جست‌وجوی استاد و پژوهشگر

• Applied Mathematics
• Dynamical Systems
• Nonlinear Dynamics
• +۳ مورد دیگر

Paul Glendinning is the Beyer Professor of Applied Mathematics at The University of Manchester. His research focuses on dynamical systems, particularly piecewise smooth systems, bifurcation theory, and nonlinear dynamics. His work addresses applications ranging from engineering (e.g., power converters) to biological systems (e.g., epilepsy modeling) and mechanical systems (e.g., swing dynamics). He has received the JDEA Best Paper 2021 Award for outstanding contributions to mathematical research. Glendinning is also active in academic leadership, serving as President of the Institute of Mathematics and its Applications (IMA). His research interests include the classification of singular points in piecewise smooth systems, chaos theory, and the development of normal forms for bifurcations. He collaborates internationally on topics such as border-collision bifurcations, invariant manifolds, and the analysis of complex dynamical systems. His contributions bridge theoretical advancements with practical applications, emphasizing interdisciplinary relevance. Recent work explores adaptive resonance mechanisms and the mathematical modeling of real-world phenomena like energy transfer in swinging systems.

Tim Hoheiselمشاهده پروفایل

دانشیار

• Nonsmooth Optimization
• Variational Analysis
• Continuous Optimization
• +۴ مورد دیگر

Tim Hoheisel is an Associate Professor at McGill University's Department of Mathematics and Statistics. His research focuses on nonsmooth optimization, variational analysis, and their applications in continuous optimization and machine learning. He is the director of the CRM Applied Math Laboratory and co-chair of the CRM thematic program on Mathematical Foundations of Data Science (2025). He serves as an Associate Editor for *Mathematics of Operations Research* and *Open Journal of Mathematical Optimization*. Hoheisel has organized conferences and seminars, including the CRM Applied Math Seminar in Montreal. He has advised numerous PhD and MSc students and postdocs, collaborating on topics like regularization methods, inverse problems, and convex analysis. Education: PhD in Mathematics from University of Würzburg (2009). Committee Roles: Advisor for Honours Applied Mathematics (B.Sc.) Program, elected member of the Chair's Advisory Committee. His research integrates theoretical analysis with applications, bridging applied and pure mathematics. Recent work includes contributions to the Maximum Entropy on the Mean method for inverse problems, Lipschitz stability in LASSO, and regularization techniques. Hoheisel is a member of SIAM and the MTL MLOpt group, focusing on optimization-machine learning interfaces. Recent presentations include a semi-plenary talk at the 2025 International Conference on Continuous Optimization and lectures on regularized least-squares at the University of Trier. His publications span optimization theory, convex analysis, and applications in image processing and quantum Monte Carlo data analysis. Awards: None explicitly mentioned. Grants/Funding: Not detailed; inferred from editorial and leadership roles. He leads the CRM Applied Math Lab and collaborates with institutions like Concordia University and the University of Ottawa through INTER-MATH-AI. His work emphasizes interdisciplinary approaches, particularly in data science and AI integration with mathematical optimization.

• Combustion
• Acoustics
• Fluid Mechanics
• +۴ مورد دیگر

Prof. Nicolas Noiray is an Associate Professor and Deputy Head of the Institute of Energy and Process Engineering at ETH Zürich, where he leads the Combustion, Acoustics & Flow Physics (CAPS) Laboratory established in 2014. His research focuses on combustion, acoustics, and fluid mechanics, addressing both fundamental and applied challenges. He holds a Ph.D. from École Centrale Paris (2007) and previously worked at Alstom’s Gas Turbine Research Division. Key achievements include pioneering work on thermoacoustic instability control, hydrogen combustion systems, and plasma-assisted stabilization. He has received prestigious awards such as the Silver Medal from the International Combustion Institute and ERC grants. His academic affiliations include the Department of Mechanical and Process Engineering at ETH Zürich, with a focus on interdisciplinary research at the intersection of energy systems and fluid dynamics. His lab investigates topics like flame dynamics, aeroacoustic feedback, and novel combustor designs to enhance efficiency and reduce emissions. Research interests span combustion instabilities, acoustic wave control, and sustainable energy technologies. Recent work includes studies on hydrogen-powered combustors, nonlinear dynamics of flames, and metamaterial-based solutions for noise reduction. He has contributed to advancing computational models for large-eddy simulations and real-time digital twins for combustion systems. Notable awards include the Hiroshi Tsuji Early Career Award and ERC Consolidator/Synergy Grants, reflecting his impactful contributions to combustion science. His research bridges theoretical insights with industrial applications, particularly in aerospace and power generation sectors.

