Selective registration that is available until 1 September, 23:59 (AoE). Notifications will be sent on 4 September.
|Ticket||Early Bird Price|
First-come-first-served registration, which starts 14 September, 18:00 CEST (GMT+2) and last until all tickets are sold out.
First-come-first-served registration for tutorials. Starts TBA in September and last until all tickets are sold out.
ML in PL Conference 2023 is the 7th edition of an annual conference focused on the best of Machine Learning both in academia and in business. Join us and...
Attend a stellar lineup of keynote and invited lectures of internationally recognized researchers, learn about state-of-the-art, and get inspired.
Take part in Call for Contributions and present your work as an engaging talk or as a captivating poster. All presenters get free entry to the conference.
Experience a friendly and inclusive atmosphere. Engage in meaningful conversations and establish lasting connections with other machine learning enthusiasts.
We are excited to announce that this year, the main part of the conference will take place at the remarkable Copernicus Science Centre in Warsaw. Thanks to this change, we will be able to host more attendees and provide you with the best experience ever!
Léon Bottou received the Diplôme d’Ingénieur de l’École Polytechnique (X84) in 1987, the Magistère de Mathématiques Fondamentales et Appliquées et d’Informatique from École Normale Supérieure in 1988, and a Ph.D. in Computer Science from Université de Paris-Sud in 1991. His research career took him to AT&T Bell Laboratories, AT&T Labs Research, NEC Labs America and Microsoft. He joined Meta AI (formerly Facebook AI Research) in 2015. The long-term goal of Léon Bottou’s research is to understand and replicate intelligence. Because this goal requires conceptual advances that cannot be anticipated, Leon’s research has followed many practical and theoretical turns: neural networks applications in the late 1980s, stochastic gradient learning algorithms and statistical properties of learning systems in the early 1990s, computer vision applications with structured outputs in the late 1990s, theory of large scale learning in the 2000s. During the last few years, Léon Bottou’s research aims to clarify the relation between learning and reasoning, with more and more focus on the many aspects of causation (inference, invariance, reasoning, affordance, and intuition.)
Volodymyr Mnih is a Research Scientist at Google DeepMind. He completed an MSc at the University of Alberta working under the supervision of Csaba Szepesvari and a PhD at the University of Toronto working under the supervision of Geoffrey Hinton. Since joining DeepMind, he has been working at the intersection of deep learning and reinforcement learning, co-developing Deep Q Networks (DQN), the asynchronous advantage actor critic (A3C), and reinforcement learning-based hard attention mechanisms.
Jiri Matas is a full professor and the head of the Visual Recognition Group, Department of Cybernetics, Czech Technical University in Prague. He holds a PhD degree from the University of Surrey, UK (1995). He has published more than 300 papers that have been cited about 64000 times. His research interests include visual tracking, object recognition, image matching and retrieval, sequential pattern recognition, and RANSAC-type optimization methods. He received the best paper prize at the British Machine Vision Conferences in 2002, 2005 and 2022, at the Asian Conference on Computer Vision in 2007 and at the Int. Conf. on Document analysis and Recognition in 2015. J. Matas served as a programme or general chair at the European Conference of Computer Vision (ECCV) in 2004, 2016, 2022 and at Computer Vision and Pattern Recognition (CVPR) in 2007 and 2022. He is an Editor-in-Chief of the International Journal of Computer Vision was an Associate Editor-in-Chief of IEEE T. Pattern Analysis and Machine Intelligence. He has co-founded two companies, Eyedea Recognition (computer vision) and Locksley (combinatorial optimization). The industrial project he has lead at the Czech Technical University (Toyota, Samsung, Hitachi, Boeing) have generated income of about 5 million euros. He is an inventor of several patents.
Massimiliano Pontil is Senior Researcher at the Italian Institute of Technology, where he leads the CSML research unit, and co-director of ELLIS unit Genoa. He is also Professor at University College London and member of the UCL Centre for Artificial Intelligence. He has been active in machine learning for over twenty-five years, working on theory and algorithms, including the areas of kernel methods, learning dynamical systems, meta-learning, multitask and transfer learning, sparse estimation, and statistical learning theory.
