Jannis Born

Title

Research Scientist in AI for Scientific Discovery

Bio

Jannis is a research scientist at IBM Research in Zurich, Switzerland. His current research interests are on AI4Science, Language Models and Quantum Machine Learning. Jannis obtained his PhD from ETH Zurich (D-BSSE) in 2022, for his work on language models for molecular design performed at IBM Research. His PhD was advised by Matteo Manica (IBM), Prof. Karsten Borgwardt (ETH) and Prof. Alan Aspuru Guzik. Before that he completed a M.Sc. at ETH/UZH Zurich (with distinction) in Neural Systems & Computation in 2019 and a B.Sc. in Cognitive Science. He has received the FXH Scientific Excellence Award in 2019 and studied and researched in Germany, England, Singapore and Switzerland.

Publications

Regress, Don’t Guess – A Regression-like Loss on Number Tokens for Language Models
- - Jonas Zausinger
  - Lars Pennig
  - et al.
- 2024
- NeurIPS 2024
Quantum Theory and Application of Contextual Optimal Transport
- - Nicola Mariella
  - Albert Akhriev
  - et al.
- 2024
- ICML 2024
Learning Drug Perturbations via Conditional Map Estimators
- - Benedek Harsanyi
  - Marianna Rapsomaniki
  - et al.
- 2024
- ICLR 2024
Using Domain Specific Languages to Enable Artificial Intelligence for Polymer and Catalyst Design
- - Nathaniel Park
  - Jannis Born
  - et al.
- 2024
- MRS Spring Meeting 2024
AI for Drug Discovery
- - Marianna Rapsomaniki
  - Jannis Born
  - et al.
- 2024
- AMLD EPFL 2024
Language models in molecular discovery
- - Nikita Janakarajan
  - Tim Erdmann
  - et al.
- 2023
- NeurIPS 2023
Characterizing pre-trained and task-adapted molecular representations
- - Celia Cintas
  - Payel Das
  - et al.
- 2023
- NeurIPS 2023
Using Foundation Models to Promote Digitization and Reproducibility in Scientific Experimentation
- - Amol Thakkar
  - Andrea Giovannini
  - et al.
- 2023
- NeurIPS 2023
Quantum Theory and Application of Contextual Optimal Transport
- - Nicola Mariella
  - Jannis Born
  - et al.
- 2023
- NeurIPS 2023
Growing strings in a chemical reaction space for searching retrosynthesis pathways
- - Federico Zipoli
  - Carlo Baldassari
  - et al.
- 2023
- ChemRxiv

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Projects

AI for Scientific Discovery
Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation
Lab that Learns
Leveraging AI foundation models and multi-cloud computing to usher in a new era of reproducible and collaborative experimentation for scientific discovery.