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) !
Two papers accpeted in NeurIPS’222. September 2022
Francesco Croce will give an invited talk on test-time defense on Sept. 7th.31. August 2022
Dr. Sijia Liu is grateful to receive a Robust Intelligence (RI) Core Small Grant Award from NSF as the PI.4. August 2022
Grateful to receive the Best Paper Runner-Up Award at UAI’22 in recognition of our work Distributed Adversarial Training to Robustify Deep Neural Networks at Scale.