Biography

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).

News

• 2023/11: I will give a talk “Towards Controllable and Personalized AI Models” at UMD CS department seminar on 11/03.
• 2023/10: We release HallusionBench focusing on the Language Hallucination and Visual Illusion of GPT-4V(ision), Llava-1.5, and other multi-modality models. Analyses and insights can be found in the preprint.
• 2023/10: Data recycling and filtering improves instruction-tuning of LLMs, leading to recycled LLMs outperforming other larger LLMs trained on new data and RLHF. We release Reflection-Tuning preprint, codebase, and the model.
• 2023/10: Two papers (How Many Demonstrations Do You Need for In-context Learning, Merging Mixture-of-Experts into One) have been accepted by EMNLP 2023.
• 2023/09: Two papers (multi-modality model distillation for task adaptation, clustered additive modeling for structured federated learning) have been accepted by NeurIPS 2023.
• 2023/07: Two papers (model-adaptive data augmentation curriculum, subclass balancing for long-tail recognition) have been accepted by ICCV 2023.
• 2023/06: How to efficiently optimize the textual instructions applied to API black-box LLMs (e.g., ChatGPT) for solving downstream tasks? Please check our recent work InstructZero, paper and code have been released.
• 2023/06: Invited talk at Purdue Statistics on “Structured Decentralized Learning”.
• 2023/06: Two papers (Meta-Vote Pruning and Eigensubspace of Temporal-Difference Dynamics) have been accepted by ECML/PKDD 2023.
• 2023/05: I will teach CMSC-421 on “Introduction to Artificial Intelligence” in Fall 2023.
• 2023/04: Three papers (training dynamics of continual learning, continual RL via sparse prompting, structured cooperative learning) have been accepted by ICML 2023. See you at Hawaii in July!
• 2023/04: One paper about personalization in federated recommendation system has been accepted by IJCAI 2023.
• 2022/12: I will teach CMSC-828A on “Fantastic Machine Learning Paradigms and Where to use Them” in Spring 2023.
• 2022/12: I will serve as an SPC (meta-reviewer) for IJCAI 2023.
• 2022/11: One XAI paper on extracting local reasoning chains for subtasks from neural networks such as ResNet and ViT has been accepted by TMLR.
• 2022/10: One paper (adversarial attacks to Question-Answer models) has been accepted by EMNLP 2022.
• 2022/09: Three papers (adversarial augmentation for continual learning, adversarial augmentation for representation learning, federated learning from pre-trained models) have been accepted by NeurIPS 2022.

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