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Jeffrey YoungView profile

Researcher

• High-Performance Computing (HPC)
• Computer Architecture
• Quantum Programming
• +6 more

Jeffrey Young is a Principal Research Scientist at Georgia Institute of Technology, working with the Partnership for Advanced Computing Environments (PACE) and leading Georgia Tech’s Open Source Program Office. His research focuses on high-performance computing (HPC), computer architecture, and novel accelerators including GPUs, FPGAs, and Arm/RISC-V processors. He leads next-generation computing strategy at PACE and directs the NSF-funded CRNCH Rogues Gallery testbed, which explores post-Moore accelerators like neuromorphic and near-memory systems. His work bridges hardware-software co-design and scientific software engineering. Recent research trends show expertise in quantum programming (Qwerty/ASDF), heterogeneous computing (Cupbop), and memory system optimization across GPUs, FPGAs, and CPUs. He has contributed to exascale workflows (HIPLZ), safe HPC libraries, and UAV co-simulation frameworks. Scientific Awards: NSF-funded CRNCH Rogues Gallery testbed (2020-2024) Education: Ph.D. in Computer Architecture (2013), advised by Dr. Sudhakar Yalamanchili Labs & Initiatives: Director, CRNCH Rogues Gallery testbed Co-Director, Georgia Tech Center for Scientific Software Engineering Director, Georgia Tech Open Source Program Office

• Particle Physics
• High Energy Physics
• Instrumentation Development
• +7 more

Richard Brenner is a Professor and Head of Department at the Department of Physics and Astronomy , Uppsala University. He is a key member of the ATLAS detector team at the CERN Large Hadron Collider (LHC) , focusing on instrumentation development and real-time data processing for dark matter detection. His work bridges semiconductor detector signals with machine learning systems , emphasizing radiation resistance in high-energy environments. Role: Head of Department of Physics and Astronomy Affiliation: Uppsala University and CERN Research Focus: Dark Matter, Higgs Boson, Particle Physics His recent 15 publications (2025) span topics like dark matter searches , Higgs boson production , vector boson fusion , and machine learning applications in data analysis. Keywords include High Energy Physics , Experimental Physics , and Quantum Interactions , with subfields such as Collider Physics , Detector Engineering , and Theoretical Modeling

Maurits HaverkortView profile

Professor

• Strongly Correlated Electron Systems
• Quantum Many-Body Physics
• X-ray Spectroscopy
• +6 more

Maurits Haverkort is a Professor at the Institute for Theoretical Physics, Heidelberg University (Germany). His research focuses on quantum many-body systems , strongly correlated electrons , and X-ray spectroscopy of complex materials under strong fields. University of Cologne (PhD in Physics, 2005) University of Groningen (M.Sc. in Physics, 2002) Research Interests : He investigates orbital and magnetic properties in heavy fermion systems , actinide materials , and correlated oxides using resonant inelastic X-ray scattering (RIXS) , ARPES , and computational tools like Quanty . His work spans crystal field theory , spin-orbit coupling , and ultrafast electron dynamics . Scientific Awards & Activities : 2018 – Editorial Board Member, Physical Review Letters 2017 – Beam Time Allocation Panel, ESRF Grenoble 2016–2018 – Swedish Research Council Panel NT-4 2012–2016 – Scientific Selection Panel, Helmholtz-Zentrum Berlin Recent Publications highlight 5f electron counting , photon-modulated bonding , and precision neutrino mass experiments , reflecting his expertise in quantum materials and advanced spectroscopy .

Georgios ZourarisView profile

Professor

• Numerical Analysis
• Finite Element Methods
• Stochastic Partial Differential Equations
• +3 more

Georgios Zouraris is a Professor at the University of Crete, where he has maintained an active research profile since earning his Ph.D. from the same institution in 1995. His work is centered in the School of Science and Engineering, focusing on advanced computational mathematics with applications in physics and engineering. Education: Ph.D. in Mathematics, University of Crete, 1995 Professor Zouraris specializes in the development and rigorous analysis of numerical methods for partial differential equations. His research spans finite element and finite difference techniques for nonlinear Schrödinger equations, logarithmic heat equations, and stochastic PDEs with space-time white noise. Key contributions include error estimation frameworks for relaxation schemes, convergence analysis of Crank-Nicolson methods, and efficiency improvements for multilevel Monte Carlo simulations. His theoretical work consistently addresses singular nonlinearities and complex domain geometries, bridging mathematical rigor with computational practicality. Analysis of his 2020-2025 publications reveals a sustained focus on high-accuracy numerical schemes for challenging PDEs, particularly those involving logarithmic singularities and stochastic forcing. Recent work demonstrates increasing sophistication in handling noncylindrical domains and coupling strategies, with applications ranging from quantum systems to material science. The publications show consistent emphasis on provable convergence rates and computational efficiency. Information regarding student advising, research grants, and laboratory facilities is not documented in the available sources. His active publication record through 2025 indicates ongoing research leadership in computational mathematics.

