Jakub Tomczak

I am an assistant professor of Artificial Intelligence in the Computational Intelligence group (led by Prof. A.E. Eiben) at Vrije Universiteit Amsterdam. My main research interests include deep learning, Bayesian inference and deep generative modeling.

About me

Jakub Tomczak is an assistant professor of Artificial Intelligence in the Computational Intelligence group (led by Prof. A.E. Eiben) at Vrije Universiteit Amsterdam. Before joining Vrije Universiteit Amsterdam, he was a deep learning researcher (Engineer, Staff) in Qualcomm AI Research in Amsterdam (10/2018 - 12/2019) and a Marie Sklodowska-Curie individual fellow in Max Welling's group at the University of Amsterdam, and an assistant professor and postdoc at Wroclaw University of Technology. His main research interests include deep generative modeling, deep learning, and Bayesian inference. Recently, I am also interested in developing novel derivative-free optimization algorithms. I am very keen on applying machine learning to biology and medicine. My CV: [here].

[NEWS] [PAPERS] [REVIEWING SERVICE]

News

2021/5/24

Our paper on applying deep learning to medical imaging has been accepted to Applied Sciences: [LINK]

2021/5/23

Our paper on applying derivative-free optimization for robotics has been published in Neurocomputing: [LINK]

2021/5/21

A project on continuous kernel convolutions led by David W. Romero (supervised by me and Prof. M. Hoogendoorn) has been awarded QIF (Qualcomm Innovation Fellowship)

2021/5/8

Our paper on data augmentation and causal theory has been accepted to ICML 2021: [LINK]

2021/3/27

Our paper on applying deep learning for agriculture has been published in Computers and Electronics in Agriculture: [LINK]

2021/2/11

Our paper on COVID-19 has been published in Scientific Reports: [LINK]

2021/2/1

Our paper on genome-wide reconstruction with gut microbiota has been accepted to Bioinformatics: [LINK]

2021/01/27

Our paper on genome-wide reconstruction of bone marrow cancer has been accepted to Int. Journal of Molecular Sciences: [LINK]

2020/12/30

Our paper on population-based parameter identification for systems biology has been accepted to Processes: [LINK]

2020/12/22

Our 3 papers have been accepted to AABI 2021: [LINK]

2020/09/26

Our paper has been accepted to NeurIPS 2020: [here].

2020/06/29

Our two papers have been accepted to the INNF+ workshop at ICML 2020: [here] and [here]

2020/06/1

Our paper has been accepted to ICML 2020: [here]

2020/05/31

Our paper has been accepted to LOD 2020: [here]

2020/04/1

Our paper has been accepted to MIDL 2020 (oral presentation): [here]

2020/03/20

Our paper has been accepted to GECCO 2020: [here]

2020/03/20

Our paper has been accepted to IEEE CEC 2020: [here]

2020/02/27

Our paper has been accepted to CVPR 2020 (oral presentation): [here]

2019/11/24

An invated talk given at MLinPL 2019. Link to the slides: [here]

2019/09/03

Our paper on combinatorial Bayesian optimization has been accepted to NeurIPS 2019. Link to the paper: [here]

2019/07/23

Our paper on video compression has been accepted to ICCV 2019. Link to the paper: [here]

2019/06/05

Our paper (together with Ewelina Weglarz-Tomczak) on ABC for Michaelis-Menten model has been accepted to FEBS Letter. Link to the paper: [here]

2019/04/19

A workshop paper is accepted to Deep Generative Models for Highly Structured Data @ ICLR 2019. Link to the paper: [here]

2019/04/05

A workshop paper is accepted to Representation Learning on Graphs and Manifolds @ ICLR 2019. Link to the paper: [here]

2018/10/03

A paper is accepted to Neural Processing Letters.

2018/05/16

Two papers are accepted as oral presentations at UAI 2018.

2018/05/16

Two papers are accepted to MIDL 2018.

2018/06/15

One paper is accepted to TADGM @ ICML 2018.

2018/05/11

One paper is accepted to ICML 2018.

2017/12/22

One paper is accepted as an oral presentation to AISTATS 2018.

2017/05/11

One paper is accepted to Benelearn 2017.

2016/11/30

One paper is accepted to Bayesian Deep Learning @ NIPS 2016.