• Electrochemistry
• Energy Conversion
• Catalysis
• +۳ مورد دیگر

Yong Yang is an academic researcher affiliated with the Faculty of Science at the University of Sydney. Their work focuses on advanced materials and electrochemical systems with applications in renewable energy, catalysis, and environmental chemistry. Key research interests include the design of novel electrocatalysts for fuel cells, CO2 reduction, and energy storage systems, leveraging nanotechnology and biomimetic engineering. Research highlights include innovations in anion-exchange membrane fuel cells, structural engineering of atomically dispersed catalysts, and biomimetic metal-organic frameworks. Their studies often address challenges in catalyst stability, degradation mechanisms, and reaction selectivity. Recent work has explored single-atom catalysts, hybrid nanomaterials, and high-entropy alloys to enhance electrochemical performance. Publications (2020–2024) emphasize interdisciplinary approaches at the intersection of material science, electrochemistry, and environmental engineering. Collaborative efforts aim to bridge fundamental research with industrial applications in sustainable energy systems and green chemistry.

• Quantum Computing
• Quantum Thermodynamics
• Machine Learning
• +۳ مورد دیگر

Natalia Ares is an Associate Professor in the Materials Department at the University of Oxford and a Tutorial Fellow at New College. She holds a Royal Society University Research Fellowship and joined Oxford in 2013. Her research focuses on advancing quantum technologies through machine learning for device control and quantum thermodynamics. Education: PhD in quantum computing (silicon-based devices) from CEA Grenoble, France Undergraduate and Master's in Physics with specialization in quantum chaos theory from University of Buenos Aires, Argentina Research Focus: Dr. Ares develops machine learning algorithms to control quantum circuits in real-time, addressing device variability challenges. Her group explores quantum thermodynamics at nanoscales, fabricating devices cooled to millikelvin temperatures to study information-thermodynamics relationships, aiming to build autonomous quantum machines like quantum engines and learning circuits. Current projects include machine learning for quantum control and thermodynamics of quantum information processing. Publication Trends (2024-2025): Recent works demonstrate strong integration of machine learning with quantum physics, focusing on quantum-dot systems, thermodynamic costs of quantum operations, symplectic learning frameworks, and high-fidelity qubit control. Publications consistently bridge theoretical quantum physics with practical engineering solutions. Awards and Recognition: Marie Skłodowska-Curie Fellowship Royal Society University Research Fellowship European Research Council Starting Grant (2020) Research Leadership: Leads the Machine Learning research group developing algorithms for quantum control. Manages experimental labs fabricating nanoscale quantum devices operating at cryogenic temperatures. Secured significant funding including ERC Starting Grant for quantum thermodynamics research.

Rodrigo Sernaمشاهده پروفایل

استادیار

• Mineral Processing
• Battery Recycling
• Circular Economy
• +۳ مورد دیگر

Rodrigo Serna is an Assistant Professor at Aalto University specializing in mineral processing and recycling. His research focuses on developing sustainable methods for extracting valuable materials from waste, particularly through flotation processes and battery recycling. He emphasizes minimizing environmental impact by optimizing resource recovery and integrating circular economy principles. His work includes advancing froth flotation techniques to separate minerals and recover metals like cobalt, nickel, and copper from tailings and battery waste. He also explores novel approaches such as electrochemical discharge for safer battery recycling and statistical entropy analysis to evaluate recycling efficiency. Key research areas include: Flotation process optimization for battery materials Circular economy metrics for resource recovery Hydrometallurgical and pyrometallurgical integration Waste valorization and exergy analysis Automated contact time analysis for bubble-particle interactions Recent studies highlight innovations like using cellulose-based frothers for greener mineral processing and developing multi-dimensional indicators to assess material and energy circularity in battery recycling systems. No scientific awards are explicitly mentioned in the provided texts. His research has been consistently published since 2011, with a strong focus on CO₂ capture technologies transitioning into modern battery recycling challenges.

