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Hee-Jong SeoView profile

Associate Professor

• Cosmology
• Large Scale Structure of the Universe
• Baryon Acoustic Oscillations
• +10 more

Hee-Jong Seo is an Associate Professor in the Department of Physics and Astronomy at Ohio University's College of Arts and Sciences, where he also serves as the Director of the Astrophysics Institute. His research focuses on high-precision cosmology using large-scale structure to probe dark energy and dark matter. Research Interests: High-precision cosmology with large-scale structure Baryon Acoustic Oscillations (BAO) from galaxy surveys and 21cm intensity mapping Redshift-space distortions and cosmic shear Mitigation of observational systematics using deep learning Constraints on neutrino mass and dark energy Galaxy-halo connection and clustering analysis The recent publications reflect a strong focus on BAO reconstruction techniques, systematic error mitigation in galaxy surveys, and cosmological parameter estimation using data from SDSS-III/BOSS, SDSS-IV/eBOSS, and DESI. His work combines theoretical modeling, numerical simulations, and observational data analysis to improve distance measurements and test fundamental physics. Scientific Awards: Department of Energy Early Career Award (DE-SC0019091) Advising and Grants: Dr. Seo has secured significant research funding as PI and Co-PI from the Department of Energy, including an Early Career Award. His grants focus on improving dark energy constraints using low-redshift large-scale structures and developing robust reconstruction methods for BAO, redshift-space distortions, and the Alcock-Paczynski effect. While no students are listed, his leadership in major collaborations suggests active mentorship within large survey teams. Labs and Teams: Dr. Seo is actively involved in major cosmological surveys, including the Dark Energy Spectroscopic Instrument (DESI), where he co-chairs the Galaxy Quasar Clustering Science Working Group, and the Sloan Digital Sky Survey (SDSS-IV/eBOSS), where he served as Tiling Coordinator and SDSS-IV Architect. He also contributes to the development of public BAO forecasting tools and collaborative research in cosmology.

• Complex Systems
• Network Theory
• Dynamical Systems
• +17 more

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.

Ömer Lütfi ŞenView profile

Professor

• Climate Modeling
• Regional Climate
• Vegetation
• +4 more

Ömer Lütfi Şen is a Professor at Istanbul Technical University's Eurasia Institute of Earth Sciences, where he conducts cutting-edge research in climate science and Earth system modeling. His work focuses on regional climate dynamics, particularly in the Euro-Mediterranean region and Turkey, with significant contributions to understanding climate change impacts and developing predictive models. Professor Şen's research interests span multiple dimensions of climate science, with particular expertise in Climate Modeling , Regional Climate dynamics, Vegetation responses to climate change, and Land Cover Change analysis. His work often intersects with practical applications for understanding Tigris River basin hydrology and broader Climate Change impacts across Earth and Planetary Sciences. With an h-index of 19 and 1,278 citations, his research output spans from 1998 to the present, with particularly productive years in 2012-2025. His recent publications demonstrate a strong focus on advanced climate modeling techniques, including variable-resolution configurations of global models, analysis of ENSO effects, and the impact of atmospheric rivers on regional climate and hydrology. These works collectively advance our understanding of climate dynamics in vulnerable regions and inform adaptation strategies, with several studies gaining significant media attention and citations. Professor Şen has supervised 17 graduate students throughout his career, contributing significantly to the development of the next generation of climate scientists. His research has been supported by numerous projects, including those examining climate change impacts on wooden species in Turkey, electricity generation and CO2 emissions, and regional climate dynamics in the Euro-Mediterranean region.

• Numerical Analysis
• Partial Differential Equations
• Fluid Mechanics
• +9 more

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.

Friedemann ZenkeView profile

Assistant Professor

• Computational Neuroscience
• Neural Network Modeling
• Spiking Neural Networks
• +7 more

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.

