Invited Speakers

Prof. Clémence Corminboeuf

Clémence Corminboeuf is a full professor and head of the Laboratory of Computational Molecular Design at EPFL. Her research focuses on the development of electronic structure methods as well as conceptual tools targeted at exploiting non-covalent interactions for applications in the fields of organic electronics and catalysis. Her most recent work involves the development of machine learning models of electronic structure properties.



Prof. Christophe Copéret

Since 2010, Christophe Copéret is a Professor in the Department of Chemistry and Applied Biosciences, ETH Zürich. His scientific interest lies at the frontiers of molecular, material, and surface chemistry as well as NMR spectroscopy with the aim to design molecularly-defined solid catalysts through detailed mechanistic studies and structure-activity relationships.

Prof. Jacqueline Cole 

Jacqueline Cole is Head of Molecular Engineering at the University of Cambridge. She concurrently holds the BASF / Royal Academy of Engineering Research Chair in Data-driven Molecular Engineering of Functional Materials, where she is engaged in data science and computational methods that predict and thence experimentally validate materials for photovoltaic, magnetic and catalytic applications. She carries a joint appointment between the Physics Department (Cavendish Laboratory) and the Department of Chemical Engineering and Biotechnology at Cambridge.



Prof. Jorge Behler

The main research interest of Jörg Behler is the development of machine learning potentials for atomistic simulations of complex systems. Since the introduction of high-dimensional neural network potentials in 2007, which in the following years have found many applications in chemistry and materials science, he has been continuously working on further extensions of the method, like the recent introduction of a fourth-generation high-dimensional neural network potential including long-range charge transfer.

Prof. Anatole von Lilienfeld 

Anatole von Lilienfeld is a professor of Computational Materials Discovery at the Faculty of Physics of the University of Vienna. He develops methods for the first principles-based sampling of chemical compound space using quantum mechanics, supercomputers, Big Data, and machine learning. He is also interested in pseudopotentials, van der Waals forces, density functional theory, molecular dynamics, and nuclear quantum effects.


Prof. Alexandre Tkatchenko

Alexandre Tkatchenko is a Professor and Head of the Theoretical Chemical Physics group at the University of Luxembourg. His group pushes the boundaries of quantum mechanics, statistical mechanics, and machine learning to develop efficient methods to enable accurate modeling and new insights into complex materials.

Prof. Klaus Robert Müller

Professor Müller has been a professor of computer science at Technische Universität Berlin since 2006; at the same time, he is directing and co-directing the Berlin Machine Learning Center and the Berlin Big Data Center, respectively. His research interests are intelligent data analysis and Machine Learning in the sciences (Neuroscience (specifically Brain-Computer Interfaces), Physics, Chemistry) and in industry.


Dr. Loïc Roch

Dr. Roch specializes in Artificial Intelligence and Machine Learning for materials discovery and automated solutions, and in software design for closed-loop experimentation. He is the co-Founder and CTO at Atinary Technologies, a Swiss-American AI/ML technology startup that enables self-driving labs for Industry 4.0. 

Prof. Nicolas Flammarion

Nicolas Flammarion is a tenure-track assistant professor in computer science at the Laboratory of Theory of Machine Learning in EPFL. His research focuses primarily on learning problems at the interface of machine learning, statistics, and optimization.


Prof. Fang Liu

Fang Liu is an Assistant Professor at Emory University. Her research focused on developing GPU accelerated electronic structure methods to describe chemical processes in the solvent environment and at electronically excited states. Currently, her work involves building computational tools to accelerate the design and discovery of functional molecules.

Dr. Stephan Andreas Schunk

Dr. Stephan Andreas Schunk holds the position of an Executive Expert/Vice President within BASF SE and hte GmbH. His fields of research are partial oxidation in gas and liquid phase, syngas production and conversion, use of renewables, and the development of alternative concepts for materials synthesis to foster new approaches in heterogeneous catalysis.


Prof. Michele Ceriotti 

Michelle Ceriotti leads the laboratory for Computational Science and Modeling, in the institute of Materials at EPFL. His research interests focus on methods for molecular dynamics and quantum simulations, the development of machine-learning techniques for the study of complex systems at the atomistic level, and their application to problems in chemistry and materials science. 

