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 & data compression, distributed learning, black-box optimization, and automated ML

Sponsors

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



News

12. February 2024

Tutorial ‘Machine Unlearning in Computer Vision: Foundations and Applications’ is accepted for presentation by CVPR’24. See you in Seattle!

16. January 2024

Four papers accepted in ICLR’24: [1] Machine unlearning for safe image generation (Spotlight); [2] DeepZero: Training neural networks from scratch using only forward passes; [3] Backdoor data sifting; [4] Visual prompting automation.

9. November 2023

[New Preprints] We are pleased to announce the release of the paper on arXiv: From Trojan Horses to Castle Walls: Unveiling Bilateral Backdoor Effects in Diffusion Models.

24. October 2023

Tutorial on ‘Zeroth-Order Machine Learning - Fundamental Principles and Emerging Applications in Foundation Models’ is accepted by ICASSP’24 and AAAI’24.

21. October 2023

[New Preprints] We are pleased to announce the release of the following papers on arXiv: [1] To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images … For Now; [2] SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation; [3] DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training.

22. September 2023

NeurIPS 2023: 3 Papers Accepted – 1 Spotlight and 2 Posters. Congratulations to Jinghan, Jiancheng, and Yuguang for their spotlight acceptance with ‘Model Sparsity Simplifies Machine Unlearning’. And kudos to Yihua, Yimeng, Aochuan, Jinghan, and Jiancheng for their poster acceptance with ‘Selectivity Boosts Transfer Learning Efficiency’.

31. August 2023

Grateful to receive a grant from Army Research Office (ARO) as the PI.

12. August 2023

Our paper on Adversarial Training for MoE has been chosen for an Oral Presentation at ICCV’23!

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