News

[CV Lab] Six papers have been accepted to CVPR’24, covering research topics on face recognition, gait recognition, person re-ID, data attribution for AI-generated images, 3D object detection, and 3D point cloud generation. For details, please visit http://cvlab.cse.msu.edu/category/publications.html

[HAL] One paper on finding near-optimal utility-fairness trade-offs has been accepted to CVPR’24. For details, please visit https://hal.cse.msu.edu/papers/

[OPTML] One tutorial on “Machine Unlearning in Computer Vision: Foundations and Applications” has been accepted to CVPR 2024

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

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.

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.

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.