Find Academic Supervisors

• Particle Physics
• Experimental Physics
• Neutrino Physics
• +5 more

Dr. Michal Rigan is a Research Fellow in the Department of Physics and Astronomy at the University of Sussex, within the School of Mathematical and Physical Sciences. His research focuses on experimental particle physics, particularly with the DUNE (Deep Underground Neutrino Experiment) and previously with the SNO+ experiment. Dr. Rigan completed his PhD in Physics at the University of Sussex, where he worked on the SNO+ experiment, focusing on calibration systems and developing a burst trigger system for supernova detection. Before his PhD, he earned an MSci in Particle Physics and Physical Chemistry from University College London, where his master's thesis involved work on the SHiP experiment in collaboration with CERN. His research interests span neutrino physics , supernova detection , liquid argon detector technology , and trigger systems development . Dr. Rigan has made significant contributions to the field through his work on calibration systems, supernova sensitivity analysis, and physics trigger implementation for large-scale particle physics experiments. His recent publications (2023-2025) demonstrate a strong focus on the DUNE experiment, with particular emphasis on neutrino interaction vertex reconstruction using deep learning, supernova pointing capabilities, detector technology development, and data analysis techniques for liquid argon time projection chambers. His work shows a progression from detector calibration and supernova detection in SNO+ to more advanced physics trigger systems and analysis techniques in DUNE. Dr. Rigan has received recognition through citations for his work, with several publications accumulating significant citation counts (up to 25 citations for the DUNE far detector vertical drift technology paper). His research has been highlighted by various platforms including news outlets, social media, and academic readership services. As an educator, Dr. Rigan teaches C++ programming to students in the School of Mathematical and Physical Sciences. His background as a full-stack developer during a gap year before his PhD provides him with practical industry experience that informs his teaching approach. He is actively involved in mentoring and would be an excellent supervisor for students interested in experimental particle physics and computational methods. Dr. Rigan's laboratory work is primarily associated with the DUNE experiment ( https://www.dunescience.org/ ), where he contributes to the development of physics trigger systems and supernova detection capabilities. His work involves close collaboration with international research teams working on this major particle physics project.

Ralph ErnstorferView profile

Professor

• Ultrafast Phenomena
• Condensed Matter Physics
• Materials Science
• +4 more

Prof. Dr. Ralph Ernstorfer is a leading physicist holding dual appointments at the Technical University Berlin (Professor, Institute for Optics and Atomic Physics) and the Fritz Haber Institute of the Max Planck Society (Group Leader, Department of Physical Chemistry). His career includes tenure as a Max Planck Research Group leader (2010–2017) and W2 scientist (2017–2021) at FHI. He specializes in ultrafast dynamics of quantum materials, with affiliations spanning TU Berlin, Max Planck Institutes, and international collaborations. His research explores electron-phonon interactions, topological materials, and surface dynamics using advanced spectroscopic methods. Key themes include: Ultrafast electron and lattice dynamics in 2D systems (e.g., bismuthene, black phosphorus) Singlet fission mechanisms in organic semiconductors Non-equilibrium phenomena in ferromagnets and ferroelectrics Development of time-resolved photoemission techniques Recent publications (2019–2025) demonstrate prolific output in high-impact journals, focusing on momentum-resolved spectroscopy, phonon scattering, and computational materials science. His lab pioneers methodologies for studying energy flow in solids at femtosecond timescales. Prof. Ernstorfer advises researchers at the Fritz Haber Institute and TU Berlin, though specific student names are not listed. His group maintains active instrumentation development for ultrafast experiments, including momentum microscopes and spectral analysis tools.

• Analytical Chemistry
• Food Safety
• Environmental Contaminants
• +6 more

Professor Roberto Romero-González is a distinguished researcher in the Department of Chemistry and Physics at the University of Almería, Spain. With an extensive publication record of 257 articles and an h-index of 49 (Scopus), he leads the Analytical Chemistry of Contaminants Research Group and serves as Principal Investigator on multiple national research projects. His research focuses on analytical chemistry with emphasis on food safety and environmental contaminants. Key areas include pesticide analysis, plastic additives determination, natural deep eutectic solvents for green extraction, biopesticide residues, and food authentication. He has developed innovative methodologies using high-resolution mass spectrometry and chromatographic techniques for detecting contaminants in complex matrices. Professor Romero-González's recent publication trends show a strong focus on emerging contaminants, particularly non-phthalate plastic additives in environmental and food samples, and the application of natural deep eutectic solvents for sustainable analytical methodologies. His work spans food safety, environmental monitoring, and method development across multiple high-impact journals. As an educator and mentor, he has supervised numerous PhD and Master's students, with at least 9 theses listing him as director or co-director. His research group provides training in advanced analytical techniques and contributes to solving real-world problems in food safety and environmental contamination. His current research is supported by the project 'Estrategias analíticas para el control de contaminantes emergentes derivados de plásticos en suelos agrícolas' (2023-2026), continuing his long-standing commitment to developing analytical solutions for emerging contaminants in agricultural and food systems.

