Lab Partnering Service Discovery

Title: Associate Professor of Physics ad Astronomy, Tufts University/Senior Scientist, Computer Science Initiative

Expertise: Quantum Computing

In 2015 Love joined the Physics Department at Tufts University as an Associate Professor with Tenure. In 2018 he joined Brookhaven National Lab’s Computational Science Initiative as a Senior Scientist in a dual appointment held concurrently with his Tufts appointment. He serves as the Chair of the Scientific Advisory Board of Zapata Computing, Inc., a Boston-based quantum software startup. He is a member of FQXi.

In quantum information science Love has worked broadly on quantum simulation, including work on quantum simulation of quantum chemistry and high energy physics and on quantum lattice-gas and quantum cellular automata models. Love has also worked on adiabatic quantum computing, the theory of entanglement, on semiclassical descriptions of quantum information including wigner functions for qubits and qudits, and on efficient simulation of subtheories of quantum mechanics that lack contextuality.  

Title: HPC Application Architect

Expertise:

• Docking
• Molecular dynamics
• Density Functional Theory Code Development
• Parallel programming (GNU parallel, MPI, OpenMP, PGAS models, etc.)

Hubertus (Huub) van Dam is a computational chemist with expertise in docking and molecular dynamics simulations. In prior work he has collaborated on improving the accuracy of docking calculations by using ab-initio molecular potentials for the electrostatic part of docking scores (DOI: 10.1063/1.2793399). He is currently supporting the National Virtual Biotechnology Laboratory (NVBL) effort to find COVID-19 drug candidates using Autodock 4.2, Dock 6 and DeepDriveMD. He also has extensive expertise in writing and supporting large parallel quantum chemistry packages. Currently, he serves as Testing and Assessment Task Lead on the Exascale Computing Project’s NWChemEx effort. NWChemEx is providing a community infrastructure for computational chemistry that takes full advantage of exascale computing technologies.

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.

Title: Assistant Computational Scientist

Expertise:

• Scientific literature processing to rapidly find protein-protein interaction candidates
• AI-based platform to accelerate discovery of novel drug compounds
• Querying and filtering interface for users to efficiently scan tens of thousands of sources

Since joining Brookhaven National Laboratory in 2018, Carlos X. Soto has been an active member of the Machine Learning Group, where he has contributed extensively toward large-scale scientific data extraction from published literature using natural language processing (NLP) techniques applied to areas such as functional genomics, drug discovery, and government reports. His contributions to machine learning for the integration of biological genomics data helped prompt an ongoing partnership with Oak Ridge National Laboratory to use NLP techniques to accelerate COVID-19 drug discovery. In 2019, Soto was part of the Brookhaven team awarded one of only two Nuclear Threat Initiative (NTI) Nuclear Security Index Challenge grants. The work, Towards a Predictive Nuclear Security Threat Model, aims to create a predictive model by integrating NTI Index data into machine learning and sentiment analysis. Presently, his work on COVID-related projects focuses on providing domain scientists with powerful new computational tools to identify patterns and insights in large volumes of documents, in particular relating to potential drug compound candidates.

Title: Associate Computational Scientist, Quantum Computing Group, Computational Science Initiative, Brookhaven National Laboratory

Expertise: Quantum Information Science

Description: Ning Bao’s research is focused on quantum information theory and quantum information science more broadly, particularly with an eye toward entanglement measures and applications of ideas from quantum information science to other aspects of physics. Before arriving at Brookhaven, he completed postdoctoral positions at Caltech and University of California, Berkeley and did his graduate work at Stanford University.

Ronald Pindak’s research is in condensed matter physics with an emphasis on the use of x-ray scattering techniques to characterize bulk, surface, and interface structures as well as their kinetics and dynamical fluctuations.   Pindak worked for 24 years at Bell Laboratories where he achieved the rank of Distinguished Member of the Technical Staff.  He has 45 years of research experience with over 100 refereed publications covering work on both soft condensed matter (complex fluids, colloids, polymers) and hard condensed matter thin films such as found in electronic and opto-electronic devices.  He has 36 years of experience using synchrotron research facilities and 17 years of experience managing synchrotron facility operations.  He currently oversees a suite of state-of-the-art beamlines at NSLS-II that are optimized for coherent, micro-beam, inelastic, resonant, and small/wide angle x-ray scattering. 

Title: Deputy Director

Expertise:

• Optimal Design of Experiments under Uncertainty
• Machine Learning
• Computational Physics
• Statistical Physics

Prior to joining Brookhaven National Laboratory in 2017, Francis “Frank” Alexander spent nearly 20 years at Los Alamos National Laboratory, finishing his tenure as the acting division leader of the lab’s Computer, Computational, and Statistical Sciences (CCS) Division. At Los Alamos, he grew in several leadership roles, including serving as deputy leader of CCS Division’s Information Sciences Group and leader of the Information Science and Technology Institute. Alexander was introduced to the DOE national laboratory complex during his postdoctoral work with Los Alamos’ Center for Nonlinear Studies and the Institute for Scientific Computing Research at Lawrence Livermore National Laboratory. He also was a research assistant professor at Boston University’s Center for Computational Science. Alexander has led many research projects and has published more than 50 papers in peer-reviewed journals. In addition to leading Brookhaven’s artificial intelligence and machine learning strategy effort, Alexander currently serves as project director of the multi-laboratory ExaLearn Co-design Center for Exascale Machine Learning Technologies, part of the Exascale Computing Project. He also leads various projects involving optimal experimental design, including for biological systems.