Martin Frank is an Associate Professor in the Department of Physics at the University of South Alabama, located in Mobile, AL. He holds a Ph.D. from Baylor University and a B.S. from Emory University. His research focuses on experimental high-energy particle physics, particularly in searches for exotic particles and phenomena using the NOvA and Mu2e experiments at Fermilab. Key areas of investigation include magnetic monopole detection, neutrino oscillations, and advanced detector technologies. Education: Ph.D., Baylor University B.S., Emory University Frank's work with the NOvA collaboration has produced significant results in neutrino physics, including groundbreaking studies on three-flavor oscillation parameters and magnetic monopole constraints. His technical expertise spans detector design optimization, data analysis methodologies, and cosmic ray monitoring systems. Recent publications highlight innovations in photoelectron yield measurements and seasonal variations in atmospheric muon events. Labs/Teams: Active member of the NOvA and Mu2e collaborations at Fermilab.
Nicolas Rubido Obrer is a Lecturer in the Department of Physics at the School of Natural and Computing Sciences, University of Aberdeen, UK. He is affiliated with the Institute for Complex Systems and Mathematical Biology (ICSMB) and previously served as an Adjunct Professor at the Physics Institute, Universidad de la República (UdelaR), Uruguay, and as a Research Fellow at the Aberdeen Biomedical Imaging Centre (ABIC). He earned his PhD in Physics from the University of Aberdeen in 2014, with a thesis on energy transmission and synchronization in complex networks. PhD in Physics – University of Aberdeen (2014) MSc in Physics – Universidad de la República (2010) BSc in Physics – Universidad de la República (2008) His research focuses on complex systems, employing network theory, dynamical systems, numerical modeling, and data analysis to study emergent behaviors such as synchronization and chaos. He applies these methods across interdisciplinary domains including brain networks (Alzheimer’s disease, sleep-wake cycles), climate modeling, and power-grid stability. He is particularly interested in network inference and reverse engineering system connectivity from observational data. The recent trend in his publications reflects a strong emphasis on interdisciplinary neuroscience, with high-impact contributions in Nature Medicine , NeuroImage , and Scientific Reports , focusing on brain aging, neurodegeneration, and neuroimaging. His work also spans climate dynamics and foundational network theory, demonstrating both applied and theoretical depth. His scientific awards include the prestigious Springer Theses Award (2015) and the Young Researchers Award at Dynamics Days Europe (2016), as well as the SUPA Studentship Prize (2011). Nicolas supervises PhD and MSc students in Physics, Mathematics, and Applied Health Sciences, and is actively involved in international collaborations with institutions in Uruguay, Brazil, Spain, Argentina, and the UK. He has led and contributed to projects on EEG-based biomarkers for aging, functional ultrasound imaging, and climate forecasting. His research integrates theoretical modeling with experimental and clinical data, advancing methodologies in network science and complex systems analysis. He is a member of the Non-Linear Physics group at UdelaR and associated with the Laboratory of Instabilities in Fluids. His ongoing work includes developing data-driven models for complex systems and exploring the role of network structure in synchronization and information transmission.
Rodica Georgeta Mihai is an Associate Professor in the Department of Informatics at the University of Bergen, Norway. She is also affiliated with NORCE Norwegian Research Centre AS, where she contributes to advanced research in drilling automation and digital well technologies. Her work bridges theoretical computer science and practical engineering applications in the energy sector. Position: Associate Professor Institution: University of Bergen Department: Department of Informatics Affiliation: NORCE Norwegian Research Centre AS Email: rodica.g.mihai@uib.no Phone: +4755584350 Her research interests lie at the intersection of computer science and petroleum engineering, focusing on drilling automation, autonomous systems, verification and validation in distributed control environments, AI safety, and graph algorithms . She explores how intelligent systems can make safe and efficient decisions in complex, uncertain drilling conditions. The recent publications demonstrate a strong trend toward applied AI in drilling operations, particularly in autonomous decision-making, safe mode transitions, distributed control, and online verification of multi-vendor systems . Earlier works were rooted in theoretical computer science, especially graph searching and structural graph theory. The shift reflects a move from foundational algorithms to real-world deployment in energy systems. Rodica Mihai is actively involved in major research initiatives, including the DigiWells project, funded by the Research Council of Norway and industry partners such as Equinor and Aker BP. Her work emphasizes system reliability, safety, and interoperability in automated drilling environments. DigiWells: Digital Well Center for Value Creation, Competitiveness and Minimum Environmental Footprint Demonstration of Automated Drilling Process Control She advises on the design of safe and resilient automation systems, ensuring smooth human-machine transitions and robust operation under uncertainty. Her contributions are presented at leading conferences such as SPE/IADC and include guest lectures at institutions like BI Norwegian Business School.
