Find Academic Supervisors

• Particle Physics
• Astrophysics
• Dark Matter Detection
• +7 more

Anastassopoulos Vassilis is a Professor at the University of Patras, affiliated with the Department of Electronics and Computers within the School of Natural Sciences. His research spans particle physics, astrophysics, and applied technologies such as remote sensing and medical imaging. He leads experimental efforts in axion detection via the CAST helioscope and explores dark matter signatures in geophysical and astrophysical contexts. His work integrates cutting-edge techniques like neural networks for image processing and decision fusion algorithms for data analysis. Research interests focus on dark matter axions, solar physics, and interdisciplinary applications including land cover classification using SAR imagery, thermal intoxication detection, and super-resolution imaging. He contributes to collaborations such as the Proton EDM experiment and the pEDM project, aiming to uncover new physics beyond the Standard Model. His expertise bridges theoretical models with experimental validation, addressing questions in fundamental physics and applied engineering. Key areas include: particle physics experiments (axion searches, proton EDM), environmental monitoring via remote sensing (coastal change detection, ship identification), biomedical imaging (OCT analysis, thermal facial features), and algorithm development (neural networks, inverse problems). His work frequently employs advanced computational methods and interdisciplinary approaches.

Enrique Martinez SaezView profile

Associate Professor

• Microstructure Evolution
• Thermodynamics and Kinetics of Alloys
• Advanced Manufacturing
• +4 more

Dr. Enrique Martinez Saez is an Associate Professor at Clemson University, specializing in computational materials science with a focus on microstructural evolution under extreme conditions. He holds appointments in the College of Engineering, Computing and Applied Sciences (CECAS), where he conducts interdisciplinary research at the intersection of materials science, physics, and data analytics. Ph.D. (2008), Polytechnic University of Madrid M.S. (2003), Polytechnic University of Madrid B.S. (2000), Polytechnic University of Madrid His research explores: Defect kinetics in irradiated materials Thermodynamics of alloy systems Mechanical behavior of heterogeneous solids Machine learning applications in materials discovery Advanced manufacturing techniques Dislocation dynamics and plasticity mechanisms Recent publications demonstrate expertise in modeling radiation effects, phase transformations, and dislocation interactions using molecular dynamics and multiscale approaches. Notable simulations include studies on FeNiCrCoMn alloys, austenitic stainless steel crystallization, and machine learning-enhanced dislocation mobility. Scientific recognition includes: 2010: Outstanding Ph.D. Thesis at Polytechnic University of Madrid 2009: Juan de la Cierva Fellowship (Spain Ministry of Science) 2008: Cum Laude Ph.D. Thesis distinction

Wanwei WuView profile

Adjunct Assistant Professor

• Particle Physics
• Neutrino Physics
• Experimental Physics
• +3 more

Wanwei Wu is an Adjunct Assistant Professor in the Department of Physics & Astronomy at the University of Pittsburgh, affiliated with the Dietrich School. His research focuses on neutrino physics, particularly using liquid argon time projection chambers (LArTPCs) in experiments like MicroBooNE and DUNE . Key areas include neutrino-nucleus interactions, cross-section measurements, and detector development. He contributes to analyzing neutrino oscillations, supernova neutrinos, and novel particle physics searches. Wu's work at MicroBooNE involves studying charged-current interactions, pion production, and suppression effects in argon. For DUNE, he explores vertex reconstruction, computing infrastructure, and detector design for Phase II. His technical contributions span energy estimation algorithms, scintillation light analysis, and machine learning applications in neutrino event reconstruction. Recent projects include validating neutrino interaction models, demonstrating neutron identification in LArTPCs, and investigating sterile neutrino signals. His research bridges experimental methods with theoretical predictions, aiming to advance our understanding of fundamental particle physics phenomena.

Kristin PerssonView profile

Professor

• Materials Science
• Computational Materials Design
• Battery Materials
• +7 more