Liviu ChioncelView profile

Professor

• Condensed Matter Physics
• Spintronics
• Electronic Structure Theory
• +3 more

Professor Liviu Chioncel leads the Theoretical Physics III group at the Institute of Physics, University of Augsburg, affiliated with the Faculty of Mathematics, Natural Sciences, and Materials Engineering. His research focuses on strongly correlated electronic systems, combining theoretical frameworks like Dynamical Mean-Field Theory (DMFT) with computational methods to study quantum materials, spintronics, and transport phenomena. Research Interests: Key areas include electronic structure calculations of correlated materials, momentum density analysis via Compton scattering and positron annihilation, non-equilibrium transport in nanodevices, half-metallic magnetism, and cluster-based quantum many-body approaches. His work bridges fundamental theory with applications in next-generation electronic and spintronic devices. Publication Trends: Recent articles (2021–2025) emphasize kagome magnets, high-entropy alloys, and correlated electron transport. Predominant themes include topological magnetism, Fermi surface engineering, and advanced computational techniques (e.g., DFT+DMFT). This reflects a focus on quantum materials design and electron correlation effects in solids. Team & Advising: Dr. Chioncel supervises PhD candidates (Dylan Jones, Shreyas Nadiger) and collaborates with postdoctoral researchers. His group investigates condensed matter systems using analytical and numerical tools, with projects spanning from ab initio methods to experimental spectroscopy validation.

• Computational Fluid Dynamics
• Combustion
• Multiphase Flow
• +5 more

Ashoke De is a Professor in the Department of Aerospace Engineering at the Indian Institute of Technology Kanpur. His research spans computational fluid dynamics, combustion, and green propellant technologies, with a focus on numerical simulations and multiphase flows. Key research areas include turbulence-chemistry interactions, aerospace propulsion systems, and renewable energy applications such as hydrokinetic rotors and fuel cell dynamics. His work employs advanced computational methods like phase field modeling, immersed-boundary approaches, and flamelet-generated manifolds. Recent publications highlight innovations in Martian spacecraft aerodynamics, green propellant combustion, and self-starting hydrokinetic turbines. Ashoke De serves as an Associate Editor for the International Journal of Energy for a Clean Environment and Computational Thermal Sciences , and as a reviewer for multiple journals. His contributions to engineering education and academic leadership are evident through collaborative tributes to peers.

• Hydrogen Systems
• Thermoacoustics
• Cryogenics
• +4 more

Konstantin I. Matveev is a Professor at the Hydrogen Properties for Energy Research (HYPER) Center within the School of Mechanical and Materials Engineering at Washington State University. His work bridges mechanical engineering and applied physics, focusing on advanced energy and marine technologies. Education: PhD in Mechanical Engineering, California Institute of Technology, 2003 MS in Applied Physics, Moscow Institute of Physics and Technology, 1996 BS in Applied Physics, Moscow Institute of Physics and Technology, 1994 His research interests include hydrogen systems, thermoacoustics, cryogenics, energy conversion, and advanced marine vehicles. He applies fundamental physical principles to develop efficient and sustainable technologies, particularly involving hydrogen at low temperatures. His work often involves modeling and simulation of complex fluid-structure and thermal-acoustic interactions. His recent publications demonstrate a strong focus on thermoacoustic phenomena in cryogenic hydrogen, acoustic losses in hydrogen systems, and advanced marine vehicle hydrodynamics. These works reflect a trend toward developing clean energy technologies using hydrogen, particularly through innovative thermoacoustic and cryogenic engineering solutions. There are currently no scientific awards listed in the provided text. Regarding advising and grants , while no formal list of students or funded projects is provided, his active publication record and leadership at the HYPER Center suggest involvement in mentoring graduate researchers and securing research funding. He collaborates with researchers such as Jacob Leachman, Jeff Collins, Kian Conroy, and Carl Bunge. He is affiliated with the HYPER Center , a research laboratory focused on advancing hydrogen as an energy carrier through fundamental and applied research in storage, transport, and utilization technologies.

