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.

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

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


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


4. June 2023

Our paper Visual Prompting for Adversarial Robustness received the Top 3% Paper Recognition at ICASSP 2023. Congrats to Aochuan, Peter (internship at OPTML in 2022), Yuguang, and Pin-Yu (IBM Research)!

24. April 2023

Two papers in ICML’23 and CFP for 2nd AdvML-Frontiers Workshop @ICML’23.

17. April 2023

A new arXiv paper is released: Model Sparsification Can Simplify Machine Unlearning (see paper and code)!

13. April 2023

Grateful to receive a grant from Lawrence Livermore National Laboratory.

1. April 2023

Call for Papers and AdvML Rising Star Award Applications in the workshop AdvML-Frontiers, ICML’23

17. March 2023

A new arXiv paper is released: Adversarial attacks can be parsed to reveal victim model information! (see [Paper])

17. March 2023

The 2nd Workshop on New Frontiers in Adversarial Machine Learning has been accepted by ICML’23

1. March 2023

Grateful to receive a grant from DSO National Laboratories.

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