جست‌وجوی استاد و پژوهشگر

Philip Moczمشاهده پروفایل

پژوهشگر

• Multiphysics simulations
• Cosmology
• Galaxy formation and evolution
• +۷ مورد دیگر

Philip Mocz is a computational research scientist at the Flatiron Institute's Center for Computational Astrophysics, part of the Simons Foundation. His work focuses on developing scalable, high-performance multiphysics simulation software with a growing emphasis on integrating modern AI techniques and automatic differentiability. Previously, he was a Computational Physicist at Lawrence Livermore National Laboratory, where he specialized in designing high-order Arbitrary Lagrangian-Eulerian (ALE) finite element methods for magnetohydrodynamics (MHD) simulations on heterogeneous computing architectures as part of the Multiphysics on Advanced Platforms Project (MAPP). Dr. Mocz earned his Ph.D. in Astrophysics from Harvard University in 2017 under Lars Hernquist, where he developed a finite-volume moving mesh magnetohydrodynamics algorithm applied to study structure formation and magnetic field amplification, integrating his solvers into the Arepo simulation code. Prior to his doctorate, he received an A.B. in Mathematics and Astrophysics from Harvard in 2012. His research spans multiphysics simulations, cosmology, galaxy formation, black hole physics, turbulence, numerical methods, and AI integration in computational astrophysics. A significant focus involves cosmological simulations of alternative dark matter candidates, particularly fuzzy dark matter. His work bridges theoretical astrophysics with high-performance computing, developing novel simulation frameworks that incorporate modern computational techniques. Dr. Mocz's publication record reveals a strong trajectory in computational methods for astrophysical problems, with increasing integration of AI techniques in recent years. His research demonstrates expertise across multiple domains including quantum mechanics applications to cosmology, turbulence modeling, and the development of advanced simulation algorithms. The interdisciplinary nature of his work connects astrophysics with computer science and applied mathematics. He maintains an active educational presence through his blog featuring approximately 100-line Python tutorials on scientific computing at the undergraduate level, published on Medium and followed on Twitter. His educational materials cover fundamental computational methods including finite difference approaches, Riemann solvers, and differentiable simulations using JAX. Dr. Mocz has served as a Teaching Fellow for Harvard courses including Astronomy 151 (Astronomical Fluid Dynamics), Applied Computation 274 (Computational Fluid Dynamics), and Applied Mathematics 205 (Advanced Scientific Computing). His outreach activities include mentoring for the LLNL DSTI Research Program, NASA Cosmic Origins Transitional Leadership Team, and Princeton Astrophysics Undergraduate Summer Research Program. Originally from Hawaii, he enjoys outdoor activities when not working. His professional presence includes active GitHub repositories (pmocz), a personal website (pmocz.github.io), and engagement on Bluesky (@philipmocz.bsky.social), where he shares insights about computational physics and scientific software development.

Marinho Bertanhaمشاهده پروفایل

دانشیار

• Econometrics
• Causal Inference
• Applied Microeconomics
• +۵ مورد دیگر

Marinho Bertanha is a tenured Associate Professor of Economics at the University of Notre Dame, serving as concurrent faculty in Statistics and a faculty fellow at the Kellogg Institute. His academic work bridges econometrics and applied microeconomics with significant contributions to causal inference methodologies. His educational background includes a Ph.D. in Economics from Stanford University (2015), an M.A. from Fundação Getúlio Vargas in Rio de Janeiro (2009), and a B.A. from Universidade de São Paulo (2006). Prior to his current position, he completed postdoctoral work at the University of Louvain and served as a visiting professor at the University of Chicago. Bertanha's research focuses on causal inference , resampling methods , and policy evaluation , with particular expertise in regression discontinuity designs, bunching estimation, and permutation testing. His methodological innovations address critical challenges in econometric identification and statistical inference, especially in contexts with strategic reporting and imperfect compliance. His publication record shows consistent high-impact output in top journals including the Journal of the American Statistical Association , Journal of Econometrics , and Review of Economics and Statistics . Recent work demonstrates increasing focus on practical implementation through software development (Stata and MATLAB packages) that has become standard in empirical economics research. Through his concurrent appointment in Statistics and faculty fellowship at the Kellogg Institute, Bertanha contributes to interdisciplinary research initiatives focusing on economic policy evaluation and statistical methodology development. His current work-in-progress examines causal effects in matching mechanisms, returns to education with strategic reporting, and tax elasticity estimation across multiple countries.

