Machine Learning Summer School
on applications in Science

26 June - 2 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 in other disciplines of Science, 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 .

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

* Yes, these images were AI-generated by us using Midjourney v4 model.

/ Speakers

Thea Klæboe Årrestad

Thea Klæboe Årrestad

ETH Zürich / CERN

Matej Balog

Matej Balog

DeepMind

Michael Bronstein

Michael Bronstein

University of Oxford

Bingqing Cheng

Bingqing Cheng

Institute of Science and Technology, Austria

Sayak Ray Chowdhury

Sayak Ray Chowdhury

Microsoft Research

Marco Cuturi

Marco Cuturi

Apple / CREST-ENSAE

Yarin Gal

Yarin Gal

University of Oxford

Anna Gambin

Anna Gambin

University of Warsaw

Jose Miguel Hernandez-Lobato

Jose Miguel Hernandez-Lobato

University of Cambridge

Stanisław Jastrzębski

Stanisław Jastrzębski

Molecule.one

Krzysztof Krawiec

Krzysztof Krawiec

Poznan University of Technology

Mario Krenn

Mario Krenn

Max Planck Institute for the Science of Light

Krzysztof Maziarz

Krzysztof Maziarz

Microsoft Research

Mathias Niepert

Mathias Niepert

University of Stuttgart

Benedek Rozemberczki

Benedek Rozemberczki

Isomorphic Labs

Żaneta Świderska-Chadaj

Żaneta Świderska-Chadaj

Warsaw University of Technology / Bayer

Christoph Weniger

Christoph Weniger

University of Amsterdam

Bartosz Zieliński

Bartosz Zieliński

Jagiellonian University / IDEAS NCBR

/ Timeline

6 March

Registration open (details here)

11 April

Registration closes

28 April

Acceptance notifications

28 April - 16 May

Time to pay the registration fee

16 May

Confirmation of participation

26 June - 2 July

/ Agenda

Monday / 26 June

8:30 - 9:00
Introduction
9:00 - 10:30
Christoph Weniger
Simulation-based inference for large forward models in physics and astronomy
10:30 - 11:00
Coffee break
11:00 - 12:30
Christoph Weniger
Simulation-based inference for large forward models in physics and astronomy
12:30 - 13:30
Lunch break
13:30 - 15:00
Bingqing Cheng
Predicting materials properties with the help of machine learning
15:00 - 15:30
Coffee break
15:30 - 17:30
Matej Balog
Algorithm discovery using reinforcement learning
17:30+
Free time / break
18:00+
Party 1

Tuesday / 27 June

9:00 - 10:30
Marco Cuturi
TBA
10:30 - 11:00
Coffee break
11:00 - 12:30
Marco Cuturi
TBA
12:30 - 13:30
Lunch break
13:30 - 15:00
Benedek Rozemberczki
Deep Learning for Drug Pair Scoring
15:00 - 15:30
Coffee break
15:30 - 17:00
Thea Aarrestad
Unlocking the Secrets of the Universe: Accelerating Discovery with Machine Learning at CERN
17:00+
Poster session

Wednesday / 28 June

9:00 - 10:30
Ania Gambin
Bayesian models, Bayesian networks, and Bayesian neural networks with applications in computational biology
10:30 - 11:00
Coffee break
11:00 - 12:30
Yarin Gal
Uncertainty in Deep Learning
12:30 - 13:30
Lunch break
13:30 - 15:00
Krzysztof Maziarz
Reaction Prediction and Retrosynthesis
15:00 - 15:30
Coffee break
15:30 - 17:00
Stanisław Jastrzębski
TBA
17:00+
Free time

Thursday / 29 June

9:00 - 10:30
Mathias Niepert
Learning with Discrete Structures and Algorithms
10:30 - 11:00
Coffee break
11:00 - 12:30
Mathias Niepert
Learning with Discrete Structures and Algorithms
12:30 - 13:30
Lunch
13:30+
Free time

Friday / 30 June

9:00 - 10:30
Jose Miguel Hernandez-Lobato
Machine Learning for Molecules
10:30 - 11:00
Coffee break
11:00 - 12:30
Jose Miguel Hernandez-Lobato
Machine Learning for Molecules
12:30 - 13:30
Lunch break
13:30 - 15:00
Mario Krenn
Towards an Artificial Muse for new Ideas in Physics
15:00 - 15:30
Coffee break
15:30 - 17:00
Mario Krenn
Towards an Artificial Muse for new Ideas in Physics
17:00+
Free time / break
18:00+
Party 2

