جست‌وجوی استاد و پژوهشگر

Umut Cetinمشاهده پروفایل

استاد

• Stochastic Analysis
• Mathematical Finance
• Market Microstructure
• +۴ مورد دیگر

Umut Cetin is a Professor of Statistics at the Department of Statistics, London School of Economics and Political Science (LSE). He holds a PhD in Applied Mathematics from Cornell University and has been affiliated with LSE since 2004, progressing through roles as Assistant Professor (2004–2010), Associate Professor (2010–2017), and full Professor since 2017. His research focuses on stochastic analysis, market microstructure, and mathematical finance, with an emphasis on equilibrium models under asymmetric information and liquidity risk. He contributes to editorial boards of journals like Market Microstructure and Liquidity and Frontiers of Mathematical Finance . Key research areas include stochastic filtering, Markov processes, and applications to financial economics. Notable achievements include the 2008 Europlace Institute of Finance Best Paper Award for work on insider trading models. He advises PhD students and teaches advanced courses such as Stochastic Processes and Markov Processes and Their Applications . Professional roles include Director of the LSE PhD Programme, member of the Steering Committee for the London Graduate School of Mathematical Finance, and external examiner for City University’s MSc Financial Economics. His recent work addresses power laws in market microstructure, equilibrium models with legal risk, and numerical methods for killed diffusions.

Jonathan Pakianathanمشاهده پروفایل

استاد

• Algebraic Topology
• Cohomology of Groups
• Lie Algebras
• +۳ مورد دیگر

Jonathan Pakianathan is a Professor of Mathematics at the University of Rochester's School of Arts & Sciences, Department of Mathematics. He holds a PhD from Princeton University (1997) and a BS in Mathematics and Physics from Caltech (1992). His research focuses on algebraic topology, cohomology of groups and Lie algebras, geometric combinatorics, and finite fields. He has held leadership roles, including Director of Graduate Studies (2014–2020) and Director of Undergraduate Studies (2003–2010). Notable awards include the Goergen Award for Excellence in Teaching (2014) and the Sloan Foundation Doctoral Dissertation Fellowship (1996). His research explores applications of topology and algebra in discrete geometry, often collaborating with Alex Iosevich and students. Recent work addresses topics like Fuglede's conjecture over finite fields, geometric configurations, and probabilistic methods in combinatorics. His articles frequently intersect harmonic analysis, group theory, and number theory, reflecting interdisciplinary strengths. Education : PhD, Princeton University, 1997 BS in Mathematics and Physics, Caltech, 1992 Awards : Goergen Award for Excellence in Undergraduate Teaching, 2014 Sloan Foundation Doctoral Dissertation Fellowship, 1996 H. J. Ryser Scholarship, 1991 Grants include an NSA Mathematical Sciences Grant (2016–2017, $110,000 total) with A. Iosevich. He advises numerous PhD students, many of whom now hold academic or research positions. His teaching spans undergraduate to graduate courses, including algebra, topology, and financial mathematics. Research groups and collaborations extend to geometric combinatorics, algebraic topology, and applications in physics and data science. Ongoing projects explore topological methods in discrete geometry and probabilistic structures over finite fields.

• Partial Differential Equations
• Numerical Analysis
• Mathematical Biology
• +۳ مورد دیگر

Professor Charles Elliott of Warwick's Mathematics Institute specializes in the numerical analysis of partial differential equations with applications in biology, materials science, and geometric evolution. His research develops finite element methods for surface PDEs, free boundary problems, and phase transitions, with recent publications on Cahn-Hilliard equations and information propagation models. Honors include the Royal Society Wolfson Research Merit Award, Humboldt Prize, and honorary doctorate from Sussex University. As Editor-in-Chief of the IMA Journal of Numerical Analysis and editor for Interfaces and Free Boundaries, he shapes research dissemination in computational mathematics. Teaching covers topics in PDEs and mathematical modeling. His theoretical work on surface evolution equations informs applications in cell motility studies and biomembrane physics, connecting mathematical rigor with biological and physical phenomena.

