AI in chemistry and beyond: Trends in the field (ChE-606)

With: Originally from Ukraine, Ganna (Anya) Gryn’ova received her BS and MSc in chemistry summa cum laude from Oles Honchar Dnipro National University. In 2014 she received a PhD in computational chemistry from Australian National University. Her doctoral thesis gathered a number of awards, including the IUPAC-Solvay International Award for Young Chemists for one of the five most outstanding PhD theses in the general area of the chemical sciences worldwide. Dr. Gryn’ova continued her research career at École Polytechnique Fédérale de Lausanne as a postdoctoral researcher working on in silico modeling of organic semiconductors. In 2016 she won the Marie Skłodowska-Curie Actions individual fellowship and focussed on the non-conventional architectures of single-molecule junctions. In 2019, Dr. Gryn’ova started her independent scientific career leading the junior research group “Computational Carbon Chemistry” (CCC) at the Heidelberg Institute for Theoretical Studies (HITS gGmbH) and Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University, Germany. The CCC group uses state-of-the-art computational chemistry and data science to explore and exploit diverse functional organic materials for applications in organocatalysis and environmental remediation. In 2021, Anya received the prestigious ERC Starting Grant for her project “PATTERNCHEM: Shape and Topology as Descriptors of Chemical and Physical Properties in Functional Organic Materials”; she is also a principal investigator in the Collaborative Research Centre SFB1249 “N-Heteropolycycles as Functional Materials” and the SIMPLAIX strategic research initiative on bridging scales from molecules to molecular materials by multiscale simulation and machine learning.
Place and room: BCH 2218
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Kobi Felton is a chemical engineer interested in solving problems at the intersection of chemical engineering, chemistry and software. He holds a Bachelor of Science in Chemical Engineering from North Carolina State University and a MPhil Research and PhD in Chemical Engineering from the University of Cambridge, where he was a recipient of the Marshall Scholarship and the Cambridge-Marshall PhD Scholarship. His current projects include: Optimization of distillation control systems, Self-optimization of reactions using bayesian algorithms, Design of experiments for time-series datasets, and Designing descriptors for chemical reactions.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Frank Noé has a background in Electrical Engineering, Computer Science and Physics and did his PhD at University of Heidelberg in 2006. He became group leader at FU Berlin in 2007 and professor in 2013. Since 2022 he is Partner Research Manager in Microsoft Research AI4Science, also located in Berlin. Frank has received two European Research Commission (ERC) grants and the early career award in Theoretical Chemistry of the American Chemical Society (ACS). He is member of the Berlin-Brandenburg academy of sciences, a fellow in the European Laboratory for Learning and Intelligent Systems (ELLIS) and an ISI highly cited researcher. Frank’s research is highly interdisciplinary and focuses on developing Machine Learning methods to address fundamental questions in the natural Sciences.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Daniel received his BSc in computer science at the Bern University of Applied Sciences in 2013 and his MSc in Bioinformatics and Computational Biology at the University of Bern in 2016. In 2020 he received his PhD in Chemistry and Molecular Sciences for his thesis “Scalable Methods for the Exploration and Visualization of Large Chemical Spaces” from the University of Bern under the supervision of  Prof. Jean-Louis Reymond. His main research interest is efficient machine learning and data visualisation applied to natural sciences, focusing on the intersection of chemistry and biology. After a two-year stay as a permanent research staff member at  IBM Research in the Team of Teodoro Laino working on machine learning for biocatalysis, he started as a postdoctoral researcher in the group of  Prof. Pierre Vandergheynst at EPFL.
Place and room: MA A1 10
Category: Conferences – Seminars

With: Jules Schleinitz. Jules is currently a postdoctoral scholar at CalTech in the group of Sarah E. Reisman and a current member of the NSF Center for Computer Assisted Synthesis. His research focuses on the development of computational and machine learning tools for organic synthesis planning through mechanistic understanding. Jules graduated in 2022 from the Ecole Normale Supérieure in Paris. His PhD intitled “Machine learning and Mechanistic Analysis” was supervised by Laurence Grimaud. Alongside with his research activities, Jules spent half of his PhD teaching chemistry at Ecole Normale Supérieure (Organic chemistry: lessons, electrochemistry: tutorials and experimental sessions, experimental projects for bachelor and master students.).
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Oh-hyeon studied computational neuroscience at EPFL with Prof. Herzog, where she researched the fundamental aspects of computer vision models. She then started her career at Novartis focusing on machine learning research for drug discovery. Soon, she will start a new challenge at a med-tech company (SynpleChem) for lab automation.  
Place and room: CH G1 495
Category: Conferences – Seminars

With: Kjell Jorner is an Assistant Professor of Digital Chemistry at ETH Zurich since January 2023. His work focuses on accelerating chemical discovery with digital tools, with a special emphasis on reactivity and catalysis. His group does interdisciplinary research, drawing from the fields of computational chemistry, cheminformatics and machine learning. Before joining ETH Zurich, he was a postdoctoral researcher with Alán Aspuru-Guzik (2021-2022) and at AstraZenecaUK (2018-2020). Kjell has a PhD from Uppsala University (2018) on computational physical organic chemistry for the photochemistry of aromatic compounds.
Place and room: CH G1 495
Category: Conferences – Seminars

With: Minkai Xu is a Ph.D. student in the Computer Science Department at Stanford University. Previously, he received his M.Sc degree from Mila and B.E. from Shanghai Jiaotong University. His research lies in probabilistic models, geometric representation learning, and ML for scientific discovery. He has published several influential papers on the above topics in top machine learning conferences (e.g., ICML, NeurIPS, ICLR, AAAI, and AAMAS) including the first diffusion models for molecular structure generation, which has been widely adopted in various drug and protein design problems. His research is generously supported by Sequoia Capital Stanford Graduate Fellowship.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Hannes Stärk is a PhD student at MIT in the CS and AI Laboratory (CSAIL) co-advised by Tommi Jaakkola and Regina Barzilay.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09#success
Category: Conferences – Seminars

