Carlo is a theoretical engineer and computational innovator. He is a Chair Professor in the Tsinghua Laboratory of Brain and Intelligence (THBI) and adjunct professor in the Department of Computer Science and the School of Biomedical Engineering at Tsinghua University. He directs the Center for Complex Network Intelligence (CCNI) in THBI, which aims to create pioneering algorithms at the interface between information science, physics of complex systems, complex networks and machine intelligence, with a focus on brain/life-inspired computing for efficient artificial intelligence and big data analysis. These computational methods are often applied to precision biomedicine, neuroscience, social and economic science. 

Aditya is a Research Scientist at Netflix Research in the Foundation Models and Inference Research group. His research focuses on the practice and theory of training and post-training, alignment, and evaluation of LLMs. Prior to this, Aditya held fellow and engineer roles at Microsoft and Google, respectively. His recent research into structured representations and use of Hyperbolic embeddings, notably within industry research, will contribute to the workshop extensively.

Mehrtash is an Associate Professor in the Department of Electrical and Computer Systems Engineering (ECSE), Monash University, Australia. His research focuses on machine learning, computer vision, and optimization, with particular interests in learning with limited supervision, continual and lifelong learning, optimization over structured spaces (e.g., Riemannian geometry), and responsible AI (e.g., machine unlearning). His talk will focus on Poincaré Kernels for Hyperbolic Representations.

Rex is an assistant professor in the Department of Computer Science at Yale University. His research focuses on algorithms for graph neural networks, geometric embeddings, explainable models, and, more recently, multi-modal foundation models involving relational reasoning. He is the author of many widely used GNN algorithms such as GraphSAGE, PinSAGE, and GNNExplainer. In addition, he has worked on a variety of applications of graph learning in physical simulations, social networks, knowledge graphs, neuroscience, and biotechnology. He developed the first billion-scale graph embedding services at Pinterest and the graph-based anomaly detection algorithm at Amazon.