Jinghui Chen

Assistant Professor, Ph.D.

Penn State University

Email: jzc5917 [at] psu [dot] edu

About

I am an Assistant Professor in the College of Information Sciences and Technology at Penn State University. I received my Ph.D. in the Department of Computer Science, University of California, Los Angeles (UCLA) working with Prof. Quanquan Gu in 2021. I received my B.E. in the Department of Electrical Engineering and Information Science at the University of Science and Technology of China in 2015.

Prospective Students: I’m looking for highly motivated PhD/intern students to join my group. The official PhD application deadline for the Fall 2024 application cycle is Dec 15, 2023 (details). If you’re interested in joining my lab, please fill and see instructions in the following form (feel free to skip optional questions).

Research Interests: 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. Recently, we are particularly interested in the following research topics:

News

Publications

Full publications on Google Scholar.
E indicates authors with equal contribution. underline indicates students supervised.

  1. Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration

    Yujia Wang, Yuanpu Cao, Jingcheng Wu, Ruoyu Chen and Jinghui Chen, in Proceedings of the 25th International Conference on Learning Representations (ICLR), Vienna, Austria, 2024.

  2. Backdoor Contrastive Learning via Bi-level Trigger Optimization

    Weiyu Sun, Xinyu Zhang, Hao Lu, Ying-Cong Chen, Ting Wang, Jinghui Chen and Lu Lin, in Proceedings of the 25th International Conference on Learning Representations (ICLR), Vienna, Austria, 2024.

  3. VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models

    Ziyi Yin, Muchao Ye, Tianrong Zhang, Jiaqi Wang, Han Liu, Jinghui Chen, Ting Wang and Fenglong Ma, in Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, 2024.

  4. On the Difficulty of Defending Contrastive Learning against Backdoor Attacks

    Changjiang Li, Ren Pang, Bochuan Cao, Zhaohan Xi, Jinghui Chen, Shouling Ji and Ting Wang, in Proceedings of the 33rd USENIX Security Symposium (USENIX), Philadelphia, PA, USA, 2024. [Paper]

  5. Unveiling the Secrets without Data: Can Graph Neural Networks Be Exploited through Data-Free Model Extraction Attacks?

    Yuanxin Zhuang, Chuan Shi, Mengmei Zhang, Jinghui Chen, Lingjuan Lyu, Pan Zhou and Lichao Sun, in Proceedings of the 33rd USENIX Security Symposium (USENIX), Philadelphia, PA, USA, 2024. [Paper]

  6. Federated Learning with Projected Trajectory Regularization

    Tiejin ChenE, Yuanpu CaoE, Yujia WangE, Cho-Jui Hsieh, and Jinghui Chen, arXiv:2312.14380, 2023. [Paper]

  7. Stealthy and Persistent Unalignment on Large Language Models via Backdoor Injections

    Yuanpu Cao, Bochuan Cao and Jinghui Chen, arXiv:2312.00027, 2023. [Paper]

  8. On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?

    Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen and Dinghao Wu, arXiv:2310.01581, 2023. [Paper]

  9. Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM

    Bochuan CaoE, Yuanpu CaoE, Lu Lin, and Jinghui Chen, arXiv:2309.14348, 2023. [Paper]

  10. IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI

    Bochuan Cao, Changjiang Li, Ting Wang, Jinyuan Jia, Bo Li and Jinghui Chen, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023. [Paper]

  11. VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models

    Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang and Fenglong Ma, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023. [Paper]

  12. A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning

    Hangfan Zhang, Jinyuan Jia, Jinghui Chen, Lu Lin and Dinghao Wu, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023. [Paper]

  13. Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks

    Tianyu Du, Zhaohan Xi, Changjiang Li, Ren Pang, Shouling Ji, Jinghui Chen, Fenglong Ma and Ting Wang, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023. [Paper]

  14. UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation

    Muchao Ye, Ziyi Yin, Tianrong Zhang, Tianyu Du, Jinghui Chen, Ting Wang and Fenglong Ma, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023. [Paper]