• Machine Learning
• Artificial Intelligence
• Big Data Analysis
• +3 more

Artem Sergeevich Ryzhikov is an Associate Professor and Research Fellow at the National Research University Higher School of Economics (HSE), working within the Faculty of Computer Science. He is affiliated with both the Institute of Artificial Intelligence and Digital Sciences and the Department of Big Data and Information Retrieval, where he leads research in the Research and Educational Laboratory of Big Data Analysis Methods. 2024: Candidate of Physical and Mathematical Sciences, National Research University Higher School of Economics 2018: Master's degree in Applied Mathematics and Computer Science, National Research University Higher School of Economics 2015: Bachelor's degree in Physics, Novosibirsk National Research State University Dr. Ryzhikov's research spans the intersection of machine learning, artificial intelligence, and high-energy physics. His work focuses on developing and applying advanced computational methods to solve complex problems in particle physics, particularly in the areas of anomaly detection, detector calibration, and analysis of large-scale experimental data from facilities like LHCb. He has made significant contributions to amplitude analyses of B-meson decays, studies of CP violation, and applications of generative models in physics instrumentation. His interdisciplinary approach bridges theoretical physics with cutting-edge machine learning techniques, enabling novel insights into fundamental particle interactions and detector performance optimization. Analysis of Dr. Ryzhikov's recent publications reveals a strong focus on applying machine learning techniques to particle physics problems, particularly in detector calibration and data analysis. His work spans both theoretical developments in amplitude analysis and practical applications of deep learning for instrumentation challenges. The research demonstrates increasing integration of AI methods with traditional physics analysis techniques, reflecting broader trends in the field. Letter of thanks from the Vice-Rector of HSE (September 2022) Bonus for publication in international peer-reviewed scientific journal (2021-2022, 2022-2023) Bonus for regular publications in international peer-reviewed scientific journals (2024-2029) Dr. Ryzhikov supervises graduate students working at the intersection of computer science and physics. His research is supported by institutional funding from HSE and likely benefits from collaborations with major physics experiments like LHCb at CERN. He regularly publishes in top physics and computer science journals, demonstrating the impact and recognition of his interdisciplinary work. Dr. Ryzhikov leads research activities within the Research and Educational Laboratory of Big Data Analysis Methods, where his team develops novel computational approaches for physics data analysis. The laboratory serves as a hub for interdisciplinary collaboration between computer scientists and physicists, fostering innovation in both fields through the application of advanced data science techniques to fundamental research questions.

• Deep Learning
• Computer Vision
• Generative Models
• +3 more

Foma Aleksandrovich Shipilov is a Lecturer and Research Intern at the National Research University Higher School of Economics (HSE), working within the Faculty of Computer Science, specifically in the Department of Big Data and Information Retrieval and the Institute of Artificial Intelligence and Digital Sciences. He began his tenure at HSE in 2025 and is actively involved in both teaching and research activities. Shipilov holds a Master's degree in "Applied Mathematics and Computer Science" from HSE (2025) and attended the Summer School on Machine Learning in Bioinformatics in 2022. His educational background has prepared him for his current roles in academia and research. His primary research interests include Deep Learning, Computer Vision, and Generative Models, with applications extending to Physics and Particle Detection systems. Shipilov's work demonstrates the interdisciplinary nature of modern machine learning applications, bridging computer science with physics research. His publications focus on applying machine learning techniques to improve particle detector systems, particularly focusing aerogel detectors used in high-energy physics experiments. Letter of gratitude from the Senior Director for Research and Development at HSE (August 2024) Ilya Segalovich Scholarship (2022) Grant of the President of the Russian Federation (2019) Shipilov teaches courses including "Introduction to Deep Learning" and "Deep Learning 1" for undergraduate programs at HSE across multiple disciplines including Applied Mathematics and Computer Science, Psychology, Public Administration, Sociology, Political Science, and Economics. Previously, he served as a Teaching Assistant for Discrete Mathematics, Algorithms and Data Structures, and Deep Learning courses. His industry experience includes a position as Assistant Engineer at Huawei Noah's Ark Lab (2020-2021), where he worked on deep learning applications for video remastering and photographic super-resolution. Shipilov is affiliated with the Research and Educational Laboratory of Big Data Analysis Methods and participates in the annual machine learning seminars held at the Voronovo Educational Center, which facilitate networking and knowledge exchange among students, faculty, and research staff.

