He is a computer scientist in the Global Security Sciences Division at Argonne National Laboratory working on a variety of Modeling and Simulation (M&S) projects. He is an integral part of the Analysis of Mobility Platforms (AMP) logistics modeling project for U.S. Transportation Command. He has also been the lead investigator on a program for the Naval Research Laboratory doing Electronic Warfare (EW) M&S, which includes both EW system modeling as well as detailed Radio Frequency (RF) propagation modeling in complex environments. Among his research interests is the development of remotely distributed deep-learning image recognition systems for Unmanned Aerial Systems (UAS) detection. He participated in numerous government and military test and evaluation events for UAS mitigation systems and did analysis on UAS threats to critical infrastructure and methods for protection. He graduated from Carnegie Mellon University with a degree in computer science and robotics and is currently pursuing a master's in analytics at the University of Chicago with an emphasis on advanced computational models, including computer vision and machine learning algorithms.
Dr. Chris Haase joins as Director of the Critical Materials Institute from GE Ventures, where he was Senior Director, leading new business creation and investment activities in the areas of oil & gas, power and renewables. With background in defense and natural resources, Chris has served as early-stage technology manager and investor in several corporate venture capital organizations, including Shell Technology Ventures Fund 1, BTG Ventures, Shell GameChanger and GE Ventures. In upstream energy, Chris served as the head business advisor to the Chief Technology Officer of Royal Dutch / Shell, managing alignment of R&D funding with the company’s long-term corporate strategy and value chains and also launching Shell’s latest venture fund, Shell Ventures. Additionally, Chris was Shell’s manager for external research, where he helped Shell close many innovative partnership agreements with universities and small enterprises in North America. With a background in numerical modeling, petrophysics and quantitative seismic interpretation, Chris has worked on oil & gas exploration and development projects, new upstream joint ventures and divestments involving assets in the Gulf of Mexico, South Atlantic, North Sea, Middle East and Australia.
A former US Department of Defense Fellow and adjunct professor at the United States Naval Academy, Chris held R&D positions with the Naval Ocean Systems Center (now SPAWAR) and Department of Defense and also served as a 10-year volunteer commercialization advisor for the National Technology Transfer Center and US Missile Defense Agency. An inventor with several patents, Chris received his Ph.D. and MS degrees in mathematics from the University of Chicago, his MBA from Erasmus University in Rotterdam and his Bachelor of Science degree, Summa Cum Laude, from Ohio State University. Chris is married to Ineke and has two sons, Mark and Peter, both studying mechanical engineering in university.
Dr. Brad Aimone is a Principal Member of the Technical Staff in the Center for Computing Research at Sandia National Laboratories, where he is a lead researcher in leveraging computational neuroscience to advance machine learning and in using brain-inspired computing platforms for future scientific computing applications. He was the Deputy PI and Neural Algorithms Core Lead on the Hardware Acceleration of Adaptive Neural Algorithms (HAANA) Grand Challenge, which was a major R&D program targeting the development of neural based computing algorithms and computing architectures from 2015-2017. Brad is currently is the PI of several efforts focused on developing neural algorithms for future scientific computing platforms.
Prior to joining the technical staff at Sandia in 2011, Dr. Aimone was a postdoctoral research associate at the Salk Institute for Biological Studies. He received his Ph.D. in computational neuroscience from the University of California, San Diego and earned his Bachelor and Master degrees in chemical engineering from Rice University. Dr. Aimone has published over fifty peer-reviewed journal articles and conference publications, and has a number of patents, book chapters, magazine articles, and invited talks.
She has expertise in adaptive and optimal control, multi-agent systems, artificial intelligence and methods of distributed optimization with strong building and heating, ventilation, and air conditioning (HVAC) system control application experience. At Pacific Northwest National Laboratory, she is responsible for development of advanced control technologies applicable to buildings and HVAC systems, power grid controls, and building to grid interaction.
He is a staff scientist at Idaho National Laboratory (INL) and a recognized expert in materials characterization and instrumentation. He has a doctorate in materials science and condenser matter physics from the University of California, Davis. His work has spanned global and nationwide collaborations. He has worked at premier nanocharacterization facilities at national laboratories and universities and has expert knowledge of scanning transmission electron microscopy, atom probe tomography and electron loss spectroscopy. His primary research interests lie in the investigation of materials and the origins of their physical properties. He has heavily leveraged the use of multidimensional microscopy, diffraction and artificial intelligence to address delays in data access and extraction, which has led to a new frontier in advanced microscopy. At INL, he continues to focus on the development and application of machine and deep learning in order to decipher and decimate information from images, spectra, and diffraction patterns to maximize the effectiveness, efficiency and utility of advanced microscopy. He is an invited academic faculty member and manager for a diverse group of postdoctoral research scientists, graduate students, and technicians across several national laboratories and universities. He is an author of 45 peer-reviewed publications, a recognized reviewer, and a technical contributing member to energy materials research. He was awarded two patents and has three patents pending, including an innovative approach to computational microscopy using machine learning.