  15. RoCourseNet: Robust Training of a Prediction Aware Recourse Model

    Hangzhi Guo, Feiran Jia, Jinghui Chen, Anna Squicciarini and Amulya Yadav, in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM), Birmingham, UK, 2023. [Paper]

  16. PAT: Geometry-Aware Hard-Label Black-Box Adversarial Attacks on Text

    Muchao Ye, Jinghui Chen, Chenglin Miao, Han Liu, Ting Wang and Fenglong Ma, in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA, 2023. [Paper]

  17. Benign Overfitting in Adversarially Robust Linear Classification

    Jinghui ChenE, Yuan CaoE, and Quanquan Gu, in Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), Pittsburgh, PA, USA, 2023. [Paper]

  18. Graph Contrastive Backdoor Attacks

    Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia and Dinghao Wu, in Proceedings of the 40th International Conference on Machine Learning (ICML), Hawaii, USA, 2023. [Paper]

  19. Multiple Models for Outbreak Decision Support in the Face of Uncertainty

    Katriona Shea, ..., Jinghui Chen, ..., Michael C. Runge., in Proceedings of the National Academy of Sciences (PNAS), 2023. [Paper]

  20. Do Language Models Plagiarize?

    Lee, Jooyoung, Thai Le, Jinghui Chen, and Dongwon Lee, in Proceedings of the ACM Web Conference (WWW), Austin, Texas, USA, 2023. [Paper]

  21. Spectral Augmentation for Self-Supervised Learning on Graphs

    Lu Lin, Jinghui Chen, Hongning Wang, in Proceedings of the 11th International Conference on Learning Representations (ICLR), Kigali Rwanda, 2023. [Paper] [Code]

  22. On the Vulnerability of Backdoor Defenses for Federated Learning

    Pei Fang and Jinghui Chen, in Proceedings of the 37th Conference on Artificial Intelligence (AAAI), Washington DC, USA, 2023. [Paper] [Code]

  23. One-shot Neural Backdoor Erasing via Adversarial Weight Masking

    Shuwen Chai and Jinghui Chen, in Proceedings of the 36th Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, 2022. [Paper] [Code]

  24. Accelerating Adaptive Federated Optimization with Local Gossip Communications

    Yujia Wang, Pei Fang and Jinghui Chen, in International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022 (FL-NeurIPS), 2022. [Paper]

  25. How Powerful is Implicit Denoising in Graph Neural Networks

    Songtao Liu, Zhitao Ying, Hanze Dong, Lu Lin, Jinghui Chen and Dinghao Wu, NeurIPS 2022 Workshop on New Frontiers in Graph Learning (GLFrontiers-NeurIPS). [Paper]

  26. The United States COVID-19 Forecast Hub dataset

    Estee Y Cramer, ..., Jinghui Chen, ..., Nicholas G. Reich, Scientific Data, 9(1), pp.1-15., 2022. [Paper]

  27. LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization

    Muchao Ye, Jinghui Chen, Chenglin Miao, Ting Wang and Fenglong Ma, in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington DC, USA, 2022. [Paper]

  28. Communication-Efficient Adaptive Federated Learning

    Yujia Wang, Lu Lin and Jinghui Chen, in Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, Maryland, USA, 2022. [Paper] [Code]

  29. Evaluation of inliidual and ensemble probabilistic forecasts of COVID-19 mortality in the US

    Estee Y Cramer, ..., Jinghui Chen, ..., Nicholas G. Reich, in Proceedings of the National Academy of Sciences (PNAS), 2022. [Paper]

  30. Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations

    Weiqi Peng and Jinghui Chen, in Proceedings of the 10th International Conference on Learning Representations (ICLR), Virtual, 2022. [Paper] [Code]

  31. Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization

    Yujia Wang, Lu Lin and Jinghui Chen, in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), Virtual, 2022. [Paper] [Code]

  32. Efficient Robust Training via Backward Smoothing

    Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu and Jingjing Liu, In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), Vancouver, BC, Canada, 2022. [Paper] [Code]