Pep Mulet Mestre is a full Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València. He is a member of the ANIMS research group, focusing on Numerical Analysis, Images, Multiresolution, and Simulation. University: Universitat de València School: Faculty of Mathematics Department: Department of Mathematics Research Group: ANIMS His primary research interests include numerical analysis, partial differential equations, fluid mechanics, image processing, and high-order finite difference methods. He has made significant contributions to WENO schemes, total variation-based image restoration, and models for sedimentation and traffic flow. The 15 most recent publications highlight a consistent focus on high-order accurate numerical methods, particularly WENO and IMEX schemes, applied to conservation laws, image processing, and biological models. His work bridges theoretical numerical analysis with practical applications in fluid dynamics and epidemiology. He has collaborated extensively with researchers such as Raimund Bürger, Rosa Donat, Antonio Baeza, and David Zorío, resulting in over 60 indexed publications since 1996. Dr. Mulet earned his PhD from the Universitat de València in 1992 with a thesis on local cohomology and duality in non-commutative Gorenstein rings, supervised by Dr. A. Verschoren. He advises graduate students and leads research projects in numerical methods for PDEs and scientific computing, though specific advisees are not listed in the provided text. There is no mention of specific grants or scientific awards in the available information.
Nicolas Franco is a Lecturer in Mathematics at the University of Namur (2022–present) and scientific collaborator at Hasselt University's Data Science Institute. His expertise spans mathematical modeling of COVID-19, noncommutative geometry applications in physics, and quantum computing. Previously, he held postdoctoral positions at Hasselt University (2020–2022), University of Namur (2015–2022), Jagiellonian University (2014–2015), and Copernicus Center for Interdisciplinary Studies (2012–2014). Education Doctor of Science: Lorentzian approach to noncommutative geometry (University of Namur, 2011) Master's in Mathematics (University of Namur, 2006) Bachelor of Music: Piano & Chamber Music (Royal Conservatory of Mons, 2003) Research Focus Franco's research integrates mathematical physics with practical applications. His primary domains include: Epidemiological Modeling : Long-term COVID-19 forecasting for the European CDC and Belgian government Quantum Structures : Causality in noncommutative geometries, κ-Minkowski spacetime constraints Quantum Computing : Robust quantum machine learning, qubit-efficient optimization Publication Trends Recent works demonstrate a shift toward quantum computing applications (2021–2024), focusing on optimization robustness and security in quantum neural networks. Earlier foundational contributions (2011–2017) established frameworks for Lorentzian distance formulas and causal structures in noncommutative geometry. Awards & Recognition Namur Resident of the Year - Science Category (2020) Multiple Belgian Mathematical Olympiad prizes (1996–2001) International math competition awards (FFJM) Professional Activities Member: European COVID-19 Scenario Hub (ECDC), RESTORE Consortium Consultant: Belgian federal government pandemic response Reviewer: Physical Review Letters, PLOS Computational Biology, etc.
