جست‌وجوی استاد و پژوهشگر

Juhi Jangمشاهده پروفایل

استاد

• Fluid Dynamics
• Gas Dynamics
• Kinetic Theory
• +۴ مورد دیگر

Professor Juhi Jang is a mathematician specializing in analysis and partial differential equations (PDEs) with applications to fluid dynamics, gas dynamics, kinetic theory, and astrophysics. She holds the rank of Professor in the Department of Mathematics at the University of Southern California (USC), where she has been since 2020. Prior to this, she served as an Associate Professor at USC (2015–2020) and UC Riverside (2014–2015), and as an Assistant Professor at UC Riverside (2010–2014). Her research focuses on the mathematical analysis of fluid and gas dynamics, singularities in PDEs, and gravitational collapse. Notable contributions include work on the Einstein-Euler system, hydrodynamic limits from kinetic equations, and nonlinear stability of expanding stars. Educationally, Jang earned a B.Sc. (summa cum laude) in Mathematics from Seoul National University (2000), followed by a M.Sc. (2004) and Ph.D. (2007) in Mathematics from Brown University. Her honors include the Frontiers of Science Award (2023), Simons Fellowship (2019–2020), and NSF CAREER grant (2014–2020). She has also organized international summer schools on mathematical fluids and served on editorial boards for journals like SIAM Journal on Mathematical Analysis and Kinetic and Related Models. Her research interests span: fluid dynamics, gas dynamics, kinetic theory, plasma physics, and mathematical physics. Key areas include moving boundary problems in compressible fluids, stability of gravitational systems, and singularity formation in fluid models. She has authored over 50 peer-reviewed articles, with recent work appearing in Annals of PDE , Archive for Rational Mechanics and Analysis , and Inventiones Mathematicae . Jang’s teaching spans graduate courses in PDEs, real analysis, and topology, alongside undergraduate calculus and topology. She has advised numerous graduate students and postdocs, contributing to the training of the next generation of mathematical analysts.

Caglar Oskayمشاهده پروفایل

استاد

• Multiscale Computational Modeling
• Composite Materials
• Structural Failure Analysis
• +۷ مورد دیگر

Caglar Oskay is the Cornelius Vanderbilt Professor of Engineering and Chair of the Department of Civil and Environmental Engineering at Vanderbilt University. He is also a Professor of Mechanical Engineering. His research focuses on multiscale computational modeling of material and structural systems under extreme conditions, with expertise in composite materials, failure mechanisms, and computational mechanics. Dr. Oskay earned his Ph.D. in Civil Engineering from Rensselaer Polytechnic Institute (2003) and has held academic roles there before joining Vanderbilt in 2006. He was honored as an ASME Fellow in 2017 and as a Chancellor Faculty Fellow in 2016. Key research areas include multiscale failure modeling, life prediction of heterogeneous materials, and computational methods for composites and multiphysics systems. His work integrates advanced simulation techniques with experimental validation, addressing challenges in infrastructure resilience, additive manufacturing defects, and quantum computing applications in engineering. Education: Ph.D., Civil Engineering, Rensselaer Polytechnic Institute (2003) M.S., Civil Engineering, Rensselaer Polytechnic Institute M.S., Applied Mathematics, Rensselaer Polytechnic Institute B.S., Civil Engineering, Middle East Technical University Recent research highlights include stochastic modeling of geotechnical infrastructure failures, quantum-enhanced finite element methods, and predictive analytics for additive manufacturing defects. He leads interdisciplinary efforts on backward erosion piping in flood protection systems and has secured grants for multiscale modeling of titanium alloys and composites. Awards: ASME Fellow (2017) Chancellor Faculty Fellow (2016) Advising and grants: Dr. Oskay’s grants include NSF-funded studies on backward erosion piping and quantum computing applications. His research group collaborates with industry partners on materials for aerospace and energy sectors, emphasizing computational tools for failure prediction and material design. His work bridges computational mechanics with practical engineering challenges, advancing methods for infrastructure resilience, advanced materials, and sustainable design through multiscale modeling innovations.

