The Speakers

Alexander Hexemer holds B.S. and M.S. degrees in Physics from Mainz University. His Masters thesis was in collaboration with the Max‐Planck Institute for Polymer Science. He earned his PhD in Materials Science under the guidance of Prof. Ed Kramer at the University of California Santa Barbara. Dr. Hexemer joined the Advanced Light Source at LBNL as a PostDoc to develop a Small and Wide Angle X‐ray Scattering beamline. He was awarded an outstanding performance award by the lab in the following year for the beamline and became a beamline scientist in 2007. He has authored over 95 peer‐reviewed articles on topics ranging from copolymer ordering to the development of high performance computing algorithms on super computers. In 2013 he received a DOE Early Career Award for his proposal for a Light Source Toolkit.

James Sethian is a professor of mathematics at UC Berkeley, as well as a Berkeley Lab Senior Faculty Scientist, Mathematics Department Head, Group Lead of the Mathematics Group, and Director of the CAMERA Center. He received his Ph.D. in applied mathematics from UC Berkeley in 1982. Sethian continued his research with an NSF postdoc fellowship at the Courant Institute of Mathematics, and then returned to Berkeley as an assistant professor in 1985. He now holds the James H. Simons Chair in Mathematics at UC Berkeley.

Professor Peter Müller-Buschbaum carries out research in the field of functional materials, with a particular focus on energy materials, e.g. solar cells and batteries. 

He studied physics in Kiel including his doctorate. Then he worked as a postdoctoral fellow at the MPI for Polymer Research in Mainz and as visiting scientist at the ILL and the ESRF in Grenoble, France. He acquired his postdoctoral teaching qualification (Habilitation) in 2002 and headed the Chair of Functional Materials at the TUM Department of Physics, before his appointment in 2018 as full professor and scientific director of the Forschungs-Neutronenquelle FRM-II and of the Heinz Maier-Leibnitz Zentrums MLZ. Since 2011, he has been the German representative at the European Polymer Federation and, since 2012, Associate Editor of the journal ACS Applied Materials & Interfaces. He also heads the Bavarian key laboratory TUM.solar and the Network for Renewable Energies (NRG) of the Munich Institute of Integrated Materials, Energy and Process Engineering (MEP).

Nathan Szymanski is a PhD candidate and NSF graduate research fellow working at UC Berkeley and Lawrence Berkeley National Lab, supervised by Professor Gerbrand Ceder. He received his Bachelor of Science in Physics & Mathematics at the University of Toledo. Nathan's research interests include the development of machine learning techniques for the interpretation of materials characterization data, with an emphasis on X-ray diffraction, as well as their integration with autonomous experimental tools for the solid-state synthesis of novel inorganic materials. 

Tanny Chavez is a computational research scientist at Lawrence Berkeley National Laboratory and is passionate about data analysis, machine learning, and high-performance computing. Fulbright alumna with significant research experience in software development, data science, and digital signal processing.

After studying physics at the Ludwig Maximilian University in Munich, Stefan Kowarik completed a doctorate in physical chemistry at Oxford University in 2006. Postdoctoral stints took him to Tübingen and Cornell Universities, and an Alexander von Humboldt scholarship to the University of California, Berkeley. In 2009, he was appointed Junior Professor of Physics at Berlin’s Humboldt University. Stefan Kowarik ​worked at the Federal Institute for Materials Research and Testing​ from 2017 until joining the University of Graz in 2020.

Prof. Billinge has more than 25 years of experience developing and applying techniques to study local structure in materials using x-ray, neutron and electron diffraction including the development of novel data analysis methods including graph theoretic, Artificial Intelligence and Machine Learning approaches. He earned his Ph.D in Materials Science and Engineering from University of Pennsylvania in 1992. After 13 years as a faculty member at Michigan State University, in 2008 he took up his current position as Professor of Materials Science and Applied Physics and Applied Mathematics at Columbia University and Physicist at Brookhaven National Laboratory. Prof. Billinge has published more than 300 papers in scholarly journals. 

He is a fellow of the American Physical Society and the Neutron Scattering Society of America, a former Fulbright and Sloan fellow and has earned a number of awards including the 2018 Warren Award of the American Crystallographic Association and being honored in 2011 for contributions to the nation as an immigrant by the Carnegie Corporation of New York, the 2010 J. D. Hanawalt Award of the International Center for Diffraction Data, University Distinguished Faculty award at Michigan State, the Thomas H. Osgood Undergraduate Teaching Award. He is Section Editor of Acta Crystallographica Section A: Advances and Foundations. He regularly chairs and participates in reviews of major facilities and federally funded programs.

Marcus Noack got his master's degree in geophysics from Friedrich-Schiller University in Jena, Germany. Working as a Ph.D. candidate at Simula Research Laboratory in  Oslo, he was able to pursue his interests in the theory of wave propagation and mathematical function optimization. There, Marcus leveraged his knowledge of theoretical and numerical physics and applied mathematics and connected it with high-performance computing to create efficient methods to model wave propagation and solve non-linear inverse problems. He graduated with a Ph.D. in applied mathematics from the University of Oslo. Starting at Lawrence Berkeley National Laboratory as a Post Doc, Marcus worked on uncertainty quantification, stochastic function approximation, and autonomous experimentations. Now, as a Research Scientist, Marcus is continuing this line of work with a focus on stochastic processes for function approximation and dimensionality reduction, function optimization, and high-performance computing while serving the autonomous-experimentation community by providing support and practical software. This work has earned him several awards, most notably the 2022 Director’s Award for Exceptional Early-Carreer Achievements. He can be reached by email at MarcusNoack@lbl.gov

Dinesh Kumar is Project Scientist with the Center for Advanced Mathematics for Energy Research Applications (CAMERA). He received his Ph.D. in Mechanical Engineering from University at Buffalo in 2012. He was awarded ALS Postdoctoral Fellowship in 2014. His work involves new techniques analyzing synchrotron data for X-ray scattering and Tomography.

Dr. Frank Schreiber primary areas of study are Thin film, Monolayer, Chemical physics, Diindenoperylene and Crystallography. Frank Schreiber has researched Thin film in several fields, including X-ray crystallography, Analytical chemistry, Morphology and Organic semiconductor. His Monolayer study results in a more complete grasp of Nanotechnology.

His Chemical physics study combines topics in areas such as Counterion, Phase, Globular protein, Condensation and Colloid. His Diindenoperylene research incorporates elements of Transmission electron microscopy, Scattering and Optics. His Crystallography research is multidisciplinary, incorporating perspectives in Small-angle X-ray scattering, Molecule, Diffraction and Adsorption.

His most cited work include:

• Structure and growth of self-assembling monolayers (1830 citations)

• Perovskite solar cells with CuSCN hole extraction layers yield stabilized efficiencies greater than 20 (703 citations)

• Step-by-Step Route for the Synthesis of Metal−Organic Frameworks (595 citations)

Wiebke is a computational research scientist in the computing group of the Advanced Light Source at Berkeley Lab working on supporting data analysis workflows for synchrotron experiments. Prior to her current position, Wiebke received a PhD in high-performance computing and visualization from KTH Royal Institute of Technology focussing on creating static overviews of temporally evolving hierarchical data and visualization of large-scale flow simulations. She also spent some time as a research and teaching assistant at the Biomimetic Robotics and Machine Learning group at the Technical University of Munich where she researched adaptive transfer functions for artificial neural networks.