  33. Do Wider Neural Networks Really Help Adversarial Robustness?

    Boxi WuE, Jinghui ChenE, Deng Cai, Xiaofei He and Quanquan Gu, in Proceedings of the 35th Advances in Neural Information Processing Systems (NeurIPS), Virtual, 2021. [Paper]

  34. Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States

    Difan Zou, Lingxiao Wang, Pan Xu, Jinghui Chen, Weitong Zhang and Quanquan Gu, ICLR 2021 Workshop on Machine Learning for Preventingand Combating Pandemics (MLPCP-ICLR). [Paper]

  35. On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

    Dongruo ZhouE, Jinghui ChenE, Yuan CaoE, Yiqi Tang, Ziyan Yang, and Quanquan Gu, NeurIPS 2020 Workshop on Optimization for Machine Learning (OPT-NeurIPS). [Paper]

  36. Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S

    COVID-19 Forecast Hub Consortium, Jinghui Chen., medRxiv:2020.08.19.20177493, 2020. [Paper]

  37. RayS: A Ray Searching Method for Hard-label Adversarial Attack

    Jinghui Chen and Quanquan Gu, in Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA 2020. [Paper] [Code]

  38. Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks

    Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao and Quanquan Gu, in Proceedings of 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, 2020. [Paper] [Code]

  39. Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models

    Xiao ZhangE, Jinghui ChenE, Quanquan Gu and David Evans, in Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Sicily, Italy, 2020. [Paper] [Code]

  40. A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks

    Jinghui Chen, Dongruo Zhou, Jinfeng Yi and Quanquan Gu, in Proceedings of the 34th Conference on Artificial Intelligence (AAAI), New York, New York, USA, 2020. [Paper] [Code]

  41. Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

    Pan XuE, Jinghui ChenE, Difan Zou and Quanquan Gu, in Proceedings of the 32nd Advances in Neural Information Processing Systems (NeurIPS), Montréal, Canada, 2018. [Paper]

  42. Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization

    Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma and Quanquan Gu, in Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. [Paper] [Code]

  43. Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization

    Jinghui Chen and Quanquan Gu, in Proceedings of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Halifax, Nova Scotia, Canada, 2017. [Paper]

  44. Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption

    Jinghui Chen, Lingxiao Wang, Xiao Zhang, and Quanquan Gu, arXiv:1704.06256, 2017. [Paper]

  45. Outlier Detection with Autoencoder Ensembles

    Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga, in Proceedings of the 17th SIAM International Conference on Data Mining (SDM), Houston, Texas, USA, 2017. [Paper]

  46. Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Optimization

    Jinghui Chen and Quanquan Gu, in Proceedings of the 32th International Conference on Uncertainty in Artificial Intelligence (UAI), New York, USA, 2016. [Paper]

  47. Improved threshold Selection by using Calibrated Probabilities for Random Forest Classifiers

    Florian Baumann, Jinghui Chen, Karsten Vogt and Bodo Rosenhahn, in Proceedings of the 12th Conference on Computer and Robot Vision (CRV), Halifax, Nova Scotia, Canada, 2015. [Paper]

  1. Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration

    Yujia Wang, Yuanpu Cao, Jingcheng Wu, Ruoyu Chen and Jinghui Chen, in Proceedings of the 25th International Conference on Learning Representations (ICLR), Vienna, Austria, 2024.

  2. Backdoor Contrastive Learning via Bi-level Trigger Optimization

    Weiyu Sun, Xinyu Zhang, Hao Lu, Ying-Cong Chen, Ting Wang, Jinghui Chen and Lu Lin, in Proceedings of the 25th International Conference on Learning Representations (ICLR), Vienna, Austria, 2024.

  3. VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models

    Ziyi Yin, Muchao Ye, Tianrong Zhang, Jiaqi Wang, Han Liu, Jinghui Chen, Ting Wang and Fenglong Ma, in Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, 2024.

  4. On the Difficulty of Defending Contrastive Learning against Backdoor Attacks

    Changjiang Li, Ren Pang, Bochuan Cao, Zhaohan Xi, Jinghui Chen, Shouling Ji and Ting Wang, in Proceedings of the 33rd USENIX Security Symposium (USENIX), Philadelphia, PA, USA, 2024. [Paper]

  5. Unveiling the Secrets without Data: Can Graph Neural Networks Be Exploited through Data-Free Model Extraction Attacks?