Antonino Di Piazzaمشاهده پروفایل

استاد

• Quantum Electrodynamics
• Strong-Field QED
• High-Energy Physics
• +۱۲ مورد دیگر

Antonino Di Piazza is a Professor of Physics at the Department of Physics and Astronomy, University of Rochester, and a Distinguished Scientist at the Laboratory for Laser Energetics (LLE). He is also a Guest at the Theory Division of the Max-Planck-Institut für Kernphysik (MPI-K), where he previously served as a Group Leader from 2009 to 2023. His academic journey includes a PhD from the University of Trieste and habilitations in both Germany and Italy, establishing his expertise in theoretical physics. His research interests lie at the forefront of quantum electrodynamics, particularly in strong and ultra-intense laser fields. He investigates fundamental phenomena such as radiation reaction, vacuum polarization, nonlinear Compton scattering, and Breit-Wheeler pair production. His work often bridges classical and quantum electrodynamics, exploring the limits of theoretical models like the local constant field approximation and developing new frameworks for tightly focused and flying-focus laser pulses. The 15 most recent publications highlight a consistent focus on strong-field QED processes. Key themes include the behavior of particles in flying focus pulses, quantum vacuum effects like birefringence, and the analytical and numerical modeling of radiation processes. His work combines deep theoretical insight with relevance to cutting-edge experimental facilities, aiming to test QED in previously inaccessible regimes. He has not received any scientific awards listed in the provided text. Dr. Di Piazza is an active educator, currently teaching Quantum Mechanics II at the University of Rochester. He has a history of lecturing and tutoring at Heidelberg University and leading numerous doctoral schools and workshops on strong-field physics. He has secured research support through collaborations with major institutions like LLE, ELI-NP, and MPI-K, enabling his theoretical work to inform and be informed by high-power laser experiments. His extensive list of invited seminars and conference talks underscores his leadership in the field. He leads the 'High-Energy Quantum Electrodynamics' research group, which is affiliated with the University of Rochester and the Max-Planck-Institut für Kernphysik. This group focuses on theoretical investigations of quantum vacuum and particle dynamics in extreme electromagnetic fields, contributing significantly to the global effort in high-field science.

Anna-Karin Tornbergمشاهده پروفایل

استاد

• Numerical Analysis
• Fluid Dynamics
• Boundary Integral Methods
• +۳ مورد دیگر

Anna-Karin Tornberg is a Professor in Numerical Analysis at the Department of Mathematics, KTH Royal Institute of Technology. She holds positions as Vice Chair of the Department of Mathematics and previously served as Head of the Numerical Analysis division (2011–2023). Her research focuses on numerical methods for PDEs, particularly boundary integral methods for fluid flows involving particles and drops. She is active in the Linne FLOW Centre and Swedish e-Science Research Center (SeRC). Key roles include membership in the Royal Swedish Academy of Engineering Sciences (IVA), Royal Academy of Sciences, and receipt of awards like the Göran Gustafsson Prize (Mathematics, 2014). She has advised numerous PhD students and postdocs, including current supervisees Anna Broms, David Krantz, and Emanuel Ström. Her work spans theoretical, computational, and applied fluid dynamics with emphasis on microfluidics and high-accuracy numerical techniques. Education includes a PhD in Numerical Analysis from KTH (2000) followed by postdoctoral positions at NYU’s Courant Institute. Promoted to Full Professor at KTH in 2012. Service roles include membership in KTH’s University Board, Faculty Council, and editorial roles at Advances in Computational Mathematics and BIT Numerical Mathematics . Active in international conferences, delivering plenary/invited lectures at ICIAM, ECM, and ICM. Research group projects include development of fast numerical methods for microfluidics and molecular dynamics simulations. Current openings for PhD candidates in numerical methods for non-elliptic PDEs in time-dependent domains. Her lab collaborates on high-performance computing and fluid-structure interaction problems.