Prof. Toshiaki Taniike

Prof. Toshiaki Taniike joined the School of Materials Science in the Japan Advanced Institute of Science and Technology in 2006 and was promoted to a full professor in 2020 in the same institute. He also serves as a director of the International Excellent Core of Materials Informatics. His main interest is in the implementation of Materials Informatics based on a combination of high-throughput experimentation, data science, and computational chemistry.


Prof. Markus Reiher

Markus Reiher is a full professor at ETH Zurich. His research covers many different areas in theoretical chemistry which range from relativistic quantum chemistry to the development of new electron-correlation theories and smart algorithms for the efficient exploration of complex chemical reaction networks.

Prof. Abigail G. Doyle

Abigail G. Doyle is the A. Barton Hepburn Professor of Chemistry in the Chemistry Department at Princeton University. She is also a co-PI for the NSF CCI Center for Computer Assisted Synthesis (C-CAS) and the DOE EFRC Bioinspired LightEscalated Chemistry (BioLEC). Her research is at the interface of organic, organometallic, physical organic, and computational chemistry, with the goal to address unsolved problems in organic synthesis through the development of catalysts, catalytic reactions, and synthetic methods.


Dr. Teodoro Laino

Teodoro Laino is Distinguished Research Staff Member and Manager of Accelerated Discovery at IBM Research-Zurich. The focus of his research is on materials simulations for industrial-related problems and on the application of machine learning/artificial intelligence technologies to chemistry and materials science problems.


Prof. Klavs F. Jensen

Klavs F. Jensen is Warren K. Lewis Professor in Chemical Engineering and Materials Science and Engineering at the Massachusetts Institute of Technology. He is a co-director of MIT’s consortium, Machine Learning for Pharmaceutical Discovery and Synthesis, which aims to bring machine learning technology into pharmaceutical discovery and development. His research interests include on-demand multistep synthesis, methods for automated synthesis, and machine learning techniques for chemical synthesis and interpreting large chemical data sets.


Prof. Sereina Riniker

Sereina Riniker is currently Associate Professor of Computational Chemistry at the Department of Chemistry and Applied Biosciences at ETH Zurich. Her research focuses on the development of methods and software for classical molecular dynamics (MD) simulations and cheminformatics and their application to biological and chemical questions.

Prof. Philipp Marquetand

Philipp Marquetand works at the University of Vienna with a focus on machine learning for simulating electronically excited states of molecules and different types of spectroscopy. He is a developer of the SHARC program package for nonadiabatic dynamics, which has been interfaced with machine learning potentials in order to model photochemistry on nanosecond time scales.


Dr. Tobias Gensch

Tobias Gensch started became research group leader at TU Berlin in 2020. His research combines lab chemistry with data science and computational chemistry for the discovery, optimization, and understanding of organometallic catalysts. This involves developing and navigating DFT-derived chemical space representations and predictive modeling as much as the wet-chemical data generation and validation of predictions.

Dr. Guillaume Godin

Guillaume Godin is Scientific Director in Artificial Intelligence at Firmenich. His broad data science team focuses on designing advanced tools to promote user experience using state-of-the-art methods in Cheminformatics, TextMining, and Machine Learning. Guillaume has extensive experience in chemistry, analytics, application design, database administration, and data automation using Artificial Intelligence.


Dr. Maud Reiter

Dr. Maud Reiter is the Director of New & Renewable Ingredients at the corporate R&D Division of Firmenich S.A. in Geneva (Switzerland).  She oversees the discovery of novel & sustainable perfumery ingredients as well as, since October 2020, the implementation of renewable versions of existing ingredients.

Dr. Guido Falk von Rudorff

Dr. Guido Falk von Rudorff  is a postdoc with Prof. von Lilienfeld at University of Vienna (since 2020) and University of Basel (2017-2020) working on Alchemical Perturbation Density Functional Theory (APDFT) and machine learning methods for molecular geometries and reaction barriers. PhD (2017) from University College London under the supervision of Prof. Blumberger.

Dr. Rolf Gueller

Rolf Gueller founded Chemspeed in 1997. Since then, he has succeeded in building a global leader in high-throughput technology for research and development. Thanks to numerous innovations in automation, digitization in research and development, Chemspeed is an internationally oriented company with a strong presence in Europe, the USA and Asia.


Dr. Sourav Chatterjee

Sourav Chatterjee is a Research Engineer at the Coperet group in ETH Zürich. His research involves data-​mining reaction from high throughput experimentation technology (HTE) and theoretical models that combine DFT calculations and machine learning to generate predictive models.