Discussions around how to effectively use and analyze limited datasets to arrive at useful solutions will be the focus of this webinar. Before embarking on those conversations, the speakers will debate how they define “limited” or “small” data including the number of data points and variables considered. Discussions around data quality; trust and interpretability; and their integration into existing big-data workflow will be presented. Finally, speakers will share their strategy for handpicking chemistry problems that could be solved using the “limited data” approach

Speakers:

Carlos Gonzalez (Moderator), Chief of the Chemical Sciences Division; National Institute of Standards and Technology

Carlos Gonzalez has been the Chief of the Chemical Sciences Division of the National Institute of Standards and Technology (NIST) since 2012. Dr. Gonzalez joined NIST in 1997 as a member of the Computational Chemistry Group within the Physical and Chemical Properties Division. He was appointed to the position of Chief, Chemical and Biochemical Reference Data Division in 2008. Previously, Dr. Gonzalez was a Postdoctoral Scholar at Carnegie Melon University under the mentorship of Prof. John A. Pople, a 1998 Nobel Laureate in Chemistry. Dr. Gonzalez received his Ph.D. in Chemistry from Wayne State University.

Johannes Hachmann (Panelist), Associate Professor, Department of Chemical and Biological Engineering; Director, Engineering Science (Data Science Focus) Graduate Program, Institute for Artificial Intelligence and Data Science; University at Buffalo, The State University of New York

Johannes Hachmann is an Associate Professor of Chemical Engineering at the University at Buffalo (UB), the Director of the Engineering Science (Data Science Focus) graduate program, a Leadership Member of the UB Institute for Artificial Intelligence and Data Science, and a Faculty Member of the New York State Center of Excellence in Materials Informatics. He earned a Dipl.-Chem. degree (2004) after undergraduate studies at the universities of Jena and Cambridge, M.Sc. (2007) and Ph.D. (2010) degrees in Chemistry from Cornell University, and he conducted postdoctoral research at Harvard University before joining the UB faculty in 2014. The research of the Hachmann Group fuses (first-principles) molecular and materials modeling with virtual high-throughput screening and modern data science (i.e., the use of machine learning, artificial intelligence, and informatics) to advance a data-driven discovery and rational/inverse design paradigm in the chemical and materials disciplines. One of the centerpieces of the group’s efforts is the creation of an open, general-purpose software ecosystem for the data-driven design of chemical systems and the exploration of chemical space. This work was recognized with a 2018 NSF CAREER Award.

Stephan Mohr (Panelist), Chief Scientific Officer; Nextmol

Stephan Mohr is co-founder and Chief Scientific Officer at Nextmol. Previously, he worked as as researcher at Barcelona Supercomputing Center (Spain), CEA Grenoble (France) and University of Basel (Switzerland). He holds a PhD in Physics, and is an expert in molecular simulations and chemicals informatics, both with respect to algorithmic developments and applications. Moreover, he has also a large experience in code development and High-Performance Computing. During his career he has authored 27 peer-reviewed papers and has been the principal investigator of several supercomputing projects on Tier-0 supercomputers. He has supervised numerous students and led R&D teams in multidisciplinary and fast-paced environments.