MLSS^D 2025

Regina Barzilay

MIT

Regina is a School of Engineering Distinguished Professor of AI & Health in the Department of Computer Science and the AI Faculty Lead at MIT Jameel Clinic. She develops machine learning methods for drug discovery and clinical AI. In the past, she worked on natural language processing. Her research has been recognized with the MacArthur Fellowship, an NSF Career Award, and the AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity. Regina is a member of the National Academy of Engineering, National Academy of Medicine, and the American Academy of Arts and Sciences.

Christina Leslie

Memorial Sloan Kettering Cancer Center

Christina did her undergraduate degree in Pure and Applied Mathematics at the University of Waterloo in Canada. She was awarded an NSERC 1967 Science and Engineering Fellowship for graduate study and did a PhD in Mathematics at the University of California, Berkeley, where her thesis work dealt with differential geometry and representation theory. She won an NSERC Postdoctoral Fellowship and did her postdoctoral training in the Mathematics Department at Columbia University in 1999-2000. She then joined the faculty of the Computer Science Department and later the Center for Computational Learning Systems at Columbia University, where she began to work in computational biology and machine learning and became the principal investigator leading the Computational Biology Group. In 2007, she moved her lab to Memorial Sloan Kettering Cancer Center, where she is currently a Member of the Computational and Systems Biology Program. Christina is well known for developing machine learning approaches for the analysis and interpretation of high-throughput biological data – in particular, bulk and single-cell transcriptomic, epigenomic, and 3D genomic sequencing data sets – with the goal of decoding gene regulation. Biological application domains include basic and cancer immunology, cancer epigenetics, and stem cell biology and cellular differentiation. She is PI, together with Alexander Rudensky, of the NCI U54 Center for Tumor-Immune System for Systems Biology at MSKCC. She is also co-leads projects in the NHGRI Impact of Genomic Variation on Function (IGVF) consortium and the NIH Common Fund 4D Nucleome (4DN) consortium.

Marwin Segler

Microsoft Research AI for Science

Marwin is a researcher within MSR AI for Science, interested in Machine Learning, Reinforcement Learning, Chemistry and Drug Discovery.

Emmanuel Bengio

Recursion / MILA

Emmanuel is an ML Scientist at Recursion's Valence Labs, working on the intersection of GFlowNets and drug discovery problems. He did his PhD under Joelle Pineau and Doina Precup at McGill/Mila, focusing on understanding generalization in deep RL.

Jakub Tomczak

Eindhoven / CZI

Jakub is a Group Leader & Principal Scientist/Research Manager with 15+ years of experience in machine learning, deep learning, and Generative AI. Proven track of leading research projects (academic: 49 AI MSc&PhD, industrial: 7 Computer Vision, 5 LLMs, 2 Foundation Models), carrying out cutting-edge research (2 patents & 1 application, 20+ conference papers: NeurIPS, ICML, ICLR, AISTATS, UAI, CVPR, ICCV, 25+ journal papers), and securing funds (2,150,000 EUR). He is experienced in and enjoying managing people (3y in the industry, 8y in academia). Effective team leader encouraging initiative & independence, and facilitating cross-functional collaboration. Jakub is well-recognized in the AI community (a PC of NeurIPS 2024, the author of a book on GenAI). The Founder and Director of Amsterdam AI Solutions.

Lixin Sun

Microsoft Research AI for Science

Lixin is a researcher at Microsoft Research (Cambridge, UK) in the AI4Science team. Her work is to accelerate materials R&D for sustainable technologies by developing machine learning methods. In particular, she is interested in accelerating atomistic modelling of long-time-scale evolution in energy materials via advanced force fields methods and dimension reduction techniques. Before joining MSR, Lixin received her PhD from MIT and worked as a postdoctoral researcher at Harvard. Her previous research focused on applying and developing novel and highly accurate atomistic simulation methods to discover functional material systems for next-generation catalysis and energy technologies.

Patrick Schwab

GSK.ai

Patrick is a Senior Director of Machine Learning and Artificial Intelligence and Head of the Biomedical AI group at GSK.ai. His work aims to advance personalised medicine by utilising machine learning, computational systems biology methods and large-scale health data, such as genetics, multi-omics, cell-based assays, and continuous measurements from smart devices and electronic health records, to better understand and treat complex diseases. Prior to joining GSK, Patrick was Principal Architect working on Machine Learning for Personalised Medicine at Roche in Basel, Switzerland and at Genentech in South San Francisco, US. Before joining Roche, He was a doctoral researcher working on Machine Learning for Healthcare at ETH Zurich. Prior to ETH Zurich, he spent 5 years building custom data-driven software solutions in industry. Patric hold a PhD in Machine Learning (2019) from ETH Zurich, Switzerland, a MSc in Computer Science (2015) with distinction from the University of Vienna, Austria and a BSc in Computer Science (2013) with honors from Technikum Vienna, Austria.