• Scientific Metadata
• Research Data Management
• FAIR Principles Implementation
• +2 more

Dr. A. K. Mistry is a Researcher at GSI Helmholtzzentrum für Schwerionenforschung GmbH in Darmstadt, Germany, specializing in scientific metadata and data management for nuclear physics experiments. They lead significant initiatives including NAPMIX (Nuclear, Astro, and Particle Metadata Integration for eXperiments) and contribute to HELPMI (Helmholtz Metadata Collaboration Initiative for Plasma and Laser-Plasma Physics). Dr. Mistry's research focuses on developing metadata frameworks that implement FAIR principles (Findability, Accessibility, Interoperability, and Reusability) for physics research data. Their work bridges nuclear physics experimentation with modern data science approaches to enable better data sharing and reuse across the international physics community. Recent projects include developing a Django & React web application for metadata generation and creating cross-domain metadata schemas that work across traditionally separate physics domains. The research output shows a strong trend toward addressing data management challenges in nuclear physics, with an emphasis on creating practical tools and standards that researchers can implement in their daily work. The publications span both theoretical framework development and practical implementation of metadata solutions for specific experimental setups. Dr. Mistry's scientific contributions are primarily in the development of research data infrastructure rather than traditional physics discoveries, positioning them at the forefront of the data science revolution in physics research. They are actively involved in mentoring through research projects and collaborations, providing students with opportunities to work at the intersection of physics and data science. Their work on metadata standards and data management tools represents an increasingly critical component of modern scientific research infrastructure.

• Nuclear Fusion
• Plasma Physics
• Radiation Detection
• +4 more

Gabriel Crosses is a Researcher in the Department of Physics "Giuseppe Occhialini" at the University of Milano-Bicocca, specializing in nuclear fusion diagnostics and radiation detection systems for tokamak reactors. His work focuses on developing advanced diagnostic tools essential for measuring and understanding plasma behavior in fusion experiments. Dr. Crosses' research interests center on radiation detection for fusion applications, with particular expertise in gamma-ray and neutron spectroscopy. He develops diagnostic systems including scintillation detectors, semiconductor-based neutron sensors, and proton recoil spectrometers. His work addresses critical challenges in measuring fusion power output, particularly for next-generation devices like ITER. Recent research incorporates machine learning techniques to improve diagnostic accuracy in complex fusion environments. His publication record shows consistent contributions to the field, with numerous articles in 2025-2026 focusing on detector development, plasma diagnostics, and analysis methods for major fusion facilities including SPARC, ITER, MAST-U, and JET. The research spans both theoretical modeling and experimental validation of diagnostic systems. Dr. Crosses collaborates extensively with international fusion research teams across multiple facilities. His work supports the development of reliable diagnostic tools necessary for advancing fusion energy research toward practical implementation.

• Nuclear Fusion
• Plasma Physics
• Neutron Spectroscopy
• +4 more

Giuseppe Gorini is a Professor in the Department of Physics "Giuseppe Occhialini" at the University of Milan-Bicocca, specializing in nuclear fusion diagnostics and plasma physics. His research primarily focuses on developing and implementing advanced diagnostic techniques for measuring fusion power in tokamak devices through neutron and gamma-ray spectroscopy. Professor Gorini's research interests center on fusion plasma diagnostics, with particular expertise in gamma-ray spectroscopy for fusion power determination, neutron detection systems, and radiation-hard detector development. His work addresses critical challenges in magnetic confinement fusion, including accurate measurement of fusion reactions, plasma behavior analysis during disruptions, and characterization of detector technologies for harsh fusion environments. His research has significant implications for the success of major fusion projects including ITER, SPARC, and other international tokamak experiments. His recent publications demonstrate a strong focus on applying cutting-edge diagnostic approaches to solve fundamental challenges in fusion research. The work spans from fundamental detector characterization to complex plasma physics analysis, with an increasing incorporation of machine learning techniques for data analysis. His research consistently addresses the critical need for accurate, reliable diagnostics in the path toward practical fusion energy. Professor Gorini actively collaborates with major fusion research institutions worldwide, contributing his diagnostic expertise to experiments at facilities including JET, MAST-U, TCV, and SPARC. His work on gamma-ray spectroscopy for fusion power measurement represents a critical capability needed for the success of ITER and future fusion power plants.

