Lab Partnering Service Discovery

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

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.

Principal Project Specialist at the Argonne National Laboratory and a Senior Scientist in Department of Computer Science at University of Chicago Consortium for Advanced Science and Engineering. He is a senior member of IEEE society and a member of Chicago Quantum Exchange. His research involves development of quantum computing algorithms, error correction/mitigation techniques, and numerical simulator of quantum systems using high-performance computing on next-generation high-performance supercomputers. The recent projects include development of quantum chemistry and combinatorial optimization quantum algorithms for NISQ quantum computers.

Dr. Alexeev received his PhD in Physical Chemistry from Iowa State University while a graduate student in Mark Gordon’s quantum chemistry group. After graduation, Dr. Alexeev became a postdoctoral fellow at Pacific Northwest National Laboratory and worked in the NWChem group led by Dr. Theresa Windus; later, he joined the Nobel Prize winner Dr. Martin Karplus’ group at Harvard University and Université de Strasbourg. 

Research Interests:

• Quantum Algorithms
• Quantum Chemistry Algorithms
• Quantum Combinatorial Optimization Algorithms
• Classical and Quantum Machine Learning
• Quantum Simulators
• High Performance Computing and Parallel Computing

Ojas Parekh is a Principal Member of Technical Staff in the Center for Computing Research at Sandia National Laboratories. Parekh is a theoretical computer scientist with a background in discrete optimization. More broadly, he has worked on combinatorial scientific computing, geospatial graph analysis, and neural-inspired computation, and he is currently leading several interdisciplinary and multi-institutional efforts that seek to understand novel ways in which quantum computation may offer resource advantages over classical computation, especially for optimization, simulation, and machine learning. Parekh received his PhD in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University in 2002 and has been at Sandia since 2010.

Title: Physicist, Collider-Accelerator Department Control Systems Head

Expertise: Particle Accelerator Physics and Technology, Computational Accelerator Physics, Particle Accelerator Control Systems, Data Science and Machine Learning in Accelerator Science, Quantum Information Science (QIS), Storage Rings for Quantum Computing

As an accelerator physicist in the Collider-Accelerator Department at Brookhaven National Laboratory (BNL), Kevin has spent over 35 years working in accelerator physics where he has gained expertise and experience in accelerator design, particle beam simulations, processing and analysis of data, particle accelerator-based data science and machine learning, as well as ion trap dynamics, crystalline beams for quantum information sciences (QIS), and ion trap-based quantum computing.

Kevin has broad experience, as a designer of the NASA Space Radiation Laboratory, a member of the RHIC design and commissioning team, and most recently as a member of the electron ion collider (EIC) project at BNL. His work extends internationally, with collaborations with researchers at CERN, Fermilab, J-PARC & KEK in Japan, as well as domestically with Stony Brook University, the University of New Mexico, and Cornell University.

Kevin and Dr. Thomas Roser are the inventors of the storage ring quantum computer, a new kind of quantum information system that utilizes a circular radio-frequency quadrupole to create an unbounded ion trap. Kevin is the principle investigator for the Storage Ring Quantum Computer project, which offers a pathway to large scale QIS.

Kevin is an author on over thirty peer reviewed publications, co-author on a book chapter in “Challenges and Goals for Accelerators in the XXI Century” (2016), and an author on over 150 conference publications. Kevin has mentored many students in his career, including three Ph.D. students from Stony Brook University.