I am passionate about the interface between molecules and machine learning: a fascinating field that encourages creative, interdisciplinary and fast-paced research. I am currently working at Microsoft Quantum, integrating machine learning models to further accelerate the platform.

Selected CV

• Machine learning engineer @ Microsoft Quantum, Jan 2025 - present
• Research intern in reinforcement learning for chemical reactions @ AI4Science, Microsoft Research, Oct. - Dec. 2023
• PhD in machine learning applied to computational chemistry @ EPFL (nominated for ISIC prize), Oct. 2019 - Sep. 2024
• MSc in physics @ Imperial College London (Distinction), Oct. 2018 - Oct. 2019
• BSc in materials chemistry @ University of Edinburgh (1st class honours & BSc project prize), Sep. 2013 - June 2017

Scientific Publications

Check my google scholar profile here.

1. M. Haeberle,^* P. van Gerwen^*, R. Laplaza, K. Briling, J. Weinreich, F. Eisenbrand and C. Cominboeuf, Mach. Learn. Sci. Tech., 2025
2. P. van Gerwen,^* K. Briling,^* C. Bunne, V. Ram Somnath, R. Laplaza, A. Krause and C. Corminboeuf, J. Chem. Inf. Model., 2024
3. P. van Gerwen, K. Briling, Y. Calvino Alonso, M. Franke and C. Corminboeuf, Dig. Discov., 2024
4. S. Gallarati, P. van Gerwen, R. Laplaza, L. Brey and C. Corminboeuf, Chem. Sci., 2024
5. S. Gallarati, P. van Gerwen, A. A. Schoepfer, R. Laplaza and C. Corminboeuf, CHIMIA, 2023, 77, 39
6. S. Gallarati, P. van Gerwen, R. Laplaza, S. Vela, A. Fabrizio and C. Corminboeuf, Chem. Sci., 2022, 13, 13782
7. P. van Gerwen, A. Fabrizio, M. Wodrich and C. Corminboeuf, Mach. Learn. Sci. Tech. 2022, 3, 045005
8. R. Fabregat,^* P. van Gerwen,^* M. Haeberle, F. Eisenbrand and C. Corminboeuf, Mach. Learn. Sci. Tech. 2022, 3, 035015
9. L. Whalley, P. van Gerwen, J.M. Frost, S. Kim, S.N. Hood and A. Walsh, J. Am. Chem. Soc. 2021, 143 (24), 9123
10. S. Kim, S. Hood, P. van Gerwen, L. Whalley and A. Walsh, J. Open Source Softw. 2020, 5 (47), 2102

^* means shared first-author.

Conferences and talks

• Poster presentation @ NCCR Catalysis Annual Meeting, June 2024
• Contributed talk @ Machine Learning in Chemical and Materials Sciences 2024, May 2024
• Invited talk @ M2D2, Valence Labs, April 2024
• Poster @Bridging length scales with machine learning, CECAM-Psi-k Research Conference, June 2023
• Invited joint talk with Vignesh Ram Somnath (ETH Zurich) @ NCCR Catalysis Annual Meeting, March 2023
• Talk and poster presentation @Ellis ML4Molecules, Nov. 2022
• Poster presentation @ Psi-k 2022, August 2022
• Poster presentation @ WATOC 2020, July 2022
• Invited talk at @AI4Science day, June 2022
• Poster presentation @ NCCR Catalysis Annual Meeting, June 2022
• Poster presentation @ AI & The Molecular World, Applied Machine Learning Days, March 2021
• Poster presentation @ ELLIS ML4Molecules, including accepted workshop mini-paper, Dec. 2021

Relevant activities

• Co-organiser of the ML4LMS workshop at ICML 2024, July 2024
• Associate student of the EPFL AI4Science initiative, 2023-2025
• Mentee of the Fix the Leaky Pipeline, 2023-2024
• Organised seminars and events on machine learning in chemistry (and beyond) @ EPFL, 2021-2023

Supervised student projects

• Master project on graph-based models to classify organocatalyst molecules, 2023
• Master project on benchmarking representations and methods for ML of chemical reaction properties, 2022, see associated @peer-reviewed workshop paper
• Bachelor project on gaussian process regression of experimental reaction yields, 2022
• Bachelor project on machine learning of conceptual DFT descriptors, 2021

Fun facts

• I love to get outdoors - particularly rock climbing - as well as on a yoga mat
• I am an avid reader of good literature - Olga Tokarczuk is my favourite author