He received a doctorate in computer science at the University of Tennessee in 2009, master’s in computer systems and software design, and his bachelor’s with a double major in computer science and mathematics with physics from Jacksonville State University. His research spans government-scale database and management systems, graphical user interface design, medical software used for surgery, gesture recognition, graph-theoretic analysis, optimization, automation, systems genetic research, magnetic resonance imaging, image processing, artificial intelligence, supercomputing, and energy-efficient buildings. He currently serves at Oak Ridge National Laboratory’s Building Technologies Research & Integration Center (BTRIC) as a subprogram manager for software tools and models with oversight of projects, involving websites, web services, databases, simulation engine development, visual analytics, supercomputing, and artificial intelligence. He has lead creation of the world’s most accurate method for calibrating a simulation model to measured data, fastest building model creator, fastest buildings simulator, and largest archive of simulated building data. He is a joint faculty member at the University of Tennessee’s Electrical Engineering and Computer Science Department, and an active member of American Society of Heating, Refrigerating and Air-Conditioning Engineers and Institute of Electrical and Electronics Engineers.
He is a research scientist specializing in crosscutting applications and advancement of sensor research to enable resilient real-time measurement and control of process variables within the nuclear and other critical industries. His research expertise includes applications of pattern recognition and machine learning techniques, instrumentation and controls, data analytics, battery modeling, risk and reliability, digital signal processing, acoustic telemetry, diagnosis/prognosis using wavelets and empirical mode decomposition, time series analysis, power management, wireless communication protocols, and wireless sensor networks. He has authored 51 peer-reviewed publications and one book chapter, and two U.S. patent applications filed. To date, he was involved in 13 research projects and has been the principal investigator for eight. He serves as a reviewer for the Institute of Electrical and Electronics Engineers (IEEE) Transactions on Image Processing, Energy Conversion, Industrial Informatics, Industrial Applications, Power Delivery, Systems, Machine and Cybernetics, Instrumentation and Measurement, and the American Nuclear Society (ANS) Transactions on Nuclear Technology. He serves as an external reviewer for U.S. Department of Energy’s Office of Science and Office of Nuclear Energy. Since 2009, he has been section editor for the Journal of Pattern Recognition Research. Since 2015, he has served as an elected member of the ANS Human Factors, Instrumentation, and Controls Division and the American Society of Mechanical Engineers Nondestructive Prognostics and Diagnostic Division since 2016.
She has more than 30 years of experience in theoretical and computational chemistry. She develops new methods and algorithms for high performance computational chemistry as well as applying those techniques to both basic and applied research. Her current application interests are rare earth and heavy element chemistry, separations, catalysis, aerosol formation, cellulose degradation, and photochemistry. Much of her research interests involve large, collaborative efforts between scientists in multiple fields working together to solve difficult scientific challenges. She is a distinguished professor in the Chemistry Department of Iowa State University. Prior to joining Ames Laboratory, she worked at Pacific Northwest National Laboratory as the lead for the NWChem development group and the Visualization and User Services Group. She also worked at the Wright Patterson Air Force Base in technology transfer and training. She received her bachelor’s in chemistry, mathematics, and computer science from Minot State University and her doctorate in physical chemistry from Iowa State University.
Craig M. Vineyard, PhD received his B.S., M.S., and PhD. degrees in computer engineering from the University of New Mexico. He pursues computing research in machine learning, neural-inspired/neuromorphic computing, and algorithmic game theory. Helping lead the Neural Exploration & Research Lab (NERL) at Sandia Labs, his research furthers the understanding of how emerging computing approaches may impact application domains spanning from remote sensing to scientific computing. He has been at Sandia National Laboratories for over 10 years and has authored over 25 papers on computational intelligence research.
He has more than 10 years of industrial and research experience in automation, instrumentation, and control. He holds a doctorate in nuclear engineering from Texas A&M University, a master’s degree in information technology and automation systems from Esslingen University of Applied Science in Germany, and a bachelor’s degree in mechanical engineering from Jordan University of Science and Technology in Jordan. In 2015, he joined Idaho National Laboratory as a research and development scientist with special focus on nuclear automation, instrumentation, and control. Before earning his doctorate, he worked at Asea Brown Boveri for 6 years and was a lead distributed control systems engineer by 2010. While pursuing his degree, he researched various nuclear engineering topics at Texas A&M University and worked for a year at the International Atomic Energy Agency (IAEA). He also worked for Daimler Chrysler-Mercedes Group and Fraunhofer Institute for Production and Automation in Germany. He is a senior Institute of Electrical and Electronics Engineers (IEEE) member and author of several publications and technical reports. He is also a reviewer of nuclear energy and IEEE journals and U.S. Department of Energy grants.
His areas of expertise include synthesis, structure, experimental thermodynamics, physical and chemical properties of intermetallic compounds containing rare earth metals, anomalous behavior of 4f-electron systems, magnetostructural phase transformations, physical properties of ultra-pure rare earth metals, caloric materials and systems, mechanochemistry, mechanically induced solid state reactions and mechanochemical transformations, and relationships between composition, structure, physical and chemical properties of materials. He is a distinguished professor of Materials, Science, and Engineering at Iowa State University, and is an FWP leader and faculty scientist at Ames Laboratory. He is a member of the Materials Research Society, Royal Society of Chemistry, and International Centre for Diffraction Data. He has his doctorate in inorganic chemistry and a bachelor’s and master’s in chemistry (with distinction), both from L’viv State University, L’viv, Ukraine.
You can use keywords such as "Advanced Materials" to find experts who focus on this area of interest.
You may search for a specific lab to see all facilities, technologies and experts found there. e.g. "Ames National Laboratory"
You can use search for a specific technology to find all laboratories and experts who have expertise in this field. e.g. "Energy Analysis"
Fill out the information below to ask your energy technology question. Our target response time is 14 business days; however, any individual may not be available to meet this target though we strive to provide a timely response.