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +۸ مورد دیگر

Dr Dalia Chakrabarty is a Reader in Statistical Data Science at the Department of Mathematics, University of York. Previously, she held positions as Senior Lecturer at Newcastle University and Lecturer at Lancaster University. Her research focuses on probabilistic methods, Bayesian inference, and machine learning applications in fields such as medicine, astronomy, and materials science. She specializes in kernel methods, random graph analysis, and causal forecasting. Notable contributions include developing methodologies for uncertainty quantification and non-parametric learning. Her academic career includes a Royal Society Dorothy Hodgkin Fellowship and supervision of students like Kane Warrior. Dr Chakrabarty's work bridges theoretical statistics and real-world challenges, with publications spanning journals like Plos One and Artificial Intelligence in Medicine . She also authored the textbook Supervised Learning: Mathematical Foundations & Real-world Applications (2024, CRC Press). Research Highlights: Inter-graph distance metrics for medical data analysis Bayesian state-space modeling of galactic dynamics High-dimensional data applications in oncology and materials science Collaborations: Maintains ties with Brunel Mathematics for PhD supervision and international research networks. Contact: Email dalia.chakrabarty@york.ac.uk , Tel: +44 (0)1904 32 1486

Ed Corriganمشاهده پروفایل

استاد

• Mathematical Physics
• Quantum Field Theory
• Integrable Systems
• +۳ مورد دیگر

Ed Corrigan is a Professor of Mathematics at the University of York, holding this position since 2011. Previously, he served as Principal of Collingwood College, Durham University (2008–2011), and held roles including Head of the Department of Mathematical Sciences at Durham University (1999–2004, 2005–2007, 2011–2015). His academic journey includes a BA (1968) and PhD (1972) from the University of Cambridge, followed by fellowships at Durham University and CERN. His research focuses on Mathematical Physics, particularly classical and quantum integrable systems, integrable defects, and two-dimensional field theories with boundaries. Notable contributions include work on affine Toda field theories, soliton solutions, and defects in field theory. He has coordinated European Networks (1992–2006) and led projects funded by EPSRC and the Royal Society. Corrigan is a Fellow of the Royal Society (FRS, 1995), the Institute of Physics (FInstP, 1999), and the Institute of Mathematics and its Applications (FIMA, 2013). His work bridges theoretical physics and mathematics, emphasizing integrability and exact solutions. He has supervised numerous students and remains active in academic leadership and research.

• Quantum Information
• Functional Analysis
• Operator Algebras
• +۳ مورد دیگر

Li Gao is a Professor at the School of Mathematics and Statistics, Wuhan University. His research bridges functional analysis and quantum information, with a focus on operator algebras and noncommutative analysis. Previously, he held positions at the University of Houston (tenure-track assistant professor, 2021–2023), Technical University of Munich (postdoc, 2021), and Texas A&M University (postdoc, 2018–2020). He earned his Ph.D. in Mathematics from the University of Illinois at Urbana-Champaign (2018) and a Bachelor's degree from Wuhan University (2012). His research explores quantum entanglement, quantum algorithms, and entropy inequalities in quantum systems. He actively organizes seminars like the Yangtze Analysis Seminar (2025), East Lake Quantum Seminar (2024), and undergraduate Quantum Information Seminars. Recent teaching includes Functional Analysis (Spring 2025) and Linear Algebra A. Key research themes include logarithmic Sobolev inequalities, quantum channel properties, and the interplay between noncommutative geometry and quantum information. His work addresses foundational questions in quantum computing, such as Shor's algorithm and Bell inequality violations, as seen in student-led seminar discussions on topics like quantum entanglement and Heisenberg uncertainty principles. Advising includes mentoring postdocs and Ph.D. students in interdisciplinary projects. He collaborates across departments like the Li Yinan School of Artificial Intelligence and Guanglin School of Mathematics. Current initiatives focus on quantum machine learning and cutting-edge quantum communication protocols.