Chris Rogersمشاهده پروفایل

استاد

• Financial mathematics
• probability theory
• stochastic analysis
• +۲ مورد دیگر

Chris Rogers is a Professor of Statistical Science within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, actively contributing to research at the intersection of probability theory, stochastic analysis, and financial applications. His academic profile reflects deep engagement with mathematical finance and theoretical probability through publications and departmental affiliations. His research spans financial mathematics, probability theory, stochastic analysis, statistics, and mathematical economics, with emphasis on rigorous mathematical frameworks for financial markets. Key themes include option pricing mechanisms, stochastic process modeling, and geometric probability applications, often addressing real-world financial instruments like Asian options and S&P500 index behaviors through advanced probabilistic techniques. Analysis of his 15 most recent publications (2016-2018) reveals consistent focus on stochastic calculus applications in finance, particularly Lévy processes, diffusion models, and optimal stopping problems. His work bridges theoretical probability with quantitative finance, demonstrating expertise in translating complex stochastic phenomena into financial modeling solutions across asset pricing, risk assessment, and market analysis domains. No scientific awards were documented in the provided source material. Information regarding PhD/Master's student supervision, research grants, or collaborative teams was not specified in the available texts, indicating absence of such details in the source documentation.

Jia-Jie Zhuمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Optimal Transport
• +۵ مورد دیگر

Jia-Jie Zhu is a machine learner and applied mathematician currently serving as head of an independent research group at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, with an upcoming appointment as tenured associate professor at KTH Royal Institute of Technology in Stockholm. Previously, he conducted postdoctoral research in machine learning at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, following doctoral studies in optimization and numerical analysis at the University of Florida. His research focuses on the mathematical foundations of machine learning and optimization, particularly at the intersection of computational algorithms, dynamical systems, and probability theory. Key areas include: Robust probabilistic machine learning algorithms Kernel methods for distribution manipulation Variational methods for optimization over probability distributions Gradient flows and optimal transport theory Wasserstein and Fisher-Rao geometry Applications in generative modeling and causal inference Dr. Zhu's recent work reveals deep connections between partial differential equations, kernel methods, and machine learning, resulting in theoretically grounded algorithms for handling distribution shifts. His publications demonstrate a consistent trajectory from foundational optimization theory to cutting-edge applications in robust learning and generative modeling, with increasing emphasis on the mathematical structures underlying modern ML systems. He has secured significant research funding, including a DFG Project on 'Optimal Transport and Measure Optimization Foundation for Robust and Causal Machine Learning' within the Priority Program 'Theoretical Foundations of Deep Learning' (SPP 2298), and actively organizes academic events such as the Workshop on Optimal Transport from Theory to Applications (OT-DOM) and upcoming sessions at ICSP 2025 and SwissMAP. As an educator, Dr. Zhu teaches nonparametric statistics at Humboldt University of Berlin and serves as area chair for major conferences including AISTATS 2025. He maintains an active research group with opportunities for master's students, PhD candidates, and postdoctoral researchers interested in the mathematical frontiers of machine learning.

Standa Živnýمشاهده پروفایل

استاد

• Theoretical Computer Science
• Discrete Mathematics
• Algorithms
• +۱۰ مورد دیگر

Standa Živný is a Professor of Computer Science at the University of Oxford and a Fellow and Tutor at Merton College. He has been a faculty member at Oxford since 2013 and was promoted to full professor in 2021. His research spans theoretical computer science and discrete mathematics, with a focus on algorithms, computational complexity, and constraint satisfaction problems (CSPs) in various forms, including optimisation, counting, and approximation. His research interests include the power and limitations of convex relaxations, sparsification, submodularity, and the algebraic and logical foundations of tractability in combinatorial problems. He has made significant contributions to understanding when and why certain problems can or cannot be efficiently solved using linear programming and other algorithmic paradigms. The recent trends in his publications show a deep engagement with approximation algorithms, hardness results, sparsification techniques, and the complexity of counting and promise problems. His work often lies at the intersection of algebra, logic, and optimisation, demonstrating the power of interdisciplinary approaches in theoretical computer science. ERC Consolidator Grant (NAASP, 2022–2027) ERC Starting Grant (PowAlgDO, 2017–2022) Royal Society University Research Fellowship (2013–2021) He actively supervises a large cohort of postdoctoral researchers and students, including PhD candidates, master’s, and undergraduate students. His leadership extends to academic service, where he serves as Editor-in-Chief of the SIAM Journal on Discrete Mathematics and holds editorial and committee positions in major journals and funding bodies. He has organised numerous workshops and research programmes at institutions such as Dagstuhl, the Isaac Newton Institute, and AIM. He is involved in major research initiatives, including a Simons Programme on symmetry in computation and an American Institute of Mathematics SQuARE on relaxations for promise CSPs.