Barry PointonView profile

Adjunct Professor

• Experimental neutrino physics
• Water Cherenkov detectors
• Super-k
• +6 more

Barry Pointon serves as an Adjunct Professor affiliated with the British Columbia Institute of Technology (BCIT) and TRIUMF, Canada's particle accelerator centre. His academic position connects him to experimental physics research infrastructure in Canada. His research spans experimental neutrino physics using water Cherenkov detectors including Super-kamiokande and Hyper-kamiokande projects, supernova neutrino detection through the SNEWS network, machine learning applications in physics, nuclear medicine research, and interdisciplinary work examining the relationship between science and religion. As an adjunct faculty member, he maintains professional connections between BCIT's academic programs and TRIUMF's research facilities, contributing specialized expertise in particle physics instrumentation and data analysis.

• Experimental Physics
• Materials Science
• Data Analysis
• +4 more

Sebastiano Vasi is a Researcher with fixed-term contract in the Department of Mathematical and Computer Sciences, Physical Sciences, and Earth Sciences at the University of Messina. His work focuses on experimental physics of matter and applications, with particular emphasis on materials science, data analysis, and physics education. His educational background includes national scientific qualifications for associate professor in two sectors: Experimental Physics of Matter (02/B1) valid from December 6, 2023 to December 6, 2034, and Applied Physics, Teaching and History of Physics (02/D1) valid from September 29, 2023 to September 29, 2034. Dr. Vasi's research interests span experimental physics, materials science, data analysis, and physics education. His work includes studies on 2D materials, ion beam physics, metabolomics, and innovative educational approaches using digital tools like Minecraft for physics education. He has made significant contributions to understanding surface modifications, nanomaterials synthesis, and environmental monitoring techniques. His research demonstrates a strong interdisciplinary approach connecting physics with practical applications in environmental science, education, and materials engineering. Start Cup Unime 2023 Finanziamento Attività di Base della Ricerca di Ateneo (FFABR Unime) 2022 Premio SIF - Società Italiana di Fisica 2020 Migliore Comunicazione Congresso Nazionale della Società Italiana di Fisica 2017 Dr. Vasi teaches multiple courses including Data Analysis, Physics, and Experimental Physics across various degree programs at the University of Messina. He is actively involved in public engagement activities, particularly in school outreach and science communication initiatives, organizing events such as MEDNIGHT (Mediterranean Night of Researchers) and Pint of Science to promote physics education to broader audiences.

Matthias Dietrich FrontzekView profile

Researcher

• Magnetism
• Neutron Scattering
• Quantum Materials
• +2 more

Matthias Dietrich Frontzek serves as an Instrument Scientist at the WAND² diffractometer within Oak Ridge National Laboratory's High Flux Isotope Reactor (HFIR). He concurrently leads the HFIR Beryllium Reflector Replacement project as matrixed scientist, chairs the high-temperature-sample-environment committee, and participates in the US-Japan cooperative neutron scattering research program. His academic foundation includes a Diploma (MS) in Physics from Technical University Dresden (2003) and a PhD in Physics from the same institution (2009), where his thesis on R2PdSi3 magnetic properties utilized neutron scattering at Helmholtz-Zentrum Berlin under Michael Loewenhaupt. Frontzek's research specializes in neutron scattering investigations of quantum magnetic phenomena, with emphasis on frustrated spin systems, topological materials, and rare-earth compounds. His work reveals intricate magnetic structures in kagome/hyperkagome lattices, Dirac/Weyl semimetals, and skyrmion-hosting materials, leveraging diffraction techniques to probe phase transitions and collective excitations at atomic scales. Recent publications demonstrate a concentrated focus on emergent quantum states where geometric frustration intersects with topological band structures. Key trends include van Hove singularity-driven spin density waves, skyrmion stabilization mechanisms in centrosymmetric materials, and quantum criticality in geometrically frustrated magnets, with neutron diffraction serving as the primary investigative tool across these diverse material systems. His scientific recognition includes: PSI Fellow at Paul Scherrer Institut Frontzek directs major infrastructure initiatives including the HFIR Beryllium Reflector Replacement project critical for reactor longevity and chairs the high-temperature-sample-environment committee supporting advanced user experiments. His international collaboration through the US-Japan program facilitates cross-continental neutron research, while instrument development efforts like ANDiE (Autonomous Neutron Diffraction Explorer) implement machine learning for real-time experimental optimization. Operating within ORNL's Powder Diffraction Group of the Diffraction Section, Frontzek manages the WAND² instrument user program that serves global researchers studying magnetic and structural properties of quantum materials through neutron powder diffraction techniques.