• Probability Theory
• Stochastic Processes
• Functional Limit Theorems
• +۷ مورد دیگر

Prof. Alexander Iksanov is a Professor in the Operations Research Department at the Faculty of Computer Science and Cybernetics, Taras Shevchenko National University of Kyiv, Ukraine. His career includes roles as Associate Professor (2002–2008), Deputy Dean of the Faculty of Cybernetics (2004–2006), and Head of the Operations Research Department since 2014. He leads the project 'Asymptotic and structural analysis of stochastic models of population dynamics' (2019–2021). Education: 1995: Bachelor’s/Master’s degree in Cybernetics from Taras Shevchenko National University of Kyiv 2000: PhD in Physics and Mathematics 2008: Habilitation in Physics and Mathematics Research Interests: Focuses on stochastic processes, random discrete structures (compositions, coalescents, branching processes), functional limit theorems, renewal theory applications, and asymptotic analysis of complex systems. Explores regeneration properties in discrete and continuous stochastic models, including perturbed random walks, random Dirichlet series, and generalized convexity analysis. Methodological Expertise: Shot noise processes, regenerative compositions, perpetuities, branching random walks, probabilistic number theory, and convex stochastic geometry. Future Projects: Includes asymptotic theory of perturbed random walks, analysis of random Dirichlet series, discounted perpetuities, generalized convexity, random vp-trees, mod-phi convergence, and complex systems analysis. Grants & Leadership: Headed project 19БФ015-01 (2019–2021). Active in collaborative research groups and international publications.

Yi Wuمشاهده پروفایل

استادیار

• Mobile Sensing & Computing
• Wearable Software Engineering
• Cybersecurity & Privacy
• +۳ مورد دیگر

Dr. Yi Wu is an Assistant Professor at the School of Computer Science within the Gallogly College of Engineering at the University of Oklahoma. His research focuses on mobile sensing, wearable computing, cybersecurity, and smart healthcare applications, leveraging machine learning and signal processing. He holds a Ph.D. in Computer Science from the University of Tennessee, Knoxville, and has conducted postdoctoral research at Emory University, alongside industry internships at Snap Inc. and Truveta. Education: Ph.D., Computer Science, University of Tennessee, Knoxville M.S., Computer Engineering, Rutgers University B.S., Automation and Engineering, University of Electronic Science and Technology of China Research Interests: Dr. Wu specializes in mobile sensing technologies for health monitoring, wearable device security, and adversarial attacks on IoT systems. His work bridges hardware innovation with software engineering, emphasizing real-world applications like cycling fitness tracking (SmarCyPad) and AR/VR security (Face-Mic). Key Themes: Human-computer interaction, privacy-preserving biomedical systems, and embedded sensor networks. Publications Trends: His recent work spans cybersecurity vulnerabilities in AR/VR systems, lightweight biosensor technologies, and astrophysical studies of rotating stellar systems. The latter appears to represent a secondary research focus or collaborative area, with publications extending to 2023 despite primary CS affiliation. Awards: None explicitly listed. Grants/Advising: No details provided in available texts. Labs/Teams: No specific lab affiliations mentioned beyond departmental resources.