Taco Cohen is a machine learning researcher (Principal Engineer) at Qualcomm AI Research in Amsterdam. He received a BSc in theoretical computer science from Utrecht University, and a MSc in artificial intelligence and PhD in machine learning (with prof. Max Welling) from the University of Amsterdam (all three cum laude). He was a co-founder of Scyfer, a company focussed on deep active learning, acquired by Qualcomm in 2017. His research is focused on geometric deep learning and reinforcement learning. During his studies he has interned at Google Deepmind (working with Geoff Hinton) and OpenAI. He received the 2014 University of Amsterdam MSc thesis prize, a Google PhD Fellowship, ICLR 2018 best paper award for “Spherical CNNs”, was named one of 35 innovators under 35 by MIT Tech Review, and won the 2022 ELLIS PhD Award and 2022 Kees Schouhamer Immink prize for his PhD research.
Gintare Karolina Dziugaite is a senior research scientist at Google DeepMind, an adjunct professor in the McGill University School of Computer Science, and an associate industry member of Mila, the Quebec AI Institute. Prior to joining Google, she led the Trustworthy AI program at Element AI / ServiceNow, and was named a Rising Star in Machine Learning in 2019. Her research combines theoretical and empirical approaches to understanding deep learning. Since her PhD, one of her main focuses has been on generalization, memorization, and, more recently, on unlearning. She has published a number of papers on network and data pruning, investigating how pruning interacts with other properties of deep learning systems, the training dynamics and the loss landscape.
Frans A. Oliehoek is Associate Professor at Delft University of Technology, where he leads a group on interactive learning and decision making, is one of the scientific directors of the Mercury machine learning lab, and is director and co-founder of the ELLIS Unit Delft. He received his Ph.D. in Computer Science (2010) from the University of Amsterdam (UvA), and held positions at various universities including MIT, Maastricht University and the University of Liverpool. Frans' research interests revolve around intelligent systems that learn about their environment via interaction, building on techniques from machine learning, AI and game theory. He has served as PC/SPC/AC at top-tier venues in AI and machine learning, and currently serves as associate editor for JAIR and AIJ. He is a Senior Member of AAAI, and was awarded a number of personal research grants, including a prestigious ERC Starting Grant.
Desmond Elliott is an Assistant Professor and a Villum Young Investigator at the University of Copenhagen. He obtained his Ph.D from the University of Edinburgh, under the supervision of Frank Keller, and he was a Postdoctoral Researcher at CWI, and the University of Amsterdam in the Netherlands. His current research interests include tokenisation-free language modelling, and multilingual and multimodal learning.
Kateřina Staňková is an associate professor at Delft University of Technology and Delft Technology Fellow, at the faculty of Technology, Policy and Management. She also co-founded Institute for Health Systems Science at her faculty. She focuses on both theory of differential and evolutionary games and their application in understanding and managing evolving systems. In the past years, she has been focusing on understanding cancer through evolutionary game theory and designing evolutionary therapies, i.e. therapies that anticipate and steer/forestall treatment-induced resistance in cancer cells. These treatments show a great promise in first clinical trials. For this work, she received the 2020 Dutch Research Council Stairway to Impact award. She leads a number of national and international projects, including European Training Network EvoGamesPlus and the Dutch Research Council VIDI project “ANTICANCER: Game Theory Empowered by Data Science and Control Theory to Improve Treatment of Metastatic Cancer”, which aims at designing evolutionary therapies for metastatic Non-Small Cell Lung Cancer.
Gergely Neu is a research assistant professor at the Pompeu Fabra University, Barcelona, Spain. He has previously worked with the SequeL team of INRIA Lille, France and the RLAI group at the University of Alberta, Edmonton, Canada. He obtained his PhD degree in 2013 from the Budapest University of Technology and Economics, where his advisors were András György, Csaba Szepesvári and László Györfi. His main research interests are in machine learning theory, with a strong focus on sequential decision making problems. Dr. Neu was the recipient of a Google Faculty Research award in 2018, the Bosch Young AI Researcher Award in 2019, and an ERC Starting Grant in 2020.
