Welcome to the OPTML Group

About Us

OPtimization and Trustworthy Machine Learning (OPTML) group is an active research group at Michigan State University. Our research interests span the areas of machine learning (ML)/ deep learning (DL), optimization, computer vision, security, signal processing and data science, with a focus on developing learning algorithms and theory, as well as robust and explainable artificial intelligence (AI). These research themes provide a solid foundation for reaching the long-term research objective: Making AI systems scalable and trustworthy.

📖 For a more detailed introduction, see our Welcome2OPTML Booklet.

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As AI moves from the lab into the real world (e.g., autonomous vehicles), ensuring its safety becomes a paramount requirement prior to its deployment. Moreover, as datasets, ML/DL models, and learning tasks become increasingly complex, getting ML/DL to scale calls for new advances in learning algorithm design. More broadly, the study towards robust and scalable AI could make a significant impact on machine learning theories, and induce more promising applications in, e.g., automated ML, meta-learning, privacy and security, hardware design, and big data analysis. We seek a new learning frontier when the current learning algorithms become infeasible, and formalize foundations of secure learning.

We always look for passionate students to join the team in terms of RA/TA/externship/internship/visiting students (more info)!

Representative Publications

Authors marked in bold indicate our group members, and “*” indicates equal contribution.

Trustworthy AI: Robustness, fairness, and model explanation

• The Fragile Truth of Saliency: Improving LLM Input Attribution via Attention Bias Optimization
  Y. Zhang, C. Wang, Y. Chen, C. Fan, J. Jia, S. Liu,
  NeurIPS’25 (Spotlight)

• Rethinking Machine Unlearning for Large Language Models
  S. Liu, Y. Yao, J. Jia, S. Casper, N. Baracaldo, P. Hase, Y. Yao, C. Y. Liu, X. Xu, H. Li, K. R. Varshney, M. Bansal, S. Koyejo, Y. Liu
  Nature Machine Intelligence, 2025.

• Salun: Empowering Machine Unlearning Via Gradient-based Weight Saliency In Both Image Classification And Generation
  C. Fan*, J. Liu*, Y. Zhang, E. Wong, D. Wei, S. Liu
  ICLR’24 (Spotlight)

• Model sparsification can simplify machine unlearning
  J. Jia*, J. Liu*, P. Ram, Y. Yao, G. Liu, Y. Liu, P. Sharma, S. Liu
  NeurIPS’23 (Spotlight)

• Revisiting and advancing fast adversarial training through the lens of bi-level optimization
  Y. Zhang*, G. Zhang*, P. Khanduri, M. Hong, S. Chang, S. Liu
  ICML’22

Scalable AI: Model & data compression, distributed learning, black-box optimization, and automated ML

• Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
  Y. Zhang*, Y. Zhang*, A. Chen, J. Jia, J. Liu, G. Liu, M. Hong, S. Chang, S. Liu
  NeurIPS’23

• Advancing Model Pruning via Bi-level Optimization
  Y. Zhang*, Y. Yao*, P. Ram, P. Zhao, T. Chen, M. Hong, Y. Wang, S. Liu
  NeurIPS’22

• Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
  G. Zhang*, S. Lu*, Y. Zhang, X. Chen, P.-Y. Chen, Q. Fan, L. Martie, L. Horesh, M. Hong, S. Liu
  UAI’22 (Best Paper Runner-Up Award)

• Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
  S. Liu, S. Lu, X. Chen, Y. Feng, K. Xu, A. Al-Dujaili, M. Hong, U.-M. O’Reilly
  ICML’20

• A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning
  S. Liu, P.-Y. Chen, B. Kailkhura, G. Zhang, A. O. Hero, P. K. Varshney
  IEEE Signal Processing Magazine, 2020

Sponsors

We are grateful for funding from Michigan State University, MIT-IBM Watson AI Lab, DARPA, Cisco Research, NSF, DSO National Laboratories, LLNL, ARO, Amazon Research, Open Philanthropy, Schmidt Sciences and CAIS (Center for AI Safety).