Alán Aspuru-Guzik, Editor in Chief
University of Toronto, Canada
Alán Aspuru-Guzik is a professor of Chemistry and Computer Science at the University of Toronto and is also the Canada 150 Research Chair in Theoretical Chemistry and a Canada CIFAR AI Chair at the Vector Institute. He is a CIFAR Lebovic Fellow in the Biologically Inspired Solar Energy program.
Alán also holds a Google Industrial Research Chair in Quantum Computing. Alán is the director of the Acceleration Consortium, a University of Toronto-based strategic initiative that aims to gather researchers from industry, government and academia around pre-competitive research topics related to the lab of the future.
Alán began his independent career at Harvard University in 2006 and was a Full Professor at Harvard University from 2013-2018. He received his B.Sc. from the National Autonomous University of Mexico (UNAM) in 1999 and obtained a PhD from the University of California, Berkeley in 2004, where he was also a postdoctoral fellow from 2005-2006.
Alán conducts research in the interfaces of quantum information, chemistry, machine learning and chemistry. He was a pioneer in the development of algorithms and experimental implementations of quantum computers and quantum simulators dedicated to chemical systems.
He has studied the role of quantum coherence in the transfer of excitonic energy in photosynthetic complexes and has accelerated the discovery by calculating organic semiconductors, organic photovoltaic energy, organic batteries and organic light-emitting diodes. He has worked on molecular representations and generative models for the automatic learning of molecular properties. Currently, Alán is interested in automation and "autonomous" chemical laboratories for accelerating scientific discovery.
Among other recognitions, he received the Google Focused Award for Quantum Computing, the Sloan Research Fellowship, The Camille and Henry Dreyfus Teacher-Scholar award, and was selected as one of the best innovators under the age of 35 by the MIT Technology Review. He is a member of the American Physical Society and an elected member of the American Association for the Advancement of Science (AAAS) and received the Early Career Award in Theoretical Chemistry from the American Chemical Society.
Matthias Degroote
Boehringer Ingelheim, Belgium
ORCID 0000-0002-8850-7708
Dr. Matthias Degroote is investigating the application of quantum computers in drug design at Boehringer Ingelheim. He is a quantum chemist who has approached the quantum many-body problem from the classical and quantum computing sides. Matthias received a PhD in physics from Ghent University working on Green’s functions and has gone on from there to do postdoctoral research in method development at Ghent University and Rice University. Since 2018 his research is centered around the application of quantum computers for which he has held postdoctoral positions at Harvard University and University of Toronto.
Cesar de la Fuente
University of Pennsylvania, USA
César de la Fuente is a Presidential Assistant Professor at the University of Pennsylvania, where he leads the Machine Biology Group whose goal is to combine the power of machines and biology to help prevent, detect, and treat infectious diseases. Specifically, he pioneered the development of the first antibiotic designed by the computer with efficacy in animals, designed algorithms for antibiotic discovery, reprogrammed venoms into antimicrobials, created novel resistance-proof antimicrobial materials, and invented rapid low-cost diagnostics for COVID-19 and other infections. De la Fuente is an NIH MIRA investigator and has received recognition and research funding from numerous other groups. Prof. de la Fuente has received over 50 awards. He was recognized by MIT Technology Review as one of the world’s top innovators for “digitizing evolution to make better antibiotics”. He was selected as the inaugural recipient of the Langer Prize, an ACS Kavli Emerging Leader in Chemistry, and received the AIChE’s 35 Under 35 Award and the ACS Infectious Diseases Young Investigator Award. In 2021, he received the Thermo Fisher Award, and the EMBS Academic Early Career Achievement Award “For the pioneering development of novel antibiotics designed using principles from computation, engineering, and biology.” Most recently, Prof. de la Fuente was awarded the prestigious Princess of Girona Prize for Scientific Research, the ASM Award for Early Career Applied and Biotechnological Research and has been named a Highly Cited Researcher by Clarivate several times. Prof. de la Fuente has given over 200 invited lectures and his scientific discoveries have yielded over 110 publications, including papers in Nature Biomedical Engineering, Nature Communications, PNAS, ACS Nano, Cell, Nature Chemical Biology, Advanced Materials, and multiple patents.
