Displaying items by tag: ai

Biography

Christopher Jarzynski received his A.B. (with high honors) in 1987 from Princeton University and his Ph.D. in 1994 from University of California, Berkeley. His research focuses on statistical mechanics and thermodynamics at the molecular level, with a particular focus on the foundations of nonequilibrium thermodynamics. His research group has worked on topics that include the application of statistical mechanics to problems of biophysical interest; the analysis of artificial molecular machines; the development of efficient numerical schemes for estimating thermodynamic properties of complex systems; the relationship between thermodynamics and information processing; quantum and classical shortcuts to adiabaticity; and quantum thermodynamics. Jarzynski is a Fellow of the American Physical Society and the American Academy of Arts and Sciences, and a UMD Distinguished University Professor. He received the 2019 Lars Onsager Prize for theoretical statistical physics, a 2020 Guggenheim Fellowship and a 2020 Simons Fellowship. In 2020, he was elected to the National Academy of Sciences.

Biography

Theoretical condensed matter physicist Maissam Barkeshli joined the UMD Department of Physics as an Assistant Professor and a JQI Fellow in August, 2016.

Barkeshli received his PhD in Physics from MIT in 2010 following a BS in Electrical Engineering and Computer Science and a BA in Physics from UC Berkeley. He was a Simons Postdoctoral Fellow at Stanford University (2010-2013) and a postdoctoral researcher at Microsoft's Station Q, located at UC Santa Barbara (2013-2016).

He works on complex many-body phenomena involving condensed matter physics, quantum field theory, quantum topology, and quantum information theory. He also works on the science of deep learning, studying fundamental principles underlying the inner workings of modern AI models. In 2018, he was awarded an Alfred P. Sloan Research Fellowship and a CAREER award from the National Science Foundation. 

Biography

Wolfgang Losert obtained his PhD from City College of the City University of New York. His research is centered on dynamical properties of Complex Systems at the convergence of physics and biology. A special focus is on applications to cancer biology. Examples of dynamical processes that are often found in complex systems are pattern formation and dynamical phase transitions. The main thrust of his work on living systems is to assess how cell motion and collective behavior are affected by physical cues, in particular the topography of the surface, surface adhesivity, and cell-cell adhesion. We discovered that cell migration can be guided by nanotopography via control of the dynamics of actin waves and that cell-surface adhesion can significantly alter the intracellular and collective cell dynamics. We also developed new tools to integrate measurements of the physical properties of living systems with biomedical phenotypes, via advanced statistical and machine learning analysis of multiple types of information, most at the single cell level.

Biography

Michelle Girvan received her B.S. in 1999 from the Massachusetts Institute of Technology and her Ph.D. in 2003 from Cornell University. Her research combines methods from statistical mechanics, dynamical systems, and graph theory to address interdisciplinary, network-related problems. She is interested in both broad theoretical approaches to complex networks as well as specific applications, especially to information cascades, epidemiology, and genetic regulatory networks.

In a 2019 podcast, she discussed her work in chaos and artificial intelligence.

In 2022, she was named a UMD Distinguished Scholar-Teacher.