Kristin Persson is a Professor of Materials Science and Engineering at the University of California, Berkeley, where she also serves as Director of the Molecular Foundry. She is a Faculty Staff Scientist at Lawrence Berkeley National Laboratory, based in the Energy Sciences Area (ESA). Her research focuses on computational materials science for energy applications, leading the internationally recognized Persson Group and the Materials Project initiative. Professor Persson's research interests center on applying atomistic and first-principles computational methods coupled with high-performance computing and machine learning to advance materials for clean energy production and storage. Her group specializes in designing novel photocatalysts, multi-valent battery electrode materials, Li-ion battery electrode materials, polar materials, and liquid electrolytes. The Materials Project, which she directs, is a multi-national effort to compute the properties of all inorganic materials and provide this data and associated analysis algorithms free of charge, with the ultimate goal of drastically reducing the time needed to invent new materials for societal needs. Her recent publications reveal a strong trend toward data-driven materials discovery, with significant focus on battery materials (particularly zinc-ion and lithium-ion systems), sustainable materials design, and the development of computational frameworks for materials prediction. There's a clear emphasis on machine learning applications in materials science, as well as growing interest in circular economy approaches for materials and chemical recycling. Member of the National Academy of Engineers (NAE) 2024 Distinguished Scientist Fellow by the U.S. Department of Energy's Office of Science Foreign member of the Royal Swedish Academy of Sciences Fellow of the Materials Research Society Fellow of the American Physical Society (APS) Cyril Stanley Smith Award recipient Recognized by Web of Science as top 1% highly cited researcher Professor Persson has mentored numerous graduate students and postdocs, many of whom have received prestigious fellowships and awards. Her research is supported by significant grants from the Department of Energy, including through the Joint Center for Energy Storage Research (JCESR) and the Electrolyte Genome project. She has led several major computational initiatives that have been recognized with the Secretary of Energy Honor Award. The Persson Group operates within the Energy Sciences Area at Lawrence Berkeley National Laboratory, collaborating closely with the Molecular Foundry (which she directs) and maintaining strong connections with experimental groups at JCAP (Joint Center for Artificial Photosynthesis) and other DOE-funded centers. The group follows a public group handbook that emphasizes collaborative research practices and open science principles.

• Integrated Photonics
• Imaging Systems
• Medical Diagnostics
• +7 more

George Giakos is a Professor and Chair of the Department of Electrical and Computer Engineering at Manhattan College, NY, and Director of the Graduate Program. Previously, he served as a Professor at the University of Akron for over 20 years, directing imaging technologies and surveillance laboratories. His research focuses on imaging systems, photonics, medical diagnostics, and defense applications. Giakos holds a PhD from Marquette University and has authored over 200 peer-reviewed articles, 16 patents, and received prestigious awards including the IEEE Fellow distinction and the 2014 IEEE-USA Professional Achievement Award. Education Ph.D., Electrical & Computer Engineering, Marquette University M.S., Nuclear Physics, Ohio University Postgraduate Diploma, Nuclear Instrumentation, University of Edinburgh Laurea, Applied Physics, University of Turin Research Interests Giakos pioneers advancements in imaging devices and systems, integrating photonics, nanotechnology, and bio-inspired techniques. His work spans medical diagnostics (e.g., cancer detection via polarimetric imaging), defense (space surveillance, autonomous systems), and semiconductor characterization. He emphasizes interdisciplinary innovation, bridging defense and bioscience through novel imaging paradigms. Articles Overview Recent work includes neuromorphic architectures for space IoT, adversarial machine learning in power grids, and interdisciplinary frameworks for systematic innovation. His research often combines AI with domain-specific applications, addressing challenges in healthcare site selection, cybersecurity, and quantum sensing. Awards IEEE Fellow (2014) ONR Distinguished Faculty Fellow (2005) AFRL Space Surveillance Fellowships (2007–2009) NASA Faculty Fellowships (2000, 2001, 2004) Advising & Grants Giakos has secured over $6M in grants as PI, collaborating with AFRL, NASA, and industry partners like Lockheed Martin. His leadership spans IEEE committees, including chairing the TC-19 Imaging Systems Technical Committee and organizing international conferences like IEEE International Conference on Imaging Systems and Techniques. Labs & Teams Directs Manhattan College’s imaging and surveillance laboratories, advancing technologies such as multifusion polarimetric detection and bio-inspired robotic vision systems. His team develops platforms like the AFRL Multifunctional Imaging Surveillance system for defense and medical applications.

