalization. And Yes we are developing these methods for LLMs, multi-modality foundation models, and RL. Our goal is to develop efficient, versatile, trustworthy, and environmentally-friendly hybrid-intelligence based on coevolution between human and machine. A list of our research topics can be found at the bottom of this webpage.

I was a visiting research scientist at Google between 2021-2022. Before that, I received my Ph.D. (thesis) from Computer Science of University of Washington, where I was a member of MELODI lab led by Prof. Jeff A. Bilmes. I have been working with Prof. Dacheng Tao (University of Sydney) as a research assistant at University of Technology, Sydney (UTS) and Nanyang Technological University (NTU). I was a research intern at Yahoo! Labs, mentored by Dr. Hua Ouyang (Apple) and Prof. Yi Chang (Jilin University), and a research intern at Microsoft Research, mentored by Dr. Lin Xiao (Facebook AI Research).


Research Topics

  • Machine Learning (2008-present)
    1. Learning over time: Curriculum Learning, Continual Learning
    2. Learning via interactions: Reinforcement Learning, Online Learning
    3. Learning across tasks/domains: Multi-task Learning, Meta-Learning, Domain Adaptation/Generalization
    4. Learning multiple models: Mixture-of-Experts (MoE), Collaborative/Cooperative Learning, Federated/Decentralized Learning
    5. Learning under noises: Noisy-Label Learning, Adversarial Learning
    6. Learning representations: Self-Supervised Learning, Dimension Reduction
    7. Sparse Learning: Compressed Sensing, Matrix Factorization, Spectral Method
    8. Optimization: Continuous, Combinatorial, Multi-Objective, Black-Box
  • Natural Language Processing (2016-present)
    1. Attention mechanisms
    2. Toxicity and Bias in NLP models
    3. Adversarial textual attack and defense
    4. Large language models (LLMs) (Reflection-Tuning, Alpagasus, and Cherry LLM created by our group)
    5. Personalization
  • Multi-modality Models (2021-present)
    1. Vision-Language Models
    2. Human-AI alignment
    3. VLM/LLM + RL and Multi-modality Embodied-AI