Silvia Corbera LopezView profile

Associate Professor

• Social Cognitive Neuroscience
• Empathy
• Prosocial Behavior
• +7 more

Silvia Corbera Lopez is an Associate Professor in the Department of Psychological Science at Central Connecticut State University. She leads the Social Cognitive and Affective Neuroscience (SCAN-LAB), focused on understanding social cognitive processes like empathy, theory of mind, and facial affect recognition in healthy individuals and clinical populations (e.g., schizophrenia, autism). Research Focus: Behavioral and neural responses to emotionally loaded stimuli, developmental trajectories of social cognition, and cross-cultural validation of cognitive assessment tools. Publications: 15+ peer-reviewed articles (2025-2006) in journals like Schizophrenia Research, Translational Psychiatry, and BMC Psychology, with recent work on AI applications in psychosis assessment and excitation/inhibition ratio analysis. Lab: Director of SCAN-LAB, mentoring undergraduate/graduate researchers in clinical, experimental, and neuroscientific methods.

Anthony MurroView profile

Professor

• Neurology
• Epilepsy
• Clinical Neurophysiology
• +7 more

Anthony Murro is a Professor in the Department of Neurology at the Medical College of Georgia. He is actively involved in clinical and academic work, with a focus on epilepsy and neurophysiology. His academic appointments and clinical expertise are centered at the Medical College of Georgia, a prominent medical institution. Research Interests: His primary research areas include epilepsy management, brain-responsive neurostimulation, detection of epileptiform activity using AI models, and SUDEP. His work bridges clinical neurology and advanced computational methods for seizure detection. This interdisciplinary focus places him at the forefront of neuromodulation and epilepsy therapeutics. Recent Research Trends: His most recent publications (2017–2020) highlight a strong focus on the long-term efficacy and safety of responsive neurostimulation devices in treating focal and mesial temporal lobe epilepsy. He also explores the use of deep learning, particularly LSTM networks, for identifying pathological EEG patterns, indicating a growing integration of AI in clinical neurophysiology. Professional Service: Member, Neurology Department Education Committee (2016–Present) Teaching: He teaches RNEU 5000: Neurology, contributing to graduate medical education. Education: MD, Medicine, State University of New York, 1981 BS, Chemistry, Rensselaer Polytechnic Institute, 1977

Carlos E Vargas-IrwinView profile

Assistant Professor

• Brain-Computer Interfaces
• Cortical Computation
• Neuroinformatics
• +4 more

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.

Jacob HerrmannView profile

Assistant Professor

• Biomedical device design & control
• Biomedical imaging and signal processing
• Computational modeling
• +3 more

Jacob Herrmann is an Assistant Professor in the Department of Biomedical Engineering at the University of Iowa College of Engineering. He joined the institution in 2022 and specializes in biomedical device design, respiratory dynamics, and computational modeling. His work focuses on mechanical ventilation optimization and pulmonary structural analysis. PhD in Biomedical Engineering from University of Iowa (2018) MS and BS in Biomedical Engineering from Boston University (2015 & 2012) His research integrates biomedical device control , multiscale imaging , and computational modeling to study ventilator-induced lung injury, pulmonary fibrosis biomechanics, and oxygenation optimization. Recent work explores multi-frequency oscillatory ventilation and AI-assisted CT imaging analysis. Key trends in his publications include: Development of computational models for lung mechanics Innovations in ventilator design and control systems Quantitative CT imaging applications in pulmonary injury Biomechanical feedback mechanisms in fibrosis Collagen network analysis in tissue failure Herrmann leads the Respiratory Dynamics Lab and maintains active memberships in the American Thoracic Society and Biomedical Engineering Society. His work addresses critical challenges in respiratory care through interdisciplinary engineering approaches.

• Neutrino Physics
• Particle Physics
• Detector R&D
• +3 more

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

Kelly R. BijankiView profile

Associate Professor

• Neuromodulation
• Affective Neuroscience
• Intracranial Electrophysiology
• +5 more

Kelly R. Bijanki, Ph.D. , is an Associate Professor at Baylor College of Medicine with joint appointments in Psychiatry and Neuroscience . As Director of Intracranial Monitoring Research , she leads the Translational Neuromodulation Lab , focusing on intracranial neural signatures of affective dysfunction and minimally-invasive therapeutic approaches.

Chris KimView profile

Professor

• Integrated circuits and systems design
• Energy-efficient computing
• Quantum-inspired computing
• +7 more

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.