The Michigan State University AI Research (MAIR), housed within the Department of Computer Science and Engineering (CSE) at Michigan State University (MSU), holds a distinguished position in the dynamic field of Artificial Intelligence (AI). With a rich history of innovation and a commitment to pushing the boundaries of technology, MAIR provides a hub of creativity and discovery in AI research. Led by a diverse team of renowned experts, MAIR endeavors include a wide range of research domains, spanning biometrics, computer vision, data mining, natural language processing, and machine learning. Leveraging interdisciplinary collaborations and state-of-the-art facilities, MAIR is not only advancing the frontiers of AI but also nurturing the next generation of AI leaders and problem solvers. Welcome to the dynamic world of AI research at MSU, where innovation knows no bounds.

Michigan State University AI Research (MAIR) provides a hub of creativity and discovery in AI research
Jan 01, 2024 - Michigan State University AI Research (MAIR) provides a hub of creativity and discovery in AI research

The Michigan State University AI Research (MAIR), housed within the Department of Computer Science and Engineering (CSE) at Michigan State University (MSU), holds a distinguished position in the dynamic field of Artificial Intelligence (AI). With a rich history of innovation and a commitment to pushing the boundaries of technology, MAIR provides a hub of creativity and discovery in AI research. Led by a diverse team of renowned experts, MAIR endeavors include a wide range of research domains, spanning biometrics, computer vision, data mining, natural language processing, and machine learning. Leveraging interdisciplinary collaborations and state-of-the-art facilities, MAIR is not only advancing the frontiers of AI but also nurturing the next generation of AI leaders and problem solvers. Welcome to the dynamic world of AI research at MSU, where innovation knows no bounds.


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OPTML Lab Tutorial on Zeroth-Order ML and Foundation Models has been accepted and will be presented at AAAI’24
Jan 01, 2024 - OPTML Lab Tutorial on Zeroth-Order ML and Foundation Models has been accepted and will be presented at AAAI’24

OPTML Lab’s latest tutorial, accepted for AAAI-24, spotlights the emerging field of zeroth-order machine learning (ZO-ML). Titled “Zeroth-Order Machine Learning: Fundamental Principles and Emerging Applications in Foundation Models,” this session will explore ZO-ML’s core theories and methodologies. It aims to showcase the integration of ZO-ML in advanced AI applications, particularly within foundation models, highlighting its potential to revolutionize current AI paradigms. This tutorial represents a significant step in marrying theoretical insights with practical AI solutions.


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SeaBird: Segmentation in Bird’s View with Dice Loss Improves Monocular 3D Detection of Large Objects
SeaBird: Segmentation in Bird’s View with Dice Loss Improves Monocular 3D Detection of Large Objects
Abhinav Kumar, Yuliang Guo, Xinyu Huang, Liu Ren, Xiaoming Liu. Conference on Computer Vision and Pattern Recognition (CVPR), 2024
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DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training
A. Chen*, Y. Zhang*, J. Jia, J. Diffenderfer, J. Liu, K. Parasyris, Y. Zhang, Z. Zhang, B. Kailkhura, S. Liu. International Conference on Learning Representations (ICLR), 2024
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SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
C. Fan*, J. Liu*, Y. Zhang, D. Wei, E. Wong, S. Liu. International Conference on Learning Representations (ICLR), 2024 (Spotlight).
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FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
F. Liu, R. Ashbaugh, N. Chimitt, N. Hassan, A. Hassani, A. Jaiswal, M. Kim, Z. Mao, C. Perry, Z. Ren, Y. Su, P. Varghaei, K. Wang, X. Zhang, S. Chan, A. Ross, H. Shi, Z. Wang, A. Jain, X. Liu. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024
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