Nils Myszkowskiمشاهده پروفایل

دانشیار

• Psychometrics
• Creativity Measurement
• Emotional and Social Competencies
• +۱۲ مورد دیگر

Nils Myszkowski is a tenured Associate Professor in the Department of Psychology at Pace University, located within the Dyson College of Arts and Sciences in New York City. He holds a PhD from Université Paris Descartes (now Université de Paris) and is recognized for his contributions to psychometric theory and creativity assessment. His work bridges psychological measurement, cognitive abilities, and occupational applications. Education: PhD in Psychology, Université Paris Descartes (now Université de Paris), France, 2013 His research focuses on improving psychometric methods to measure intellectual, emotional, creative, and aesthetic abilities, particularly in occupational settings. Key areas include creativity-related traits (aesthetic sensitivity, divergent thinking), emotional and social competencies (empathy, leadership, stress coping), and advanced quantitative methods such as item response theory and statistical modeling. He applies these in contexts like occupational health, managerial creativity, and consumer behavior. The 15 most recent publications highlight a consistent trajectory in psychometrics and creativity measurement, with increasing innovation in modeling techniques (e.g., AR-IRT, Poisson models), applications in virtual reality, and explorations of emotional and personality dynamics. There is a strong emphasis on methodological rigor, interdisciplinary application, and software development for research. Scientific Awards: Berlyne Award for Outstanding Contributions by an Early Career Scholar, American Psychological Association (Division 10), 2020 Nils Myszkowski is the co-principal investigator on a National Science Foundation-funded project titled 'Developing, Field-Testing, and Validating an Elementary Algebra Concept Inventory Database For Use In The College Context' (2024–2025), demonstrating active grant acquisition and leadership in educational psychometrics. He has advised students through mentored lab courses and doctoral seminars, though specific advisee names are not listed. His professional service includes editorial board membership for Psychology of Aesthetics, Creativity and the Arts and memberships in key societies such as the Society for the Psychology of Aesthetics, Creativity, and the Arts and the International Society for the Study of Individual Differences. He has developed three open-source R libraries to support psychometric analysis, most notably the 'jrt' package for judgment data. His book Item Response Theory for Creativity Measurement (Cambridge University Press, 2024) consolidates his theoretical and practical contributions to the field.

Marc Delarueمشاهده پروفایل

استاد

• Structural Biology
• Computational Structural Biology
• DNA Repair
• +۱۲ مورد دیگر

Marc Delarue is a Professor and Head of Structure at the Laboratory of Architecture and Dynamics of Biological Macromolecules, part of the Department of Structural Biology and Chemistry at the Institut Pasteur in Paris, France. He leads multiple research projects focused on DNA repair, DNA replication, ion channels, and computational structural biology. Institution: Institut Pasteur School: Department of Structural Biology and Chemistry Department: Architecture and Dynamics of Biological Macromolecules Role: Head of Structure, Principal Investigator His research integrates experimental and computational approaches, including X-ray crystallography, cryo-EM, molecular dynamics, and electrostatic modeling, to understand macromolecular structure-function relationships. Key interests include DNA polymerases, NHEJ repair in cancer, ligand-gated ion channels, and the development of simulation software such as MinActionPath and NOMAD-Ref. The recent publications highlight a strong trend in structural mechanisms of DNA-processing enzymes, ion channel pharmacology, and computational modeling of macromolecular transitions. These works frequently appear in high-impact journals and involve collaborations across disciplines and institutions. Marc Delarue has mentored numerous PhD students and postdoctoral researchers, including Jérémie Piton, Jerome Gouge, Frederic Poitevin, and Leonardo Betancurt Anzola. His projects are supported by internal and transversal funding initiatives at the Institut Pasteur, such as the Cancer Initiative and INCEPTION convergence program, though specific grant names are not detailed. He is actively involved in software development for structural bioinformatics, leading projects like MinActionPath and NOMAD-Ref, which are used to simulate conformational changes and analyze dynamics in large macromolecular complexes.

