Machine Learning Seminars (ChE-650) – Fall

The Machine Learning Seminars are organized by Prof. Berend Smit, Prof. Clémence Corminboeuf and Prof. Jiri Vanicek, the seminars usually take place on Tuesdays at 15:15. Unless indicated otherwise, the lecture takes place on Zoom.

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PAST SEMINARS

"Machine learning in chemistry and beyond" (ChE-650) seminar by Xiaowei Jia (University of Pittsburgh)

14-12-202114-12-2021

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

"Machine learning in chemistry and beyond" (ChE-650) seminar by Bharath Ramsundar: "Language based Pre-training for Drug Discovery"

07-12-202107-12-2021

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

"Machine learning in chemistry and beyond" (ChE-650) seminar by Sereina Riniker (ETH Zurich)

23-11-202123-11-2021

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

"Machine learning in chemistry and beyond" (ChE-650) seminar by Alexandre Tkatchenko: On Electrons and Machine Learning Force Fields

16-11-202116-11-2021

With: Alexandre Tkatchenko is a professor at the Department of Physics and Materials Science (and head of this department since January 2020) at the University of Luxembourg, where he holds a chair in Theoretical Chemical Physics. Tkatchenko also holds a distinguished visiting professor position at Technical University of Berlin. His group develops accurate and efficient first-principles computational models to study a wide range of complex materials, aiming at qualitative understanding and quantitative prediction of their structural, cohesive, electronic, and optical properties at the atomic scale and beyond. He has delivered more than 250 invited talks, seminars and colloquia worldwide, published 180 articles in prestigious journals (h-index of 69 with more than 24,000 citations; Top 1% ISI highly cited researcher in 2018-2020), and serves on the editorial boards of Science Advances and Physical Review Letters. Tkatchenko has received a number of awards, including APS Fellow from the American Physical Society, Gerhard Ertl Young Investigator Award of the German Physical Society, Dirac Medal from the World Association of Theoretical and Computational Chemists (WATOC), van der Waals prize of ICNI-2021, and three flagship grants from the European Research Council: a Starting Grant in 2011, a Consolidator Grant in 2017, and Proof-of-Concept Grant in 2020.
Online: https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUWJyQT09
Category: Conferences – Seminars

"Machine learning in chemistry and beyond" (ChE-650) seminar "Towards fully digital R&D in chemistry and process engineering" by Alexei Lapkin (University of Cambridge)

09-11-202109-11-2021

With: Alexei Lapkin
Online: https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUWJyQT09
Category: Conferences – Seminars

"Machine learning in chemistry and beyond" (ChE-650) seminar by Marwin Segler (Microsoft Research)

19-10-202119-10-2021

With: Dr. Marwin Segler is Senior Researcher at Microsoft Research. Before that, he was researcher at BenevolentAI and PhD student at WWU Muenster, where he worked on planning chemical synthesis with deep neural networks and symbolic AI.
Online: https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUWJyQT09
Category: Conferences – Seminars

"Machine learning in chemistry and beyond" (ChE-650) seminar by Prof. Andrew White (University of Rochester): Making cool stuff with deep learning

05-10-202105-10-2021

With: Andrew White graduated from Rose-Hulman Institute of Technology in 2008 with a BS in chemical engineering. While at Rose, he spent a year studying at the Otto-von Guericke Universität and the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Dr. White completed a PhD in chemical engineering at the University of Washington in 2013. The thesis topic was the creation of non-fouling biomimetic surfaces with computational modeling. Next, Dr. White worked with Professor Greg Voth at University of Chicago as a Post-doctoral fellow in the Institute for Biophysical Dynamics from 2013-2014. In Chicago, he developed new methods for combining simulations and experiments. Dr. White joined the University of Rochester in Chemical Engineering in 2015 and is currently an associate professor. He has joint appointments in the Chemistry Department, Biophysics, Materials Science, and Data Science programs. Dr. White received a National Science Foundation CAREER award in 2018 and an Outstanding Young Investigator Award from the National Institutes of Health in 2020. Dr. White has authored a textbook on deep learning for molecules and materials, which is freely available at https://whitead.github.io/dmol-book.
Online: https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUWJyQT09
Category: Conferences – Seminars

"Machine learning in chemistry and beyond" (ChE-650) seminar by Prof. Volker Deringer (University of Oxford)

21-09-202121-09-2021

With: Volker Deringer studied chemistry at RWTH Aachen University (Germany), where he obtained his diploma (2010) and doctorate (2014) under the guidance of Richard Dronskowski. In 2015, he moved to the University of Cambridge as a fellow of the Alexander von Humboldt Foundation; in 2017, he was awarded a Leverhulme Early Career Fellowship at the same institution. He joined the Inorganic Chemistry Laboratory of the University of Oxford in September 2019. In addition to his Associate Professorship in the Department, he holds a Tutorial Fellowship at St Anne’s College, Oxford.
Online: https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUWJyQT09
Category: Conferences – Seminars

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