Tomasz Kościółek

Sano

Tomasz obtained his M.Sc. in chemistry from Jagiellonian University in 2010 and a PhD in biological sciences from University College London (UK) in 2016. In years 2016-2019 he was a post-doctoral research associate in Rob Knight’s group at University of California San Diego (USA) and 2019-2022 we was a group leader in bioinformatics at the Małopolska Centre of Biotechnology, Jagiellonian University in Kraków. He is working on the development and application of computational methods to better understand the function and dynamics of the human gut microbiome. He has contributed to the development of a suite of tools commonly used in microbiome analyses – QIIME 2 and Qiita – and he is also working on state-of-the-art machine learning and statistical methods to predict protein function (deepFRI) or to model the dysbiosis and dynamics of the microbiome. The goal of his group is to build a multi-level understanding of the microbiome from genes, through structures, to functions and therapies.

Kaja Milanowska-Zabel

Ardigen SA

Kaja is a precision medicine enthusiast and a bioinformatics researcher. Her personal and professional ambitions led her to exploit the importance of different data analysis approaches in human health. In 2016, Kaja joined Ardigen and started research activities, primarily in the area of oncology. In 2018, after becoming one-third of Ardigen’s management board, she took over a new position and the responsibility of the R&D projects. Currently, she is a Business Development Executive.

Andreas Bender

Khalifa University

Andreas is a Professor for Machine Learning in Medicine at the Department of Medicine at Khalifa University, Abu Dhabi. Before he has been Professor for Molecular Informatics at Cambridge University and a Director for Digital Life Sciences at Nuvisan/Berlin, as well as Associate Director for Data Science and AI in the Clinical Pharmacology & Safety Sciences group at AstraZeneca/Cambridge, UK. On the entrepreneurial side, Andreas was involved in setting up Healx Ltd. and PharmEnable Ltd., both based in Cambridge/UK, as well as Pangea Bio, located in London/UK as well as Berlin/Germany. He received his PhD from the University of Cambridge and worked in the Lead Discovery Informatics group at Novartis in Cambridge/MA as well as at Leiden University in the Netherlands before his current post.

Bożena Augustyn

KCRI

Bożena is a Clinical Project Manager at KCRI specializing in clinical research and biostatistics with experience in managing international clinical trials for medical devices in cardiology. Before joining KCRI, Bożena held key roles in clinical operations, oncology research, and data management at Ardigen. With a PhD in Biological Sciences from Jagiellonian University and research experience at the University of Helsinki and Tampere University of Technology, she has worked across academia and industry, focusing on the intersection of biomedical science and data-driven research.

Bartosz Grzybowski

UNIST

Bartosz is a Distinguished Professor of Chemistry at the Ulsan National Institute of Science and Technology (UNIST, South Korea) and a Director of the IBS Center for Algorithmic and Robotized Synthesis (CARS) located therein. He is also affiliated with the Institute of Organic Chemistry, Polish Academy of Sciences. Although he has spent a large fraction of his research career on esoteric problems of self-assembly and non-equilibrium systems, he considers his most impactful discoveries to be in the area of computer-driven synthesis (e.g., the Chematica/Synthia and Allchemy programs). The chemical algorithms and robotics systems Grzybowski develops find applications in both academic and industrial settings, and have ramifications for the issues of global chemical production, circular economy, and national security. Grzybowski is an author of 300+ articles (H=92), and over the years received numerous accolades of which the 2016 Feynman Prize and the 2022 Foundation for Polish Science Prize are closest to his heart.

Miguel Angel Bautista

Apple

Miguel is a Research Scientist in the Machine Learning Research (MLR) group at Apple, where he works on generative modeling, geometric deep learning, and unsupervised learning. His current focus is on developing domain-agnostic diffusion and flow matching models across multiple data domains, including vision, 3D and biology data. Prior to joining Apple, Miguel was a Postdoctoral Fellow at the University of Heidelberg, where he contributed to some of the earliest approaches for self-supervised learning in vision. He earned his PhD from the University of Barcelona, specializing in Error-Correcting Representations for Multi-class problems, spending a significant portion of his PhD at Carnegie Mellon University. His research has been recognized with national awards in Spain for both his Master's and PhD theses. Miguel's long-term goal is to develop probabilistic models that leverage large-scale data to understand and simulate the physical world, pushing the boundaries of generative modeling beyond traditional domains to achieve a unified modeling paradigm.