Jianfeng Zhangمشاهده پروفایل

استاد

• Stochastic Analysis
• Backward Stochastic Differential Equations
• Stochastic Controls and Games
• +۲ مورد دیگر

Jianfeng Zhang is a Professor in the Department of Mathematics at the University of Southern California (USC). His research focuses on Stochastic Analysis, Backward Stochastic Differential Equations, Stochastic Controls and Games, Stochastic Numerics, and Mathematical Finance. He earned his Ph.D. in Mathematics from Purdue University in 2001, preceded by an M.S. in Computational Finance and a B.S. in Mathematics from Fudan University. Prof. Zhang has held faculty positions at USC since 2003, advancing from Assistant Professor (2003–2007) to Associate Professor (2007–2012) and his current rank (2012–present). His academic career also includes a Visiting Assistant Professorship at the University of Minnesota (2001–2003). His research interests emphasize theoretical advancements in stochastic processes and their applications to finance and control theory. Key contributions include works on Mean Field Games, Viscosity Solutions, and Optimal Stopping Problems. He co-authored Contract Theory in Continuous-Time Models (2012) and Backward Stochastic Differential Equations (2017). Recent publications explore topics such as stochastic volatility control, viscosity solutions for master equations, and dynamic programming in mean field settings. His work often bridges theoretical rigor with applications in finance and optimal control.

Luis Barrales Moraمشاهده پروفایل

دانشیار مدعو

• Materials Science
• Metallurgy
• Computational Materials Modeling
• +۵ مورد دیگر

Luis Barrales Mora is an Adjunct Associate Professor at the Georgia Institute of Technology campus in Lorraine, France. He holds a B.S. in Aeronautics (2001), M.S. in Metallurgy (2003) from Mexico's National Polytechnic Institute, and a Ph.D. in Materials Science (2008) from RWTH-Aachen University, Germany. His research focuses on microstructure evolution in metals, thermal stability of nanocrystalline materials, and computational materials modeling. Dr. Barrales-Mora’s work spans topics such as massively parallel materials simulation, grain boundary migration mechanisms, and heat treatment of high-Mn steels and high-entropy alloys. Notable achievements include developing a 3D level-set grain growth algorithm for ccNUMA architecture, which achieved a 20x speedup in parallel simulations. His research group also investigates low-loss electrical steels for electromobility applications. Distinctions include the DFG Outstanding Young Scientists Grant (2008), the Conference on Recrystallization and Grain Growth Award (2007), and a DAAD doctoral grant (2004). His publications span over two decades, addressing grain growth dynamics, phase-field modeling, and texture analysis in metals. He has led research groups at RWTH-Aachen, including a two-year interim directorship at the Institute of Physical Metallurgy. His contributions bridge computational and experimental approaches to advance material design and performance.

Alexander Tichaiمشاهده پروفایل

استاد پژوهشی

• Nuclear Physics
• Many-Body Theory
• Theoretical Physics
• +۳ مورد دیگر

Alexander Tichai is an ERC Group Leader at the Technical University of Darmstadt's Institute of Nuclear Physics, leading the DeformedNuclei research group. His academic journey includes a PhD in Physics (2017) and dual bachelor's and master's degrees in Physics and Mathematics from TU Darmstadt. Prior to his current role, he held postdoctoral positions at CEA Saclay/Université Paris-Saclay (2018-2019) and TU Darmstadt (2019-2025). His research focuses on nuclear many-body theory , including open-shell nuclei, symmetry-broken correlation expansions, and tensor factorization techniques. He specializes in ab initio methods for nuclear structure and dynamics, with applications to neutron stars, neutrinos, and dark matter. Key techniques include coupled-cluster theory, Bogoliubov perturbation methods, and low-rank matrix decompositions. His work bridges theoretical physics and computational science, addressing challenges in heavy-nuclei simulations and precision nuclear modeling. Collaborations include the EUSTRONG and ChiralEFT4DM projects, supported by ERC funding. Tichai’s research also explores interdisciplinary applications like eigenvector continuation and quantum information entropy. He advises the DeformedNuclei research group at TU Darmstadt and contributes to the STRONGINT initiative. His publications emphasize computational advancements for nuclear many-body problems, including data compression and numerical stability in large-scale simulations.

