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.

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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

• Understanding and Improving Visual Prompting: A Label-Mapping Perspective
  A. Chen, Y. Yao, P.-Y. Chen, Y. Zhang, S. Liu
  CVPR’23

• 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

• Reverse Engineering of Imperceptible Adversarial Image Perturbations
  Y. Gong*, Y. Yao*, Y. Li, Y. Zhang, X. Liu, X. Lin, S. Liu
  ICLR’22

• How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
  Y. Zhang, Y. Yao, J. Jia, J. Yi, M. Hong, S. Chang, S. Liu
  ICLR’22

• Proper Network Interpretability Helps Adversarial Robustness in Classification
  A. Boopathy, S. Liu, G. Zhang, C. Liu, P.-Y. Chen, S. Chang, L. Daniel
  ICML’20

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

• 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

• An ADMM Based Framework for AutoML Pipeline Configuration
  S. Liu*, P. Ram*, D. Vijaykeerthy, D. Bouneffouf, G. Bramble, H. Samulowitz, D. Wang, A. Conn, A. Gray,
  AAAI’20

Sponsors

We are grateful for funding from Michigan State University, MIT-IBM Watson AI Lab, DARPA, Cisco Research, NSF, DSO National Laboratories, and LLNL.