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Ivar Martin is a condensed matter theorist in the Material Science Division.
His interests include equilibrium properties of materials, including superconductivity and magnetism, as well as nonequilibrium. Recently he has been particularly interested in the ways to create new quantum states by means of strong periodic and quasiperiodic driving.
His other interests include microscopic theory of quantum decoherence and quantum measurement, ways to implement unusual correlated states in quantum hardware, and classical nonlinear phenomena of phase and mode locking.
Martin got his undergraduate degree from the Moscow State University, and PhD from the University of Illinois at Urbana-Champaign. In 1999 he went to Los Alamos National Lab, first as a postdoc and then as a staff member. He came to Argonne in 2013.
Dr. Iadecolais a theoretical physicist using diverse analytical and numerical tools to study a variety of topics in quantum condensed matter. A graduate of Brown University (Sc.B., 2012), he received his Ph.D. in Physics from Boston University in 2017. He then became a JQI Theoretical Postdoctoral Fellow at the NIST-University of Maryland Joint Quantum Institute until 2019, when he joined Iowa State University as an Assistant Professor. Research in his group focuses on out-of-equilibrium quantum systems and topological phases with a view towards emerging quantum technologies. On the nonequilibrium side, he studies properties of highly-excited many-body states and the surprising phenomena they harbor that challenge deeply ingrained intuition based on quantum statistical mechanics. On the topological side, he focuses on states of matter whose properties cannot be understood within the traditional paradigm of spontaneous symmetry breaking, and which could enable the robust storage and manipulation of quantum information. In addition to thinking about new phenomena, he grapples with ways to realize them in electronic and photonic systems, or using near-term quantum platforms.
Jonathan Carter is the Associate Laboratory Director for Computing Sciences at Lawrence Berkeley National Laboratory (Berkeley Lab). The Computing Sciences Area at Berkeley Lab encompasses the National Energy Research Scientific Computing Division (NERSC), the Scientific Networking Division (home to the Energy Sciences Network, ESnet) and the Computational Research Division.
Dr. Carter's research interests are in the evaluation of system architectures and algorithms for high-performance computing, and in computational chemistry and physics simulations. Recently he has been engaged in a project to look at computer architectures beyond the end of Moore's Law and has focused on techniques to perform simulations for computational chemistry using newly developed quantum computing test-beds. He brings a unique perspective to his work, formed from using computing resources as a domain scientist, from performing performance analyses of computer architectures, and from his experience in moving large-scale computational systems from idea to reality.
Carter joined Computing Sciences as part of the National Energy Research Scientific Computing (NERSC) Division at the end of 1996, working with a broad range of scientists to optimize applications, transition projects from shared-memory vector systems to massively parallel systems, and providing in-depth consulting for materials scientists and chemists using NERSC. He became group leader of the consulting group at the end of 2005. During his time at NERSC, he led or played a lead role in teams that procured and deployed three of the fastest computing systems in the world.
Areas of expertise: quantum computing, beyond Moore's Law computer architectures, high-performance computing (HPC) / supercomputing, and computational chemistry.
Irfan Siddiqi received his AB (1997) in chemistry & physics from Harvard University. He then went on to receive a PhD (2002) in applied physics from Yale University, where he stayed as a postdoctoral researcher until 2005. Irfan joined the physics department at the University of California, Berkeley in the summer of 2006. In 2006, Irfan was awarded the George E. Valley, Jr. prize by the American Physical Society for the development of the Josephson bifurcation amplifier. In 2007, he was awarded the Office of Naval Research Young Investigator Award, the Hellman Family Faculty Fund, and the UC Berkeley Chancellor’s Partnership Faculty Fund.
His group, the Quantum Nanoelectronics Laboratory, investigates the quantum coherence of various condensed matter systems ranging from microscopic nanomagnets such as single molecule magnets to complex macroscopic electrical circuits. To measure the electric and magnetic properties of these quantum systems, they are developing novel microwave frequency quantum-noise-limited amplifiers based on superconducting Josephson junctions formed by both oxide tunnel barriers and carbon nanotube weak links. Current topics of research include the dependence of quantum coherence on system complexity, the non-equilibrium quantum statistical mechanics of non-linear oscillators, the quantum coherence of single molecules, and topological architectures for maximum coherence in superconducting circuits.
