# About event
The ML in PL Conference is an event focused on the best of Machine Learning both in academia and in business. This year, after two years of remote events, we've decided to organize a hybrid event for the first time.
Learn from the top world experts
Share your knowledge
Meet the ML community
Feel the unique atmosphere
# Invited Speakers
Petar Veličković is a Staff Research Scientist at DeepMind, Affiliated Lecturer at the University of Cambridge, and an Associate of Clare Hall, Cambridge. He holds a PhD in Computer Science from the University of Cambridge (Trinity College), obtained under the supervision of Pietro Liò. His research concerns geometric deep learning—devising neural network architectures that respect the invariances and symmetries in data (a topic I’ve co-written a proto-book about). For his contributions, he is recognised as an ELLIS Scholar in the Geometric Deep Learning Program. Particularly, his focus is graph representation learning and its applications in algorithmic reasoning (featured in VentureBeat). He is the first author of Graph Attention Networks—a popular convolutional layer for graphs—and Deep Graph Infomax—a popular self-supervised learning pipeline for graphs (featured in ZDNet). Petar’s research has been used in substantially improving travel-time predictions in Google Maps (featured in the CNBC, Endgadget, VentureBeat, CNET, the Verge and ZDNet), and guiding intuition of mathematicians towards new top-tier theorems and conjectures (featured in Nature, Science, Quanta Magazine, New Scientist, The Independent, Sky News, The Sunday Times, la Repubblica and The Conversation).
Matthias Bethge is a Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy. He is also co-initiator of the European ELLIS initiative and co-founder of Deepart UG, and Layer7 AI GmbH.
Farah Shamout is an Assistant Professor in Computer Engineering at NYU Abu Dhabi, where she leads the Clinical Artificial Intelligence Laboratory, and an Affiliated Faculty at NYU Radiology, New York. Her research interests lie in developing machine learning and data science approaches to tackle real-world clinical problems. Farah completed her DPhil (PhD) in Engineering Science at the University of Oxford as a Rhodes Scholar and was a member of Balliol College. Prior to her doctoral studies, she completed her BSc in Computer Engineering (cum laude) at NYU Abu Dhabi.
Piotr Miłoś is a professor at the Polish Academy of Sciences and a research team leader at IDEAS NCBR. He specializes in machine learning and has a strong background in probability theory. He is a leader of a research group focusing on a range of topics related to sequential decision-making. The group develops new methods for domains that require reasoning (e.g., theorem proving). These include new planning algorithms and deep learning techniques for sequential modeling. Another principal line of the group's research is continual learning, focusing on reinforcement learning settings. Prof Milos actively works toward developing a machine learning community in Poland. This includes hosting a reinforcement learning seminar and co-organize a reinforcement learning course (first in Poland).
Cheng Zhang is a Principal Researcher, leading causal AI for decision making at Microsoft Research Cambridge (MSRC), UK. She is an expert in deep generative models, causal discovery, causal inference, and decision-making under uncertainty. She has published in all top venues in machine learning, including NeurIPS, ICML, AIStats, UAI, and AAAI. Apart from research expertise, she is also experienced in enabling real-world impact in different domains.
Kyunghyun Cho is an associate professor of computer science and data science at New York University and CIFAR Fellow of Learning in Machines & Brains. He is also a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving PhD and MSc degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He received an honour of being a recipient of the Samsung Ho-Am Prize in Engineering in 2021. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.
# Previous Editions
# Call for Contributors
We are very excited to invite you to submit your proposals for contributed talks and posters for ML in PL ‘22 Conference!
All accepted talks and posters will be presented during the Main Conference and their authors will receive a free ticket.
A detailed description of the Call for Contribution can be found here.
# Students' Day
Registration for Students’ Day for #MLinPL Conference 2022 is now open! We are very excited to invite you to submit your talk's proposals. Your talks should be about 20 minutes long.
Participation in Students' Day is free of charge. Moreover, all accepted speakers will receive tickets for the whole ML in PL Conference 2022!
Check out detailed information here.
Don't hesitate: the deadline for submissions passes 14 October 2022.
# Scientific Board
Ewa Szczurek is an assistant professor at the Faculty of Mathematics, Informatics and Mechanics at the University of Warsaw. She holds two Master degrees, one from the Uppsala University, Sweden and one from the University of Warsaw, Poland. She finished PhD studies at the Max Planck Institute for Molecular Genetics in Berlin, Germany and conducted postdoctoral research at ETH Zurich, Switzerland. She now leads a research group focusing on machine learning and molecular biology, with most applications in computational oncology. Her group works mainly with probabilistic graphical models and deep learning, with a recent focus on variational autoencoders.
Henryk Michalewski obtained his Ph.D. in Mathematics and Habilitation in Computer Science from the University of Warsaw. Henryk spent a semester in the Fields Institute, was a postdoc at the Ben Gurion University in Beer-Sheva and a visiting professor in the École normale supérieure de Lyon. He was working on topology, determinacy of games, logic and automata. Then he turned his interests to more practical games and wrote two papers on Morpion Solitaire. Presenting these papers at the IJCAI conference in 2015 he met researchers from DeepMind and discovered the budding field of deep reinforcement learning. This resulted in a series of papers including Learning from memory of Atari 2600, Hierarchical Reinforcement Learning with Parameters, Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes and Reinforcement Learning of Theorem Proving.