Ioan Andricioaeiمشاهده پروفایل

استاد

• Theoretical and Computational Physical Chemistry
• Molecular Biophysics
• Enhanced Sampling Techniques
• +۴ مورد دیگر

Ioan Andricioaei is a Professor in the Department of Chemistry at the University of California, Irvine (UCI), affiliated with the School of Physical Sciences. His research focuses on theoretical and computational molecular biophysics, particularly in developing novel sampling techniques and simulating biomolecular processes. Key areas include enhanced sampling in trajectory space, contractile tail dynamics, DNA-binding machines, and connections between molecular dynamics and NMR relaxation. Positions: Faculty member at UCI since his appointment, leading the Andricioaei Group. Education: Ph.D. in Theoretical Chemistry (not explicitly stated but inferred from research trajectory). Research Interests : His work bridges theoretical chemistry and biophysics, addressing challenges such as simulating rare events in biomolecules, understanding viral infection mechanisms via contractile tails, and modeling protein-DNA interactions. Techniques include weighted ensemble milestoning, stochastic path integrals, and massively parallel molecular dynamics. Advising & Collaborations: Supervises students like Dhiman Ray (Postdoc) and Moises Romero (PhD candidate). Collaborates with institutions like the Italian Institute of Technology and TeraGrid. Alumni include Gianmarc Grazioli (Assistant Professor at San Jose State) and Aaron T. Frank (Assistant Professor at University of Michigan). Labs/Teams: Directs the Andricioaei Group, which develops computational tools and applies them to problems in virology, drug delivery, and fundamental biophysical processes.

Shengwen Wangمشاهده پروفایل

مدرس

• Geometric Analysis
• Partial Differential Equations
• Differential Geometry
• +۴ مورد دیگر

Dr. Shengwen Wang is a Lecturer in Geometric Analysis at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences and the Centre for Geometry, Analysis and Gravitation. He holds a PhD from Johns Hopkins University (2018) and has held postdoctoral positions at SUNY Binghamton, QMUL, and the University of Warwick. His research focuses on geometric analysis and partial differential equations, emphasizing regularity theory and singularity analysis of geometric flows like mean curvature flow and Allen-Cahn equations, with applications in geometric topology and mathematical relativity. Research Interests: His work explores the formation of singularities in geometric PDEs, including minimal surfaces, Ricci flows, and their applications. He has contributed to entropy stability, topological rigidity of self-shrinkers, and curvature estimates in geometric flows. Publications: Recent work includes studies on entropy stability (J. Reine Angew. Math., 2020), topological rigidity (Int. Math. Res. Not., 2020), and mean curvature flow singularities (Nonlinear Anal., 2025). His articles often address energy quantization, transition layers, and curvature estimates in geometric contexts. Awards: George E. Owen Fellowship (2012-2015), Grad Student Travel Grant (2018). Teaching: Courses include Partial Differential Equations, Differential Geometry, and Mean Curvature Flow. Professional Activities: Referee for leading journals (e.g., Journal of Differential Geometry), organizer of QMUL’s Internal School Colloquium, and active in geometric analysis conferences. He is part of the Centre for Geometry, Analysis and Gravitation, fostering interdisciplinary research in geometric flows and their applications.

• Heavy Ion Collisions
• Quark-Gluon Plasma
• General Relativity
• +۴ مورد دیگر

Dr. W. van der Schee is an Assistant Professor at Utrecht University's Faculty of Science, within the Department of Theoretical Physics (ITF). He also holds a staff position at CERN. His research focuses on heavy ion collisions, quark-gluon plasma dynamics, and gravitational wave studies using holographic methods. Key areas include numerical relativity, neutron star equations of state, and tensor networks. He received the NWO Veni award in 2017. Educational background: PhD in Theoretical Physics (cum laude, Utrecht University, 2014), postdoctoral work at MIT (2014-2018) and Utrecht University (2016-2020), supported by a VENI grant. He teaches courses on quantum chromodynamics and particle physics field theory. Research highlights include Bayesian analysis of heavy-ion data using the Trajectum framework, gravitational wave studies from neutron star mergers, and quantum null energy condition explorations. His group includes PhD students Olaf Massen and Maaike Bakker, and postdoc Javier Subils. Ongoing projects involve holographic jet dynamics and neutron star merger simulations. Awards: NWO Veni award (2017). Active in both university and CERN-based collaborations, advancing theoretical frameworks for high-energy and gravitational physics phenomena.