Kevin StensonView profile

Professor

• Particle Physics
• Supersymmetry
• High-Energy Physics
• +3 more

Kevin Stenson is a Professor and Associate Chair for Graduate Studies in the Department of Physics at the University of Colorado Boulder. His research focuses on experimental particle physics, particularly within the CMS experiment at CERN's Large Hadron Collider (LHC). He leads investigations into supersymmetry (SUSY), exploring its implications for dark matter, the Higgs boson mass, and grand unified theories. His work involves analyzing high-energy proton-proton collision data to search for SUSY particles and test fundamental physics principles. Affiliations: CMS Collaboration, University of Colorado Boulder Physics Department Key Research Areas: Supersymmetry, Dark Matter, LHC Physics, Collider Experiments His research interests emphasize the search for supersymmetric particles in all-hadronic decay modes, leveraging the CMS detector's capabilities. He contributes to the CMS experiment's operational and technical advancements, including detector performance optimization and data analysis methodologies. Recent research trends include analyzing high-mass dilepton events, top squark searches, and novel machine learning techniques for background estimation. His work bridges theoretical models with experimental validation, addressing gaps in the Standard Model through precision measurements and new physics searches. Collaborations include the CMS global team at CERN and the US CMS group. He actively participates in detector upgrades for future LHC runs, ensuring readiness for higher luminosity conditions.

• Knowledge Management
• Uncertainty Management
• Belief Function Theory
• +4 more

BEN AYED Safa is a Researcher-Lecturer at CESI, affiliated with the LINEACT research group and the Department of Engineering and Numerical Tools. Their work focuses on knowledge management, uncertainty modeling under belief function theory, decision support systems, and predictive maintenance. They hold dual PhDs in Computer and Automatic Engineering (Artois University, France, 2020) and Computer Science applied to Management (University of Tunis, Tunisia, 2020), along with advanced degrees in Business Intelligence and Data Mining. Currently collaborating on the ANR SCOPES project (2022-2025), they teach modules in computer science, networks, and electrical engineering at undergraduate levels. Their research emphasizes CBR systems maintenance, evidential fusion methods, and AI-driven predictive analytics. Recent work includes advancements in collaborative perception modeling and uncertainty quantification in industrial systems. Education : PhD in Computer and Automatic Engineering (LGI2A, Artois University, 2020) PhD in Computer Science applied to Management (LARODEC, University of Tunis, 2020) Master of Research in Business Intelligence (Institut Supérieur de Gestion, Tunisia, 2017) Master in Data Mining & Knowledge Management (Polytech'Nantes, France, 2017) Bachelor in Computer Science applied to Management (Institut Supérieur de Gestion, Tunisia, 2015) Research Interests : Belief Function Theory applications in CBR systems Evidential clustering and policy design Predictive maintenance frameworks Machine learning for uncertainty quantification Information fusion in autonomous systems Key Contributions : Recent work bridges belief function theory with industrial AI challenges, including energy forecasting, turbofan prognosis, and ITER divertor modeling. Their methods enhance case-base maintenance through evidential integration, addressing dynamic uncertainty in real-world systems. Grants & Supervision : Participating in the ANR SCOPES project (2022-2025) and planning thesis supervision by 2025. Their research spans academic-industrial collaboration in PHM frameworks and smart building analytics.

Tim RupertView profile

Professor

• Nanocrystalline Metals and Alloys
• Grain Boundaries and Interfaces
• Defect Engineering
• +6 more

Tim Rupert is a Professor of Materials Science and Engineering and Director of the Hopkins Extreme Materials Institute (HEMI) at Johns Hopkins University's Whiting School of Engineering. His research focuses on nanocrystalline metals, defect engineering, and materials for extreme environments, aiming to enhance structural and energy technologies. Education: BS and MS in Mechanical Engineering (Johns Hopkins University, 2007), PhD in Materials Science & Engineering (MIT, 2007). His work integrates computational and experimental methods to study grain boundaries, defect phases, and interfacial phenomena. Recent breakthroughs include scalable processing of bulk nanocrystalline alloys and defect-driven strengthening mechanisms. Rupert co-founded Foundation Alloy to commercialize novel metal alloys. Awards: 2025 Brimacombe Medalist (TMS), ARPA-E CHADWICK Grant (2023). Grants/Leadership: Leads HEMI, directs the Nanoscale Mechanics & Materials Lab, and collaborates on initiatives like the DMREF program. His research spans nuclear energy, defense materials, and additive manufacturing. Labs/Teams: PI of the Nanoscale Mechanics & Materials Lab, which explores defect-dominated materials and interfaces using advanced characterization techniques.

