I am Professor of Software for Data-Intensive Applications at the University of Wuppertal. Together with my team, I explore problems in machine learning and uncertainty quantification at the intersection of computer science and applied mathematics. Our research develops methods and applications for data-intensive challenges in engineering, quantum chemistry, and climate science.
We are continuously looking for talented PhD candidates and Postdocs. Feel free to reach out via e-mail to explore joint possibilities.
Recent news
- Open Source Python Package for Multifidelity Machine Learning ReleasedThe first full-fledged version of multifidelity machine learning software has been released by Vivin Vinod under the MIT license as a pip package for use in quantum chemistry applications.
- Chemical space sampling with novel active learning cuts training data cost by an order of magnitudeVivin Vinod and Peter Zaspel have developed a faster, more efficient way to train machine learning models on complex chemical data.
- Pioneering Research on Excitation Energy Transfer in Light-Harvesting Systems Published in Advanced Theory and SimulationsD. Lyu, V. Vinod, M. Holzenkamp, Y. M. Holtkamp, S. Maity, C. R. Salazar, U. Kleinekathöfer, P. Zaspel. Excitation Energy Transfer between Porphyrin Dyes