Jacek Tabor in his scientific work deals with broadly understood machine learning, in particular with deep generative models. He is also a member of the GMUM group (gmum.net) aimed at popularization and development of machine learning methods in Cracow.
Jan Chorowski is an Associate Professor at Faculty of Mathematics and Computer Science at University of Wrocław. He received his M.Sc. degree in electrical engineering from Wrocław University of Technology and Ph.D. from University of Louisville. He has visited several research teams, including Google Brain, Microsoft Research and Yoshua Bengio’s lab. His research interests are applications of neural networks to problems which are intuitive and easy for humans and difficult for machines, such as speech and natural language processing.
Prior to joining Yahoo! Research Krzysztof Dembczyński was an Assistant Professor at Poznan University of Technology (PUT), Poland. He has received his PhD degree in 2009 and Habilitation degree in 2018, both from PUT. During his PhD studies he was mainly working on preference learning and boosting-based decision rule algorithms. During his postdoc at Marburg University, Germany, he has started working on multi-target prediction problems with the main focus on multi-label classification. Currently, his main scientific activity concerns extreme classification, i.e., classification problems with an extremely large number of labels. His articles has been published at the premier conferences (ICML, NeurIPS, ECML) and in the leading journals (JMLR, MLJ, DAMI) in the field of machine learning. As a co-author he won the best paper award at ECAI 2012 and at ACML 2015. He serves as an Area Chair for ICML, NeurIPS, and ICLR, and as an Action Editor for MLJ.
Krzysztof Geras is an assistant professor at NYU School of Medicine and an affiliated faculty at NYU Center for Data Science. His main interests are in unsupervised learning with neural networks, model compression, transfer learning, evaluation of machine learning models and applications of these techniques to medical imaging. He previously completed a postdoc at NYU with Kyunghyun Cho, a PhD at the University of Edinburgh with Charles Sutton and an MSc as a visiting student at the University of Edinburgh with Amos Storkey. His BSc is from the University of Warsaw. He also completed industrial internships in Microsoft Research (Redmond and Bellevue), Amazon (Berlin) and J.P. Morgan (London).
Marek Cygan is currently an associate professor at the University of Warsaw, leading a newly created Robot learning group, focused on robotic manipulation and computer vision. Additionally, CTO and co-founder of Nomagic, a startup delivering smart pick-and-place robots for intralogistics applications. Earlier doing research in various branches of algorithms, having an ERC Starting grant on the subject.
Piotr Miłoś is an Associate Professor at the Faculty of Mathematics, Mechanics and Computer Science of the University of Warsaw. He received his Ph.D. in probability theory. From 2016 he has developed interest in machine learning. Since then he collaborated with deepsense.ai on various research projects. His focus in on problems in reinforcement learning.
Przemysław Biecek obtained his Ph.D. in Mathematical Statistics and MSc in Software Engineering at Wroclaw University of Science and Technology. He is currently working as an Associate Professor at the Faculty of Mathematics and Information Science, Warsaw University of Technology, and an Assistant Professor at the Faculty of Mathematics, Informatics and Mechanics, University of Warsaw.
Razvan Pascanu is a Research Scientist at Google DeepMind, London. He obtained a Ph.D. from the University of Montreal under the supervision of Yoshua Bengio. While in Montreal he was a core developer of Theano. Razvan is also one of the organizers of the Eastern European Summer School. He has a wide range of interests around deep learning including optimization, RNNs, meta-learning and graph neural networks.
Tomasz Trzciński is an Associate Professor at Warsaw University of Technology since 2015, where he leads a Computer Vision Lab. He was a Visiting Scholar at Stanford University in 2017 and at Nanyang Technological University in 2019. Previously, he worked at Google in 2013, Qualcomm in 2012 and Telefónica in 2010. He is an Associate Editor of IEEE Access and MDPI Electronics and frequently serves as a reviewer in major computer science conferences (CVPR, ICCV, ECCV, NeurIPS, ICML) and journals (TPAMI, IJCV, CVIU). He is a Senior Member of IEEE and an expert of National Science Centre and Foundation for Polish Science. He is a Chief Scientist at Tooploox and a co-founder of Comixify, a technology startup focused on using machine learning algorithms for video editing.
Viorica Patraucean is a research scientist in DeepMind. She obtained her PhD from University of Toulouse on probabilistic models for low-level image processing. She then carried out postdoctoral work at Ecole Polytechnique Paris and University of Cambridge, on processing of images, videos, and point-clouds. Her main research interests revolve around efficient vision systems, with a focus on deep video models. She is one of the main organisers of EEML summer school and has served as program committee member for top Computer Vision and Machine Learning conferences.
# ML in PL Association
We are a group of young people who are determined to bring the best of Machine Learning to Central and Eastern Europe by creating a high-quality event for every ML enthusiast. Although we come from many different academic backgrounds, we are united by the common goal of spreading the knowledge about the discipline.Learn more about ML in PL Association
Vice Project Leader
Call for Contributions Coordinator
Finance Team Coordinator
Legal Team Coordinator
Panel Team Coordinator
Scientific Program Coordinator
Special Ops Coordinator
Call for Contributions Team
Call for Contributions Team
Scientific Program Team
Scientific Program Team
Special Ops Team