Edoardo M. Ponti is a Lecturer (≈ Assistant Professor) in Natural Language Processing at the University of Edinburgh, where he is part of the "Institute for Language, Cognition, and Computation" (ILCC), and an Affiliated Lecturer at the University of Cambridge. Previously, he was a visiting postdoctoral scholar at Stanford University and a postdoctoral fellow at Mila and McGill University in Montreal. In 2021, he obtained a PhD in computational linguistics from the University of Cambridge, St John’s College. His main research foci are modular deep learning, sample-efficient learning, faithful text generation, computational typology and multilingual NLP. His research earned him a Google Research Faculty Award and 2 Best Paper Awards at EMNLP 2021 and RepL4NLP 2019. He is a board member and co-founder of SIGTYP, the ACL special interest group for computational typology, and a scholar of the European Lab for Learning and Intelligent Systems (ELLIS). He is a (terrible) violinist, football player, and an aspiring practitioner of heroic viticulture.
Philippe Preux is a professor in Computer Science at the Université de Lille, France. He has been active in research in artificial intelligence for 30 years now, mostly dealing with machine learning and data mining in the last 2 decades, especially reinforcement learning. He has been the head of the SequeL research group at Inria/CNRS/Université de Lille since 2006, a group now renamed Scool. His research ranges from fundamental algorithmic and methodological questions to applications of reinforcement learning in collaboration with companies. Philippe currently focuses his efforts on applications related to health or sustainable development. He has hosted ICML 2015, and co-organized various scientific events such as the European Workshop on Reinforcement Learning in 2008 and 2018, as well as the Reinforcement Learning Summer School in 2019.
Jane Dwivedi-Yu is a researcher at Meta AI. Her current research focuses on enhancing capabilities of language models along several dimensions, including tool usage, editing, and evaluating representation harms and notions of morality and norms internalized by these models. She is also interested in building large-scale personalized recommender systems by leveraging principles from affective computing, work which was cited among the top 15 AI papers to read in 2022. Before joining Meta, she completed her PhD in Computer Science at University of California, Berkeley and Bachelors at Cornell University.
Join us for an engaging panel discussion on Open Source, where we will delve into the intricacies of opening AI models. Our panelists will provide valuable insights and thought-provoking perspectives on the benefits and risks associated with this practice.
Yannic runs the world’s largest YouTube channel dedicated to Machine Learning Research. His video topics range from technical analysis of new papers to covering the ML community’s recent news and developments, as well as mini-research projects. He holds a PhD in ML from ETH Zurich and is a co-founder of the Swiss LegalTech startup DeepJudge.
Polish Hospital Federation / AI Coalition in Healthcare / Data Lake / Donate your Data Foundation
Ligia Kornowska is Medical Doctor, Managing Director of the Polish Hospital Federation, the largest hospital organization in Poland. Leader of „AI Coalition in Healthcare” for Poland. Co-founder and Chair of the Board in Data Lake and Donate your Data Foundation. Listed among „100 most influential people in Polish healthcare” for the last 4 years, laureate of "25 under 25” and „30 under 30” Forbes list. Held managerial positions in leading polish medical startups. Speaker and moderator at the most important conferences related to the healthcare sector.
Omar Sanseviero is a lead machine learning engineer at Hugging Face, where he works at the intersection of open source, community, and product. Omar leads multiple ML teams that work on topics such as ML for Art, Developer Advocacy Engineering, ML Partnerships, Mobile ML, and ML for Healthcare. Previously, Omar worked at Google as a Software Engineer on Google Assistant and TensorFlow. In Google Assistant, Omar worked with on-device language models, model training and quality refinement, and serving infrastructure.
During our panel discussion, immerse yourself in the captivating "Generative Models" realm. Prepare to be enthralled as our panelists explore the profound impact of Generative AI and its wide-ranging implications. We will delve into the potential opportunities and risks arising from these models.
