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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.
Emilio received his B.S. in Electrical Engineering and Physics from Missouri University of Science and Technology in 2007. After graduating he worked for the NASA Marshall Space Flight Center developing non-destructive evaluation techniques for applications related to the US space program. He completed his PhD in Electrical Engineering from the Massachusetts Institute of Technology in 2013 where he worked on high-frequency high-power THz sources and the development of Nuclear Magnetic Resonance spectrometers using Dynamic Nuclear Polarization. His thesis was on the first photonic-band-gap gyrotron travelling wave amplifier which demonstrated record power and gain levels in the THz frequency band.
He completed his postdoc at MIT with a joint appointment in the Nuclear Reactor Lab and the Research Laboratory for Electronics at MIT where he demonstrated the first acceleration of electrons with optically generated THz pulses. He joined the Technology Innovation Directorate at SLAC in August of 2015 where he continues his work on high power, high-frequency vacuum electron devices; optical THz amplifiers; electron-beam dynamics; and advanced accelerator concepts. His recent interests include ultrafast characterization; defect formation in semiconductors; and superconducting and solid-state devices for quantum information.”
Dr. Agarwal's research focuses on scientific tools that enable sharing of scientific experiments, advanced networking infrastructure to support sharing of scientific data, data analysis support infrastructure for eco-science, and cybersecurity infrastructure to secure collaborative environments. Dr. Agarwal is the coordinator for ML4Sci, the Lab-wide machine learning initiative. Dr. Agarwal is a Senior Fellow at the Berkeley Institute for Data Science and an Inria International Chair. Dr. Agarwal also leads teams developing data server infrastructure to significantly enhance data browsing and analysis capabilities and enable eco-science synthesis at the watershed-scale to understand hydrologic and conservation questions and at the global-scale to understand carbon flux. Some of the projects Dr. Agarwal is working on include: Enviromental Systems Science Digital Infrastructure for a Virtual Ecosystem (ESS-DIVE), Watershed Function SFA, AmeriFlux Management Project, FLUXNET, NGEE Tropics, and International Soil Carbon Network. Dr. Agarwal received her Ph.D. in electrical and computer engineering from University of California, Santa Barbara and a B.S. in Mechanical Engineering from Purdue University.
A Computational Social Scientist in the Decision and Infrastructure Sciences Division at Argonne National Laboratory and Senior Scientist at Large of the Consortium for Advanced Science and Engineering (CASE) at the University of Chicago. His interests are particularly focused in System Dynamics Modeling and Judgment and Decision Making.
Dr. Martinez-Moyano is also a Senior Fellow at the Institute for Science and Engineering (NAISE) of Northwestern University and Lecturer at the Graham School of the University of Chicago.
Dr. Martinez-Moyano is President of the System Dynamics Society (2018) and Managing Editor of the System Dynamics Review. He has published in academic journals such as Organization Science, Journal of Public Administration Research and Theory, ACM Transactions on Modeling and Computer Simulation (TOMACS), Computers & Security, System Dynamics Review, and Government Information Quarterly.
Dr. Martinez-Moyano received a Ph.D. in Decision and Policy Science from the University at Albany, State University of New York, and MBA, M.Sc., and B.Eng degrees from the Monterrey Institute of Technology.