With: Rafael Gomez-Bombarelli is the Jeffrey Cheah Career Development Professor in MIT’s Department of Materials Science and Engineering. His work aims to fuse machine learning and atomistic simulations for designing materials and their transformations. Through collaborations at MIT and beyond, Rafael’s group develops new practical materials such as catalysts, therapeutic peptides, organic electronics or electrolytes for batteries. Rafael joined MIT in 2018 after earning BS, MS, and PhD (2011) degrees in chemistry from Universidad de Salamanca (Spain) and carrying postdoctoral work at Heriot-Watt, Harvard and at Kyulux North America. His machine learning work has received faculty awards from the Dreyfus Foundation and Google. Rafael was a co-founder of Calculario, a Harvard spinout company, and served as Chief Learning Officer of ZebiAI, a drug discovery startup.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Santiago Miret is an AI Researcher at Intel Labs where he focuses on applying AI for scientific problems with an emphasis on materials discovery and materials understanding. Through this effort, Santiago manages a wide range of academic collaborations focused on applying AI for scientific application. Among these collaboration, there have been notable engagements with the Matter Lab led by Alán Aspuru-Guzik at the University of Toronto and various AI laboratories at MILA in Montreal that have led to cross-institutional publications at various machine learning venues. Santiago was a primary organizer of the 1st AI for Accelerated Materials Discovery (AI4Mat) workshop at NeurIPS 2022, which brought together domain experts from various fields of materials science and AI to exchange research work and ideas in an interdisciplinary forum. Prior to working at Intel Labs, Santiago obtained his PhD in Materials Science and Engineering from the University of California, Berkeley.
Place and room: CH G1 495
Category: Conferences – Seminars

With: Rianne van den Berg is a Principal Researcher at MSR. 
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Julia Westermayr is an Assistant Professor at the University of Leipzig.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Geemi Wellawatte is a PhD student at the University of Rochester, NY, with Prof. Andrew White. She completed her BSc. in Computational Chemistry in 2017 at the University of Colombo Sri Lanka. Her research work focuses on developing computational models to solve chemistry problems, particularly in coarse-grained modeling, deep learning in chemistry, and explainable AI.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Rocío Mercado is currently a post-doc at MIT working with Professor Connor Coley.  Previously, she completed an industrial post-doc at AstraZeneca in the Molecular AI team where she worked on graph molecular generative models for small molecule drug design. Before that, she completed a PhD in Chemistry with Professor Berend Smit at UC Berkeley and EPFL in molecular simulation. Rocío will be starting an assistant professorship at Chalmers University of Technology in the Data Science and AI division working on data-driven molecular design.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Anirudh Nambiar is a Senior Engineer in the Synthetics Process Development Department at Amgen in Cambridge, Massachusetts. He completed his PhD in Chemical Engineering at MIT working with Klavs Jensen where he developed robotic flow synthesis platforms equipped with decision-making algorithms to optimise reaction outcomes.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Gabe is an assistant professor at the departments of Chemistry and Chemical Engineering at Carnegie Mellon University, in Pittsburgh, USA. Gabe was born and raised in the countryside of the state of Rio de Janeiro, Brazil, where he received his B.Sc. He earned his Ph.D. in Fall 2018 from Florida State University, where he was awarded the LASER Fellowship in 2014 and the 2016-2017 IBM Ph.D Scholarship. At FSU, Gabe’s research was centered on the relationship between molecular structure and reactivity, focusing on the development and applications of stereoelectronic effects. For his work at FSU, in 2018, Gabe received several awards for his work in computational chemistry, including his selection for the CAS SciFinder Future Leaders Program. In 2019, Gabe joined the University of Toronto as a Postdoctoral Research Fellow in the Matter Lab, led by Professor Alán Aspuru-Guzik. In 2020, Gabe was awarded the prestigious NSERC Banting Postdoctoral Fellowship with the project “Designing Catalysts with Artificial Intelligence,” and has been featured on the “Next Great Impossible” series by Merck/Milipore-Sigma. Gabe joined the Journal of Chemical Information and Modeling as an Early Career Board member in 2021.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

With: Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. He obtained my Ph.D. degree at the University of Minnesota, under the supervision of Prof. Vipin Kumar. Prior to that, he got his B.S. and M.S. from the University of Science and Technology of China (USTC) and State University of New York at Buffalo.
Online: https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUWJyQT09
Category: Conferences – Seminars

With: Bharath received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He did his PhD in computer science at Stanford University where he studied the application of deep-learning to problems in drug-discovery. At Stanford, Bharath created the deepchem.io open-source project to grow the deep drug discovery open source community, co-created the moleculenet.ai benchmark suite to facilitate development of molecular algorithms, and more. Bharath’s graduate education was supported by a Hertz Fellowship, the most selective graduate fellowship in the sciences. After his PhD, Bharath co-founded Computable a startup that built better tools for collaborative dataset management. Bharath is currently working actively on growing the DeepChem community and on exploring a few early projects still in stealth. Bharath is the lead author of “TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning”, a developer’s introduction to modern machine learning, with O’Reilly Media, and the lead author of “Deep Learning for the Life Sciences”
Online: https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUWJyQT09
Category: Conferences – Seminars

With: Sereina Riniker is currently Associate Professor of Computational Chemistry at the Department of Chemistry and Applied Biosciences of ETH Zurich. 
Online: https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUWJyQT09
Category: Conferences – Seminars