Polish Academy of Science / IDEAS NCBR
Łukasz Kuciński is an Assistant Professor at the Institute of Mathematics of the PAS, a Senior Research Scientist at IDEAS NCBR, and a Member of the ELLIS Society. His ambition is to design and implement AI agents that can learn to solve problems autonomously in a self-improvement manner. His research is published at CORE A* conferences, such as NeurIPS and ICLR, and covers machine learning and sequential decision-making topics, including reinforcement learning, planning, game theory, automated theorem proving, or alignment of large language models. Prior to his current roles, he worked at PFSA as Vice-Director, where he led a risk modeling team. He obtained a master's degree from the Faculty of Mathematics, Informatics, and Mechanics of the University of Warsaw and a Ph.D. in mathematics from the Polish Academy of Sciences.
Join us for a panel discussion on the intriguing concept of "Human in the Loop." Delve into the topic of training models with human involvement as our panelists shed light on harnessing human expertise to enhance AI models. We will explore the effectiveness of leveraging human insights, assess their inherent value, and uncover the true potential they hold.
Piotr Wygocki is researcher and innovator, co-founder of MIM Fertility. A graduate of the University of Warsaw with a PhD in computer science and a double master's degree in computer science and mathematics. Assistant Professor at the University of Warsaw. Experienced expert in both theoretical and commercial aspects of computer science. Winner of the Kaggle offline competition during KaggleDays Warsaw 2018. Outstanding specialist in AI solutions in reproductive health.
IT University of Copenhagen
Veronika Cheplygina's research focuses on limited labeled scenarios in machine learning, in particular in medical image analysis. She received her Ph.D. from Delft University of Technology in 2015. After a postdoc at the Erasmus Medical Center, in 2017 she started as an assistant professor at Eindhoven University of Technology. In 2020, failing to achieve various metrics, she left the tenure track of search of the next step where she can contribute to open and inclusive science. In 2021 she started as an associate professor at IT University of Copenhagen. Next to research and teaching, Veronika blogs about academic life at https://www.veronikach.com. She also loves cats, which you will often encounter in her work.
University of A Coruña / LIDIA
Eduardo Mosqueira-Rey is a tenured Associate Professor at the University of A Coruña (Spain) and member of the research group LIDIA (Laboratory for Research and Development in Artificial Intelligence). His research focuses on the development of Machine Learning and Quantum Computing algorithms applied usually to health problems. His recent work is centred on the definition of new types of interaction between humans and machine learning algorithms known as “Human-in-the-Loop Machine Learning''. Within this line, he is the principal investigator of the project “Analysis of human-in-the-loop machine learning strategies and its application to pancreatic cancer research (HITL-ML)” funded by the Spanish State Research Agency (AEI). He is also a research member of the NEASQC (NExt ApplicationS of Quantum Computing) European project developing the use case “Quantum Rule-Based Systems (QRBS) for breast cancer detection.
Below we present the draft of the conference agenda, it may change. The details will be announced in the following days.
Due to limited number of seats, attending tutorials require separate registration (see tickets section).
We are very excited to invite all to submit proposals for talks, posters, and tutorials for ML in PL Conference 2023! Like every year, the majority of the conference program will be dedicated to presenting our participants' work and research.
If you would like to present a talk or a poster, please check the details of the Call for Contributions that can be found here. And if you are interested in giving a tutorial, you can find details of Call for Tutorials can be found here.
Start of Early Bird (selective) registration
Call for Tutorials submissions deadline
1 September, 23:59 (AoE)
Call for Contributions submissions deadline / End of Early Bird registration period
Early bird tickets acceptance notifications
Talks and posters acceptance notifications
14 September, 18:00 CEST (GMT+2)
Regular ticket sales start
26 - 29 October
ML in PL Conference 2023
Save the date, add the conference to your calendar:
The conference is organized by a non-profit 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 knowledge about the discipline.
