Ivo F. Sbalzarini

Ivo F. Sbalzarini is the Professor of Scientific Machine Learning at the Department of Mathematical Modeling and Machine Learning (DM3L) of the University of Zurich, Switzerland. Before joining the University of Zurich, Ivo was in the Faculty of Computer Science of Dresden University of Technology since 2014, where he held the Chair of Scientific Computing for Systems Biology at the Institute of Artificial Intelligence. Ivo was also previously a tenured Senior Research Group Leader with the Max Planck Institute of Molecular Cell Biology and Genetics since 2012, and a Director of the Center for Systems Biology Dresden (CSBD). In addition, he was the Dean of the Faculty of Computer Science of Dresden University of Technology between 2021 and 2025. He is the founder and head of the MOSAIC Group.

During 1997-2002, Ivo studied Mechanical Engineering at ETH Zurich (Switzerland), with majors in Computational Fluid Dynamics and Control. In 2002 he received his Diploma (equivalent M.Sc.) in Mechanical Engineering, which was awarded the Willi Studer Prize. Between 2002 and 2006, Ivo was a Ph.D. student with the Computational Science and Engineering Lab at ETH Zurich, where he pioneered the close collaboration between Biology and Computational Science at ETH Zurich. In January 2006, he received his Doctorate in Computer Science (Dr. sc. techn.) under the supervision of Professor Petros Koumoutsakos and Professor Ari Helenius. His thesis work was awarded the Chorafas Prize for 2006 by the Dimitris N. Chorafas Foundation of the Weizmann Institute of Science.

Ivo intermediately stayed at the NASA Ames Research Center, USA (2000), at the Center for Turbulence Research at Stanford University, USA (2002), and at the California Institute of Technology in Pasadena, USA (2005). In addition, he was an invited Professor of Biology at Ecole Normale Supérieure (ENS), Paris, France (2007) and a research group leader in Bioinformatics at the Mediterranean Institute for Life Sciences in Split, Croatia (2007-2008).

In April 2006, Ivo was named Assistant Professor for Computational Science in the Department of Computer Science of ETH Zurich (Switzerland), where he developed and implemented the concept of a creative trans-disciplinary research group, the MOSAIC Group. In 2012, the MOSAIC Group moved from ETH Zurich to Dresden, Germany, where it became one of the four founding groups of the Center for Systems Biology Dresden (CSBD) of the Max Planck Society. In 2014, Ivo was named full Professor of Computer Science at Dresden University of Technology and in 2026 at the Department of Mathematical Modeling and Machine Learning (DM3L) of the University of Zurich.

Ivo is a life-long honorary member of the Technical Society of Zurich (TGZ) and was a member of the workgroup “Swiss National Strategic Plan for High Performance Computing and Networking”, advising the Federal Government of Switzerland. Ivo previously was the Vice Dean of the Faculty of Computer Science from 2014-2021 and Dean/Spokesperson of the International Max Planck Research School for Cell, Developmental, and Systems Biology (IMPRS-CellDevoSys) from 2018-2021, whose Co-Spokesperson he was before 2018. In 2021, Ivo was elected Dean of the Faculty of Computer Science. Ivo also served on the Steering and Executive Committees of the Center for Systems Biology Dresden (CSBD), the Steering Committee of the Center for Advanced Systems Understanding (CASUS) as a representative of the Max Planck Society, and as the head of TU Dresden’s Center for Advanced Modeling and Simulation (CAMS). He also was the head of the unit “Bio-Data Science and Physics-Informed AI” of the DFG Cluster of Excellence Physics of Life (PoL) and of the Applied Data Science and AI Area in the Federal Center of Competence for Scalable Data Analytics and AI, ScaDS.AI, on whose board of directors he also served. He was the founder of TU Dresden’s international Masters Program “Computational Modeling and Simulation”, whose coordinator and dean of studies he also was 2018-2022.

Ivo’s work focuses on developing, applying, and teaching numerical methods for point-cloud data. This includes methods for consistently computing derived quantities on scattered point locations, methods for regularizing training and inference on irregular data, and methods from numerical analysis to improve the performance, robustness, and efficiency of machine learning.