Friedemann Zenke is an Assistant Professor at the University of Basel and a Junior Group Leader at the Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland. His research lies at the intersection of computational neuroscience, machine learning, and neuromorphic engineering, focusing on modeling memory formation and information processing in neural networks. Assistant Professor, University of Basel (2022–present) Junior Group Leader, FMI (2019–present) SNSF Eccellenza Fellow (2022–2027) Education: PhD, School of Computer and Communication Sciences, EPF Lausanne, Switzerland (2014) Diplom in Physics, University of Bonn and Australian National University (2009) Postdoctoral Fellow, Stanford University (2015–2017) Sir Henry Wellcome Postdoctoral Fellow, University of Oxford (2017–2019) His research interests center on understanding how plasticity mechanisms—such as Hebbian, homeostatic, and predictive plasticity—enable learning and memory in biologically inspired neural networks. He develops computational models using spiking and rate-based networks, leveraging high-performance computing and machine learning tools. His work integrates theoretical analysis from dynamical systems and statistical physics with practical dimensionality reduction techniques to compare model outputs with experimental data. A major focus is on surrogate gradient methods for training non-differentiable spiking networks, enabling their application in neuromorphic hardware. The recent publications highlight a strong trend toward bridging theoretical neuroscience with practical AI and hardware applications. Key themes include credit assignment in spiking networks , energy-efficient neuromorphic learning , biologically plausible plasticity rules , and benchmarking frameworks for emerging neural models. His work increasingly emphasizes the co-design of algorithms and hardware for next-generation brain-inspired computing systems. Scientific Awards: SNSF Eccellenza Fellowship (2022–2027) Wellcome Trust Postdoctoral Fellowship (2016–2019) Swiss National Science Foundation Postdoctoral Fellowship (2015–2016) Teaching Award, EPFL (2012) Marie Curie PhD Fellowship (2010–2014) DAAD Fellowship (2006) Friedemann Zenke leads an active research group at FMI, advising multiple PhD students and mentoring postdoctoral fellows. His research is supported by competitive grants, including the SNSF Eccellenza grant. He is a key member of the Computational Neuroscience Initiative Basel , fostering interdisciplinary collaboration between theoretical and experimental neuroscience. His lab develops large-scale neural network simulations and contributes to open tools for evaluating spiking neural networks, such as the Heidelberg Spiking Data Sets. Future work aims to further unify principles of biological learning with scalable, efficient AI systems.
Carlos E Vargas-Irwin is an Assistant Professor of Neuroscience (Research) at Brown University. His work focuses on understanding how networks of neurons represent and transform information, with particular emphasis on the relationship between cortical neuron activity and upper limb motion control. He is affiliated with the Department of Neuroscience in the School of Engineering and collaborates extensively with researchers across Brown's neuroscience and engineering communities. Assistant Professor of Neuroscience (Research), Brown University NIH Director's New Innovator Award recipient (DP2NS111817) Member of the Donoghue Lab at Brown University Extensive collaborator with John Donoghue (14 co-publications) Dr. Vargas-Irwin's research interests center around brain-computer interfaces , cortical computation , neuroinformatics , and neuroprosthetics . His work aims to understand how neural networks encode movement information and transform it into action. He has developed mathematical 'neural decoder' models capable of reconstructing complex limb movements based solely on neural activity and has examined how visual information shapes motor planning for grasping. His research has significant implications for developing brain-controlled neuroprosthetic assistive devices for individuals with paralysis. Analysis of Dr. Vargas-Irwin's recent publications reveals a strong focus on decoding neural signals for brain-computer interfaces, particularly in human patients with tetraplegia. His work spans multiple aspects of neural decoding including movement intention, force representation, and the relationship between different neural signal types (spikes, local field potentials). He has made significant contributions to computational methods for neural data analysis, including the Spike Train Similarity Space (SSIMS) framework, which has been openly shared with the neuroscience community. NIH Director's New Innovator Award: DP2NS111817 (NINDS) 'Synergistic Effector/Environment encoding: A new perspective on motor cortex and brain-computer interfaces' Dr. Vargas-Irwin collaborates extensively with leading researchers in neuroscience and engineering at Brown University, including John Donoghue (with whom he has 14 co-publications), Matthew Harrison, Leigh Hochberg, and Arto Nurmikko. His research is supported by significant NIH funding through the prestigious New Innovator Award. He has developed openly shared software tools for neuroscience data analysis that have fostered multiple ongoing collaborations across the neuroscience community. His work is primarily conducted through the Donoghue Lab at Brown University, which focuses on understanding neural coding and developing brain-computer interfaces. The lab brings together expertise in neuroscience, engineering, and computational methods to tackle fundamental questions about how the brain controls movement and how this knowledge can be applied to assistive technologies for people with neurological disorders.