2016/10/25

One paper is accepted to Machine Learning in Computational Biology @ NIPS 2016.

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Publications



Journal articles

  1. Falko Lavitt, Demi J. Rijlaarsdam, Dennet van der Linden, Ewelina Weglarz-Tomczak, Jakub M.Tomczak, Deep learning and transfer learning for automatic cell counting in microscope images of human cancer cell lines, Applied Sciences, 2021, [MDPI]
  2. Gongjin Lan, Maarten van Hooft, Matteo De Carlo, Jakub M.Tomczak, A.E.Eiben, Learning locomotion skills in evolvable robots, Neurocomputing, 2021, [ScienceDirect]
  3. Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai, Deep learning for white cabbage seedling prediction, Computers and Electronics in Agriculture, 2021, [ScienceDirect]
  4. Ewelina Weglarz-Tomczak, Jakub M. Tomczak, Michal Talma, Malgorzata Burda-Grabowska, Miroslaw Giurg, Stanley Brul, Identification of ebselen and its analogues as potent covalent inhibitors of papain-like protease from SARS-CoV-2, Scientific Reports, 2021, [Nature], [pdf]
  5. Ewelina Weglarz-Tomczak, Jakub M. Tomczak, Stanley Brul, M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models, Bioinformatics, 2021, [OxfordAcademic], [pdf]
  6. Ewelina Weglarz-Tomczak, Demi J. Rijlaarsdam, Jakub M. Tomczak, Stanley Brul, GEM-based metabolic profiling for Human Bone Osteosarcoma under different glucose and glutamine availability, International Journal of Molecular Sciences 22 (3), 1470, 2021 [MDPI], [pdf]
  7. Ewelina Weglarz-Tomczak, Jakub M. Tomczak, A.E. Eiben, Stanley Brul, Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae, Processes 9 (1), 98, 2021 [MDPI], [pdf]
  8. Jakub M. Tomczak, Ewelina Weglarz-Tomczak, Estimating kinetic constants in the Michaelis‐Menten model from one enzymatic assay using Approximate Bayesian Computation, FEBS Letters, online 3 October 2019, doi.org/10.1002/1873-3468.13531, [Wiley], [pdf]
  9. Jakub M. Tomczak, Szymon Zaręba, Siamak Ravanbakhsh, Russell Greiner, Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines, Neural Processing Letters, online 3 October 2017, DOI: 10.1007/s11063-018-9923-4, [SpringerLink], [pdf]
  10. Magda Drewniak, Ewelina Węglarz-Tomczak, Katarzyna Ożga, Ewa Rudzińska-Szostak, Katarzyna Macegoniuk, Jakub M. Tomczak, Magdalena Bejger, Wojciech Rypniewski, Łukasz Berlicki, Helix-loop-helix peptide foldamers and their use in the construction of hydrolase mimetics, Bioorganic Chemistry, Vol. 81, pp. 356-361, DOI: 10.1016/j.bioorg.2018.07.012, [ScienceDirect]
  11. Adam Gonczarek, Jakub M. Tomczak, Szymon Zaręba, Joanna Kaczmar, Piotr Dąbrowski, Michał Walczak, Interaction prediction in structure-based virtual screening using deep learning, Computers in Biology and Medicine, online 14 September 2017, DOI: 10.1016/j.compbiomed.2017.09.007, [ScienceDirect], [pdf]
  12. Maciej Zięba, Sebastian Tomczak, Jakub M. Tomczak, Ensemble Boosted Trees with Synthetic Features Generation in Application to Bankruptcy Prediction, Expert Systems with Applications, Vol. 58, pp. 593-101, 2016, DOI: 10.1016/j.eswa.2016.04.001, [ScienceDirect], [pdf], [Draft]
  13. Jakub M. Tomczak, On some properties of the low-dimensional Gumbel perturbations in the Perturb-and-MAP model, Statistics and Probability Letters, ACCEPTED March 29 2016, DOI: 10.1016/j.spl.2016.03.019, [ScienceDirect], [pdf]
  14. Jakub M. Tomczak, Adam Gonczarek, Learning invariant features using Subspace Restricted Boltzmann Machine, Neural Processing Letters, ACCEPTED March 29 2016, DOI: 10.1007/s11063-016-9519-9, [SpringerLink], [pdf], [Draft]
  15. Adam Gonczarek, Jakub M. Tomczak, Articulated tracking with manifold regularized particle filter, Machine Vision and Applications, Volume 27, Issue 2, pp 275–286, DOI:10.1007/s00138-016-0748-8, [SpringerLink], [pdf]
  16. Jakub M. Tomczak, Learning Informative Features from Restricted Boltzmann Machines, Neural Processing Letters, DOI:10.1007/s11063-015-9491-9, [SpringerLink], [pdf]
  17. Jakub M. Tomczak, Maciej Zięba, Probabilistic combination of classification rules and its application to medical diagnosis, Machine Learning, Vol. 101, Issue 1, pp. 105-135, DOI:10.1007/s10994-015-5508-x, [SpringerLink], [pdf]
  18. Jakub M. Tomczak, Maciej Zięba, Classification Restricted Boltzmann Machine for comprehensible credit scoring model, Expert Systems with Applications, Volume 42, Issue 4, March 2015, DOI:10.1016/j.eswa.2014.10.016, [ScienceDirect], [pdf]
  19. Maciej Zięba, Jakub M. Tomczak, Boosted SVM with active learning strategy for imbalanced data, Soft Computing, August 2014, Pages 99–108, DOI: 10.1007/s00500-014-1407-5, [Scopus], [pdf]
  20. Maciej Zięba, Jakub M. Tomczak, Jerzy Świątek, Marek Lubicz, Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients, Applied Soft Computing, Volume 14, Part A, January 2014, Pages 99–108, DOI: 10.1016/j.asoc.2013.07.016, [ScienceDirect], [pdf], [DATA] , [UCI ML Repository]
  21. Jakub M. Tomczak, Adam Gonczarek, Decision rules extraction from data stream in the presence of changing context for diabetes treatment, Knowledge and Information Systems, 2013, Vol. 34, Issue 3, pp. 521-546, DOI: 10.1007/s10115-012-0488-7 [SpringerLink], [pdf]