Philip Wadlerمشاهده پروفایل

استاد

• Theoretical Computer Science
• Functional Programming
• Programming Language Foundations
• +۶ مورد دیگر

Philip Wadler is Professor of Theoretical Computer Science at the University of Edinburgh and Senior Research Fellow at IOHK. He is an ACM Fellow, Fellow of the Royal Society, and Fellow of the Royal Society of Edinburgh. His work spans programming language design, type systems, and formal verification, with significant contributions to Haskell, Java, and XQuery. He has held leadership roles in ACM SIGPLAN and served on editorial boards for major journals. Research Interests: Wadler's research focuses on the foundations of programming languages , including Gradual and session typing Language-integrated query Functional and logic programming XML data models Parametricity and free theorems Verification of smart contracts Publication Trends: Recent articles emphasize type safety, formal verification, and blockchain applications. Key themes include gradual typing (blame calculus), session types for concurrency, and logical foundations of programming. His 2015–2025 papers show sustained focus on type theory and language design . Awards & Recognition: POPL Most Influential Paper (2003 for 1993 work) SIGPLAN Distinguished Service Award Best Paper SBMF 2018 Royal Society-Wolfson Fellowship (2004–2009) ACM Fellow (2007) Fellow of Royal Society of Edinburgh (2005) Advising & Grants: He has supervised numerous PhD students in programs like the Centre for Doctoral Training in Pervasive Parallelism. His EPSRC Programme Grant "From Data Types to Session Types" (2013–2020) funded major advances in concurrency theory. Current work with IOHK explores blockchain verification using Haskell-based Plutus.

Alexandros Kontogiannisمشاهده پروفایل

پژوهشگر ارشد

• Fluid Dynamics
• Applied Mathematics
• Bayesian Inference
• +۱۰ مورد دیگر

Alexandros Kontogiannis is a research fellow at the University of Cambridge, Department of Engineering, specializing in fluid dynamics and applied mathematics. His work combines Bayesian inference, machine learning, and physics-informed algorithms to solve inverse problems in magnetic resonance velocimetry (MRV) and fluid-structure interaction. EPSRC National Fellow in Fluid Dynamics Member of Energy, Fluids and Turbomachinery Division Research Focus: Development of digital twin frameworks that integrate MRV data with Navier-Stokes equations to reconstruct flowfields, infer rheological parameters in non-Newtonian fluids, and estimate hidden quantities like pressure and wall shear stress. Key innovations include: Physics-informed compressed sensing for sparse MRV data Simultaneous boundary shape and flowfield estimation Bayesian turbulence model parameter learning Scientific Awards: ASME Fluids Engineering Division Graduate Student Scholar (2021) Technical Chamber of Greece (TEE) Award (2018) Limmat Foundation Academic Excellence (2017) Mentzelopoulos Scholarship for international studies (2017) Greek State Scholarships Foundation Award (2012) Key Contributions: Algorithms for 3D flow reconstruction with adaptive discretization, viscous signed distance field regularization, and multi-objective aerodynamic shape optimization. His methodologies enable 27x reductions in MRI scanning time while maintaining diagnostic accuracy.

Tamas Monaمشاهده پروفایل

پژوهشگر

• Epidemiology
• Plant Pathology
• Meteorology
• +۵ مورد دیگر

Dr. Tamas Mona is a Postdoctoral Research Associate in the Department of Plant Sciences at the University of Cambridge , affiliated with the Epidemiology and Modelling Group . His work focuses on environmental suitability models for large-scale epidemiological forecasting, particularly in wheat rust outbreaks across Africa, the Middle East, and Asia. Collaborations include the UK Met Office, CIMMYT, and institutions in Ethiopia, Kenya, Bangladesh, and Nepal. PhD in Environmental Sciences (2019), Eötvös Loránd University MSc in Meteorology (2013), Eötvös Loránd University BSc in Physics with Meteorology (2011), Eötvös Loránd University Research interests integrate meteorological applications with epidemiological models to predict crop disease outbreaks. Key projects involve tracking transmission pathways for stem rust pathogens and analyzing how irrigation creates green bridges for intercontinental pathogen spread. Publications emphasize environmental science and computational epidemiology . Current collaborations span Sub-Saharan Africa (EIAR, ATI, KARLO) and South Asia (BWMRI, NARC) through initiatives like the Global Food Security IRC . Modeling frameworks developed by Mona contribute to policy advisory systems for emerging pest threats, aligning with DEFRA and UK government strategies.