Chao Zhang is a tenured Researcher at the Physics Department of Brookhaven National Laboratory , specializing in neutrino oscillation experiments. His work spans reactor and accelerator-based neutrino studies, focusing on fundamental parameters like mass ordering and CP-violation. Education: Ph.D. from California Institute of Technology (2010), B.S. from University of Science and Technology of China (2002). Research Interests include neutrino physics, detector R&D (liquid argon time projection chambers, water-based liquid scintillators), and statistical methods for oscillation analysis. He leads efforts in the DUNE , MicroBooNE , SBND , Daya Bay , and PROSPECT projects. Scientific Awards DOE Office of Science Early Career Research Program Award (2017) Breakthrough Prize in Fundamental Physics (2015, Daya Bay collaboration) Email: czhang@bnl.gov
Chris Kim serves as a Professor in the Department of Electrical and Computer Engineering at the University of Minnesota's College of Science and Engineering. He holds the prestigious McKnight Presidential Endowed Chair and was named a Distinguished McKnight University Professor in 2022, one of the highest honors at the university. His research focuses on designing energy-efficient, robust, and intelligent integrated circuits and systems with expertise in building chips and collaborating across materials, devices, algorithms, systems, and signal processing. Professor Kim's research interests span quantum-inspired computing, cryogenic computing, machine learning hardware, neuromorphic computing, hardware security, internet-of-things, medical devices, sensor networks, and radiation hardened chips. His group has transferred key technologies to the semiconductor industry, including silicon odometer circuits for measuring circuit wear out, radiation monitoring circuits, and SPICE models for magnetic tunnel junctions. Analysis of Professor Kim's recent publications reveals a strong trend toward quantum-inspired computing and combinatorial optimization using coupled oscillator-based Ising chips. His work bridges theoretical computer science with practical circuit implementation, with significant contributions in electromigration characterization, aging sensors, and compute-in-memory architectures. The research demonstrates a clear trajectory from fundamental circuit design to application-specific implementations for real-world problems. Intel Outstanding Researcher Award (2024) for contributions to coupled oscillator based Ising chip research Semiconductor Research Corporation (SRC) Sustainable Future award (2024) for quantum-inspired computing chips McKnight Presidential Endowed Chair (2024) Distinguished McKnight University Professor (2022) Louis John Schnell Professor in Electrical and Computer Engineering (2021) Professor Kim actively mentors numerous Ph.D. and Master's students, with over 50 graduates who now work at leading technology companies including Intel, Apple, Samsung, and NVIDIA. His research is supported by significant grants from the National Science Foundation, Semiconductor Research Corporation, Samsung Electronics, and the Department of Defense. Current projects include electromigration lifetime characterization, energy-efficient circuits for cryogenic operation, characterization of single event effects in DRAM chips, and development of CMOS oscillator-based Ising computers. The VLSI Research Group at the University of Minnesota, led by Professor Kim, focuses on developing core circuit technologies for smart and energy-efficient integrated systems. The group has produced notable achievements including a 48-spin all-to-all connected Ising solver chip published in Nature Electronics (featured on the cover), and a quantum-inspired Ising chip with nearly 2,000 coupled ring oscillators. The group maintains strong industry connections and has transferred multiple technologies to semiconductor companies.