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +7 more

Jonathan Voersaa Wenshøj is an academic researcher at the Department of Computer Science, University of Copenhagen. He contributes to the Machine Learning section's activities spanning theoretical foundations and applications in diverse domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological modeling. The section participates in the SCIENCE AI Centre and collaborates with initiatives like TreeSense for global tree resource analysis. His research intersects machine learning with quantum computing, medical informatics, and sustainability. Recent publications highlight applications in environmental monitoring, healthcare diagnostics, and energy-efficient AI systems. The department provides advanced compute resources including a powerful cluster for intensive machine learning tasks. This researcher's work appears in diverse machine learning domains, with recent publications addressing quantum-inspired architectures, explainable AI in medical imaging, and sustainable computing practices. The section actively hosts events including seminars, conferences, and PhD defences related to machine learning advancements.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +5 more

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

• Cybersecurity
• Cyber Analytics
• Digital Forensics
• +4 more

Nicole L. Beebe is a Professor at the Alvarez College of Business, The University of Texas at San Antonio , specializing in cybersecurity, cyber analytics, and digital forensics. With over two decades of experience spanning academia, government, and industry, she has contributed extensively to research on insider threats, IoT security, and threat hunting. Ph.D. in Business Administration (Information Technology), UTSA MS in Criminal Justice, Georgia State University BS in Electrical Engineering, Michigan Technological University Her research explores cybersecurity challenges in emerging technologies, including quantum computing, IoT, and large language models. She has pioneered studies on cyberbullying dynamics, forensic automation, and AI-driven threat detection. Recent publications focus on adversarial image obfuscation , VR for security operations , IoT forensic methodologies , and deepfake detection frameworks , reflecting interdisciplinary work at the intersection of security, AI, and digital evidence. 2022 Best Paper Award, Journal of Network & Computer Applications Senior Member, IEEE and ACM Senior Fellow, Information Systems Security Association As an Associate Editor for Computers & Security , she shapes the field through peer review. Her $14M+ in funding from NSF, DHS, and DoD underscores her impact on advancing cybersecurity research and education.

Scott E. ParkerView profile

Professor

• Plasma Physics
• Magnetic Fusion Energy
• Computational Physics
• +4 more

Scott E. Parker is a Professor of Physics at the University of Colorado Boulder and a Fellow at both the Renewable and Sustainable Energy Institute (RASEI) and the Center for Integrated Plasma Studies (CIPS). His expertise lies in theoretical and computational plasma physics for magnetic fusion energy. Education: Ph.D. in Engineering Science, University of California, Berkeley B.S. in Nuclear Engineering and Mathematics, University of Wisconsin, Madison His research centers on plasma turbulence simulation using advanced computational methods. Key areas include gyrokinetic theory, tokamak edge physics, and quantum information science applications to plasma modeling. The group develops particle-based simulations in five-dimensional phase space to study transport in magnetically confined plasmas, utilizing massively parallel computing and scientific visualization. Analysis of his publications reveals consistent focus on numerical techniques for plasma turbulence, particularly trapped electron modes and zonal flows. His work bridges fundamental kinetic theory with practical fusion reactor challenges, demonstrating qualitative agreement with experimental transport observations in tokamaks. Awards: RASEI Fellow CIPS Fellow Professor Parker leads the Parker Research Group, mentoring graduate students in computational plasma physics. The group collaborates on extreme-scale computing projects for fusion energy research, though specific grant details are not provided in source materials. Their work is integral to advancing predictive capabilities for next-generation fusion devices. The research group maintains active projects in magnetic fusion energy, utilizing GEM simulations and multiscale kinetic methods to address edge turbulence and transport phenomena critical for reactor viability.

Shivakant MishraView profile

Professor

• Distributed Computing & Networking
• Social Computing
• Edge Computing
• +6 more

Shivakant Mishra is a Professor in the Department of Computer Science at the University of Colorado, Boulder, and currently on leave as a Program Director at the NSF's CSR (Computer Systems Research) program. He holds roles as Site Co-Director of the NSF IUCRC Pervasive Personalized Intelligence Center and co-founded the Colorado Research Center for Democracy and Technology. Affiliations: Department of Computer Science, College of Engineering and Applied Sciences Professional Roles: NSF Program Director, Center Leadership Education: Ph.D. in Computer Science (University of Arizona), M.S. (Southern Illinois University), B.Tech. (IIT Bombay). Research focuses on distributed systems, edge computing, socio-technical systems for environmental justice, CyberSafety, and technology's role in democracy. Key projects include the C70 community impact study and smart agriculture systems. His work integrates technology with societal challenges, such as mitigating highway construction impacts and combating cyberbullying. Teaching includes courses on operating systems, distributed systems, and special topics like democracy through technology. Advised Ph.D. students Fei Hu and Jinpeng Miao. Professional activities include organizing conferences (e.g., DSN 2017, CyberSafety workshops) and serving on NSF panels.