Jean-Christophe Olivo-Marinمشاهده پروفایل

استاد

• Bioimage Analysis
• Computational Biology
• Machine Learning
• +۱۳ مورد دیگر

Jean-Christophe Olivo-Marin is the Head of the Biological Image Analysis Unit and Director of the Carnot Pasteur Institute for Microbes and Health at the Institut Pasteur in Paris, France. He previously served as Director of the Department of Cell Biology and Infection (2010–2014) and continues to lead impactful research at the intersection of biology, computation, and imaging. His research focuses on bioimage informatics , developing computational methods in Machine learning and deep learning for image analysis Bayesian tracking and optical flow algorithms Statistical modeling of spatial patterns in biological systems Mathematical imaging and computational cell biophysics Digital pathology and cancer microenvironment analysis His work enables rigorous quantitative analysis of complex biological phenomena such as cell motility, host-pathogen interactions, and neural dynamics. His recent publications (2024–2025) reflect a strong trend toward AI integration in bioimaging , with advances in deep learning-based segmentation (e.g., Deep ContourFlow), large-scale image annotation (SAMJ), and frameworks for evaluating neuron tracking. There is also a focus on spatial analysis in disease contexts , particularly in cancer and neurodevelopment, combining deep learning with spatial statistics. He has been awarded research funding for projects including Next-generation Structured Illumination Microscopy (SIM) Machine learning for cancer detection using FFOCT Compressive sensing in biological imaging Statistical analysis of spatial coupling in bioimaging (SODA) Olivo-Marin actively mentors students and postdoctoral researchers and contributes to open science through the development and maintenance of Icy , a widely used open-source bioimage analysis platform. He regularly participates in international conferences such as QBI, ICPR, and NEUBIAS, and leads advanced training courses, including the upcoming Advanced Bioimage Analysis with Artificial Intelligence (AI) course at Institut Pasteur in 2026. He leads a dynamic research unit with PhD students, postdocs, and engineers working on cutting-edge image analysis challenges. The team fosters collaboration across disciplines and institutions, promoting open science and community-driven software development.

Clement Mouhotمشاهده پروفایل

استاد

• Partial Differential Equations
• Functional Inequalities
• Stochastic Processes
• +۲ مورد دیگر

Clement Mouhot is a Professor of Mathematical Sciences at the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge. He is affiliated with the Cantab Capital Institute for the Mathematics of Information, contributing to interdisciplinary research at the intersection of mathematics and information science. Professor Mouhot's research spans several interconnected areas of mathematical analysis. His primary focus lies in partial differential equations, particularly those arising in kinetic theory and mathematical physics. He has made significant contributions to the study of the Boltzmann equation, Vlasov-Poisson system, and related kinetic models. His work often addresses fundamental questions about regularity, stability, and long-time behavior of solutions to these equations. Mouhot's research also extends to functional inequalities, stochastic processes, and the mathematical foundations of statistical mechanics, demonstrating a deep connection between analysis and physical principles. Analysis of Professor Mouhot's publication record reveals a sustained focus on Landau damping phenomena, Boltzmann equation theory (particularly the non-cutoff case), and kinetic Fokker-Planck equations. His work frequently bridges multiple mathematical disciplines, combining techniques from harmonic analysis, functional analysis, and probability theory. A notable theme throughout his research is the establishment of quantitative decay estimates and convergence rates to equilibrium for various kinetic models, often requiring innovative approaches to handle the complex structure of these equations. Professor Mouhot has maintained a strong collaborative network, with frequent co-authorship with leading researchers in mathematical physics and analysis, including Cédric Villani (with whom he published the influential paper "On Landau damping"), José Alfredo Cañizo, Stéphane Mischler, and others. His research continues to address fundamental questions at the intersection of analysis and mathematical physics, with implications for understanding complex systems in statistical mechanics and plasma physics.