Cristiano Porcianiمشاهده پروفایل

استاد

• Cosmology
• Large-scale Structure
• Galaxy Formation
• +۲ مورد دیگر

Cristiano Porciani is Professor of Astrophysics at the University of Bonn's Argelander Institute for Astronomy, specializing in cosmological structure formation and galaxy evolution. He leads a research group working on numerical simulations of large-scale structure and theoretical cosmology. His research focuses on dark matter distribution, galaxy bias, and cosmological parameter estimation using perturbation theory and high-performance computing. Recent work examines relativistic effects in large-scale structure and intensity mapping techniques. Publications show strong emphasis on Euclid mission science, including instrument characterization, survey simulations, and cosmological tests. Article trends reveal consistent development of statistical methods for analyzing next-generation sky surveys. Supervises 9 graduate students working on cosmological simulations, galaxy clustering statistics, and radiative transfer modeling. Leads research projects within the Euclid Consortium and Transregional Collaborative Research Centre.

Mattias Villaniمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.

Hamid Hezariمشاهده پروفایل

دانشیار

• Analysis
• Partial Differential Equations
• Spectral Geometry
• +۳ مورد دیگر

Hamid Hezari is an Associate Professor in the Department of Mathematics at the University of California, Irvine (UCI). He holds a PhD from Johns Hopkins University (2009) and was a CLE Moore Instructor at MIT (2009–2012), with a postdoctoral fellowship at the Mathematical Sciences Research Institute (MSRI) during 2010. His research focuses on analysis and partial differential equations, particularly semiclassical/microlocal analysis and its applications in spectral geometry and quantum chaos. Key research interests include spectral geometry, quantum chaos, inverse problems, and the analysis of eigenfunctions. He has published over 25 peer-reviewed articles in top journals such as the Annals of Mathematics, Journal of Spectral Theory, and Communications in Mathematical Physics. His work explores topics like spectral rigidity of geometric domains, nodal set properties, and Bergman kernel asymptotics. Teaching responsibilities at UCI include courses in analysis, partial differential equations, and graduate-level topics like microlocal analysis. He has been supported by grants and has contributed to collaborative research with leading institutions. No specific awards are highlighted in the provided texts, though his prolific publication record underscores his scholarly impact. Hezari’s research often bridges mathematical physics and pure analysis, with applications to understanding geometric and dynamical systems through spectral methods.

Dora Erdosمشاهده پروفایل

مدرس ارشد

• Algorithm Design
• Data Mining
• Combinatorial Optimization
• +۳ مورد دیگر

Dora Erdos is a Senior Lecturer and Director of Undergraduate Studies in the Department of Computer Science at Boston University. She specializes in algorithmic challenges, data mining, and combinatorial optimization with a focus on network-based problems. Her work bridges theoretical computer science and practical applications in education technology and network analysis. Education: PhD in Computer Science, Boston University (2015) MSc in Pure Mathematics, Eotvos University (Advisor: Andras Frank) Postdoctoral Research at Brown University's Raphael Lab (Advisor: Ben Raphael) Research Interests: Erdos investigates algorithms for network analysis, including centrality measures, graph reconstruction, and optimization problems in educational systems. She develops scalable methods for tensor factorization and content placement in navigational networks. Her work often integrates combinatorial approaches with real-world applications. Professional Roles: As Director of Undergraduate Studies, Erdos oversees academic advising and curriculum development. She emphasizes student accessibility, maintaining office hours and encouraging direct communication via email (edori@bu.edu). Recent Research Trends: Her publications (2011–2017) focus on network-centric problems such as centrality evaluation frameworks, boolean tensor decomposition, and team formation algorithms for educational scheduling. These contributions highlight her dual expertise in theoretical algorithm design and applied educational technology.

Plamen Angelovمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +۵ مورد دیگر

Prof. Plamen Angelov holds a Chair in Intelligent Systems and is Director of Research at Lancaster University's School of Computing and Communications. He leads the Lancaster Intelligent, Robotic, and Autonomous Systems (LIRA) Centre, uniting 70+ faculty across 15 departments. With a PhD (1993) and DSc (2015), he is a Fellow of IEEE, IET, ELLIS, and AAIA. His research focuses on explainable AI, evolving systems, and computational intelligence, with 400+ publications (h-index 65) in top venues like TPAMI and IEEE Transactions. Notable achievements include the Dennis Gabor Award (2020) and ranking in Stanford's Top 0.2% AI researchers (2024). He leads projects funded by UK Research Councils, ESA, DSTL, and industry. As Editor-in-Chief of Springer's Evolving Systems , he drives standards in explainable AI through IEEE's P2976 Working Group. His work spans autonomous systems, cybersecurity, and biomedical applications through initiatives like AI4EO and H-UNIQUE.

