جستجوی استاد راهنمای دانشگاهی

Ronald D. Haynesمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• Optimization Algorithms
• +۲ مورد دیگر

Ronald D. Haynes is a Full Professor and Chair of Scientific Computing Graduate Programs in the Department of Mathematics and Statistics at Memorial University of Newfoundland. He leads research in numerical methods for PDEs and industrial-scale optimization problems. His work develops advanced domain decomposition techniques, adaptive mesh methods, and parallel computing approaches for solving complex physical systems. Applications include modeling pitting corrosion of materials, predicting rock strength for drilling optimization, and simulating multiphase fluid flows in porous media. Recent publications demonstrate innovations in mesh adaptation, parallel algorithms, and machine learning applications for industrial problems. Collaborative projects have addressed reservoir simulation, drill bit analysis, and corrosion prediction through integrated computational approaches. Professor Haynes has received the President's Award for Outstanding Research (2018) and Dean of Science Distinguished Teaching Award (2017). He serves as Co-editor-in-chief of the CAIMS Mathematics in Science and Industry Journal and was President-Elect of the Canadian Applied and Industrial Mathematics Society (2023-2025). He maintains active doctoral supervision with current research groups focusing on domain decomposition methods, closest point algorithms, and optimization techniques. Industry partnerships include projects with ExxonMobil and Global Maritime addressing drilling optimization and mooring design challenges.

Yunfeng Jiangمشاهده پروفایل

استاد

• Algebraic Geometry
• Mathematical Physics
• Moduli Spaces
• +۳ مورد دیگر

Yunfeng Jiang is a Professor of Mathematics at the University of Kansas, affiliated with the Department of Mathematics in the College of Liberal Arts & Sciences. His research focuses on algebraic geometry, with a particular emphasis on moduli spaces of stable maps, sheaves, and their enumerative invariants such as Gromov-Witten and Donaldson-Thomas invariants. His work intersects theoretical physics, including string theory and gauge theory, exploring connections between algebraic structures and physical theories. Recent research has addressed smoothing of surface singularities, equivariant geometry, and the interplay between birational geometry and enumerative invariants. He has contributed to the study of moduli spaces of general type surfaces, Vafa-Witten invariants, and S-duality conjectures on K3 surfaces. His work often involves advanced techniques like virtual fundamental classes and obstruction theories. Jiang has taught extensively at KU, including courses on linear algebra, calculus, algebraic topology, and specialized topics in algebraic geometry. His research is supported by the NSF (DMS-2401484) and has been disseminated through over 50 peer-reviewed publications in top journals like *Mathematische Annalen*, *Advances in Mathematics*, and *Pure and Applied Mathematics Quarterly*. His research trends emphasize geometric structures in higher dimensions, moduli problems, and applications to physics, with notable contributions to the quantum cohomology of moduli spaces and the study of root stacks in algebraic geometry.

Gourab Rayمشاهده پروفایل

دانشیار

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

Gourab Ray is an Associate Professor in the Department of Mathematics and Statistics at the University of Victoria, Faculty of Science. He holds a PhD from the University of British Columbia, Vancouver. His research focuses on the intersection of probability theory, geometry, and mathematical physics, particularly large-scale patterns in stochastic models inspired by physics. Key areas include random planar maps, random walks, lattice spin models, dimer models, Gaussian free field properties, and Liouville quantum gravity. Recent work emphasizes establishing Gaussian free field-like behaviors in dimer models across various graphs and surfaces. He teaches courses such as MATH 236: Introduction to Real Analysis and MATH 555: Topics in Probability. His publications span leading journals including Inventiones Mathematicae , Annals of Probability , and Probability Theory and Related Fields . Notable contributions include studies on unimodular hyperbolic triangulations, half-planar map classifications, and conformal invariance in dimer models. No specific awards are listed for Dr. Ray, though his work has been recognized in peer-reviewed venues. He actively contributes to academic service, including roles on graduate committees and research collaborations. His research group engages with theoretical and applied aspects of probability theory, often bridging discrete and continuous mathematical frameworks.

Stephen Pankavichمشاهده پروفایل

استاد

• Partial Differential Equations
• Kinetic Theory
• Mathematical Biology
• +۴ مورد دیگر

Stephen Pankavich is a Professor and Department Head in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. He holds a PhD in Mathematical Sciences from Carnegie Mellon University, with research focused on partial differential equations, kinetic theory, and mathematical biology. His work bridges theoretical analysis and computational methods, addressing challenges in plasma dynamics, epidemiological modeling, and multiscale systems. Education: PhD, Mathematical Sciences, Carnegie Mellon University (2005) MS, Mathematical Sciences, Carnegie Mellon University (2001) BS, Mathematical Sciences, Carnegie Mellon University (2000) Research interests include the analytical and numerical study of collisionless plasmas, HIV dynamics, and epidemiological models. He has received awards such as the W.M. Keck Mentorship Award and the Colorado School of Mines Alumni Teaching Award. His articles explore topics like plasma decay rates, HIV therapy models, and particle-tracking algorithms. He has advised over 20 graduate and undergraduate students, contributing to impactful research in applied mathematics and computational science.