Jason E. Hein
University of British Columbia, Canada
ORCID: 0000-0002-4345-3005
Jason Hein received his BSc in Biochemistry in 2000 and PhD in asymmetric reaction methodology in 2005 from the University of Manitoba (NSERC PGS-A/B, Prof. Philip G. Hultin). In 2006, he became an NSERC postdoctoral research fellow with Professor K. Barry Sharpless and Professor. Valery V. Fokin at the Scripps Research Institute in La Jolla, CA.
In 2010, he became a senior research associate with Professor Donna G. Blackmond at the Scripps Research Institute. He began his independent career at the University of California, Merced in 2011, employing in-situ kinetic reaction analysis to rapidly profile and study complex networks of reactions. In 2015, he moved to the University of British Columbia to continue the development of automated reaction analytical technology to serve mechanistic organic chemistry.
His research has resulted in a collection of prototype modular robotic tools and integrated analytical hardware which create the first broadly applicable automated reaction profiling toolkit geared toward enabling autonomous research and discovery. He was the co-lead of Project ADA; the world's first autonomous discovery platform for thin film materials, supported by Natural Resources Canada, co-PI of the MADNESS team supported by the DARPA Accelerated Molecular Discovery Program and an Associate Director of the Acceleration Consortium spearheaded by the University of Toronto.
Kedar Hippalgaonkar
Nanyang Technological University and Institute of Materials Research and Engineering, Singapore
ORCID ID: 0000-0002-1270-9047
Assistant Professor Kedar Hippalgaonkar is a joint appointee with the Materials Science and Engineering Department at Nanyang Technological University (NTU) and a Senior Scientist at the Institute of Materials Research and Engineering (IMRE) at the Agency for Science Technology and Research (A*STAR). He is a 2020 NRF Fellow and MOE Inauguration Grant Awardee and has received the Materials Horizons (2021) and JMCA (2019) Emerging Investigatorship.
He is leading the multi-PI Accelerated Materials Development for Manufacturing (AMDM) program focusing on the development of new materials, processes and optimization using Machine Learning, AI and high-throughput computations and experiments in electronic, thermoelectric, polymeric and structural materials. He led the Pharos Program on Hybrid (inorganic-organic) thermoelectrics for ambient applications from 2016-2020.
Dr. Hippalgaonkar’s interests lie in designing functional materials, especially for energy applications. He has fundamental knowledge in solid state physics, 1D (nanowires) and 2D (TMDCs), as well as inorganic-organic (hybrid) materials. His approach to materials by design is built on creating and utilizing materials data by high-performance computing and high-throughput experiments to synthesize and characterize materials for optical and electronic properties.
In addition, he is using machine learning and data science for materials discovery. His background is in transport properties of materials, specifically in understanding their thermal, optical and thermoelectric properties. He is keen on developing tools such as process optimization, design of experiments and materials, and process fingerprinting from materials development to device applications.
Linda Hung
Toyota Research Institute, USA
ORCID ID: 0000-0002-1578-6152
Linda Hung is a Senior Research Scientist in the Accelerated Materials Design and Discovery division at Toyota Research Institute (TRI). She obtained her PhD in applied and computational mathematics from Princeton University, and held research positions at the Ecole Polytechnique (France), the University of Illinois Chicago, and the National Institute of Standards and Technology before joining TRI in 2017.
She has a background in density functional theory and other first-principles simulation methods, with applications in computational spectroscopy. Her current work explores how machine learning can accelerate materials simulation, and how to integrate data-driven methods into discovery workflows. Her research focuses on energy materials and involves the development of software tools aiming to shorten the materials innovation timeline.
Joshua Schrier
Fordham University, USA
ORCID: 0000-0002-2071-1657
Joshua Schrier is a physical chemist interested in computational methods to accelerate the discovery of new materials by using a combination of physics-based simulations, cheminformatics, machine learning, and automated experimentation. He is the Kim B. and Stephen E. Bepler Professor of Chemistry at Fordham University in New York City. Prior to joining Fordham in 2018, he was an associate professor at Haverford College, and a Luis W. Alvarez computational sciences postdoctoral fellow at Lawrence Berkeley National Laboratory. As a faculty member, he has received awards including the Dreyfus Teacher-Scholar, U.S. Department of Energy Visiting Faculty, and Fulbright scholar awards.
