Invited SPEAKERs

Krishna Murthy Jatavallabhula is a postdoc at MIT, working with Antonio Torralba and Josh Tenenbaum. He previously completed his PhD working at Mila and the University of Montreal, advised by Liam Paull. His research focuses on building computational world models to help embodied agents perceive, reason about, and act in the physical world. He has led the organization of multiple workshops on themes spanning differentiable programming, physical reasoning, 3D vision and graphics, and ML research dissemination. His research has been recognized with graduate fellowship awards from NVIDIA and Google (2021); a best paper award from Robotics and Automation letters (2019); and an induction to the RSS Pioneers cohort (2020).

Talk title: Structured world modeling with 3D scene graphs

Volker Tresp is a professor at Ludwig Maximilian University of Munich (LMU) and with the Munich Center for Machine Learning ( MCML). He received his Diploma degree in physics from the University of Göttingen in 1984 and M.Sc., M.Phil. and Ph.D. degrees from Yale University in 1986 and 1989, respectively. His team has been doing pioneering work on machine learning with knowledge graphs, temporal knowledge graphs, and scene graph analysis.  The work on the Tensor Brain reflects his interest in mathematical models for cognition and neuroscience. In 2020, he became a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). As co-director (with Kristian Kersting and Paolo Frasconi), he leads the ELLIS program "Semantic, Symbolic and Interpretable Machine Learning".

Talk title: Perception, Memory and Semantic Decoding

Bryan Perozzi is a Research Scientist on the Graph Mining team at Google. He works in the areas of data mining and knowledge discovery, machine learning, network science, and natural language processing. He received his M.Sc. degree from Johns Hopkins University and his Ph.D. degree from Stony Brook University. Bryan's research at Stony Brook focused on applying representation learning (or deep learning) to applications in social network analysis (including link prediction, user profiling, etc.) and natural language processing.

Talk title: Giving a Voice to Your Graph: Representing Structured Data for LLMs