Areas of expertise: quantum computing, condensed matter physics, superconducting qubits, quantum limited amplifiers, quantum circuits
Thomas Schenkel is a physicist and senior scientist at Lawrence Berkeley National Laboratory, where he is the interim Director of the Accelerator Technology and Applied Physics Division (http://atap.lbl.gov/). Thomas received his Ph.D. in physics from the Goethe University in Frankfurt. Following time as a postdoc at Lawrence Livermore National Laboratory, he joined Berkeley Lab. His research interests include novel accelerator concepts, materials far from equilibrium, exploration of fusion processes, and spin qubit architectures. Thomas also teaches a graduate course on particle accelerators at UC Berkeley.
Thomas worked on variations of time-of-flight mass spectrometry to characterize the environment of bio-molecules as a postdoc. This theme has now come up in the current Covid-19 crisis with new ideas for mass spectrometry and imaging of viruses in droplets.
COVID-19-related research: "Laser, Biosciences Researchers Combine Efforts to Study Viruses in Droplets"
Areas of expertise: accelerators, fusion, lasers, quantum, spin qubits
Haruko Wainwright received her MS in nuclear engineering (2006), MA in statistics (2010), and PhD in nuclear engineering (2010) at University of California, Berkeley. Her initial research interest was to investigate the environmental impact of nuclear waste and nuclear weapon productions. Her PhD dissertation focused on Bayesian geostatistical inverse modeling for subsurface characterization at the uranium-contaminated DOE Hanford site. Since then, she has broadened her research interest to various environmental problems, including Arctic ecosystem responses to climate change, groundwater contamination, and deep-subsurface CO2 storage. In addition to working in many interdisciplinary projects, she is a deputy lead of the site application thrust in the Advanced Simulation Capability for Environmental Management project, leading the site application at the Savannah River Site F-Area. She is also on the leadership team of Institute for Resilient Communities, which aims to prepare communities for radiological and other disasters through research, education and outreach activities.
For more information: https://eesa.lbl.gov/profiles/haruko-murakami-wainwright/
COVID-19-related research: "Using Machine Learning to Estimate COVID-19âs Seasonal Cycle"
Ben Brown is a statistical scientist in the Environmental Genomics and Systems Biology division within Berkeley Labâs Biosciences Area. He specializes in the development of novel machine algorithms, usually for the biological and environmental sciences at Berkeley Lab. His group develops âthird-waveâ learning algorithms that combine the interpretability and reliability of classical statistics with the predictive performance of deep learning. They specialize in designing learning paradigms for complex, high-dimensional systems that enable accurate uncertainty quantification, model discovery, feature selection, and inference. Dr. Brown's expertise include statistics, machine learning, deep learning, and artificial intelligence.
COVID-19-related research: "Using Machine Learning to Estimate COVID-19's Seasonal Cycle". Other principal investigators include: Eoin Brodie, Nicola Falco, Dan Feldman, Zhao Hao, Chaincy Kuo, Joshua Ladau, and Haruko Wainwright.
Professor of Chemistry, received his B.S. in 1997 from Pennsylvania State University, where he worked in the group of Prof. Ayusman Sen on palladium-catalyzed co- and terpolymerizations. He earned his Ph.D. from the University of California, Berkeley in 2003 under the guidance of Prof. T. Don Tilley, primarily focused on the development of new catalytic C–H bond functionalizations. Following postdoctoral work at the Swiss Federal Institute of Technology (ETH Zürich) with Antonio Togni investigating catalytic asymmetric hydroamination and hydrophosphination, Aaron joined the chemistry faculty at Iowa State University in 2005. He was promoted to associate professor in 2011, and to professor in 2016.
Dr. David Stracuzzi is a Principal Member of Technical Staff at Sandia National Laboratories and has been studying machine learning and artificial intelligence for 20 years. He currently leads several projects that apply data-driven modeling and uncertainty analysis methods to tasks related to remote sensing data, pattern-of-life data, geophysical data, and data related to physics-based simulations. Prior to joining Sandia in 2010, Dr. Stracuzzi was a member of the research faculty at Arizona State University working on computational cognitive architectures for developing intelligent agents.
Tim Draelos has been at Sandia for over 32 years and received his Ph.D. at UNM in 1998, focusing on constructive neural networks. He has spent the last ten years conducting deep learning R&D, including work on seismic signal detection, phase identification, and event discrimination. He chaired special sessions on Machine Learning in Seismology at the 2016 and 2017 Seismological Society of America annual meetings and 2017 American Geophysical Union fall meeting. He has taught classes on machine and deep learning and was the founder and general chair of the 1st three Sandia Machine Learning and Deep Learning Workshops, starting in 2017. He has published papers in the Bulletin of the Seismological Society of America, Seismological Research Letters, and various machine learning conferences.