Lab Partnering Service Discovery
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Title: HPC Application Architect
- 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: Assistant Computational Scientist
- 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.
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
- 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.