Rui Antonio Loja Fernandesمشاهده پروفایل

استاد

• Differential geometry
• Poisson geometry
• Integrable systems
• +۴ مورد دیگر

Rui Antonio Loja Fernandes is a Professor of Mathematics at the University of Illinois with expertise in differential geometry, Poisson geometry, integrable systems and Lie theory. His research focuses on geometric structures with particular emphasis on groupoids and Poisson manifolds. His primary research areas include: Differential geometry and its theoretical foundations Poisson geometry and related mathematical structures Integrable systems and their geometric interpretations Lie theory, especially Lie groupoids and algebroids Geometric structures on manifolds Professor Fernandes' scholarly work demonstrates consistent focus on the theoretical aspects of geometric structures, with publications showing strong connections between differential geometry, mathematical physics, and algebraic frameworks. His research output reflects deep engagement with fundamental questions in modern geometry. His significant professional recognition includes: AMS Fellow (2017) for contributions to Poisson geometry and Lie algebroids Simons Fellow in Mathematics (2018) recognizing scientific impact Professor Fernandes maintains an active research program with numerous scholarly publications including research articles, books, and commentaries. His recent work includes the 2025 monograph "The Global Solutions to a Cartan's Realization Problem" and the 2024 textbook "Lectures on Differential Geometry" for advanced undergraduate students. He has also contributed to the mathematical community through interviews such as his conversation with Alan Weinstein published in the Notices of the American Mathematical Society.

• Stochastics
• Probability Theory
• Stochastic Processes
• +۲ مورد دیگر

Dr. Franz Nestmann serves as a Researcher at the Institute of Stochastics within the Department of Mathematics at Karlsruhe Institute of Technology (KIT). His academic profile includes teaching responsibilities across multiple semesters, with current appointments for Sommersemester 2025 including Mathematics 2 for Economics, associated exercises, and a specialized seminar in mathematical economics. His research concentrates on fundamental stochastic processes including Poisson processes and Markov chains, with significant applications in mathematical economics and fractal geometry. This interdisciplinary work bridges theoretical probability frameworks with practical economic modeling, particularly evident through his advanced courses in stochastic systems and geometric analysis of complex structures.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

• Plasma Physics
• Low Temperature Plasmas
• Plasma Modeling and Simulation
• +۵ مورد دیگر

Ralf Peter Brinkmann is a Professor and Chair holder of Theoretical Electrical Engineering at Ruhr University Bochum, Faculty of Electrical Engineering and Information Technology, since 2000. He is actively involved in research as a member of the Center for Plasma Science and Technology and a board member of the Collaborative Research Centre 591 ("Universal Behaviour of Non-Equilibrium Plasmas: Heating, Transport, and Structure Formation") since 2003. His office is located at Room ID 1/127, Universitätsstraße 150, D-44801 Bochum, with contact via brinkmann@tet.rub.de. His educational background includes: Diploma in Physics (1984) from Ruhr-University Bochum Diploma in Electrical Engineering (1985) from Ruhr-University Bochum PhD (Dr. rer. nat.) in theoretical astrophysics (1986) from Ruhr-University Bochum Brinkmann's research centers on low-temperature plasma physics, specifically modeling and simulation of technical plasmas, theory of plasma edge layers, and model-based plasma diagnostics. He investigates electron dynamics, power absorption mechanisms, and sheath behavior in capacitive coupled radio frequency discharges, magnetron plasmas, and atmospheric pressure plasma jets, with focus on kinetic effects and electromagnetic phenomena. Analysis of his recent publications (2023-2025) reveals strong emphasis on electromagnetic particle-in-cell simulations, time-resolved plasma transition studies (E-H, capacitive-inductive), and thermodynamic quasi-equilibria in high-power magnetrons. Key trends include rotational spoke dynamics in ExB plasmas, nonlocal electron transport, and diagnostic tools like the multipole resonance probe. Scientific awards: No scientific awards listed for Brinkmann in the provided text Brinkmann has advised PhD and Master's students (specific names unlisted) and secured major research funding through Collaborative Research Centre 591 under the German Research Foundation. His work includes industry collaborations with Siemens AG/Infineon Technologies and academic partnerships with Stanford University, University of Chicago, and Old Dominion University. He leads research within the Center for Plasma Science and Technology and Collaborative Research Centre 591, developing advanced plasma models and diagnostic probes. His team focuses on computational frameworks for plasma edge layers, magnetron discharge kinetics, and industrial plasma process optimization, particularly for semiconductor and thin-film applications.