• Visualization
• Computer Graphics
• Flow Visualization
• +۳ مورد دیگر

Tino Weinkauf is a Professor of Visualization and Head of the Division of Computational Science and Technology at KTH Royal Institute of Technology in Stockholm. His work bridges computer science and applied mathematics, with a focus on visualization and topological data analysis. He leads research in visualizing complex data from fields like fluid dynamics, neurobiology, and human-computer interaction. Education: Ph.D. in Computer Science (not explicitly stated in provided texts, but inferred from career trajectory). Research interests include flow visualization, topological methods for data analysis, and interactive visualization techniques. He develops tools like the TopoInVis Toolkit (TTK) and contributes to infrastructure such as the Swedish Research Infrastructure for Visualization Support (InfraVis). His work emphasizes applications in turbulence modeling, biomedical imaging, and user-centered design. Teaching: Responsible for courses such as Advanced Topics in Visualization and Computer Graphics , Information Visualization , and Introduction to Visualization and Computer Graphics . Supervises degree projects in Computer Science and Engineering across specializations like Machine Learning and Interactive Media Technology. Publications focus on topological data analysis, flow segmentation, and algorithm optimization. Notable projects include binary segmentation of turbulent flows and interactive reward tuning systems for preference elicitation. Labs/Teams: Leads the Division of Computational Science and Technology at KTH, fostering interdisciplinary research in computational methods and visualization technologies.

Stephan Stolzمشاهده پروفایل

استاد

• Geometric Topology
• Algebraic Topology
• Differential Geometry
• +۳ مورد دیگر

Stephan Stolz is a Professor in the Department of Mathematics at the University of Notre Dame, affiliated with the College of Science. His research focuses on geometric and algebraic topology, differential geometry, and questions related to positive scalar curvature and moduli spaces. He holds a Vardiplom from the University of Bielefeld (1975), a Diplom from the University of Bonn (1979), and a Ph.D. from Mainz University (1984). Stolz has made significant contributions to understanding the interplay between topology and geometry, particularly in the classification of manifolds with specific curvature properties. His work often bridges algebraic topology with geometric analysis, including studies on elliptic cohomology, supersymmetric field theories, and the Gromov-Lawson conjecture. He has organized initiatives like the RTG: Geometry and Topology (2016) and contributed to conferences such as the Topology and Field Theories summer school (2012). His research outputs span over four decades, addressing topics like spinor bundles on loop spaces, factorization algebras, and the relationship between differential forms and supersymmetric field theories. While no formal awards are listed, his extensive publication record reflects sustained academic impact in his fields. Stolz collaborates actively with institutions like UC San Diego and has supervised projects involving algebraic structures in quantum field theories. His office is located in 166A Hurley Bldg, and he remains a key figure in geometric topology and related mathematical physics areas.

• Control Systems
• Model Predictive Control
• Power Systems Engineering
• +۳ مورد دیگر

Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.

Matthias S. Maierمشاهده پروفایل

دانشیار

• Multiscale Methods
• Computational Fluid Dynamics
• Finite Element Methods
• +۳ مورد دیگر

Matthias S. Maier is an Associate Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. His research focuses on multiscale methods, computational fluid dynamics, and finite element software development, particularly with the deal.II library. He organizes an annual undergraduate summer school on PDE modeling and simulation. Research Interests: Multiscale effects in Maxwell’s equations, computational fluid dynamics, finite element methods, and numerical analysis. His work includes studies on surface plasmon-polaritons, homogenization theory, and high-performance computing for hyperbolic systems. Developed ryujin, a high-performance finite-element solver for compressible flows. Contributed to deal.II, a widely used open-source finite element library. Recipient of NSF awards (DMS 1912847, DMS 2045636) and AFOSR funding. Teaching includes courses on numerical methods (Math 417, 610), mathematical modeling (Math 442), and finite element methods (Math 676). He advises graduate students in applied mathematics and computational science. Labs/Teams: Core developer of deal.II and contributor to the ryujin framework. Collaborates with interdisciplinary teams in physics and engineering.

