Markus Buehler
MRS Bulletin and Massachusetts Institute of Technology
  

Kristina Kareh
Nature

Simon Batzner, DeepMind
Simon Batzner is a Research Scientist at Google DeepMind. His research interests lie in large-scale deep learning and its applications in physics. Prior to joining DeepMind, he worked with Boris Kozinksy on building deep learning systems for large-scale simulations, in particular on learning symmetry-preserving representations of geometric structures. His PhD research was a finalist for the 2023 ACM Gordon Bell Prize, also known as the "Nobel Prize of Supercomputing". Batzner holds a PhD in Applied Mathematics from Harvard University and a Master's Degree from MIT.
 

Cate Brinson, Duke University
L. Cate Brinson is the Sharon and Harold Yoh Professor and Donald Alstadt Department Chair of the Mechanical Engineering and Materials Science Department at Duke University. She obtained her PhD from Caltech and was faculty at Northwestern University prior to joining Duke. She is an expert in the broad area of mechanics of materials, with emphasis on complex hierarchical materials and polymer based systems, and merging concepts of data science into materials. Experimental and computational work spans the range of molecular interactions, micromechanics and macroscale behavior. Current research foci include nanostructured polymers, interfacial behavior, structural metamaterials and AI and data platforms for material query and design. Her awards include the Eringen Medal of SES, the Nadai Medal of ASME, the Bessel Prize of the Humboldt Foundation and a Fellow of many professional societies. She served on the SES Board of Directors and is a founding member of the Materials Research Data Alliance (MaRDA).
  

Michele Ceriotti, EPFL
Michele Ceriotti received his PhD in Physics from ETH Zürich working with Michele Parrinello, and spent three years in Oxford as a Junior Research Fellow at Merton College and as a member of the group of David Manolopoulos. Since 2013 he has led the laboratory for Computational Science and Modeling, in the institute of Materials at EPFL, focusing on method development for the atomistic modeling of matter, bridging quantum mechanics, statistical physics and machine learning. If you're curious about grants and prizes he has received, you'll find them on Google. If you're curious to know things that he thinks have had an impact, beside the papers, Ceriotti is proud to have contributed to the development of several open-source software packages, including http://ipi-code.org and http://chemiscope.org, and to have served the atomistic modeling community as an associate editor of the Journal of Chemical Physics, as a moderator of the physics.chem-ph section of the arXiv, and as an editorial board member of Physical Review Materials. 
   

Rose Cersonsky, University of Wisconsin-Madison
Rose K. Cersonsky is the Michael and Virginia Conway Assistant Professor of Chemical and Biological Engineering at the University of Wisconsin-Madison. She received her Bachelor of Science degree in Materials Science and Engineering with a minor concentration in Computer Science from the University of Connecticut in 2014. She went on to obtain her PhD in Macromolecular Science and Engineering from the University of Michigan in 2019 alongside Professor Sharon C. Glotzer, focusing on the self-assembly behavior and optical properties of colloidal nanoparticles. Following her doctoral work, she collaborated with Prof. Michele Ceriotti as a postdoctoral researcher at École Polytechnique Fédérale de Lausanne, working on developing and applying hybrid supervised-unsupervised machine learning models for data-driven studies of molecular design. Her research group at UW-Madison, established in 2023, centers on developing techniques for and using data science and machine learning to unify our understanding of molecular motion and interactions across length scales. She and her group lead the development of scikit-matter, a scikit-learn-affiliated package for quantitative structure-property relations in materials research, and are core developers of chemiscope, an interactive visualizer for data-driven analyses of molecular datasets. Cersonsky’s work has been recognized with a number of awards, including being named one of Matter’s “35 under 35” in Materials Research, the Victor K. LaMer Award from the Colloids Division of the American Chemical Society, the Biointerfaces Institute Innovator Award, and the Charles G. Overberger Award for Excellence in Research.  In addition to research, she has devoted herself to scientific service, leading and coordinating multiple outreach programs and publishing work in educational journals on community engagement and gender equity. Recently, she released the commentary “Not yet defect-free: the current landscape for women in computational materials research,” in npj Computational Materials, highlighting the persisting inequities for women in her field. 
  

Chi Chen, Microsoft Quantum
Chi Chen leads quantum and AI applications in materials science as an Engineering Manager at Microsoft Quantum. His contributions to the scientific community include widely adopted machine learning models such as MEGNet and M3GNet, which have enabled many materials discoveries across the research community. His computational work has led to the successful synthesis of several novel materials, including solid-state battery electrolytes, structural ceramics, and electret polymers for energy harvesting. He also develops AI-powered tools including Copilot and large language model agents. His current research interests lie at the intersection of quantum computing, AI, and materials science.
       