Roxanne P. Springerمشاهده پروفایل

استاد

• Quantum Chromodynamics
• Weak Interactions
• Effective Field Theory
• +۴ مورد دیگر

Professor Roxanne P. Springer is a faculty member at Duke University's Department of Physics within Trinity College of Arts & Sciences, specializing in weak interactions and quantum chromodynamics (QCD). Her research explores fundamental symmetry violations and hadronic structure through effective field theories. Education: Ph.D. in Physics from California Institute of Technology (1990) Research Interests: Springers work focuses on hadronic parity violation, large-Nc expansion, and precision nuclear physics. She applies pionless effective field theory (EFT(π/)) to study neutron-deuteron capture, two-nucleon interactions, and neutrinoless double-beta decay. Her projects bridge QCD symmetries with nuclear dynamics. Publication Trends: Recent works emphasize large-Nc scaling, parity-violating observables, and cross-section calculations in nuclear reactions. Key topics include Wigner-SU(4) symmetry, renormalization group constraints, and hadronic structure analysis. Scientific Awards: POWRE Visiting Professorship (1998-1999) Grants: Principal investigator for DOE-funded projects on lattice QCD and effective field theory since 2005. Co-PI for multiple DOE grants on high-energy nuclear physics from 1990-2005. Includes support for strangeness physics and extreme energy density studies. Teaching: Taught graduate courses on quantum mechanics, quantum field theory, and nuclear physics since 2022. Led methods courses for physics research. Advising: Chaired thesis and preliminary committees for students including James Wheeler (2019), Adryanna Major (2020), and Qiaofeng Liu (2021). Mentored graduate students Xincheng Lin, H Nguyen, and Son Nguyen through 2021.

• Model Checking
• Symbolic Execution
• Compositional Verification
• +۷ مورد دیگر

Corina Pasareanu is an ACM Fellow and IEEE ASE Fellow serving as a Principal Scientist at Carnegie Mellon University's CyLab Security and Privacy Institute and Technical Professional Leader for Data Science at NASA Ames Research Center through KBR. Her work bridges formal methods, software verification, and artificial intelligence to ensure the safety and security of complex systems, particularly autonomous systems and machine learning applications. Dr. Pasareanu received her academic training at: Ph.D. in Computer Science, Kansas State University (2001) M.S. in Computer Science, University Politehcnica of Bucharest (1995) B.S. in Computer Science, University Politehcnica of Bucharest (1994) Her research focuses on developing formal verification techniques that can provide mathematical guarantees about the behavior of complex software systems. She specializes in applying model checking, symbolic execution, and compositional verification methods to challenges in autonomy, security, and AI safety. Her recent work addresses the verification of systems incorporating machine learning components, particularly neural networks used in safety-critical applications like autonomous vehicles. She investigates how to ensure these systems behave correctly even when their perception components have uncertainties or are subject to adversarial attacks. Analysis of her recent publications shows a strong trend toward verifying AI and machine learning systems, particularly focusing on neural networks in autonomous systems. Her work increasingly addresses the challenges of Large Language Models, examining both their vulnerabilities to attacks and methods to defend against them. She also continues to advance traditional software verification techniques while adapting them to modern programming languages and paradigms. Dr. Pasareanu has received numerous prestigious awards recognizing her contributions to the field: ACM Fellow IEEE ASE Fellow ETAPS Test of Time Award (2021) ASE Most Influential Paper Award (2018) ESEC/FSE Test of Time Award (2018) ISSTA Retrospective Impact Paper Award (2018) ACM Impact Paper Award (2010) ICSE 2010 Most Influential Paper Award (2010) As an advisor, Dr. Pasareanu mentors several PhD students and postdoctoral researchers, often in collaboration with other faculty members at CMU. Her students focus on cutting-edge research at the intersection of formal methods and AI safety. Her research is supported by substantial funding from diverse sources including NSF, DARPA, NASA, AWS, and industry partnerships. She leads multiple projects focused on AI security, formal verification of neural networks, and software analysis techniques. Dr. Pasareanu also plays a significant role in the broader research community, serving as Program/General Chair for major conferences including ICSE 2025, and as an associate editor for IEEE TSE and STTT. Dr. Pasareanu leads research teams working on projects like "Trinity: Neurosymbolic Learning and Reasoning" (DARPA) and "HUGS: Human-Guided Software Testing and Analysis" (NSF). Her work often involves interdisciplinary collaboration between computer scientists, formal methods experts, and domain specialists to address complex safety challenges in autonomous systems.