Fayçal Ikhouane is a Professor at the Department of Mathematics , Universitat Politècnica de Catalunya (UPC-BarcelonaTech) , affiliated with the School of Engineering of Barcelona East (EEBE) . His academic career spans over two decades, focusing on hysteresis modeling, nonlinear control, and system identification. University: Universitat Politècnica de Catalunya (UPC-BarcelonaTech) School: School of Engineering of Barcelona East (EEBE) Department: Department of Mathematics Email: faycal.ikhouane@upc.edu Research Interests revolve around hysteresis and its applications in structural engineering, mechatronics, and control systems. His work includes: Theoretical analysis of hysteresis models (Bouc-Wen, Duhem, LuGre) Adaptive backstepping control for nonlinear systems Parametric identification of uncertain systems Seismic isolation and vibration control Applications to piezoelectric and magnetorheological actuators Article Trends show a consistent focus on mathematical modeling of hysteresis (46 indexed journal articles), control algorithms (58 conference papers), and structural health monitoring. His 2017-2022 publications emphasize asymmetric hysteresis, data-driven identification, and Lyapunov-based stability analysis. Scientific Awards : Premi extraordinari de doctorat 2006/2007 (Industrial Engineering) Advising : Supervised Fuad Mohammad Naser 's PhD thesis on hysteresis model consistency. Active in the OMGRAPH - Optimisation Methods on Graphs and CoDAlab - Control, Data and Artificial Intelligence research groups.
Dr. Tim Raupach is a Scientia Fellow (Level C) at the University of New South Wales in the School of Biological, Earth & Environmental Sciences . His research focuses on the impacts of climate change on hailstorms , convective processes , and precipitation microstructure , using high-resolution numerical weather simulations and remote sensing techniques to analyze small-scale precipitation variability. Education: PhD, École Polytechnique Fédérale de Lausanne (2016) Master of Science, Australian National University (2009) Bachelor of Software Engineering, Australian National University (2004) Research Interests include: Climate change impacts on severe weather Advanced hailstorm and thunderstorm analysis Improvement of remote sensing techniques (radar, microwave links) Statistical modeling of precipitation patterns Recent Work Trends reflect studies on hail damage potential in Australian cities, hail-hazard mapping across Australia, Bayesian hailstorm detection frameworks , and multi-hazard climate risk assessments for decision-making. Teaching: In 2022, he served as primary convenor for CLIM3001/6001 Climate Systems Science . Research Supervision: He supervises students including Isabelle Greco and Quincy Tut .
Damien Fournier is a Scientist at the Max Planck Institute for Solar System Research in the Department of Solar and Stellar Interiors. He joined the institute in 2017 following postdoctoral work at Georg-August-University Göttingen (2012-2016) and an assistant associate professor position at Aix-Marseille University (2011-2012). He holds a Ph.D. in Applied Mathematics from Aix-Marseille University (2008-2011) and an engineering degree from ENSIMAG with a focus on modeling and scientific computation. His research specializes in computational helioseismology , with dual focus areas: Forward modeling of wave propagation dynamics in solar interiors Inverse problem resolution for reconstructing interior perturbations from surface seismic measurements Secondary interests include Reynolds stress analysis, noise estimation methodologies, and Pinsker estimators for flow inversions. Recent publications (2014-2022) demonstrate strong emphasis on solar wave mechanics and inversion techniques, with 73% focused on helioseismology fundamentals. Dominant themes include: Solar Rossby wave dynamics under rotational effects (26% of recent works) Computational advancements in wave equation solutions and kernel development (33%) Meridional flow structure and convection zone dynamics (20%) He contributes to academic training through co-supervision of bachelor/master/PhD candidates. Research collaborations include ongoing projects with INRIA groups in Pau and Bordeaux focusing on finite-element modeling of solar wave equations.