Yousung Jung
Seoul National University, South Korea
ORCID ID: 0000-0003-2615-8394
Yousung Jung is a Professor of Chemical and Biological Engineering at Seoul National University. His research background and current interests involve quantum chemistry and machine learning to develop efficient methods for fast and accurate simulations of complex molecular and materials systems and their applications toward the understanding of molecules and materials for new discovery. Some of his recent works include using data science and machine learning to understand the structure-property-synthesizability relations for molecules and materials and using the obtained knowledge for inverse design. He received his PhD in Theoretical Chemistry from the University of California, Berkeley, with Martin Head-Gordon. After postdoctoral work at Caltech with Rudy Marcus, he joined the faculty at KAIST in 2009 and recently moved to Seoul National University in 2023. He has received the following awards: the Hanseong Science Award from Hanseong Son Jae Han Foundation; the KAIST Technology Innovation Award; the Pole Medal by the Asia-Pacific Association of Theoretical and Computational Chemists; a Korean Chemical Society Young Physical Chemist Award, and a KCS-Wiley Young Chemist Award.
Anat Milo
Ben-Gurion University of the Negev, Israel
ORCID ID: 0000-0003-1552-8193
Anat Milo received her BSc/BA in Chemistry and Humanities from the Hebrew University of Jerusalem in 2001, her MSc from UPMC Paris in 2004 with Berhold Hasenknopf, and her PhD from the Weizmann Institute of Science in 2011 with Ronny Neumann. Her postdoctoral studies at the University of Utah with Matthew Sigman focused on developing physical organic descriptors and data analysis approaches for chemical reactions. At the end of 2015 she returned to Israel to join the Department of Chemistry at Ben-Gurion University of the Negev, where her research group develops experimental, statistical, and computational strategies for identifying molecular design principles in catalysis with a particular focus on stabilizing and intercepting reactive intermediates by second sphere interactions.
Lilo D. Pozzo
Institution: University of Washington (Chemical Engineering)
ORCID ID: 0000-0001-7104-9061
Lilo Pozzo is the Boeing-Roundhill Professor of Chemical Engineering and interim chair of the Department of Materials Science and Engineering at the University of Washington in Seattle.
Her research focuses on controlling and manipulating the structure of soft matter for applications in healthcare, alternative energy, chemical manufacturing and separations. Her group also focuses on developing and utilizing experimental high-throughput tools and techniques to accelerate deployment timelines for new materials. She is an expert in the use of neutron and x-ray scattering techniques for the analysis of colloids and polymers.
Professor Pozzo obtained her BS in Chemical Engineering from the University of Puerto Rico at Mayagüez in 2001 and her PhD in Chemical Engineering from Carnegie Mellon University in Pittsburgh PA in 2006. She also worked at the NIST Center for Neutron Research as a postdoctoral fellow, and has served at the University of Washington since 2007. She has also been recognized with the Early Career Award from the US Department of Energy and with the C3E Award for Women in Clean Energy.
Ekaterina Skorb
Institution: ITMO University, Russia
Ekaterina Skorb obtained her PhD in physical chemistry. She was a postdoc and Alexander-von-Humboldt fellow at the Max Planck Institute of Colloids and Interfaces (MPIKG, Germany) with Prof. Helmuth Möhwald. From 2013 she worked with Prof. Peter Fratzl (MPIKG) as a group leader. She was a visiting scholar at Harvard (USA) in Prof. George Whitesides’ group from 2016 to 2017. Since 2017 she has worked at ITMO University, Saint Petersberg, Russia where she is now a Full Professor and Director of the Infochemistry Scientific Center (ISC).
An interdisciplinary view on problems from various directions – chemistry, mathematics, IT, and biology – helps to solve complex issues and guide science development for the next generation. There are several ambitious projects running now at the ISC, such as synthetic cells, chemical computing, and chemical origins-of-life as well as sensor, lab-on-a-chip, and implant development.