Legal Team Coordinator
Speakers Team Coordinator
Panel Team Coordinator
Visual Identity Coordinator
Call for Contributions Coordinator
Speakers Team Coordinator
Infrastructure Team Coordinator
Special Ops Coordinator
Premeetings and Tutorials Team
Special Ops Team
Special Ops Team
Call for Contributions Team
Special Ops Team
Premeetings and Tutorials Team
Research Platform Team
Call for Contributions Team
Call for Contributions Team
Special Ops Team
We have invited a group of outstanding researchers and entrepreneurs to serve on the scientific board of the conference. We consult the event's program with them to guarantee the best scientific level.
Warsaw University of Technology / University of Warsaw
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.
University of Wrocław / Pathway
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.
University of Warsaw / Nomagic
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.
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.
New York University
Krzysztof Geras is an associate 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).
Stanislaw Jastrzebski serves as the CTO and Chief Scientist at Molecule.one, a biotech startup in the drug discovery space. He is passionate about improving the fundamental aspects of deep learning and applying it to automate scientific discovery. He completed his postdoctoral training at New York University in deep learning. His PhD thesis was based on work on foundations of deep learning done during research visits at MILA (with Yoshua Bengio) and the University of Edinburgh (with Amos Storkey). He received his PhD from Jagiellonian University, advised by Jacek Tabor. Beyond academia, he gained industrial experience at Google, Microsoft and Palantir. In his scientific work, he has published at leading machine learning venues (NeurIPS, ICLR, ICML, JMLR, Nature SR). He is also actively contributing to the machine learning community as an Area Chair (most recently NeurIPS '23) and as an Action Editor for TMLR. At Molecule.one, he leads technical teams working on software for synthesis planning based on deep learning, public data sources, and experiments from a highly automated laboratory.
Poznan University of Technology
Agnieszka Ławrynowicz is an associate professor of AI and an experienced researcher in combining machine learning with symbolic approaches such as knowledge graphs. She is passionate about her work and continually seeks new ways to apply her knowledge to help solve real-world problems. She has led and participated in many R&D projects, including research in computational food, digital humanities, and social good. She enjoys things that are simple to use but elegant and creative.
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.
IDEAS NCBR / University of Warsaw
Piotr Miłoś is an associate professor in the Polish Academy of Sciences, a team leader at IDEAS NCBR and a member of the ELLIS Society. He is interested in methods that can deliver robust decision-making capabilities in complex scenarios. This covers many scenarios including continual learning, automated reasoning in mathematics, planning algorithms and sequential modelling.
Inez Okulska is the Head of the Department of Linguistic Engineering and Text Analysis at the NASK National Research Institute. After completing a colorful humanistic path (which included, among others, linguistics, comparative literary studies, cultural studies, philosophy), culminating in a doctorate in translation studies and a postdoctoral fellowship at Harvard University, she completed master’s studies in automation and robotics at the WEiTI faculty of the Warsaw University of Technology. Scientifically interested in the semantic and pragmalinguistic potential of grammar, explores proprietary vector representations of text and their algebraic potential. She implements projects related to cybersecurity, primarily at the level of detection and classification of undesirable content.
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.
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.
IDEAS NCBR / University of Warsaw
Piotr Sankowski is a professor at the Institute of Informatics, University of Warsaw, where he received his habilitation in 2009 and where he received a doctorate in computer science in 2005. His research interest focuses on practical application of algorithms, ranging from economic applications, through learning data structures, to parallel algorithms for data science. In 2009, Piotr Sankowski received also a doctorate in physics in the field of solid state theory at the Polish Academy of Sciences. In 2010 he received ERC Starting Independent Researcher Grant, in 2015 ERC Proof of Concept Grant, and in 2017 ERC Consolidator Grant. He is a president of IDEAS NCBR – a research
University of Warsaw
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.
Jagiellonian University (GMUM)
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.
Warsaw University of Technology / Tooploox / Jagiellonian University
Tomasz Trzciński (DSc, WUT'20; PhD, EPFL'14; MSc, UPC/PoliTo'10) is an Associate Professor at Warsaw University of Technology, where he leads a Computer Vision Lab, and at Jagiellonian University of Cracow (GMUM). He is also a Computer Vision Group Leader at IDEAS NCBR, a publicly-funded Polish Center for AI. 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, member of ELLIS Society, member of the ALICE Collaboration at CERN 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.
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