    Yuanxin Zhuang, Chuan Shi, Mengmei Zhang, Jinghui Chen, Lingjuan Lyu, Pan Zhou and Lichao Sun, in Proceedings of the 33rd USENIX Security Symposium (USENIX), Philadelphia, PA, USA, 2024. [Paper]

  6. Federated Learning with Projected Trajectory Regularization

    Tiejin ChenE, Yuanpu CaoE, Yujia WangE, Cho-Jui Hsieh, and Jinghui Chen, arXiv:2312.14380, 2023. [Paper]

  7. Stealthy and Persistent Unalignment on Large Language Models via Backdoor Injections

    Yuanpu Cao, Bochuan Cao and Jinghui Chen, arXiv:2312.00027, 2023. [Paper]

  8. On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?

    Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen and Dinghao Wu, arXiv:2310.01581, 2023. [Paper]

  9. Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM

    Bochuan CaoE, Yuanpu CaoE, Lu Lin, and Jinghui Chen, arXiv:2309.14348, 2023. [Paper]

  10. IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI

    Bochuan Cao, Changjiang Li, Ting Wang, Jinyuan Jia, Bo Li and Jinghui Chen, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023. [Paper]

  11. VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models

    Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang and Fenglong Ma, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023. [Paper]

  12. A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning

    Hangfan Zhang, Jinyuan Jia, Jinghui Chen, Lu Lin and Dinghao Wu, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023. [Paper]

  13. Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks

    Tianyu Du, Zhaohan Xi, Changjiang Li, Ren Pang, Shouling Ji, Jinghui Chen, Fenglong Ma and Ting Wang, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023. [Paper]

  14. UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation

    Muchao Ye, Ziyi Yin, Tianrong Zhang, Tianyu Du, Jinghui Chen, Ting Wang and Fenglong Ma, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2023. [Paper]

  15. RoCourseNet: Robust Training of a Prediction Aware Recourse Model

    Hangzhi Guo, Feiran Jia, Jinghui Chen, Anna Squicciarini and Amulya Yadav, in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM), Birmingham, UK, 2023. [Paper]

  16. PAT: Geometry-Aware Hard-Label Black-Box Adversarial Attacks on Text

    Muchao Ye, Jinghui Chen, Chenglin Miao, Han Liu, Ting Wang and Fenglong Ma, in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA, 2023. [Paper]

  17. Benign Overfitting in Adversarially Robust Linear Classification

    Jinghui ChenE, Yuan CaoE, and Quanquan Gu, in Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), Pittsburgh, PA, USA, 2023. [Paper]

  18. Graph Contrastive Backdoor Attacks

    Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia and Dinghao Wu, in Proceedings of the 40th International Conference on Machine Learning (ICML), Hawaii, USA, 2023. [Paper]

  19. Do Language Models Plagiarize?

    Lee, Jooyoung, Thai Le, Jinghui Chen, and Dongwon Lee, in Proceedings of the ACM Web Conference (WWW), Austin, Texas, USA, 2023. [Paper]

  20. Spectral Augmentation for Self-Supervised Learning on Graphs

    Lu Lin, Jinghui Chen, Hongning Wang, in Proceedings of the 11th International Conference on Learning Representations (ICLR), Kigali Rwanda, 2023. [Paper] [Code]

  21. On the Vulnerability of Backdoor Defenses for Federated Learning

    Pei Fang and Jinghui Chen, in Proceedings of the 37th Conference on Artificial Intelligence (AAAI), Washington DC, USA, 2023. [Paper] [Code]

  22. One-shot Neural Backdoor Erasing via Adversarial Weight Masking

    Shuwen Chai and Jinghui Chen, in Proceedings of the 36th Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, 2022. [Paper] [Code]

  23. Accelerating Adaptive Federated Optimization with Local Gossip Communications

    Yujia Wang, Pei Fang and Jinghui Chen, in International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022 (FL-NeurIPS), 2022. [Paper]