• Continuum Mechanics
• Metamaterials
• Composite Materials
• +۵ مورد دیگر

William Parnell is a Professor of Applied Mathematics at the University of Manchester's School of Mathematics. His research focuses on continuum mechanics, metamaterials, and industrial composites, with applications in soft tissue mechanics and acoustic wave manipulation. He leads the Mathematics of Waves and Materials (MWM) group and co-founded the Manchester Materials Modelling Centre (M3C). He has held roles including EPSRC Fellowship 'NEMESIS' (2014-2019) and its extension, contributing to transformative materials science. Education: BSc Mathematics (First Class), University of Bristol (1996-1999) MSc Mathematical Modelling and Scientific Computing (Distinction), University of Oxford (1999-2000) PhD in Applied Mathematics, University of Manchester (2001-2004) His research interests span elastic wave propagation, cloaking, and viscoelastic modeling. He has pioneered hyperelastic cloaking techniques and developed mathematical methods for metamaterials. His work contributes to UN Sustainable Development Goals related to advanced materials and digital innovation. Key achievements include the 2019 Whitehead Prize and over 80 publications. His grants include funding for microstructured material design and collaborations with Thales UK and the National Physical Laboratory. Grants & Awards: EPSRC Fellowships (NEMESIS and extension) Whitehead Prize (2019) Labs/Teams: MWM Group (focusing on waves and materials) M3C (Manchester Materials Modelling Centre)

Patrick Jennyمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Turbulent Reactive Flows
• Multiphase Flow Modeling
• +۳ مورد دیگر

Prof. Patrick Jenny is a Full Professor at the Department of Mechanical and Process Engineering and Head of the Institute of Fluid Dynamics at ETH Zurich. His research focuses on computational fluid dynamics (CFD), numerical methods for turbulent and multiphase flows, and reservoir simulation. He has held positions at ChevronTexaco and Cornell University, and received the National Latsis Prize 2005. PhD in CFD from ETH Zurich (1997) Postdoctoral work at Cornell University (1997–1999) Senior Researcher at ChevronTexaco (1999–2003) Research interests include: turbulent reactive flows, PDF modeling, multi-scale reservoir simulation, and data assimilation in engineering systems. He teaches courses on fluid dynamics, turbulence, and computational methods. Over 100 peer-reviewed publications span topics like fracture modeling, LES/RANS coupling, and particle-laden flows. His work bridges academia and industry, addressing challenges in energy systems, environmental engineering, and numerical algorithms. Winner: National Latsis Prize 2005 Led over 20 PhD projects and collaborates with institutions globally. His lab develops open-source tools for CFD and energy systems analysis.

Zachary E. Rossمشاهده پروفایل

استاد

• Seismology
• Geophysics
• Machine Learning
• +۷ مورد دیگر

Zachary E. Ross is a Professor of Geophysics at the California Institute of Technology (Caltech) and holds the William H. Hurt Scholar distinction since 2021. His research integrates machine learning, computational mathematics, and seismology to analyze earthquakes and fault zones using large seismic datasets. Education B.S., University of California, Davis (2009) M.S., California Polytechnic State University, San Luis Obispo (2011) Ph.D., University of Southern California (2016) His research focuses on high-resolution imaging of fault zones, understanding earthquake sequences in space and time, and applying artificial intelligence to seismic data analysis. He develops scalable algorithms for waveform inversion, ground-motion synthesis, and real-time seismic monitoring. Recent publications highlight his work on neural operators for wave propagation, AI-driven seismicity analysis, and induced earthquake dynamics. He teaches advanced courses including Ge 264 – Machine Learning in Geophysics and Ge 271 – Dynamics of Seismicity . Scientific Awards William H. Hurt Scholar (2021–present)