Saturday / 1 July

9:00 - 10:30
Żaneta Swiderska-Chadaj
Machine Learning in medical image analysis - challenges and opportunities
10:30 - 11:00
Coffee break
11:00 - 12:30
Sayak Ray Chowdhury
Differential Privacy in Reinforcement Learning
12:30 - 13:30
Lunch break
13:30 - 15:00
Michael Bronstein
TBA
15:00 - 15:30
Coffee break
15:30 - 17:00
Michael Bronstein
TBA
17:00+
Free time

Sunday / 2 July

9:00 - 10:30
Krzysztof Krawiec
TBA
10:30 - 11:00
Coffee break
11:00 - 12:30
Bartosz Zieliński
Interpretable Deep Learning with Prototypical Parts
12:30 - 13:30
Lunch
13:30 - 14:00
Closing remarks

/ Venue

Jagiellonian University
Faculty of Mathematics and Information Technologies

Address:
Profesora Stanisława Łojasiewicza 6,
30-348 Kraków

/ Organizers

Jagiellonian University in Krakow
Group of Machine Learning Research
ML in PL Association
Ideas NSBR
Bio-inspired artificial neural networks
Tomasz Trzciński
Tomasz Trzciński

IDEAS NCBR / Jagiellonian University

Adam Pardyl
Adam Pardyl

IDEAS NCBR / Jagiellonian University

Marcin Przewięźlikowski
Marcin Przewięźlikowski

IDEAS NCBR / Jagiellonian University

Filip Szatkowski
Filip Szatkowski

IDEAS NCBR / ML in PL / Warsaw University of Technology

Sylwia Piskorska
Sylwia Piskorska

IDEAS NCBR

Karolina Matusiak
Karolina Matusiak

IDEAS NCBR

Adam Goliński
Adam Goliński

ML in PL / Apple

Maciej Chrabąszcz
Maciej Chrabąszcz

ML in PL / Warsaw University of Technology

Maja Jabłońska
Maja Jabłońska

ML in PL

Marek Wydmuch
Marek Wydmuch

ML in PL / Poznan University of Technology

Franek Budrowski
Franek Budrowski

ML in PL / G-Research

Marek Masiak
Marek Masiak

ML in PL / University College London

Michał Tyrolski
Michał Tyrolski

ML in PL / Deepflare

Sebastian Dziadzio
Sebastian Dziadzio

ML in PL / University of Tübingen

Andrzej Krupka
Andrzej Krupka

ML in PL / University of Warsaw

Magdalena Cebula
Magdalena Cebula

ML in PL / Orange

Alicja Ziarko
Alicja Ziarko

ML in PL / University of Warsaw

Dawid Mączka
Dawid Mączka

ML in PL / University of Warsaw

Jakub Walendowski
Jakub Walendowski

ML in PL / University of Warsaw

Aleksandra Możwiłło
Aleksandra Możwiłło

ML in PL

Jakub Myśliwiec
Jakub Myśliwiec

ML in PL / Utrecht University

Kamil Bladoszewski
Kamil Bladoszewski

ML in PL / Amazon Web Services (AWS)

Bartek Krzepkowski
Bartek Krzepkowski

IDEAS NCBR / ML in PL

Marcin Możejko
Marcin Możejko

ML in PL / University of Warsaw

/ Project

The project, whose goal is to internationalize the Jagiellonian University of Kraków in the fields of machine learning and neurobiology through the implementation of a series of summer schools, is carried out under the SPINAKER program financed by the National Agency for Academic Exchange (NAWA).

Projekt, którego celem jest umiędzynarodowienie Uniwersytetu Jagiellońskiego w obszarach uczenia maszynowego oraz neurobiologii poprzez realizację cyklu intensywnych warsztatów w ramach szkół letnich, realizowany jest w ramach programu SPINAKER finansowanego przez Narodową Agencję Wymiany Akademickiej (NAWA).

Zapytania ofertowe: wkrótce.