• Simulation
• Computational Probability
• Queueing Theory
• +۲ مورد دیگر

Peter W. Glynn is the Thomas Ford Professor in the Department of Management Science and Engineering (MS&E) at Stanford University's School of Engineering, and also holds a courtesy appointment in the Department of Electrical Engineering. Additionally, he serves as a Senior Fellow of the Hong Kong Institute for Advanced Study at City University of Hong Kong. His distinguished career spans over four decades, with significant contributions to the fields of simulation, computational probability, and stochastic modeling. Professor Glynn received his Ph.D. in Operations Research from Stanford University in 1982 and his B.S. with Honors in Mathematics from Carleton University in 1978. His academic journey began at the University of Wisconsin at Madison (1982-1987) before returning to Stanford, where he has held various leadership positions including Deputy Chair of MS&E (1999-2005), Director of Stanford's Institute for Computational and Mathematical Engineering (2006-2010), and Chair of MS&E (2011-2015). His research interests focus on simulation , computational probability , queueing theory , statistical inference for stochastic processes , and stochastic modeling . Professor Glynn's work has developed algorithms widely used across the field of Monte Carlo simulation, with applications in financial risk management, service systems engineering, logistics, and retail operations. His recent publications demonstrate continued innovation in areas such as numerical methods for stochastic systems, rare-event simulation, and analysis of queueing systems under various traffic conditions, showing a strong trajectory of advancing both theoretical foundations and practical applications. Professor Glynn's scholarly contributions have been recognized with numerous prestigious awards, including: Fellow of INFORMS (2007) Fellow of the Institute of Mathematical Statistics (1998) John von Neumann Theory Prize from INFORMS (2010) Member of the US National Academy of Engineering (2012) Lifetime Professional Achievement Award, INFORMS Simulation Society (2021) Philip McCord Morse Lecturer, INFORMS (2020) Throughout his career, Professor Glynn has mentored numerous doctoral students whose research has made significant contributions to operations research and related fields. His editorial service has been extensive, including founding Editor-in-Chief of Stochastic Systems and service on the editorial boards of leading journals in operations research, probability, and statistics. His professional service extends to numerous advisory boards and committees at national and international levels, reflecting his standing as a leader in his field.

• Applied Geometry
• Discrete Differential Geometry
• Numerical Analysis
• +۳ مورد دیگر

Max Wardetzky is a Professor at the Institute for Numerical and Applied Mathematics within the Faculty of Mathematics and Computer Science at the University of Göttingen, Germany. His office is located at Lotzestraße 16-18, 37083 Göttingen, and he can be reached via email at wardetzky@math.uni-goettingen.de or by phone at +49 551 39 26778. Professor Wardetzky leads the Discrete Differential Geometry Lab at the University of Göttingen, where he conducts research at the intersection of mathematics, computer science, and geometry processing. His work bridges theoretical foundations with practical applications in computer graphics and scientific computing. His primary research interests include: Applied Geometry Discrete Differential Geometry Numerical Analysis Geometry Processing Physical Simulation Computer Graphics Professor Wardetzky's extensive publication record demonstrates significant contributions to the field of discrete differential geometry and its applications. His work shows a consistent focus on developing mathematically rigorous yet computationally efficient methods for geometric problems. Key trends in his research include the development of discrete analogues of smooth geometric objects, the study of convergence properties between discrete and continuous models, and the application of these methods to problems in computer graphics and physical simulation. Professor Wardetzky has made substantial contributions to the theoretical foundations of discrete differential geometry while maintaining strong connections to practical applications. His work on discrete Laplacians, curvature approximations, and geometric flows has influenced both theoretical mathematics and practical geometry processing algorithms.