Philip JohnsonView profile

Professor

• Quantum Computing
• Theoretical Physics
• Condensed Matter Physics
• +3 more

Philip Johnson is a Professor and Chair of the Department of Physics at American University (AU), where he has been since 2006. He also serves as Director of the Integrated Space Science and Technology Institute (ISSTI), supporting over 20 AU faculty and external partners like NASA's Goddard Space Flight Center. His research focuses on quantum computing, superconducting qubits, ultracold atoms, and effective interactions in few-body systems. He holds a PhD in Theoretical Physics from the University of Maryland and completed postdoctoral work at NIST and the University of Maryland's superconducting quantum computing group. His academic leadership roles include Associate Dean of Research for AU's College of Arts and Sciences and service on the American Physical Society's council. His research explores quantum control, nonequilibrium dynamics, and applications in quantum sensing and metrology. Key areas include ultracold bosons in optical lattices, nonlocal interactions, and hybrid machine learning approaches for quantum systems. He collaborates with institutions like the Joint Quantum Institute and Johns Hopkins Applied Physics Laboratory. Johnson's recent work advances theoretical frameworks for few-atom systems and superconducting qubits, with publications addressing topics like topological properties of interactions and correlations in quantum systems. His contributions span experimental and theoretical physics, emphasizing interdisciplinary applications in space science and technology through ISSTI.

Michael DeWeeseView profile

Associate Professor

• Nonequilibrium Statistical Mechanics
• Machine Learning Theory
• Systems Neuroscience
• +3 more

Michael DeWeese is an Associate Professor of Physics and Neuroscience at the University of California, Berkeley. His research spans nonequilibrium statistical mechanics, machine learning theory, and systems neuroscience. He holds a BA in Physics from UC Santa Cruz (1988) and a PhD in Physics from Princeton (1995). Before joining UC Berkeley in 2007, he held postdoctoral positions at the Salk Institute and Cold Spring Harbor Laboratory. His work integrates principles from physics, neuroscience, and machine learning to address fundamental questions in theoretical and experimental biology, computation, and statistical mechanics. DeWeese Lab Website provides further details on ongoing projects and collaborations. Education: BA in Physics, UC Santa Cruz (1988) PhD in Physics, Princeton University (1995) Research Interests: Nonequilibrium Statistical Mechanics: Focuses on thermodynamic optimization, active matter, and non-equilibrium processes. Machine Learning Theory: Develops first-principles models to explain neural network performance and efficient algorithms for probabilistic models. Systems Neuroscience: Uses biologically inspired models to understand neural coding, sensory processing, and computational roles of neural networks. Advising & Grants: While no formal student advisees are listed, his lab actively collaborates across disciplines. Funding sources are not explicitly mentioned but likely involve NSF, NIH, or DOE grants based on research themes. His work on quantum control and neural networks suggests potential ties to interdisciplinary funding initiatives. Labs & Teams: Directs the DeWeese Lab, which bridges physics, neuroscience, and machine learning. Collaborations include institutions like the Helen Wills Neuroscience Institute (UC Berkeley).

Richard ScalettarView profile

Professor

• Condensed Matter Physics
• Quantum Phase Transitions
• Superconductivity
• +5 more

Distinguished Professor of Physics at the University of California Davis College of Letters and Science since 1989. Primary affiliation with the Department of Physics, with significant cross-disciplinary collaborations in Applied Mathematics and Computer Science through NSF and DOE grants. Research focuses on quantum many-body phenomena in condensed matter systems and ultracold atomic gases. Expertise spans magnetism, superconductivity, metal-insulator transitions, and quantum phase transitions. Pioneers advanced Quantum Monte Carlo simulation techniques, particularly determinant quantum Monte Carlo for Hubbard and electron-phonon models. Current work investigates spatial inhomogeneities in quantum phases and strong interparticle interactions. Recent publications reveal growing integration of machine learning with quantum simulation. Research trends indicate deepening exploration of SU(N) symmetric systems, flat-band quasicrystals, photonic quantum simulators, and neural quantum states. Increasing emphasis on interdisciplinary approaches combining condensed matter theory, quantum information science, and computational mathematics. Key methodological focus remains on overcoming fermionic sign problems and developing scalable numerical algorithms. Principal investigator for major grants from the National Science Foundation (NSF), Department of Energy (DOE), Office of Naval Research (ONR), and Defense Advanced Research Projects Agency (DARPA). Significant funding through NSF Information Technology Research and DOE Scientific Discovery through Advanced Computing Programs for quantum simulation algorithm development.

