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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
Bert de Jong leads the Computational Chemistry, Materials, and Climate Group, which advances scientific computing by developing and enhancing applications in key disciplines, as well as developing tools and libraries for addressing general problems in computational science.
de Jong is the director of the LBNL Quantum Algorithms Team QAT4Chem, the team director of the Accelerated Research for Quantum Computing (ARQC) Team AIDE-QC, both funded by DOE ASCR, focused on developing software stacks, algorithms, and computer science and applied mathematics solutions for chemical sciences and other fields on near-term quantum computing devices. He is also a co-PI on the ARQC team FAR-QC (led out of Sandia). He is also part of LBNL’s quantum testbed, developing superconducting qubits. He is the LBNL lead for the Basic Energy Sciences Quantum Information Sciences project (led out of PNNL), where he is focusing on new approaches for encoding wave functions and embedding quantum systems. In addition, he is a co-PI on an LBNL led HEP funded quantum information science projects.
de Jong is a co-PI within the DOE ASCR Exascale Computing Project (ECP) as the LBNL lead for the NWChemEx effort, contributing to the development of an exascale computational chemistry code. He is the LBNL lead for the Basic Energy Sciences SPEC Computational Chemistry Center (led out of PNNL), where he is working on reduced scaling MCSCF and beyond GW approaches for molecules.
He leads an LBNL funded effort on machine learning for chemical sciences, focused on developing deep learning networks (GANs and autoencoders) for the prediction of structure-function relationships and its inverse, with a demonstrating in mass spectrometry. As part of this effort, his team developed the ML4Chem Python package.
Areas of expertise: software and algorithms for near-term quantum computing devices, machine learning, supercomputing, computational chemistry.
Lawrence Berkeley National Laboratory (Berkeley Lab), a U. S. Department of Energy Office of Science national lab managed by the University of California, delivers science solutions to the world â solutions derived from hundreds of patented and patent pending technologies plus scores of copyrighted software tools and published, peer-reviewed manuscripts.
Berkeley Lab has more than one hundred cutting-edge research projects using AI to find new scientific solutions to national problems. Through this effort, computer scientists, mathematicians, and domain scientists are collaborating to turn burgeoning datasets into scientific insights. Visit Berkeley Labâs Machine Learning for Science site for more information.
Berkeley Labâs advanced materials expertise is applied to innovation in batteries and other energy storage technologies, semiconductors, and photovoltaics. Additional energy-related areas of expertise include grid modernization and security, bio-based fuels and chemicals and building energy and demand response. Several National User Facilities are available for collaborative engagement: the Advanced Light Source, Molecular Foundry, National Energy Research Scientific Computing Center (NERSC), Energy Sciences Network, and the Joint Genome Institute. Other specialized facilities include FLEXLAB for building energy research and the Advanced Biofuels Process Demonstration Unit.
Ernest Orlando Lawrence, the lab's founder, believed team science yielded the greatest discoveries. That belief is reflected today in interdisciplinary teams and collaborative projects connecting Berkeley Lab, industry, and other research organizations. Berkeley Lab's Intellectual Property Office, connects industry partners with lab innovations and unique facilities to enable lab-to-market transition.
Heather Gray is an experimental particle physicist working on the ATLAS experiment at the Large Hadron Collider (LHC) just outside Geneva in Switzerland. She has broad interests in particle physics, but the primary focus of her research is the Higgs boson -- the most recently discovered elementary particle, the only known elementary scalar of nature and the final piece of the remarkably successful Standard Model. She studies the properties of the Higgs boson and, in particular, how it interacts with different types of quarks, including top, bottom and charm quarks. Other research interests include the development of track reconstruction algorithms, silicon detectors and algorithms for quantum computers. A theme throughout her research is applications of machine learning.
Areas of expertise: physics/astrophysics, quantum computing, AI/machine learning, accelerators, dark energy/dark matter, particle physics, Higgs boson
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.