David J. Norrisمشاهده پروفایل

استاد

• Materials Engineering
• Optical Materials
• Nanotechnology
• +۴ مورد دیگر

Prof. David J. Norris is a Full Professor at ETH Zurich's Department of Mechanical and Process Engineering and Director of the Optical Materials Engineering Laboratory. He holds a B.S. in Chemistry from the University of Chicago (1990) and a Ph.D. in Physical Chemistry from MIT (1995). His research focuses on engineering materials to achieve novel optical properties, particularly semiconductor nanocrystals (quantum dots) and plasmonic films. Notable awards include the Max Rössler Prize (2015) and ERC Advanced Grant (2014-2019). Research interests span nanoscale optical phenomena, including exciton dynamics in colloidal systems and plasmonic nanofocusing. He has pioneered studies on magic-sized semiconductor nanocrystals and developed methods for high-throughput characterization of atomically thin semiconductors. His work bridges nanotechnology and photonics, addressing applications in lasers, sensors, and energy systems. Awards also include the Credit Suisse Award for Best Teaching (2015) and fellowships from the American Physical Society and AAAS. He serves on editorial boards for ACS Photonics and Nano Letters , reflecting his leadership in nanophotonics and materials science. Grants include an ERC Advanced Grant supporting his exploration of optical materials. His lab’s innovations include template-stripping techniques for plasmonic devices and plasmon-enhanced catalysis. Past roles include Director of Graduate Studies at the University of Minnesota and an Alexander von Humboldt Fellowship at TU Munich (2006-2007).

Benno Liebchenمشاهده پروفایل

استاد

• Soft Matter Physics
• Active Matter
• Colloidal Systems
• +۳ مورد دیگر

Prof. Benno Liebchen holds a faculty position at the Technische Universität Darmstadt within the Institute for Condensed Matter Physics , part of the Faculty of Physics. He leads the Liebchen Group , dedicated to advancing research in the Theory of Soft Matter , focusing on active matter, colloidal systems, and non-equilibrium phenomena. His work explores collective behavior in self-propelled particles, phase transitions in active fluids, and adaptive strategies in smart materials. Research Interests include: Active matter dynamics and pattern formation Non-equilibrium statistical mechanics Biophysical systems and biomimetic design Computational modeling of soft matter Recent publications highlight breakthroughs in intelligent active particles , self-reverting vortices , and motility-induced phase coexistence . His lab develops tools like the AMEP Python package to analyze active systems. Teaching responsibilities include advanced modules in soft matter physics. Collaborative projects involve interdisciplinary approaches to microswimmer behavior and machine learning-driven optimization of collective systems. Contact: +49 6151 16-24509 / Office: S2|04 104

Ed Chienمشاهده پروفایل

استادیار

• Computer Science
• Graphics
• Computational Engineering
• +۳ مورد دیگر

Ed Chien is an Assistant Professor at Boston University's Department of Computer Science within the College of Arts & Sciences. He specializes in applying differential geometry and topology to graphics, computational engineering, and machine learning. Previously, he was a postdoctoral researcher at MIT's CSAIL and Bar-Ilan University. His work focuses on mathematically rigorous solutions to problems in geometric data processing and optimal transport. Education PhD in Mathematics, Rutgers University (2015) A.B. in Mathematics & Physics, Dartmouth College (2009) Research Highlights Dr. Chien's research includes fundamental studies on hexahedral mesh topology for Finite Element Modeling, optimal transport applications in machine learning, and singularity-free geometric algorithms. His work bridges theoretical mathematics with computational tools for engineering and graphics. Publications span top venues like NeurIPS, Eurographics, and SIGGRAPH, reflecting contributions to geometry processing and machine learning intersections. Program committee roles include Eurographics SGP (2019) and AAAI (2020). Awards & Recognition No specific awards listed, but notable service includes program committee memberships and peer-reviewed contributions to leading conferences. Grants & Advising Active in mentoring through academic positions but no explicit grant details provided. Research focuses on advancing geometric algorithms and optimal transport methodologies.

Celia Reinaمشاهده پروفایل

دانشیار

• Materials Science
• Computational Mechanics
• Non-equilibrium Thermodynamics
• +۳ مورد دیگر

Celia Reina is an Associate Professor in the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania’s School of Engineering and Applied Science (SEAS). Her research focuses on multiscale modeling of materials, bridging statistical mechanics, thermodynamics, and machine learning. She develops novel frameworks for predicting non-equilibrium material behavior using data-driven methods and uncertainty quantification. Her work emphasizes integrating computational tools like neural networks (Stat-PINNs, VONNs) with physical principles to model dissipative systems, phase transitions, and mesoscale dynamics. Key areas include coarse-graining techniques, epistemic uncertainty analysis, and predictive modeling of complex materials under dynamic loading. Recent publications highlight advancements in stochastic systems, resonant metamaterials, and the derivation of thermodynamic models from particle-level fluctuations. She leads efforts in experimental-simulation co-design to enhance predictive capabilities in materials science.

