I am a Ph.D. student in Computer Science at University of Washington and a member of MELODI lab led by Prof. Jeff A. Bilmes. I will be joining University of Maryland, College Park as a tenure-track assistant professor at the Department of Computer Science and affiliated with UMIACS in 2022. My research interests are in machine learning, optimization, and natural language processing. I have published ~60 papers at NeurIPS, ICML, ICLR, AISTATS, EMNLP, NAACL, COLING, KDD, ICDM, AAAI, IJCAI, ISIT, Machine Learning (Springer), IEEE 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.
My recent works study how, why, and when to translate human learning strategies (e.g., curriculum, retention, sub-tasking, curiosity, exemplar learning, collaborative learning, etc.) to improve machine learning in the wild (e.g., with unlabeled, biased, noisy, redundant or distributed data, extrapolation to unseen tasks/environments). Our works are built upon empirical/theoretical analysis to the learning dynamics of neural networks and tools from discrete and continuous optimization. Our goal is to develop efficient, versatile, trustworthy, and environmentally-friendly hybrid-intelligence based on coevolution between human and machine. A list of my research topics can be found below.
I have been a research assistant at University of Technology, Sydney (UTS) and Nanyang Technological University (NTU), supervised by Prof. Dacheng Tao (University of Sydney). I was a research intern at Yahoo! Labs, supervised by Hua Ouyang (Apple) and Yi Chang (Jilin University), and a research intern at Microsoft Research, supervised by Lin Xiao (Facebook AI Research). I also work closely with several members and students of Australian AI Institute.
- 2021/12: One paper of federated prototype learning has been accepted to AAAI 2022.
- 2021/11: I will serve as an SPC for SIGKDD 2022.
- 2021/09: Three papers (1 spotlight for Submodular Partitioning, Curriculum RL and Planning, Class-Disentanglement) have been accepted to NeurIPS 2021. Congratulations to Shuang Ao and Kaiwen Yang for their first paper!
- 2021/09: One paper of sentiment bias has been accepted to EMNLP 2021 (findings).
- 2021/08: I will serve as an SPC for AAAI 2022.
- 2021/02: I am selected as an expert reviewer for ICML 2021.
- 2021/01: One paper of curriculum learning and training dynamics has been accepted to AISTATS 2021.
- 2021/01: Three papers (RoCL for curriculum noisy-label learning, AutoLRS for auto-learning rate schedule, IPN for prototype zero-shot learning) have been accepted to ICLR 2021.
- 2021/01: One paper of knowledge graph completion has been accepted to WWW 2021.
- 2020/10: Selected among the top 10% of high-scoring reviewers for NeurIPS 2020.
- 2020/09: One paper of curriculum learning and training dynamics has been accepted to NeurIPS 2020.
- 2020/06: One paper of curriculum semi/self-supervised learning has been accepted to ICML 2020.
- Machine Learning (2008-present)
- Curriculum Learning (for 2-6 below, using tools in 7-8)
- Self-supervised/Semi-supervised Learning
- Reinforcement Learning
- Collaborative Learning on graphs/networks, Ensemble Method
- Robust Learning on Noisy Data
- Meta-Learning, Few-shot/Zero-shot Learning
- Training Dynamics and Geometry of Neural Networks
- Continuous-discrete Optimization, Submodular Optimization
- Spectral Method for Matrix Factorization and Graphical Models
- Matrix and Tensor: Low-rank Approximation, Completion, Robust PCA, NMF
- Compressed Sensing (1-bit and k-bit measurements), Sparse Learning
- Dimension Reduction, Manifold Learning
- Natural Language Processing (2016-present)