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Yue Lu, a Harvard College Professor and Gordon McKay Professor of Electrical Engineering and of Applied Mathematics at Harvard University, presented a talk entitled "Asymptotic theory of in-context learning by linear attention" at the Machine Learning Seminar on December 10, 2024.

Xinlei Chen, esearch scientist at FAIR, Meta AI, presented a talk entitled "Three Episodes of Ablative Research" at the Machine Learning Seminar on December 3, 2024.

Katie Zhao, Assistant Professor in the Department of Electrical and Computer Engineering at the University of Minnesota, Twin Cities, presented a talk entitled "Marrying Application-Level Opportunities with Algorithm-Hardware Co-Design Towards Ubiquitous Edge Intelligence" at the Machine Learning Seminar on November 26, 2024.

Nicolas Loizou, Assistant Professor in the Department of Applied Mathematics and Statistics and the Mathematical Institute for Data Science (MINDS) at Johns Hopkins University, where he leads the Optimization and Machine Learning Lab, presented a talk entitled "Recent Advances in Min-max Optimization: Convergence Guarantees and Practical Performance" at the Machine Learning Seminar on November ...

Ethan Fang, Associate Professor of Biostatistics & Bioinformatics at Duke University, presented a talk entitled "Estimation and Inference for Assortment Optimization" at the Machine Learning Seminar on October 8, 2024.

Heng Yang, Assistant Professor of Electrical Engineering in the School of Engineering and Applied Sciences at Harvard University, presented a talk entitled "Control-oriented Clustering of Visual Latent Representation" at the Machine Learning Seminar on October 29, 2024.

Yuandong Tian, Research Scientist Director in Meta AI Research (FAIR), presented a talk entitled "Towards a Unified Framework of Neural and Symbolic Decision Making" at the Machine Learning Seminar on October 22, 2024.

Panel 2, KGML in the Age of Generative AI, at KGML2024 at the University of Minnesota Twin Cities. Moderator Ananth Grama (Purdue) and panelists Tanya Berger-Wolf (Ohio State), Wei Ding (UMass Boston), Shuiwang Ji (Texas A&M), and Gengchen Mai (UT Austin).

Dr. Krishna Garikapati, University of Michigan, spoke on "Fokker-Planck-Inverse Reinforcement Learning: A physics-constrained approach to Markov Decision Process models of cell dynamics" at KGML2024 at the University of Minnesota Twin Cities.

Dr. Josef Uyeda, an evolutionary biologist and Associate Professor of Biological Sciences at Virginia Tech, spoke on "Accelerating discovery in biodiversity science and systematics with knowledge-guided machine learning" at KGML2024 at the University of Minnesota Twin Cities.

Arvind Renganathan, graduate student in Computer Science & Engineering at the University of Minnesota, spoke on "Knowledge Guided Machine Learning for Task Aware Modeling: An Approach for Few Shot Learning in Heterogeneous Systems (KGML-TAM)" at KGML2024 at the University of Minnesota Twin Cities.

Praveen Ravirathinam, graduate student in Computer Science & Engineering at the University of Minnesota, spoke on "Towards a Knowledge guided Multimodal Foundation Model for Spatio-Temporal Remote Sensing Applications" at KGML2024 at the University of Minnesota Twin Cities.

Dr. Gengchen Mai, Assistant Professor at the Department of Geography and the Environment, University of Texas at Austin, spoke on "Towards a Foundation Model for GeoAI" at KGML2024 at the University of Minnesota Twin Cities.

Dr. Yiqun Xie, Assistant Professor in Geospatial Information Science at the University of Maryland, spoke on "Improving Theory-based Simulation Models with Knowledge-Guided Learning" at KGML2024 at the University of Minnesota Twin Cities.

11 Jin KGML2024

10 Panel 1 KGML2024

7 Oliver KGML2024

9 Tayal KGML2024

8 Yu KGML2024

6 Varadharajan KGML2024

5 McEachran KGML2024

4 Zwart KGML2024

1 Kumar KGML2024 Welcome

3 Jia KGML2024

2 Karpatne KGML2024

Renbo Zhao 20240409

James Zou 20240326

Yexiang Xue 20240319

Quanquan Gu 20240312