Jonathan Weareمشاهده پروفایل

استاد

• Monte Carlo sampling
• Multiscale analysis
• Randomized numerical linear algebra
• +۲ مورد دیگر

Jonathan Weare is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds affiliations with the Faculty of Arts and Science and the Graduate School of Arts and Science. His academic journey includes roles as an Associate Professor at the University of Chicago (2014–2019) and Assistant Professor (2011–2014), following postdoctoral work as a Courant Instructor at NYU. He earned his Ph.D. in Mathematics from UC Berkeley in 2007. His research focuses on stochastic algorithms and models, with applications in astrophysics, biophysics, computational chemistry, and climate science. Key areas include Monte Carlo methods, rare event simulation, and machine learning-driven scientific analysis. Collaborations with domain experts ensure his work addresses real-world challenges in diverse fields. Recent publications emphasize advancements in trajectory stratification, rare event prediction using machine learning, and efficient algorithms for high-dimensional problems. Notable contributions include the BAD-NEUS framework and AI-based solar system instability predictions. His group’s interdisciplinary approach bridges computational methods with scientific inquiry. Weare has advised numerous students and mentored postdocs, fostering talent in applied mathematics and computational science. His work on Mercury’s orbital dynamics and extreme weather prediction showcases the societal impact of his research. Current projects explore AI applications in weather modeling and rare event analysis, leveraging cutting-edge machine learning techniques. Labs/Teams: His research group at Courant develops stochastic algorithms and collaborates with interdisciplinary teams in computational chemistry, climate science, and astrophysics. Key collaborations include the University of Chicago and Columbia University.

Sang-Hyun Ohمشاهده پروفایل

استاد

• Nanophotonics
• Plasmonics
• Biosensing
• +۲ مورد دیگر

Sang-Hyun Oh is Distinguished McKnight University Professor and Sanford P. Bordeau Chair in Electrical and Computer Engineering at University of Minnesota. His research develops nano-optical tools for biomedical applications, specializing in plasmonic biosensors, nanophotonic devices, and optical manipulation techniques. Key innovations include nanofluidic platforms for single-molecule analysis, high-Q metasurfaces for vibrational spectroscopy, and waveguide-integrated optical tweezers. Recent projects focus on diagnostic technologies such as Nano-QuIC for Parkinson's detection and computational design of upconversion materials. His laboratory advances nanofabrication methods including template stripping and atomic-layer lithography to create plasmonic nanostructures with atomic-scale precision. Collaborative projects bridge photonics, neuroscience, and clinical medicine to develop next-generation biosensors.

Andrea Collevecchioمشاهده پروفایل

استاد

• Probability
• Mathematical Physics
• Statistical Mechanics
• +۳ مورد دیگر

Andrea Collevecchio is a Professor in the School of Mathematics at Monash University, Australia, where he has been a faculty member since 2012. His research focuses on the intersection of Probability, Mathematical Physics, and Statistical Mechanics, with particular expertise in stochastic processes and theoretical modeling. He earned his PhD in Statistics from Purdue University in 2004, followed by postdoctoral positions in Italy and Germany. In 2006, he became Assistant Professor at Ca’Foscari University in Venice before joining Monash University. Collevecchio specializes in Reinforced Processes and Large Deviations, investigating complex systems through random walk models. His work bridges abstract probability theory with applications in statistical mechanics, examining phenomena like memory effects in stochastic processes and phase transitions in lattice systems. Recent research emphasizes hypercube structures, non-reversible dynamics, and reinforcement mechanisms. His 2021-2025 publications reveal a concentrated focus on hypercube random walks, with increasing exploration of non-reversible processes, vertex-reinforced dynamics, and bootstrap methods. These works consistently apply probabilistic frameworks to problems in mathematical physics, demonstrating strong connections between theoretical probability and physical modeling. Collevecchio has secured multiple research grants including ARC-funded projects on self-interacting random walks (2023-2026) and random walks with long memory (2018-2022). He contributes to interdisciplinary initiatives like the Smart Vehicles project for dementia support (2025-2027) and actively organizes academic events including the AIM Day series connecting mathematics, AI, and industry applications.

• Fluid Dynamics
• Magnetohydrodynamics
• Turbulence
• +۳ مورد دیگر

Kengo Deguchi is a Senior Lecturer in the School of Mathematics at Monash University. His research focuses on fluid dynamics, magnetohydrodynamics, and turbulence phenomena. He leads and collaborates on ARC-funded projects exploring flow control via topography, vortex dynamics in complex flows, and mathematical descriptions of magneto-hydrodynamic turbulence. Notable awards include the 2018 Faculty of Science Research Excellence Award and Vice-Chancellor’s Early Career Excellence Award. Education: Doctorate in Fluid Dynamics (details not specified in text) His research interests emphasize nonlinear instabilities, vortex dynamics, and coherent structures in shear flows. Recent work investigates Taylor-Couette flow chaos, subcritical transitions, and MHD dynamos. Over 40 publications span topics like turbulence statistics, chaotic patterns, and fluid instabilities. Key projects include investigating vortex persistence in counter-rotating systems and developing mathematical frameworks for MHD turbulence. He has secured funding through ARC grants (2017-2026) and collaborates internationally with experts like Prof. Hall and Prof. Blackburn. Grants: $A 2.6M+ in ARC funding (2017-2026) Labs/Teams: Collaborative fluid dynamics research groups focused on experimental and computational turbulence studies