Dmitry Kobak

dmitry.kobak@ugent.be

Google Scholar | Lab website | CV

ORCID Github Pubmed Dblp arXiv

@hippopedoid on Bluesky
@amoeba at CrossValidated.SE

Since April 2026, I am a professor at the Department of Mathematics, Computer Science, and Statistics at Ghent University and a principal investigator at the VIB Center for AI & Computational Biology (VIB.AI), working on machine learning and data science for biological applications.

Prior to that, I was a group leader in the Hertie AI institute at Tübingen University. I am a member of the ELLIS society.

I am interested in self-supervised and unsupervised learning, in particular contrastive learning, manifold learning, and dimensionality reduction for 2D visualization of scientific datasets. The lab is working with image data, text data, graph data, and single-cell RNA-seq data in neuroscience contexts. I am also increasingly interested in interpretability of biological foundation models.

My night-science hobby is statistical forensics. I have been involved in the analysis of Russian electoral falsifications, war fatalities, Covid-19 excess mortality, and LLM usage in academic publishing.

News

April 2026: Moved to Ghent and started my lab! Crazy :) I am hiring!

Teaching

In Ghent: upcoming.

In Tübingen (2020–25), I was teaching an introductory course on machine learning for MSc students in neuroscience and data science. In winter semester 2020/21, due to the Covid pandemic, the class was held online and the lectures were recorded in a studio.

• Videos: Tübingen Machine Learning / Introduction to Machine Learning
• Slides: 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11

In Heidelberg (2023/24), I taught a BSc course Einführung ins Machinelle Lernen (in German) and a MSc seminar Transformers, large language models, and their use in physics.

Supervision

• Sebastian Damrich → junior group leader at Hertie AI

• Rita González Márquez
• 2025: Niklas Böhm → postdoc at European Space Agency

• Mehdi Merbah
• Lena Holzwarth
• 2025: Moritz Christ
• 2023: Marius Keute
• 2021: Fynn Bachmann → PhD student at the University of Zürich
• 2021: Rita González Márquez → stayed as a PhD student
• 2020: Niklas Böhm → stayed as a PhD student

Recorded talks

• Contrastive and neighbor embedding methods for data visualisation, April 2023, Network Seminar.
• Контрастные методы и методы ближайших соседей для визуализации данных, May 2023, SciBerloga Seminar.
• What are 2D neighbour embeddings of scRNA-seq data actually useful for?, April 2022, ICLR GTRL workshop.
• Neighbour embeddings for scientific visualization, June 2021, ELLIS Life Heidelberg.

Research

This is a partial list of [co-]first-/last-author papers grouped by topic; see Google Scholar for the complete list. Most papers are open access; for the ones that are not, I provide PDFs. Twitter icons link to the respective Twitter threads.

Self-supervised learning

• González-Márquez et al., Cropping outperforms dropout as an augmentation strategy for training self-supervised text embeddings  (TMLR, 2026)
• Draganov et al., On the importance of embedding norms in self-supervised learning  (ICML, 2025)
• Böhm et al., Unsupervised visualization of image datasets using contrastive learning  (ICLR, 2023) 

Neighbour embeddings

• Böhm, Keute et al., Node embeddings via neighbor embeddings  (TMLR, 2025)
• Lause et al., The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense  (PLoS Comp Bio, 2024) 
• Damrich et al., Visualizing single-cell data with the neighbor embedding spectrum  (bioRxiv, 2024)
• González-Márquez et al., Learning representations of learning representations  (DMLR workshop @ ICLR, 2024) 
• González-Márquez et al., The landscape of biomedical research  (Patterns, 2024) 
• Damrich et al., From t-SNE to UMAP with contrastive learning  (ICLR, 2023) 
• Bachmann et al., Wasserstein t-SNE  (ECML/PKDD, 2022) 
• González-Márquez et al., Two-dimensional visualization of large document libraries using t-SNE  (GTRL workshop @ ICLR, 2022) 
• Böhm et al., Attraction-repulsion spectrum in neighbor embeddings  (JMLR, 2022) 
• Kobak & Linderman, Initialization is critical for preserving global data structure in both t-SNE and UMAP  (Nature Biotechnology, 2021)  
• Kobak et al., Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations  (ECML/PKDD, 2020) 
• Kobak & Berens, The art of using t-SNE for single-cell transcriptomics  (Nature Communications, 2019) 

