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 Azure Quantum, integrating machine learning models to further accelerate the platform.

Selected CV

• Machine learning engineer @ Microsoft Azure Quantum, Jan 2025 -
• Research intern in reinforcement learning for chemical reactions @ AI4Science, Microsoft Research, Oct. - Dec. 2023
• PhD in machine learning applied to computational chemistry @ EPFL, Oct. 2019 - Sep. 2024
• MSc in physics @ Imperial College London, Oct. 2018 - Oct. 2019

Scientific Publications

• M. Haeberle,^* P. van Gerwen^*, R. Laplaza, K. Briling, J. Weinreich, F. Eisenbrand and C. Cominboeuf, Under review at Mach. Learn.: Sci. Technol., 2025
• P. van Gerwen,^* K. Briling,^* C. Bunne, V. Ram Somnath, R. Laplaza, A. Krause and C. Corminboeuf, J. Chem. Inf. Model., 2024
• P. van Gerwen, K. Briling, Y. Calvino Alonso, M. Franke and C. Corminboeuf, Dig. Discov., 2024
• S. Gallarati, P. van Gerwen, R. Laplaza, L. Brey and C. Corminboeuf, Chem. Sci., 2024
• P. van Gerwen, M. Wodrich, R. Laplaza and C. Corminboeuf, Mach. Learn. Sci. Tech. 2023, 4, 048002
• S. Gallarati, P. van Gerwen, A. A. Schoepfer, R. Laplaza and C. Corminboeuf, CHIMIA, 2023, 77, 39
• S. Gallarati, P. van Gerwen, R. Laplaza, S. Vela, A. Fabrizio and C. Corminboeuf, Chem. Sci., 2022, 13, 13782
• P. van Gerwen, A. Fabrizio, M. Wodrich and C. Corminboeuf, Mach. Learn. Sci. Tech. 2022, 3, 045005
• R. Fabregat,^* P. van Gerwen,^* M. Haeberle, F. Eisenbrand and C. Corminboeuf, Mach. Learn. Sci. Tech. 2022, 3, 035015
• L. Whalley, P. van Gerwen, J.M. Frost, S. Kim, S.N. Hood and A. Walsh, J. Am. Chem. Soc. 2021, 143 (24), 9123
• 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-present
• 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 GPR of experimental reaction yields, 2022
• Bachelor project on fragment-based approaches to machine learning of conceptual DFT descriptors, 2021