• Hyperspectral Imaging
• Nuclear Fuel Inspection
• Corrosion Detection
• +7 more

Neil Cockbain is a Visiting Professor in the Department of Electronic and Electrical Engineering within the Faculty of Engineering at the University of Strathclyde, United Kingdom. His research focuses on advanced imaging techniques for nuclear and industrial applications. His primary research interests include Hyperspectral Imaging , Nuclear Fuel Inspection , Corrosion Detection , and Signal Processing for Remote Sensing . These areas are central to ensuring safety and reliability in nuclear material handling and storage environments. The recent publications highlight a strong trend in applying hyperspectral and multispectral imaging to detect corrosion, map salt concentrations, and inspect nuclear fuel—demonstrating a consistent focus on non-destructive evaluation using cutting-edge optical sensing technologies. The work integrates engineering principles with data science for real-world industrial monitoring solutions. Scientific Contributions: Active contributor to IEEE Sensors Journal and WHISPERS conference proceedings. Research aligns with UN Sustainable Development Goals in Industry, Innovation, and Infrastructure (SDG 9). Dr. Cockbain collaborates extensively with experts in nuclear engineering and signal processing. While specific grant details and student supervision are not disclosed, his co-authorship on multiple peer-reviewed works indicates ongoing research activity and technical leadership in imaging for nuclear applications.

• Artificial Intelligence
• Machine Learning
• Digital Signal Processing
• +7 more

Jaime Zabalza is a Lecturer and Strathclyde Chancellor's Fellow in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. He is actively affiliated with the Centre for Signal and Image Processing (CeSIP) and the Advanced Nuclear Research Centre (ANRC), where he contributes to cutting-edge research in artificial intelligence and signal processing for industrial applications, particularly in the nuclear sector. His research interests include Artificial Intelligence , Machine Learning , Digital Signal Processing , Hyperspectral Imaging , and Software Engineering , with applications focused on solving real-world industrial challenges. He has led and contributed to multiple research projects in collaboration with industrial partners and institutions such as the European Space Agency. The recent publications highlight a strong trend in applying AI and signal processing techniques to nuclear inspection, debris analysis, and hyperspectral imaging. These works span video denoising in high-radiation environments, classification of nuclear materials, and corrosion detection using advanced spectral and textural features—demonstrating a consistent focus on robust, real-world deployable AI tools for critical infrastructure. Strathclyde Global Talent Programme Chancellor's Fellowship Recipient Turing Network Development Award: Trailblazers Competition Recipient Lightning talk judges choice winner at Game Changers Innovation festival (Whitehaven, England) IEEE Brain Data Bank Challenges and Competitions Recipient (2018, 2017) IET V&I Best PhD Thesis Award (only one in UK per year) Jaime Zabalza has served as a PhD examiner and convener, and is involved in multiple funded research projects. He has secured grants from organizations such as the European Space Agency and industry partners, focusing on video analytics, debris removal, and AI-based navigation. He supervises student research and contributes to knowledge exchange initiatives. He is an active member of the Centre for Signal and Image Processing (CeSIP) and collaborates closely with the Advanced Nuclear Research Centre (ANRC). His work involves interdisciplinary teams and international partners, particularly in projects related to hyperspectral imaging, nuclear inspection, and autonomous systems.

Raghu RamanujanView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Automated Planning
• +4 more

Raghu Ramanujan is an Associate Professor of Mathematics and Computer Science at Davidson College , where he joined in 2012. He teaches across the computer science curriculum and supervises student research projects in Artificial Intelligence, automated planning, combinatorial search, and machine learning. Education Ph.D., M.S. in Computer Science from Cornell University B.S. in Electrical and Computer Engineering from Purdue University Ramanujan's research focuses on interdisciplinary applications of Artificial Intelligence, including automated planning, adversarial search, and machine learning techniques. His work often bridges theoretical AI with practical implementations in physics, detector modeling, and computational creativity. He has published extensively on point-cloud analysis, Monte Carlo tree search, and generative models. His recent publications emphasize machine learning for physics-based problems, particularly in detector simulation and jet physics, alongside advancements in adversarial search and reinforcement learning. Key trends include cross-domain applications of GANs, optimization in sparse tensor computation, and temporal dynamics modeling in reaction systems. Ramanujan mentors student research and integrates his expertise into curriculum development. While no formal awards are listed, his contributions to AI research and education are significant.

Yexiang XueView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Computational Science and Engineering
• +6 more

Yexiang Xue is an Assistant Professor in the Department of Computer Science at Purdue University, part of the College of Science. He joined Purdue in Fall 2018. His research focuses on integrating machine learning and probabilistic reasoning to enable optimal decision-making in high-dimensional, uncertain environments. Key areas include computational sustainability, materials science, robotics, and medical AI. Education: PhD in Computer Science, Cornell University (2018), advised by Carla Gomes and Bart Selman. B.Sc. in Computer Science, Peking University, China (2011). Research Interests: Xue develops cross-cutting computational methods for scientific discovery, constraint-embedded machine learning, and AI-driven sustainability. His work spans symbolic regression, probabilistic models, and applications in robotics, healthcare, and materials science. Recent efforts include end-to-end physics model discovery and integrating decision diagrams into neural networks. Awards: NSF CAREER Award (2024). IAAI Innovative Application Award (2017) for Phase-Mapper AI platform in materials discovery. Advising & Grants: Mentored multiple undergraduates (e.g., Luming Tang, Runzhe Yang) pursuing graduate studies. His NSF CAREER grant supports AI-driven scientific discovery. Teaching & Service: Taught courses like Statistical Machine Learning (CS 578). Serves on AAAI, UAI, and IJCAI program committees. Media coverage includes NSF News, Science, and MIT Technology Review.