Topological data analysis

• Damrich et al., Persistent homology for high-dimensional data based on spectral methods
  (NeurIPS, 2024)

Statistical methods for transcriptomic and multi-omic data analysis

• REVIEW: Ahlmann-Eltze, Barkmann, Lause et al., Representation learning of single-cell RNA-seq data  (RNA, 2026)
• Lause et al., Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs  (bioRxiv, 2023)
• Lause et al., Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data  (Genome Biology, 2021) 
• Bernaerts et al., Sparse bottleneck networks for exploratory analysis and visualization of neural Patch-seq data  (arXiv, 2020)
• Kobak et al., Sparse reduced-rank regression for exploratory visualisation of paired multivariate data  (Journal of Royal Statistical Society C, 2021) 

Machine learning & statistical theory

• Greydanus & Kobak, Scaling down deep learning with MNIST-1D  (ICML, 2024)
• Kobak et al., The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization  (JMLR, 2020) 

Patch-seq data analysis

• Scala* & Kobak* et al., Phenotypic variation in transcriptomic cell types in mouse motor cortex  (Nature, 2021) 
• Scala* & Kobak* et al., Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas  (Nature Communications, 2019)

Election forensics

• Kobak et al., Suspect peaks in Russia's “referendum” results  (Significance, 2020) 
• Kobak et al., Putin's peaks: Russian election data revisited  (Significance, 2018)
• Kobak et al., Statistical fingerprints of electoral fraud?  (Significance, 2016)
• Kobak et al., Integer percentages as electoral falsification fingerprints  (The Annals of Applied Statistics, 2016)

Excess mortality / Covid-19 forensics / LLM forensics

• Kobak et al., Delving into LLM-assisted writing in biomedical publications through excess vocabulary  (Science Advances, 2025) 
• Kobak et al., War fatalities in Russia in 2022–2023 estimated via excess male mortality: a research note  (Demography, 2025)
• Kobak, Underdispersion: A statistical anomaly in reported Covid data  (Significance, 2022)  
• Karlinsky & Kobak, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset  (eLife, 2021) 
• Kobak, Excess mortality reveals Covid's true toll in Russia  (Significance, 2021) 

Previous work and education

In 2013–2016 I was a postdoc in the Machens lab at Champalimaud Institute in Lisbon, working on statistical analysis of electrophyisological population recordings from the cortex.

• Kobak* & Pardo-Vazquez* et al., State-dependent geometry of population activity in rat auditory cortex  (eLife, 2019)
• Kobak* & Brendel* et al., Demixed principal component analysis of neural population data  (eLife, 2016)

In 2007–2012 I did my PhD in the Mehring lab, initially at Freiburg University and later at Imperial College London, working on computational motor control.

• Bashford* & Kobak* et al., Motor skill learning decreases movement variability and increases planning horizon  (Journal of Neurophysiology, 2022)
• Kobak & Mehring, Adaptation paths to novel motor tasks are shaped by prior structure learning  (The Journal of Neuroscience, 2012)

In 2000–2007 I studied computer science (BSc) at St. Petersburg ITMO University and then theoretical physics (MSc) at St. Petersburg State University.

Before that, I attended St. Petersburg Classical Gymnasium #610. I was part-time teaching computer science and physics there in 2002–2006 while studying at university. In 2004, together with a friend, I made a website 610.ru that is still online (with some changes).

Reviewing

Year  Reviews
-------------
2019  1
2020  15
2021  20
2022  22
2023  28
2024  23
2025  26
2026  4

Venue              Reviews (>1)
-------------------------------
NeurIPS            31
ICML               18
ICLR               13
ECML               12
TMLR               12
Bioinformatics     5
Genome Biology     5
AISTATS            3
JMLR               3
Nature Biotech     2
Nature Comms       2
Nature Comms Bio   2
Nature Methods     2
PLoS Comp Bio      2
PLoS ONE           2
Political Analysis 2

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Area chair: ICLR, ICML, NeurIPS (all from 2026)
Action editor: TMLR (from 2025)

Last updated: May 13, 2026