Kalin Nonchev
Doctoral Candidate in Computer Science, ETH Zurich
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My name is Kalin and I am from Karlovo, Bulgaria. I am a doctoral candidate under the supervision of Prof. Gunnar Rätsch in the Institute for Machine Learning at ETH Zurich. During my bachelor’s in bioinformatics at the TU Munich and LMU, I worked with Prof. Julien Gagneur on rare disease genomics. I held a scholarship awarded by the Bavarian Ministry of Science, Research, and Art and the German Academic Exchange Service (DAAD). During my master’s at ETH Zurich, I contributed to projects in single-cell analysis at the Functional Genomics Center Zurich and medical device applications at Roche Diagnostics. My research focuses on understanding the gap between genotype and phenotype in rare diseases. I am committed to advancing machine learning research in biomedicine to improve clinical decision-making. A large part of the human genome is still not understood; hence I want to contribute my knowledge and time to the interpretation of multi-omics data. I believe this is the key to more accurate disease diagnoses for patients, and, more importantly, the well-proven rationale for their therapies. |
Research collaboration
I am very open to research collaborations and mentoring BSc and MSc students. Open projects. Please reach out if interested.
Highlights
- Invited talk at 10x Genomics Single Cell & Spatial Discovery Symposium, Boris Laukas, 2026, Bern, Switzerland
- Contributed talk at NeurIPS 2025 Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences, 2025, San Diego, USA
- Conference Neural Information Processing Systems (NeurIPS) 2025, San Diego, USA
- Visit the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, Caroline Uhler, 2025, Boston, Massachusetts, USA
- Visit BGI Genomics’ headquarters in Shenzhen, China as a winner of MICOS EU and participate in the DUT-BGI International Summer School, 2025, Shenzhen, China
- Invited talk at Google Genomics Deep Dives Series, Yuchen Zhou, 2025, Boston, Massachusetts, USA
- Conference Integrating Cutting-Edge Biotechnologies and AI for Advancing Human Health (RNA-AI 2025), Monte Verita, Ascona, Switzerland
- 1st place award at the Autoimmune Disease Machine Learning Challenge organized by the Broad Institute of MIT and Harvard and Crunch Lab
- Our approach outperformed over 8000 submissions in predicting spatial transcriptomics from H&E images.
- Invited talk at the University of Basel, Department of Biomedicine and EMBL Heidelberg, Judith Zaugg, 2025, Basel, Switzerland
- Conference Biological Data Science CSHL 2024, New York, USA - DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images poster presentation.
- Conference Intelligent Systems for Molecular Biology (ISMB) 2024, Montreal, Canada - Representation learning for multi-modal spatially resolved data poster presentation.
- 1st place award at Mammoth International Contest On Omics Sciences in Europe 2024 organized by China National GeneBank, BGI Genomics, MGI and CODATA
- Our approach outperformed over 150 participants in representation learning for spatial transcriptomics.
- Belgrade Bioinformatics Conference 2024, Belgrade, Serbia
- Talk as a finalist at the Mammoth International Contest On Omics Sciences in Europe 2024 organized by China National GeneBank, BGI Genomics, MGI and CODATA.
- Conference Kipoi Summit “New horizons in Computational Regulatory Genomics” 2023, Zugspitze, Germany
- Talk on “Representation learning for multi-modal spatially resolved data”
- Conference PharmaCamp “Drug discovery in the era of Precision Medicine and Digitalization” 2023, Bern, Switzerland
- Open Day Functional Genomics Center Zurich 2022, Zurich, Switzerland
- Poster titled “Single-cell RNA analysis of Xenograft Tumor and its Host Environment”
- Healthcare Xplorer Challenge Conference of Roche 2022, Zurich, Switzerland
- Talk on “Interpretable Machine Learning models for automated quality assessment of measurement results” aiming to enhance the development of medical and diagnostic products through machine learning
Publications
- Towards Cross-Sample Alignment for Multi-Modal Representation Learning in Spatial Transcriptomics
Justina Dai*, Kalin Nonchev*, Viktor H Koelzer+, Gunnar Rätsch+
ICLR 2026 Learning Meaningful Representations of Life | GitHub - DeepSpot2Cell: Predicting Virtual Single-Cell Spatial Transcriptomics from H&E images using Spot-Level Supervision
Kalin Nonchev*, Glib Manaiev*, Viktor Hendrik Koelzer+, Gunnar Rätsch+
NeurIPS 2025 Imageomics | GitHub - Molecularly informed analysis of histopathology images using natural language
Moritz Schaefer*, Kalin Nonchev*, Animesh Awasthi*, Jake Burton*, Viktor Hendrik Koelzer, Gunnar Rätsch+, Christoph Bock+
ICML 2025 FM4LS - DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images
Kalin Nonchev, Sebastian Dawo, Karina Selina, Holger Moch, Sonali Andani, Tumor Profiler Consortium, Viktor Hendrik Koelzer+, and Gunnar Rätsch+
MedRxiv 2025 | GitHub - Representation learning for multi-modal spatially resolved transcriptomics data
Kalin Nonchev, Sonali Andani, Joanna Ficek-Pascual, Marta Nowak, Bettina Sobottka, Tumor Profiler Consortium, Viktor Hendrik Koelzer+, and Gunnar Rätsch+
MedRxiv 2024 | GitHub
| Some movies… | Some books… | Some quotes… |
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Well, I have to finish this resume in the near future…