  24. How Powerful is Implicit Denoising in Graph Neural Networks

    Songtao Liu, Zhitao Ying, Hanze Dong, Lu Lin, Jinghui Chen and Dinghao Wu, NeurIPS 2022 Workshop on New Frontiers in Graph Learning (GLFrontiers-NeurIPS). [Paper]

  25. LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization

    Muchao Ye, Jinghui Chen, Chenglin Miao, Ting Wang and Fenglong Ma, in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington DC, USA, 2022. [Paper]

  26. Communication-Efficient Adaptive Federated Learning

    Yujia Wang, Lu Lin and Jinghui Chen, in Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, Maryland, USA, 2022. [Paper] [Code]

  27. Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations

    Weiqi Peng and Jinghui Chen, in Proceedings of the 10th International Conference on Learning Representations (ICLR), Virtual, 2022. [Paper] [Code]

  28. Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization

    Yujia Wang, Lu Lin and Jinghui Chen, in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), Virtual, 2022. [Paper] [Code]

  29. Efficient Robust Training via Backward Smoothing

    Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu and Jingjing Liu, In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), Vancouver, BC, Canada, 2022. [Paper] [Code]

  30. Do Wider Neural Networks Really Help Adversarial Robustness?

    Boxi WuE, Jinghui ChenE, Deng Cai, Xiaofei He and Quanquan Gu, in Proceedings of the 35th Advances in Neural Information Processing Systems (NeurIPS), Virtual, 2021. [Paper]

Research

The research of our lab is focused on different aspects of machine learning (efficiency, robustness, interpretability, responsibility, trustworthiness) and their applications in computer vision, graph learning, anomaly detection, cybersecurity, recommendation systems, computational genomics, etc. Some of our current research projects are:

Trustworthiness and Saftey Issues in Large Language Models
Studying the vulnerabilities inside the current LLMs and how we can improve them for better trustworthiness.
Optimization in Machine Learning
Studying the convergence of machine learning optimizers including adaptive gradient optimizers and designing new generation of optimizers for deep learning.
Poisoning/Backdoor Attacks and Defenses
Studying the effects of poisoning and backdoor attacks on deep learning models, as well as how to mitigate those threats.
Federated Machine Learning
Addressing the emerging challenges for Federated Learning in practical scenarios such as data and model heterogeneity, communication efficiency, as well as security and privacy issues.
Adversarial Robustness in Machine Learning
Evaluating, understanding, and improving adversarial robustness in deep learning as well as studying the theoretical foundations behind adversarial training and robust learning.
Robustness in Graph Nerual Networks
Improving the robustness of the current graph neural networks again graph structural/feature perturbations.

Students

Current Ph.D. Students Current Undergrad/Master/Intern Students Alumni
  • Avi Bewtra (Undergrad at PSU, Fall 2021 - Spring 2022)
  • Weiqi Peng (Research Intern, Fall 2021 - Spring 2022, Now at Amazon)
  • Shuwen Chai (Research Intern, Fall 2021 - Spring 2022, Now a Ph.D. student at Northwestern University)
  • Pei Fang (Research Intern, Fall 2021 - Fall 2023, Now at Ant Financial)
  • Weiyu Sun (Research Intern, Summer 2022 - Fall 2023)
  • Tiejin Chen (Research Intern, Summer 2022 - Fall 2022, Now a Ph.D. student at ASU)
  • Aryan Harshanan Patil (Undergrad at PSU, Fall 2022 - Spring 2023)
  • Sirui Qi (Undergrad at PSU, Fall 2022 - Spring 2023)
  • Sooraj Narayanan Sekar (Undergrad at PSU, Fall 2022 - Spring 2023)
  • Jingcheng Wu (Research Intern, Spring 2023 - Fall 2023, Now at Google)
  • Ruoyu Chen (Research Intern, Spring 2023 - Fall 2023)

Teaching

Acadamic Service

Senior Program Committee Program Committee/Reviewer Journal Reviewer