OPtimization and Trustworthy Machine Learning (OPTML) group (Group Website) 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)!
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Repositories
- Pion Public
Rethinking Muon Beyond Pretraining: Spectral Failures and High-Pass Remedies for VLA and RLVR
- LLM-Biometrics Public
[COLM26] Who Built This Model? Tracing LLM Lineage via Spectral Fingerprints in Weight Space
- MemorizationBarrier Public
This is the code for paper: Breaking Memorization Barriers in LLM Code Fine-Tuning via Information Bottleneck for Improved Generalization
- OPTML-Group.github.io Public
- Unlearn-Sparse Public
[NeurIPS23 (Spotlight)] "Model Sparsity Can Simplify Machine Unlearning" by Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu
- ReVa Public
- VLM-Safety-Unlearn Public
[ICLR26] Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning
- Unlearn-Trace Public
[ICLR26] Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs
- SIFT Public
[Arxiv] Official repo for "Subspace Control: Turning Constrained Model Steering into Controllable Spectral Optimization"
- CyclicReflex Public
[ICLR26] "CyclicReflex: Improving Reasoning Models via Cyclical Reflection Token Scheduling" by Chongyu Fan, Yihua Zhang, Jinghan Jia, Alfred Hero, Sijia Liu
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