• Materials Science
• Magnetic Materials
• High-Entropy Alloys
• +۳ مورد دیگر

Esmat Dastanpour Hosseinabadi is a postdoctoral researcher at the Department of Materials Science and Engineering, KTH Royal Institute of Technology. His research focuses on exploring the relationship between structure and magnetic properties of materials , with an emphasis on developing high- and medium-entropy alloys and green solutions for hard metals . He bridges atomistic calculations with experimental results, particularly targeting Co binder phase alternatives in hard metal products to reduce harmful emissions during manufacturing and use. His research interests include Magnetocaloric effect in alloys for energy applications Nanocrystallization and thermal stability of soft magnetic materials Computational modeling using DFT techniques Environmental engineering for sustainable materials design Recent work highlights include investigations into Al-Cr-Mn-Co high-entropy alloys for room-temperature magnetic refrigeration and phase decomposition in CrFeCoNi alloys. His studies often integrate experimental data with computational simulations to optimize material properties. Publications emphasize alloy design , material characterization , and environmentally conscious material science . Collaborations span experimental and theoretical approaches to advance materials for energy and structural applications .

Jack Tuszynskiمشاهده پروفایل

مدرس

• Biophysics
• Cancer Chemotherapy
• Computational Drug Design
• +۵ مورد دیگر

Jack Tuszynski is a Full Lecturer in the Department of Physics at the University of Alberta's Faculty of Science. He also holds the prestigious Allard Chair in Experimental Oncology at the Cross Cancer Institute. His academic career includes roles as a Professor, Associate Professor, and Assistant Professor across multiple institutions, including the University of Alberta and Memorial University of Newfoundland. He has a PhD in Condensed Matter Physics from the University of Calgary and has held postdoctoral and visiting positions in Germany, Denmark, France, Belgium, Israel, and China. His research focuses on computational biophysics and oncology, particularly the design of targeted anticancer drugs using molecular dynamics simulations. Key projects include studying tubulin and microtubule behavior under electric fields, developing TTFields-based therapies, and exploring the structural and electrical properties of proteins. He has published nearly 400 peer-reviewed papers and serves on editorial boards for multiple journals, including the Journal of Biological Physics and Quantum Biosystems. Tuszynski has received significant recognition, including the Allard Chair appointment and Fellow status with the National Institute for Nanotechnology. His work is funded by grants from Canadian, U.S., and European agencies. He teaches courses like PHYS 124 (Particles and Waves), emphasizing physics applications in life sciences. His research group explores interdisciplinary topics such as p53 protein modeling, microtubule motor proteins, and personalized medicine through entropy analysis. Collaborative efforts include designing novel colchicine derivatives and investigating TTFields' intracellular mechanisms to improve chemotherapy efficacy with reduced side effects.

Vlad Yaskinمشاهده پروفایل

استاد

• Convex Geometry
• Geometric Functional Analysis
• Geometric Tomography
• +۳ مورد دیگر

Vlad Yaskin is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on Convex Geometry, Geometric Functional Analysis, and applications of Harmonic Analysis to convex bodies. He has held academic positions at the University of Alberta since 2008, advancing from Assistant to Associate and eventually Full Professor. His editorial role includes serving as an Associate Editor for the Rocky Mountain Journal of Mathematics since 2022. Yaskin earned his Ph.D. in Mathematics from the University of Missouri-Columbia (2006), advised by Alexander Koldobsky, and an M.Sc. from Kharkiv State University (1997), advised by Yuri Aminov. His teaching accolades include the 2012 Teaching Award from the University of Alberta and a Distinguished Teaching Award from the University of Missouri (2003). His research emphasizes geometric inequalities, convex body properties, and tomographic uniqueness. Notable contributions include work on Busemann-Petty problems, Grünbaum-type inequalities, and Fourier transform applications in convex geometry. He has supervised numerous graduate students and postdoctoral researchers, fostering collaborations in geometric analysis and harmonic analysis. Yaskin has organized major conferences such as the 2024 Summer School on Probability and Machine Learning and the 2023 BIRS workshop on Harmonic Analysis and Convexity. His funding includes multiple NSERC Discovery Grants, reflecting sustained research excellence.