Machine Learning Summer School
on Drug and Materials Discovery

1 - 6 July / Kraków, Poland

0 days / 00 hours / 00 minutes / 00 seconds

/ About

is a summer school providing a didactic introduction to a range of modern topics in Machine Learning and their applications for Drug and Materials Discovery, primarily intended for research-oriented graduate students. The school features a line-up of internationally recognized researchers who will talk with enthusiasm about their subjects. Our goal is to provide a unique opportunity to learn from and connect with the leading experts in the scenic setting of the historic city of Kraków, Poland. This school is the next edtion of and .

The more details about this year speakers and registration process will be published soon.

If you have any questions about the school, don't hesitate to contact us by email mlss@mlinpl.org.

/ Speakers

Regina Barzilay photo

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, American Academy of Arts and Sciences, and the National Academy of Medicine.

Marwin Segler photo

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 photo

Emmanuel Bengio

Recursion / MILA

Emmanuel is a Senior ML Scientist at Valence Labs @ Recursion, working on GFlowNet and drug-discovery from Mila, Montreal. Emmanuel got my PhD and CS Master's at McGill, as a part of Mila, under the supervision of Joelle Pineau and Doina Precup. My Master's thesis was on conditional computation, and PhD thesis on generalization and optimization in deep RL. He have a Computer Science B.Sc. from the DIRO at Université de Montréal. Emmanuel is mainly interested in Machine Learning, especially Deep Learning and Reinforcement Learning and mixing both, he is also into compiler design and implementation, and generally programming as a topic. Lately, He has been working at the intersection of ML and drug design using our all new GFlowNet framework.

Jakub Tomczak photo

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 photo

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 photo

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 photo

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 photo

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 photo

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.

and more is coming!

/ Timeline

3 February

Early Bird application opens

8 March (AoE)

Early Bird application closes

9 March

Regular application opens

22 March

Early Bird acceptance notifications

19 April (AoE)

Regular application closes

30 April

Regular notification

16 May (AoE)

Deadline for paying the registration fee

1 - 6 July

/ Venue

Jagiellonian University
Faculty of Mathematics and Computer Science

Address:
Profesora Stanisława Łojasiewicza 6,
30-348 Kraków

/ Organizers

/ Contact

If you have any question about the event don't hesitate to contact us by email or via our social media: