جست‌وجوی استاد و پژوهشگر

Qingguang Zhangمشاهده پروفایل

استادیار

• Neuroscience
• Cerebrovascular Physiology
• Neurovascular Coupling
• +۳ مورد دیگر

Qingguang Zhang serves as an Assistant Professor in the Department of Physiology and the Neuroscience Program at Michigan State University, with an additional affiliation in the BioMolecular Science Gateway. His academic role centers on advancing cerebrovascular physiology through interdisciplinary engineering and biological approaches. Dr. Zhang's research program investigates the fundamental mechanisms governing nervous-vascular system interactions for optimal brain function. His laboratory employs multimodal imaging to observe brain dynamics during natural behaviors in mouse models, combined with cellular and molecular techniques to dissect cell-type-specific signaling. Key research domains include neurovascular coupling, cerebrovascular remodeling during aging, respiratory modulation of cerebral blood flow, and brain microcirculation dynamics, spanning neuroscience, physiology, and biomedical engineering with translational applications for neurological disorders. Analysis of his 2019-2025 publications reveals consistent focus on awake-behaving animal methodologies, cerebrovascular network adaptations, and the interplay between respiratory patterns, locomotion, and cerebral oxygenation. His work demonstrates methodological innovation in physiological monitoring and imaging, with implications for improving disease screening and treatment strategies in brain research. No scientific awards or honors were explicitly mentioned in the available documentation. While the provided materials describe Dr. Zhang's research program and publications, specific details regarding graduate student mentorship, research grants, or funded projects were not included in the current text. The Zhang Laboratory operates within Michigan State University's Department of Physiology, maintaining a research environment dedicated to elucidating biophysical principles of brain microcirculation. The lab integrates engineering perspectives with physiological inquiry to address fundamental questions about blood flow regulation during normal function and disease states, with recent investigations extending to gut-brain hydraulic interactions and cellular mechanisms of neurovascular coordination.

DARRIGOL Olivierمشاهده پروفایل

پژوهشگر

• History of Physics
• Philosophy of Science
• 19th Century Physics
• +۳ مورد دیگر

Olivier Darrigol is a Directeur de Recherches at the CNRS affiliated with Université Denis Diderot (Paris). His academic journey includes being an ancien élève of the École Normale Supérieure (1975-1979), Agrégation de physique (1978), a Thèse de troisième cycle on quantum field theory history (Paris 1, 1982), and a Habilitation in history of science (Paris 7, 1992). He is a frequent visitor at the Office for History of Science and Technology, UC-Berkeley. Research Interests : History of physics (19th-20th centuries) and its interplay with philosophy, focusing on topics like electromagnetism, quantum theory, hydrodynamics, and relativity. His recent articles explore fluctuation-dissipation relations, Poincaré’s electrodynamics, and structuralism in 19th-century science. Scientific Awards : Prix Pictet (2000) Prix Grammaticakis-Neumann (2004) Prix Georges Charpak (2018) Estoire Prize (2022) Notable Publications : Authored books including Relativity Principles and Theories from Galileo to Einstein (2022), Atoms, Mechanics, and Probability (2018), and The History of Optics (2012). Co-edited volumes for the Poincaré Seminar and Erwin Schrödinger: Philosophy and the Birth of Quantum Mechanics .

• Numerical Linear Algebra
• Partial Differential Equations
• Integral Equations
• +۷ مورد دیگر

Difeng Cai is an Assistant Professor in the Department of Mathematics at Southern Methodist University. His research focuses on developing efficient algorithms for numerical linear algebra, partial differential equations, and integral equations, with applications spanning physics, statistics, machine learning, and deep learning. University: Southern Methodist University Department: Mathematics Academic Rank: Assistant Professor Professor Cai's work addresses computational challenges in scientific computing and data science by leveraging mathematical properties to design scalable algorithms. His recent interests involve integrating deep learning techniques, such as generative models, with traditional numerical methods to solve complex scientific problems. His research trends highlight interdisciplinary applications of numerical linear algebra and machine learning in scientific computing. Publications appear in venues like Numerical Linear Algebra and Its Applications , Numerische Mathematik , and SIAM Journal on Scientific Computing , emphasizing algorithm design, error estimation, and kernel matrix approximation.

Anuj Karpatneمشاهده پروفایل

دانشیار

• Knowledge-guided Machine Learning (KGML)
• Spatio-temporal Data Mining
• Environmental Science Data Analysis
• +۵ مورد دیگر

Anuj Karpatne is an Associate Professor in the Department of Computer Science at Virginia Tech, leading the Knowledge-guided Machine Learning (KGML) Lab. He joined VT in 2018 and earned tenure in 2023. His research focuses on integrating scientific knowledge into machine learning, pioneering the KGML paradigm to create explainable and generalizable AI solutions for interdisciplinary scientific challenges. He holds a PhD from the University of Minnesota (2017) and an M.Tech. from IIT Delhi (2011). Research Interests: Knowledge-guided ML frameworks (KG-Loss, KG-Neural Architectures, Hybrid-Science-ML) Applications in physics (fluid dynamics, quantum mechanics), environmental sciences (aquatic systems, remote sensing), and biology (organismal biology, virology) Interdisciplinary collaborations across 15+ institutions for NSF-funded projects Recent Contributions: Developed Fish-Vista dataset for biological trait analysis Co-organized KDD 2024 workshop on Generative Models for Sustainable Development Secured $18M NSF PIPP II grant as co-PI for pandemic prediction systems Grants & Leadership: Lead PI on NSF CAREER Grant ($586K) for unifying ML with scientific knowledge Co-PI on NSF Imageomics Institute ($15M) integrating biology with AI Site PI for macrosystems biology grant ($1.1M) modeling lake water quality Labs & Teams: KGML Lab at VT collaborates with 30+ scientists across disciplines Leadership roles in NSF PIPP Institute and Imageomics Center

Beate Sickمشاهده پروفایل

استاد

• Machine Learning
• Medical Imaging
• Deep Learning
• +۳ مورد دیگر

Beate Sick is a Professor at the Zurich University of Applied Sciences School of Engineering, specializing in Data Analysis and Statistics. Her work bridges machine learning, probabilistic modeling, and medical applications, particularly in stroke outcome prediction, uncertainty quantification, and statistical methods for healthcare.

Enrique Zuazuaمشاهده پروفایل

استاد

• Partial Differential Equations
• Control Theory
• Machine Learning
• +۲ مورد دیگر

Alexander von Humboldt Professor and Chair for Dynamics, Control, Machine Learning and Numerics at Friedrich-Alexander-Universität Erlangen-Nürnberg. Holds dual PhD from University of Basque Country and Université Pierre et Marie Curie. Secondary affiliations at University of Deusto and Autonomous University of Madrid. Research integrates partial differential equations, control theory, and machine learning. Develops computational frameworks for system optimization and dynamic modeling. Current focus includes neural transport in normalizing flows and PDE-based machine learning architectures. Recipient of Alexander von Humboldt Professorship (2019), three European Research Council Advanced Grants, and W.T. Reid Prize (2022). Editor-in-Chief of Mathematical Control and Related Fields. Founded Basque Center for Applied Mathematics and established Computational Mathematics Chair at Deusto Foundation. Supervised 30+ PhD students with work cited in 300+ publications.

• Planetary Systems Formation
• Astrophysical Disks Dynamics
• Circumstellar Dust Physics
• +۳ مورد دیگر

Pawel Artymowicz is a Professor at the University of Toronto Scarborough (UTSC) focusing on the origin and evolution of planetary systems. His research spans astrophysical disk dynamics, circumstellar dust physics, and binary star interactions. He holds a Ph.D. from the Polish Academy of Science (1990). Research Interests: Artymowicz investigates planetary formation mechanisms, protoplanetary disk evolution, and binary star-disk interactions. He explores topics like irradiation instabilities in accretion disks, gravitational instabilities in gas disks, and the role of turbulence in planetesimal formation. His work bridges theoretical modeling with observational studies using instruments like the Gemini NICI Planet-Finding Campaign. Key Contributions: Artymowicz has pioneered studies on Type III planetary migration, disk-planet interactions, and the dynamics of embedded protoplanets. His research on binary systems' impact on disk structures has advanced understanding of star formation processes. He also explores black hole dynamics and AGN phenomena through disk evolution frameworks. Recent Work Trends: Recent articles highlight advancements in 3D flow simulations around protoplanets, modal analysis of disk instabilities, and direct imaging of substellar companions in young stellar systems. His studies emphasize high-contrast imaging techniques for exoplanet detection and debris disk characterization. Grants & Advising: While specific grants/students aren't listed, his prolific publication record indicates active research leadership. He collaborates on large-scale observing campaigns like the Gemini NICI project, contributing to exoplanet demographics in B/A-type stars. Labs/Teams: Affiliated with the University of Toronto's astronomy department and UTSC's research initiatives, though specific lab names aren't provided. His work integrates theoretical astrophysics with observational astronomy.

Bernhard Rabusمشاهده پروفایل

استاد

• Synthetic Aperture Radar (SAR)
• Maritime Applications of SAR
• Landslide Monitoring
• +۵ مورد دیگر

Bernhard Rabus is a Professor and holder of the Industrial Research Chair in Synthetic Aperture Radar (SAR) at Simon Fraser University's School of Engineering Science. His work focuses on SAR technologies, with emphasis on maritime applications and novel land applications using multi-channel SAR systems. He leads the SARlab and collaborates with institutions like IEEE. Rabus earned his Ph.D. in Geophysics from the University of Alaska Fairbanks and an M.Sc. from the Technical University of Munich. His research integrates advanced interferometric and polarimetric techniques to study glaciers, landslides, and environmental dynamics. Recent projects include landslide deformation analysis, glacier motion tracking, and SAR-based wildlife monitoring. Rabus teaches graduate courses like ENSC895/EASC609 and advises students on SAR applications. His work is funded through industrial and academic partnerships, addressing challenges in geohazard monitoring and remote sensing innovation. Education: Ph.D., Geophysics, University of Alaska Fairbanks (1997); M.Sc., Technical University of Munich (1992) Affiliations: IEEE, SARlab Director Labs/Teams: SARlab, collaborating with NASA, ESA, and industry partners Research Highlights: Rabus pioneers SAR applications for maritime domain awareness, landslide prediction, and glacier dynamics. His team develops novel algorithms for interferometric analysis and airborne SAR systems. Recent studies include Fels Slide displacement tracking and Arctic ice cap stability assessments. Outcomes contribute to disaster risk reduction, climate science, and environmental policy. Awards: Industrial Research Chair in SAR (SFU), recognized for advancing SAR technology in geoscience and engineering. Grants/Projects: Funded by NSERC, NASA, and industry partnerships for SAR sensor development and environmental monitoring initiatives.

Tony Harkinمشاهده پروفایل

دانشیار

• Applied and Computational Mathematics
• Fluid Mechanics
• Dynamical Systems
• +۴ مورد دیگر

Anthony A. Harkin is an Associate Professor in the Department of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. His academic roles include teaching and research in applied mathematics, fluid mechanics, and dynamical systems. He holds a B.S. from SUNY Brockport, M.S. from MIT, and Ph.D. from Boston University, with postdoctoral research at Harvard University. His expertise spans computational mathematics, fluid dynamics, and network science, with a focus on mathematical modeling and partial differential equations. Education: B.S. in Math/Physics/Computer Science, SUNY College at Brockport (1993) M.S. in Applied Mathematics, Massachusetts Institute of Technology (1995) Ph.D. in Mathematics, Boston University (2000) Postdoctoral Fellow in Applied Mathematics, Harvard University (2001–2005) Research Interests: Fluid Mechanics: Bubble dynamics, multiphase flow, and nonlinear acoustics Dynamical Systems: Perturbation theory, renormalization group methods, and bifurcation analysis Network Science: Community detection, graph theory, and clustering algorithms Data Science: Hyperspectral image processing and machine learning applications Teaching Contributions: Harkin has taught advanced courses at RIT, Harvard, and Boston University in areas such as mathematical modeling, partial differential equations, dynamical systems, and linear algebra. He received the RIT Provost's Award for Excellence in Teaching (2008) and Harvard's Certificate of Distinction in Teaching (2004). Publications: His research spans fluid dynamics, network theory, and mathematical modeling, with notable contributions on bubble dynamics, graph modularity, and renormalization group methods. Recent work includes studies on Rayleigh collapse of bubbles and community detection in networks. Professional Activities: Co-founder of icitizen.com, a political networking platform. Active in editorial roles, including as editor of the International Journal of Applied Nonlinear Science (2013).

Jean Lécureuxمشاهده پروفایل

مدرس

• Buildings and their boundaries
• Poisson boundaries and boundary maps
• Rigidity
• +۳ مورد دیگر

Jean Lécureux is a Lecturer at the Department of Mathematics within the Faculty of Sciences of Orsay at the University of Paris-Saclay. His research focuses on geometric group theory, specifically exploring buildings, their boundaries, Poisson boundaries, rigidity phenomena, and random walks in CAT(0) spaces. He has supervised the PhD of Corentin Le Bars (2023), currently a postdoc at the Weizmann Institute, and is currently advising Antoine Derimay's PhD. His work spans advanced topics such as the Normal Subgroup Theorem for groups acting on Ã₂ buildings, superrigidity in algebraic group actions, and geometric density of invariant random subgroups. Notable contributions include foundational studies on CAT(0) cubical complexes and boundary maps in infinite-dimensional Hermitian symmetric spaces. He holds a PhD from 2009 under Bertrand Rémy and an habilitation (2020) titled 'Bords et rigidité en courbure négative ou nulle.' Publications highlight interdisciplinary approaches to geometric structures, with key journals including Annales scientifiques de l'École normale supérieure, American Journal of Mathematics, and Groups, Geometry, and Dynamics. His pedagogical contributions include a 2017 paper on evaluation strategies in university mathematics education.

Frédéric Paulinمشاهده پروفایل

استاد

• Hyperbolic Geometry
• Low-Dimensional Topology
• Geometric Group Theory
• +۶ مورد دیگر

Frédéric Paulin is a Professor at the Université Paris-Saclay, affiliated with the Department of Mathematics of Orsay (Institut de Mathématique d'Orsay, UMR 8628 CNRS). His research focuses on hyperbolic geometry, geometric group theory, ergodic theory, and their connections to number theory and dynamical systems. He has extensively studied topics such as Teichmüller spaces, discrete subgroups of Lie groups, and Diophantine approximation. Paulin has taught advanced courses across various institutions, including the École Normale Supérieure and Université Paris-Sud, covering topology, differential geometry, dynamical systems, and algebra. He has organized international conferences like the GDR Platon meetings and contributed to significant research in areas like equidistribution of geodesics and counting problems in negative curvature. His work bridges pure mathematics disciplines, with applications in geometry, number theory, and topology. He actively participates in academic service, including editorial roles and conference organization, furthering collaborative research in his fields.

Alexander Schwingمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Robotics
• +۷ مورد دیگر

Alexander Schwing is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliations to the Coordinated Science Laboratory and the Computer Science Department. His research focuses on machine learning, computer vision, and structured prediction, emphasizing algorithms for deep networks, multivariate distributions, and 3D scene understanding. He has held postdoctoral positions at the University of Toronto and completed his PhD at ETH Zurich. Educations: PhD in Computer Science (ETH Zurich, 2014) Diploma in Electrical Engineering & IT (Technical University of Munich, 2010) Research interests include generative modeling, embodied agents, video segmentation, and reinforcement learning. He has developed influential frameworks like XMem for video object segmentation and MaskRNN for instance-level tracking. His work emphasizes reproducibility and open-source releases. Key awards include the NSF CAREER Award, Amazon Research Award, and NVIDIA GPU donations. He has advised over 25 students, many of whom have pursued roles at top tech firms and academia. Current research explores structured prediction, multi-agent systems, and 3D reconstruction. His labs collaborate with industries like Samsung and Adobe, and he teaches courses on machine learning and pattern recognition.

Zhizhen Zhaoمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Imaging
• High Energy Physics
• +۵ مورد دیگر

Zhizhen Zhao is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois. He holds additional appointments as Associate Professor in the Coordinated Science Lab, Department of Statistics, and Department of Mathematics, and is an Affiliate at the Carl R. Woese Institute for Genomic Biology. He is also recognized as a William L. Everitt Faculty Fellow. His research encompasses machine learning, computational imaging, high-energy physics, climate modeling, cryo-electron microscopy, and quantum computing. He develops algorithms for complex data analysis, inverse problems, and interdisciplinary scientific applications. Recent publications (2023–2025) focus on generative AI, FAIR-compliant models for physics, climate prediction, and advanced imaging techniques. Key trends include deep learning for inverse problems, community detection in networks, and energy informatics. Scientific Awards: William L. Everitt Faculty Fellow He collaborates with the Coordinated Science Lab and the Carl R. Woese Institute for Genomic Biology, contributing to cross-disciplinary initiatives in AI, physics, and computational biology.

Jisu Kimمشاهده پروفایل

استادیار

• Applied Data Science
• Migration and Societal Change
• Mobility and Migration
• +۵ مورد دیگر

Dr. Jisu Kim is an Assistant Professor in the Department of Interdisciplinary Social Science at Utrecht University, Netherlands. She was previously a research scientist at the Max Planck Institute for Demographic Research (MPIDR) in the Digital and Computational Demography department. Her expertise lies in Mobility and Migration, Applied Data Science, Big Data, and Social Networks, with a focus on using innovative data analytics to study migration dynamics and societal integration. She holds a Ph.D. in Data Science from Scuola Normale Superiore, Italy. Her research interests include exploring big data sources to understand migration’s socio-political and cultural contexts, particularly using social media data. She is fluent in Korean, English, French, and Italian, and skilled in Python, R, and Stata for statistical analysis. Dr. Kim has received notable awards, including the DGD Best Paper Award (2025) for her work on gender differences in migration processes and the Best Poster Award at Complenet 2023. She has organized multiple interdisciplinary workshops (e.g., MIMODE 2024/2023/2022) and co-led the Population and Social Data Science Summer Incubator Program in 2024. Her grants include the Seed Money Award from Utrecht University. She has advised and mentored in summer programs, contributed to courses like the Research Practicum, and actively participates in academic networks such as CSS council (2021–2024). Her work spans collaboration with institutions like the Paris School of Economics, Imperial College London, and OECD/IOM/UN initiatives.

Jose M Penaمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Artificial Intelligence
• Probabilistic Graphical Models
• +۴ مورد دیگر

Jose M Pena is a Senior Associate Professor and Head of Unit at Linköping University's Department of Computer and Information Science, specializing in the Division of Statistics and Machine Learning (STIMA). His research focuses on probabilistic graphical models including Bayesian networks, Markov networks, and chain graphs within machine learning and artificial intelligence. His research interests center on causal inference, machine learning, and statistical modeling. He actively develops methods for causal discovery, counterfactual reasoning, and sensitivity analysis using normalizing flows and graphical models. His work bridges theoretical statistics with practical applications in complex data analysis. His recent publications (2020-2025) demonstrate a strong focus on causal machine learning, with particular emphasis on directed acyclic graphs (DAGs), counterfactual modeling, and handling unobserved confounding. Key trends include the integration of deep learning with causal structures, robust causal inference under distribution shifts, and scalable methods for high-dimensional causal analysis. As Head of Unit within STIMA, he contributes to the international master's programme in Statistics and Machine Learning. The division hosts significant research activities in modern data analysis and is part of Linköping University's Department of Computer and Information Science—one of northern Europe's largest departments in this field.