Quan Zhouمشاهده پروفایل

استادیار

• Markov chain Monte Carlo sampling
• Bayesian methodology
• Variable selection
• +۷ مورد دیگر

Quan Zhou is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing advanced sampling methods, particularly Markov chain Monte Carlo (MCMC) algorithms, with applications in Bayesian methodology, variable selection, stochastic optimization, and statistical genetics. He holds a BS from Fudan University and a PhD from Baylor College of Medicine, followed by a postdoctoral fellowship at Rice University. He teaches courses such as Mathematical Probability, Multivariate Analysis, and Advanced Stochastic Processes. Notable contributions include work on informed MCMC samplers, Schrödinger bridge theory, and high-dimensional structure learning. He advises PhD students like Hyunwoong Chang (now at UT Dallas) and Guanxun Li (Beijing Normal University). Active in academic service, he served as President of the Southeastern Texas Chapter of the American Statistical Association (SETCASA).

Emanuele Dottoمشاهده پروفایل

دانشیار

• Algebraic Topology
• Hermitian K-theory
• Topological Hochschild Homology
• +۳ مورد دیگر

Emanuele Dotto is an Associate Professor in the Mathematics Institute at the University of Warwick, specializing in algebraic topology with focus on Hermitian K-theory, topological Hochschild homology, and equivariant homotopy theory. His research bridges algebraic structures and homotopical methods , particularly through the development of Poincaré categories and real topological Hochschild homology theories. Current projects investigate connections between Witt vectors, cyclic homology, and K-theoretic invariants. He leads seminars on advanced topics including cyclotomic spectra, Franke's algebraicity conjecture, and Adams spectral sequences. Recent editorial responsibilities include the Journal and Bulletin of the London Mathematical Society. EPSRC New Investigator Award recipient (2022-2025) DFG-funded research through SPP 1786 Priority Program

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Discrete Optimization
• +۷ مورد دیگر

Nikhil Bansal holds the prestigious Patrick C. Fischer Professorship of Theoretical Computer Science in the Department of Computer Science & Engineering at the University of Michigan's College of Engineering. His research program has established him as a leading figure in theoretical computer science, with significant contributions to algorithm design and analysis, particularly in discrete optimization problems. Bansal's research focuses on theoretical computer science with emphasis on design and analysis of algorithms for discrete optimization problems. His work spans multiple areas including discrepancy theory, approximation algorithms, randomized algorithms, combinatorial optimization, complexity theory, machine learning theory, and probability. He has made significant contributions to understanding the limits of approximation algorithms and developing novel techniques for combinatorial optimization problems. Analysis of Bansal's recent publications reveals a strong focus on discrepancy theory, online algorithms, and combinatorial optimization. His work often bridges theoretical computer science with discrete mathematics and probability theory. A recurring theme across his publications is the development of novel algorithmic techniques for solving NP-hard problems with provable guarantees. His research has evolved from foundational work in approximation algorithms to more recent contributions in quantum computing complexity and stochastic optimization. Patrick C. Fischer Professor of Theoretical Computer Science Bansal has advised numerous PhD students including Marek Elias, Shashwat Garg, and Greg Koumoutsous, as well as mentoring several postdoctoral researchers. He has served on editorial boards for top journals including Journal of the ACM, Theory of Computing, and Stochastic Models, and has been active on program committees for major conferences such as STOC, FOCS, SODA, and ICALP, including serving as chair for ICALP 2021. Bansal has organized multiple academic workshops including the STOC 2020 Workshop on Recent Advances in Discrepancy and Applications, several SDP Days at CWI Amsterdam, and the Semester on Bridging Continuous and Discrete Optimization at UC Berkeley in Fall 2017.

Nikhil Bansalمشاهده پروفایل

استاد

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Discrete Mathematics
• +۳ مورد دیگر

Nikhil Bansal is a Professor in Theoretical Computer Science at the University of Michigan, Ann Arbor. He earned his PhD from Carnegie Mellon University and previously worked at IBM Research, TU Eindhoven, and CWI Amsterdam. His research focuses on algorithm design, discrepancy theory, and combinatorial optimization. Education: PhD, Carnegie Mellon University Bansal's work bridges classical and quantum computing, with recent publications exploring k -Forrelation, vector balancing, and stochastic scheduling. His algorithmic approaches often combine geometric insights and probabilistic methods. Scientific Awards: Patrick C. Fischer Professor of Theoretical Computer Science NSF Career Award (2023) He has advised numerous PhD and postdoctoral researchers, including Marek Elias, Shashwat Garg, and Makrand Sinha. Bansal actively contributes to program committees (ICALP 2021, STOC 2020, FOCS 2018) and organizes workshops on discrepancy theory and optimization.