Cecilia Clementi, Freie Universität Berlin
Cecilia Clementi is the Einstein Professor of Physics at FU Berlin since 2020, after 19 years as a Professor of Chemistry at Rice University in Houston, Texas, USA. Clementi obtained her Ph.D. in Physics at SISSA, Italy, and was a postdoctoral fellow at the University of California, San Diego. Her research focuses on the development and application of methods for the modeling of complex biophysical processes, by means of molecular dynamics, statistical mechanics, coarse-grained models, experimentaldata, and machine learning. Clementi’s research has been recognized by a US National Science Foundation (NSF) CAREER Award (2004), the Robert A. Welch Foundation Norman Hackerman Award in Chemical Research (2009), and the Hamill Innovation Award (2007 and 2014).
  

Daniela Rus, Massachusetts Institute of Technology
Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus' research interests are in robotics, artificial intelligence, and machine learning, and their applications toward a better world. Rus is a senior visiting fellow at MITRE Corporation, a MacArthur Fellow, a fellow of ACM, AAAI, AAAS, and IEEE, a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences. She is the recipient of the Engelberger Award for robotics, the IEEE RAS Technical Award, the John Scott Medal, and the IEEE Edison Medal. Rus earned her PhD in Computer Science from Cornell University.
      

Aidan Toner-Rodgers, Massachusetts Institute of Technology
Aidan Toner-Rodgers is a second-year PhD student in Economics at MIT. His research focuses on the economics of science and innovation, using tools from industrial organization, game theory, and labor. Before joining MIT, Aidan spent two years as a research analyst at the Federal Reserve Bank of New York, working on topics in macroeconomics and monetary policy. He holds a B.A. in Mathematics and Economics from Macalester College.
 

Johnson Tsun-Hsuan Wang, Massachusetts Institute of Technology
Johnson Tsun-Hsuan Wang is a final-year PhD in EECS at MIT CSAIL, working with Professor Daniela Rus. His research focuses on the intersection between robotics, machine learning and simulation, with applications on robot design, control and autonomous driving. Before joining MIT, he received his Bachelor and Master in Electrical Engineering in National Tsing Hua University in Taiwan. Also, he worked part-time/full-time at Liquid AI, MIT-IBM Watson AI Lab, and Uber Advanced Technologies Group.
   

Max Welling, University of Amsterdam
Max Welling is a full professor and research chair in machine learning at the University of Amsterdam and a Merkin distinguished visiting professor at Caltech. He is co-founder and CAIO of the startup CuspAI in Materials Design. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he served on the founding board. His previous appointments include Partner and VP at Microsoft Research, VP at Qualcomm Technologies, professor at UC Irvine. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate Prof. Gerard ‘t Hooft. He then switched fields to focus on machine learning, first as a postdoc at Caltech under supervision of Prof. Pietro Perona and then as postdoc under supervision of Nobel laureate Prof. Geoffrey Hinton at UCL & U. Toronto. Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the Neurips foundation since 2015, he is co-founder of the European Lab for Learning and Intelligence Systems (ELLIS) and served on its board until 2021, he has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair and co-founder of MIDL 2018. Welling is recipient of the ECCV Koenderink Prize in 2010, and the 10 year Test of Time awards at ICML in 2021 and ICLR in 2024.
   

Xiaoying Zhuang, Leibniz University Hannover
Xiaoying Zhuang’s key research area is computational materials design for nano composites, metamaterials and nanostructures as well as computational methods for multiphysics and multiscale modelling. Zhuang obtained her PhD in Durham University, UK in 2011, which is followed by her postdoc in Norwegian University of Technology in Trondheim and then as a faculty staff in Tongji University. In 2015, she was awarded with the Sofja-Kovalevskaja Programme from Alexander von Humboldt Foundation that brought her to Germany and she focused on the modelling and optimization of polymeric nanocomposite. Her ongoing ERC Starting Grant is devoted to the optimization and multiscale modelling of piezoelectric and flexoelectric nano structures. In 2018, she was awarded with Heinz-Maier-Leibnitz Prize and Curious Mind Award. In 2020, she was awarded with the honor of Heisenberg-Professor from German Research Foundation (DFG). In 2023, she is awarded with the KJ Bathe Prize and 2024 first Qidi Prize for female scientists in recognition of her contribution in research and education.