Conferences (selected)

  1. Maximilian Ilse, Jakub M. Tomczak, Patrick Forre, Selecting Data Augmentation for Simulating Interventions, ICML 2021, [PDF]
  2. Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling, The Convolution Exponential and Generalized Sylvester Flows, NeurIPS 2020, [PDF]
  3. Ioannis Gatopoulos, Maarten Stol, Jakub M. Tomczak, Super-resolution Variational Auto-Encoders, INNF+ @ ICML 2020, [PDF], [CODE]
  4. Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling, The Convolution Exponential, INNF+ @ ICML 2020, [PDF]
  5. David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn, Attentive Group Equivariant Convolutional Networks, ICML 2020, [PDF], [CODE]
  6. Alessandro Zonta, Ali El Hassouni, David W. Romero, Jakub M. Tomczak, Generative Fourier-based Auto-Encoders: Preliminary Results, LOD 2020, [PDF]
  7. Jakub M. Tomczak, Ewelina Węglarz-Tomczak, A.E. Eiben, Differential Evolution with Reversible Linear Transformations, GECCO 2020, [PDF], [CODE]
  8. Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, and Max Welling, DIVA: Domain Invariant Variational Autoencoder, MIDL 2020, [PDF]
  9. Davide Abati, Jakub M. Tomczak, Tijmen Blankevoort, Simone Calderara, Rita Cucchiara, Babak Ehteshami Bejnordi, Conditional Channel Gated Networks for Task-Aware Continual Learning, CVPR, 2020, [PDF]
  10. Ioannis Gatopoulos, Romain Lepert, Auke Wiggers, Giovanni Mariani, Jakub M. Tomczak, Evolutionary Algorithm with Non-parametric Surrogate Model for Tensor Program Optimization, IEEE CEC 2020, [PDF]
  11. ChangYong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling, Combinatorial Bayesian Optimization using the Graph Cartesian Product, NeurIPS, Vancouver, Canada, 2019, [PDF], [CODE]
  12. Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco S. Cohen, Video compression with rate-distortion autoencoders, ICCV, Seol, South Korea, 2019, [PDF]
  13. Tim Davidson, Jakub M. Tomczak, Efstratios Gavves, Increasing Expressivity of a Hyperspherical VAE, NeurIPS Workshop on Bayesian Deep Learning, Vancouver, Canada, 2019, [PDF]
  14. Tim Davidson, Luca Falorsi, Nicola de Cao, Thomas Kipf, Jakub M. Tomczak, Hyperspherical Variational Auto-Encoders, UAI, Monterey, California, the USA, 2018, [arxiv], [PDF], [CODE], [BLOG]
  15. Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling, Sylvester Normalizing Flow for Variational Inference, UAI, Monterey, California, the USA, 2018, [arxiv], [PDF]
  16. Philip Botros, Jakub M. Tomczak, Hierarchical VampPrior Variational Fair Auto-Encoder, ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden, 2018, [arxiv], [PDF]
  17. Maximilian Ilse, Jakub M. Tomczak, Max Welling, Attention-based Deep Multiple Instance Learning, ICML, Stockholm, Sweden, 2018, [arxiv], [PDF], [CODE]