Quentin BuatView profile

Assistant Professor

• Nuclear & Particle Experiment
• Particle Physics
• High Energy Physics
• +3 more

Quentin Buat is an Assistant Professor in the Department of Physics at the University of Washington, part of the College of Arts & Sciences. His research focuses on experimental particle physics, particularly exploring phenomena beyond the Standard Model using the ATLAS experiment at CERN. He specializes in tau lepton measurements, electroweak sector analysis, and developing machine learning tools for particle detection. He is also involved in designing the PIONEER experiment to study rare pion decays. Dr. Buat holds a Ph.D. in Physics from Université de Grenoble, France (2013). Before joining UW, he was a postdoctoral fellow at Stony Brook University and a CERN Research Fellow. His professional activities include presenting talks on topics such as tau lepton applications at the LHC, ATLAS Higgs boson studies, and PIONEER's experimental goals. His research interests span particle physics instrumentation, data analysis methodologies, and interdisciplinary applications of machine learning in experimental physics.

• Radiation Detection
• Data Science
• Gamma-ray Spectroscopy
• +7 more

Clair Julia Sullivan is an Adjunct Assistant Professor in the Department of Nuclear, Plasma & Radiological Engineering at the University of Illinois Urbana-Champaign, affiliated with the Grainger College of Engineering. She holds additional affiliations with the National Center for Supercomputing Applications (NCSA), the Department of Informatics, and the Department of Computer Science and Engineering. Sullivan earned her PhD in Nuclear Engineering from the University of Michigan in 2002, preceded by dual BS degrees in Physics and Astronomy (1997) and an MS in Nuclear Engineering (1998). Her research focuses on radiation detection technologies, data science applications in nuclear security, isotope identification algorithms, and big data analytics for sensor networks. She has contributed to critical areas like nuclear nonproliferation, sensor network optimization, and novel neutron detector materials. Notable awards include the DARPA Young Faculty Award (2014) and the Mary Jane Oestmann Professional Women’s Achievement Award (2015). Sullivan serves as an Associate Editor for the IEEE Transactions on Nuclear Sciences and holds teaching honors including Excellence in Undergraduate Teaching (2015, 2013). Her interdisciplinary work bridges nuclear engineering with computer science, contributing to advancements in automated radiation analysis systems.

Daniel L.J. ThorekView profile

Associate Professor

• Precision medicine
• Translational drug development
• Therapeutic and diagnostic agents
• +2 more

Dr. Daniel L.J. Thorek is an Associate Professor of Radiology and Biomedical Engineering at Washington University School of Medicine, where he co-leads the Oncologic Imaging Program at the Siteman Cancer Center. He directs the Molecular Radiotherapy + Imaging Laboratory within the Precision Radiotheranostics Translation Center, focusing on developing diagnostic and targeted therapies for oncology, inflammation, and infectious diseases using computational, in vitro, and preclinical approaches. His research integrates engineering principles in biotechnology and radiology to advance quantitative imaging and targeted therapeutics. Key areas include radiopharmaceutical therapy optimization, molecular imaging probe development, and nanoparticle-based diagnostics. His lab employs machine learning and computational modeling to understand disease mechanisms and improve treatment outcomes. Dr. Thorek's publications emphasize innovative approaches in radioimmunotherapy, theranostics, and molecular imaging. Recent work demonstrates strong focus on prostate cancer treatment optimization, alpha-particle therapies, and infectious disease imaging. Major scientific recognition includes: 2024 Distinguished Investigator Award 2021 Ocean Imagination Award Steven Wynn Young Investigator Award (2013-2017) Society of Nuclear Medicine Postdoctoral Scholar Award He currently leads multiple federally funded projects, including a $1.25M DOE grant for hydrogen production research and an NSF grant for nanocrystal synthesis. His laboratory collaborates with national laboratories and international institutions to translate discoveries into clinical applications.