Our paper is accepted to AAAI 2022: 'Efficient Robust Training via Backward Smoothing' Our paper is accepted to AISTATS 2022: 'Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization' Our paper is accepted to ICLR 2022: 'Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations' Chen recieved received Cisco Faculty Research Award! Our paper is accepted to ICML 2022: 'Communication-Efficient Adaptive Federated Learning' Our paper is accepted to KDD 2022: 'LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization' Our paper is accepted to NeurIPS 2022: 'One-shot Neural Backdoor Erasing via Adversarial Weight Masking'

Our paper is accepted to AAAI 2023: 'On the Vulnerability of Backdoor Defenses for Federated Learning' Our paper is accepted to ICLR 2023: 'Spectral Augmentation for Self-Supervised Learning on Graphs' Our paper is accepted to WWW 2023: 'Do Language Models Plagiarize?' Our paper is accepted to ICML 2023: 'Graph Contrastive Backdoor Attacks' Our paper is accepted to UAI 2023: 'Benign Overfitting in Adversarially Robust Linear Classification' Our paper is accepted to KDD 2023: 'PAT: Geometry-Aware Hard-Label Black-Box Adversarial Attacks on Text' Our paper is accepted to CIKM 2023: 'RoCourseNet: Robust Training of a Prediction Aware Recourse Model' We are organizing the Workshop on Federated Learning in the Age of Foundation Models at NeurIPS 2023 Submissions are welcome! Five papers are accepted to NeurIPS 2023!

Efficient optimization strategies for training large scale foundataion models/federated learning (adaptive gradient optimizers, parameter-efficient training, etc.).

Security and privacy issues for other emerging machine learning models (multimodal foundation models, federated learning, diffusion models, etc.).

Trustworthiness and safety issues in Large Language Models (LLM alignments, LLM robustness, etc.).

Recently, we are particularly interested in the following research topics: My research interests broadly include the theory and applications in different aspects of machine learning, with particular interests on building efficient and trustworthy machine learning models. If you’re interested in joining my lab, please fill and see instructions in the following form (feel free to skip optional questions). The official PhD application deadline for the Fall 2024 application cycle is ( details). Prospective Students: I’m looking for highly motivated PhD/intern students to join my group. in the Department of Electrical Engineering and Information Science at the University of Science and Technology of China in 2015. in the Department of Computer Science, University of California, Los Angeles (UCLA) working with Prof. I am an Assistant Professor in the College of Information Sciences and Technology at Penn State University.

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