  18. Nathan Ing, Beatrice S. Knudsen, Arkadiusz Gertych, Jakub M. Tomczak, Max Welling, Isla P. Garraway, Eric Miller, A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology, Medical Imaging with Deep Learning (MIDL), Amsterdam, the Netherlands, 2018, [OpenReview], [PDF]
  19. Jakub M. Tomczak, Maximilian Ilse, Max Welling, Marnix Jansen, H.G Coleman, Marit Lucas, Kikki de Laat, Martijn de Bruin, Henk Marquering, Myrtle J. van der Wel, Onno J. de Boer, C. Dilara Savci Heijink, Sybren L. Meijer, Histopathological classification of precursor lesions of esophageal adenocarcinoma: A Deep Multiple Instance Learning Approach, Medical Imaging with Deep Learning (MIDL), Amsterdam, the Netherlands, 2018, [OpenReview], [PDF]
  20. Jakub M. Tomczak, Max Welling, VAE with a VampPrior, AISTATS, the Canary Islands, 2018, [arxiv], [PDF], [CODE]
  21. Jakub M. Tomczak, Maximilian Ilse, Max Welling, Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification, NIPS Workshop on Medical Imaging Meets NIPS, Long Beach, the USA, 2017 [arxiv], [PDF]
  22. Leonard Hasenclever, Jakub M. Tomczak, Rianne van den Berg, Max Welling, Variational Inference with Orthogonal Normalizing Flows, NIPS Workshop on Bayesian Deep Learning, Long Beach, the USA, 2017, [PDF]
  23. Jakub M. Tomczak, Max Welling, Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow, Benelearn 2017, Eindhoven, the Netherlands, 2017 [arxiv], [PDF], [CODE]
  24. Jakub M. Tomczak, Max Welling, Improving Variational Auto-Encoders using Householder Flow, NIPS Workshop on Bayesian Deep Learning, Barcelona, Spain, 2016 [arxiv], [PDF], [CODE]
  25. Adam Gonczarek, Jakub M. Tomczak, Szymon Zaręba, Joanna Kaczmar, Piotr Dąbrowski, Michał J. Walczak, Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening, NIPS Workshop on Machine Learning in Computational Biology, Barcelona, Spain, 2016 [arxiv], [PDF]

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Reviewer/AC



Conferences

NeurIPS (2018, 2019, 2020, AC: 2021), ICML (2019, 2020, 2021), ICLR (2019, 2020, 2021), AISTATS (2019, 2020, 2021), ICML-GR (2019), ICLR-RLGM (2019), CVPR-URDVL (2019), Medical Imaging with Deep Learning (2018), CVPR-BNIVU (2018), NeurIPS-Bayesian Deep Learning (2017, 2018), ACIIDS (2013, 2014, 2015), ICSS (2013, 2016), ISAT (2012, 2013, 2014, 2015, 2016), NCA (2014)

Journals

IEEE Transactions on Pattern Analysis and Machine Intelligence, Bioinformatics, Medical Image Analysis, Expert Systems with Applications, BMC Bioinformatics, IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Neural Systems & Rehabilitation Engineering, European Journal of Operation Research, Neural Processing Letters, Operations Research and Decisions, Knowledge-Based Systems, International Journal of Approximate Reasoning, Biocybernetics and Biomedical Engineering, Transactions on Image Processing
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