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.
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.
Tags: MAIR News
The Data Science and Engineering Lab is set to present a tutorial at AAAI-24, “Large-Scale Graph Neural Networks: Navigating the Past and Pioneering New Horizons.” Focusing on Graph Neural Networks (GNNs) and their scalability in handling large-scale, real-world graphs, this session will explore the challenges and cutting-edge techniques in deploying GNNs effectively. Ideal for engineers and researchers, the tutorial promises a deep dive into scalable GNN strategies, their evaluation, and practical applications. Details are available on the tutorial’s website: https://sites.google.com/ncsu.edu/largescalegnn/home/aaai_2024.
Tags: Data Science and Engineering Lab News
Abhinav Kumar, Yuliang Guo, Xinyu Huang, Liu Ren, Xiaoming Liu. Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Tags: Monocular 3D object detection
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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
Tags: Scalable Machine Learning
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C. Fan*, J. Liu*, Y. Zhang, D. Wei, E. Wong, S. Liu. International Conference on Learning Representations (ICLR), 2024 (Spotlight).
Tags: Trustworthy Machine Learning
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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
Tags: Biometric Recognition