Professor Dr. Sebastian Böser is a faculty member at the Johannes Gutenberg University Mainz, holding the PRISMA Professorship for Experimental Astroparticle Physics within the School of Physics, Mathematics and Computer Sciences. His research focuses on neutrino astronomy, precision measurement of neutrino oscillations, and the development of advanced photosensors for large-scale experiments like IceCube. He contributes to Research Area B: "Origin of Mass and Physics Beyond the Standard Model" at JGU. Born in 1977 in Starnberg, Bavaria Diploma in Physics, Technical University Munich PhD, DESY Zeuthen (2006) His work includes extending the IceCube neutrino observatory to lower energy thresholds for probing neutrino mass hierarchy and CP-violation, as well as designing novel light sensors using wavelength-shifting materials. He also investigates extragalactic supernova neutrinos and cosmic ray origins through high-energy neutrino flux analysis. Scientific awards include the Tiburtius Recognition Award (2007) for his PhD thesis "Acoustic detection of high-energy cascades in ice". He has participated in major collaborations including IceCube, ATLAS, and the ARENA 2005 workshop.
Paolo Carniti is a Professor in the Department of Physics 'Giuseppe Occhialini' at the University of Milano-Bicocca, School of Science. With over 700 publications listed in the institutional repository, he maintains an active research program in experimental particle physics, particularly focused on precision measurements in heavy flavor physics. Professor Carniti's research spans multiple areas within particle physics, with significant contributions to the LHCb experiment at CERN. His work primarily focuses on precision measurements of CP violation, rare decays, and mixing phenomena in beauty and charm meson systems. He also contributes to detector development and data analysis techniques, as evidenced by his work on liquid argon time projection chambers for neutrino experiments. His research bridges theoretical predictions with experimental verification, contributing to our understanding of fundamental particle interactions and potential physics beyond the Standard Model. The recent publications demonstrate consistent activity across multiple frontiers of particle physics, from precision measurements at the LHC to detector development for future neutrino experiments. Carniti frequently collaborates with large international teams, particularly on the LHCb experiment, where he contributes to analyses of B-meson and D-meson decays, as well as developing novel analysis techniques. As a senior researcher with extensive publication record, Professor Carniti likely supervises graduate students and postdoctoral researchers, though specific names aren't listed in the available information. His work on detector development suggests involvement in significant experimental grants supporting hardware and software development for particle physics experiments. Professor Carniti's research activities span multiple experimental collaborations, including LHCb at CERN and ProtoDUNE for neutrino physics, indicating leadership roles in these international projects. His work on both heavy flavor physics and detector development demonstrates versatility across theoretical analysis and experimental instrumentation.
Dr. Abdullah Makkeh is a Senior Scientist at the University of Göttingen's Department of Data-driven Analysis of Biological Networks, headed by Michael Wibral, and a Guest Scientist at the Max Planck Institute for Dynamics and Self-Organization in Göttingen under Viola Priesemann's Complex Systems Theory group. Previously, he served as a Postdoc at the University of Tartu in both Theoretical Computer Science (Dirk Oliver Theis) and Computational Neuroscience (Raul Vicente) groups. Education: PhD in Informatics (2018, University of Tartu, Supervisor: Dirk Oliver Theis) MSc in Mathematics (2013, Lebanese University, Supervisor: Bassam Mourad) BSc in Mathematics (2011, Lebanese University) His research focuses on extending information theory to study computation in intelligent systems like the brain and artificial neural networks (ANNs). He has developed interpretable information-theoretic learning rules for ANNs (Makkeh et al., 2025) and analyzed reinforcement learning agents to reveal emergent computation mechanisms (Engel et al., 2022; Ehrlich et al., 2023). Current work applies these methods to enhance large language model (LLM) interpretability. His publications span information-theoretic frameworks, predictive coding, and neural oscillation analysis, with a 2024 Royal Netherlands Academy of Arts and Sciences (KNAW) recognition. He co-teaches courses in Bayesian Inference, Information Theory, and Discrete Mathematics, and organizes annual workshops on information theory in computational neuroscience. Scientific Awards: Royal Netherlands Academy of Arts and Sciences (KNAW) (2024) Dr. Makkeh contributes to open-source research tools via GitHub and collaborates with interdisciplinary teams across neuroscience, computer science, and mathematics. His work bridges theoretical foundations with applied machine learning through rigorous mathematical frameworks.
