Bang An

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Hi, I'm Bang!

I am a fourth-year PhD student in Computer Science at University of Maryland. I'm very fortunate to be advised by Prof. Furong Huang and work on Machine Learning as a member of UMIACS. Before coming to UMD, I was a research staff member at IBM Research China. I received my bachelor’s degree from Northeastern University (China) and my master’s degree from Tsinghua University.

My research focuses on Reliable Machine Learning. I am particularly interested in understanding and improving the algorithmic fairness, generalization, robustness, and interpretability of deep learning models. I believe that reliable machine learning is a critical element in enabling AI to better serve and support humanity.

Benchmarking the Robustness of Image Watermarks
Bang An*, Mucong Ding*, Tahseen Rabbani*, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang
[website] [code]
AutoDAN: Automatic and Interpretable Adversarial Attacks on Large Language Models
Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, Tong Sun
[website] [code]
On the Possibilities of AI-Generated Text Detection
Souradip Chakraborty*, Amrit Singh Bedi*, Sicheng Zhu, Bang An, Dinesh Manocha, Furong Huang
Selected Publications
More Context, Less Distraction: Zero-shot Visual Classification by Inferring and Conditioning on Contextual Attributes
Bang An*, Sicheng Zhu*, Michael-Andrei Panaitescu-Liess, Chaithanya Kumar Mummadi, Furong Huang
ICLR 2024.
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation
Mucong Ding, Bang An, Yuancheng Xu, Anirudh Satheesh, Furong Huang
ICLR 2024.
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Xiaoyu Liu, Jiaxin Yuan, Bang An, Yuancheng Xu, Yifan Yang, Furong Huang .
NeurIPS 2023.
Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
Sicheng Zhu, Bang An, Furong Huang, Sanghyun Hong.
ICML 2023.
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
Bang An, Zora Che, Mucong Ding, Furong Huang.
NeurIPS 2022.
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Mucong Ding, Tahseen Rabbani, Bang An, Evan Z Wang, Furong Huang.
NeurIPS 2022.
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
Sicheng Zhu*, Bang An*, Furong Huang.
NeurIPS 2021.
Adaptive Transfer Learning on Graph Neural Networks
Xueting Han, Zhenhuan Huang, Bang An, Jing Bai.
KDD 2021.
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference
Bang An, Jie Lyu, Zhenyi Wang, Chunyuan Li, Changwei Hu, Fei Tan, Ruiyi Zhang, Yifan Hu and Changyou Chen
EMNLP 2020.
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, Changyou Chen
ACL 2020.
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
Paiheng Xu*, Yuhang Zhou*, Bang An, Wei Ai, Furong Huang
Trustworthy and Socially Responsible Machine Learning (NeurIPS Workshop), 2022.
  • Outstanding Graduate Assistant Award of UMD (top 2%), 2023.
  • NeurIPS 2022 Travel Award
  • Reviewer: ICML 2022, NeurIPS 2022, ICML 2023, NeurIPS 2023, ICLR 2024, NAACL 2024