• Quantum Computing
• Quantum Gases
• Atomic Physics
• +4 more

Servaas Kokkelmans is a Full Professor and leader of the Coherence and Quantum Technology group in the Department of Applied Physics at Eindhoven University of Technology (TU/e). He is also the scientific director of the Center for Quantum Materials and Technology Eindhoven (QT/e) and co-founder of the Eindhoven Hendrik Casimir Institute. His research focuses on strongly-interacting quantum gases, neutral atom quantum computing, and ultracold atomic systems. He holds a PhD from TU/e (2000) after postdoctoral work at JILA (USA) and Laboratoire Kastler Brossel (France). Notable roles include coordinating action line 1 (Research & Innovation) at Quantum Delta NL and leading the KAT-1 project on Rydberg atom quantum computing. Awards include the NWO Vici (2015) and Vidi (2003) grants. Education: MSc (1996) and PhD (2000) in Physics from TU/e. Career progression: Assistant Professor (2004–2017), Associate Professor (2017–2023), Full Professor (2023–present). Key research areas include quantum computing architectures, ultracold collisions, Feshbach resonances, and Efimov physics. Active in teaching courses like Quantum Optics and Quantum Information, and Hybrid Quantum Computing. Research highlights include foundational work on quantum control via electric/magnetic fields and contributions to quantum simulation platforms. His work spans experimental and theoretical studies, with over 249 publications and 51 supervised works. Collaborations involve institutions globally. Media engagements include interviews on quantum technology advancements and leadership roles in national quantum initiatives.

Hadi SalmasianView profile

Professor

• Lie Groups
• Representation Theory
• Algebra
• +4 more

Hadi Salmasian is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa, Faculty of Science. He holds a PhD from Yale University and specializes in Lie groups, Lie algebra representations, and related areas of algebra and mathematical physics. Education: PhD in Mathematics, Yale University Research Interests: Dr. Salmasian's research focuses on representation theory of Lie groups and Lie superalgebras, quantum groups, invariant theory, and applications to mathematical physics. His work explores topics such as Capelli eigenvalue problems, spherical functions on supergroups, and polynomiality of faithful dimensions for nilpotent groups. Recent Research Trends: His publications emphasize the interplay between algebraic structures (e.g., Weyl algebras, quantum homogeneous spaces) and representation-theoretic techniques, with applications to quantum computing and cryptography. Notable themes include classification of dual pairs in Lie supergroups and development of hash functions over finite groups. Awards: No awards explicitly mentioned in the provided texts. Advising & Grants: Supervised students include Manal Al-Zahrani, Mengyuan Cao (co-supervised with Monica Nevins), Dene Lepine, and Mitra Mansoori. Postdoctoral researchers include Ali Assem Mahmoud (co-supervised with Anne Broadbent and Monica Nevins). Research activities involve collaboration with groups in algebra and Lie theory. Labs/Teams: Active in research groups focused on algebra, Lie theory, and representation theory within the Department of Mathematics and Statistics at the University of Ottawa.

Alan EdelmanView profile

Professor

• Julia Programming
• High Performance Computing
• Linear Algebra
• +3 more

Alan Edelman is a Professor at the Massachusetts Institute of Technology (MIT), renowned for his work in high-performance computing, linear algebra, and random matrix theory. He is a co-creator of the Julia programming language, which emphasizes efficiency and versatility for scientific computing. His research integrates mathematics and computer science, focusing on numerical methods, parallel computing, and applications in fields like quantum control and climate modeling. Edelman leads projects such as Oceananigans.jl for geophysical fluid dynamics and Circuitscape for connectivity analysis in conservation biology. His academic contributions span theoretical advancements in matrix theory and practical implementations of computational tools. Edelman collaborates across disciplines, bridging scientific computing with machine learning and quantum physics. His work on differentiable programming frameworks and GPU acceleration has impacted both academic research and industry applications. Key Projects: Julia programming language, Oceananigans.jl, Circuitscape Research Themes: High-performance computing, numerical linear algebra, random matrices, scientific machine learning Affiliations: MIT’s Theory of Computation Community of Research, PI of multiple NSF-funded projects Edelman’s recent work emphasizes interdisciplinary applications, including climate policy modeling and automated materials discovery. His publications reflect a blend of foundational mathematics and cutting-edge computational techniques.