Jakub M. Tomczak, Ph.D.
Chan Zuckerberg Initiative

Generative Artificial Intelligence (GenAI) has revolutionized how we model complex phenomena, yet its true potential in life and molecular sciences remains largely untapped. In this talk, I will discuss the transition from model-centric to system-centric generative AI — moving beyond isolated deep models toward integrated, interpretable, and scientifically grounded generative systems. I will present key advances from my research spanning probabilistic and deep generative modeling, including Variational Autoencoders with VampPrior and diffusion-based extensions, attention-based and mixed models for multi-instance and multi-modal biological data, and joint generative–predictive diffusion frameworks that unify representation learning and explainability. These developments enable principled modeling of genomic, molecular, and biomedical image data under uncertainty, symmetry, and multi-scale constraints. Finally, I will introduce the concept of Generative AI Systems (GenAISys) — architectures that connect multiple foundation models with simulators and domain knowledge — and outline their role as the next frontier for agentic, reliable AI in scientific discovery and healthcare innovation.

Jakub Tomczak is a Generative AI leader with over 15 years of experience in machine learning, deep learning, and Generative AI. He has led extensive research across academia and industry, contributing three patents, numerous publications at top conferences (including NeurIPS, ICML, and CVPR), and securing EUR 2.3M in direct funding while contributing to consortia that have collectively obtained over EUR 100M. Jakub has managed teams for more than a decade and has served as a fractional AI leader for organizations such as eBay, Qualcomm, and multiple startups. He was Program Chair of NeurIPS 2024 and is the author of Deep Generative Modeling, the first comprehensive textbook on Generative AI. He is also the founder of Amsterdam AI Solutions.

AI in Science Institute
Center for Machine Learning and Intelligent Systems