جست‌وجوی استاد و پژوهشگر

Eric C.K. Cheungمشاهده پروفایل

دانشیار

• Insurance Risk Theory
• Ruin Theory
• Aggregate Claims Analysis
• +۶ مورد دیگر

Eric C.K. Cheung is an Associate Professor in the Department of Statistics and Actuarial Science at the University of New South Wales (UNSW), where he has worked since July 2017. Previously, he held positions at the University of Hong Kong (HKU) from 2010 to 2017, including Assistant Professor (2010–2016) and Associate Professor (2016–2017). He earned his BSc (Actuarial Science) from the University of Hong Kong and MMath and PhD in Actuarial Science from the University of Waterloo. His research focuses on insurance risk theory, ruin theory, stochastic processes, and financial mathematics. He has secured multiple grants, including from the Australian Research Council and the Society of Actuaries. Currently, he supervises PhD and Honours students in areas like risk analysis and financial modeling. Cheung teaches actuarial science courses at UNSW, HKU, and the University of Waterloo. His work appears in top journals like Insurance: Mathematics and Economics , and he is an Associate Editor of this journal. He has advised over 10 students at HKU and UNSW.

• Reactive flow and transport modeling
• Geochemical processes
• Groundwater dynamics
• +۵ مورد دیگر

Martin Andersen is an Associate Professor with the Water Research Laboratory and School of Civil and Environmental Engineering at the University of New South Wales (UNSW). His research focuses on hydrogeology, groundwater dynamics, and surface water-groundwater interactions, with particular emphasis on coastal zone processes and reactive flow and transport modeling. Dr. Andersen's research interests span several critical areas in hydrogeology and environmental science. His work on reactive flow and transport modeling examines how chemical reactions affect water movement through geological formations. He investigates geochemical processes and groundwater dynamics in coastal zones, which is crucial for understanding saltwater intrusion and managing coastal aquifers. His research on surface water-groundwater interactions helps inform sustainable water resource management, particularly in Australia's diverse hydrological environments. Andersen has made significant contributions to understanding how climate variability affects groundwater recharge processes and the implications for water security in arid and semi-arid regions. Analysis of Dr. Andersen's recent publications reveals a strong focus on understanding complex hydrological systems through innovative methodologies. His work spans multiple disciplines including hydrogeology, climate science, biogeochemistry, and computational modeling. A key trend in his research is the investigation of groundwater dynamics in various environmental contexts - from arid zone aquifers to coastal wetlands and fractured rock systems. He employs diverse approaches including field monitoring networks, laboratory experiments, statistical modeling, and paleoclimate reconstructions to address pressing water resource challenges. His research has significant implications for sustainable groundwater management, climate adaptation strategies, and ecosystem conservation. Dr. Andersen is actively involved in several research teams and laboratories. As an Associate Director in the School of Civil and Environmental Engineering at UNSW, he contributes to the Water Research Laboratory's mission of advancing water science and engineering. His work often involves interdisciplinary collaborations across hydrology, geology, environmental science, and climate research. Through his leadership in establishing monitoring networks and conducting field experiments, he has helped build valuable infrastructure for ongoing water research in Australia.

• Convex Geometry
• Geometric Tomography
• Integral Geometry
• +۳ مورد دیگر

Daniel A. Klain is a Professor in the Department of Mathematics & Statistics at the University of Massachusetts Lowell , part of the College of Sciences. His career focuses on geometric and discrete mathematics, with significant contributions to Convex Geometry and its intersections with probability and combinatorics. Education: Ph.D. in Mathematics (1994), Massachusetts Institute of Technology B.S. in Mathematics (1990), Massachusetts Institute of Technology Research Interests span Convex Geometry, Geometric Tomography, Integral Geometry, and Combinatorics. His work explores geometric inequalities, valuations, and symmetrization techniques, often bridging classical geometry with modern probabilistic and discrete methods. Article Trends highlight his focus on Convex Geometry and Integral Geometry, with studies on Steiner symmetrization, shadow covering, and valuations. His publications also reflect interests in geometric probability, number theory, and educational insights. Scientific Awards and Grants: Mathematical Sciences Teaching Excellence Award (2010, 2003) Sigma Xi Young Faculty Award (2000) Jon A. Bucsela Prize in Mathematics (1990) NSF Graduate Fellowship (1990) National Merit Scholar (1986) NSF grants (2003, 1998, 1996) for convex geometry and geometric analysis Service and Collaborations: Active in teaching, research, and academic service, Klain has co-authored works in geometric probability and presented at numerous international workshops and seminars. His career integrates rigorous mathematical inquiry with educational innovation.

• Mathematical Analysis
• Partial Differential Equations
• Stochastic Homogenization
• +۴ مورد دیگر

Felix Otto is a Director at the Max Planck Institute for Mathematics in the Sciences and an Honorary Professor for Analysis and Mathematical Modeling at the University of Leipzig. His research focuses on pattern formation, energy landscapes, and scaling laws, with contributions to stochastic homogenization, PDEs, and optimal transport. He has held academic positions at the University of California and the Hausdorff Center for Mathematics. Notable grants include projects on microstructure formation in thin coatings and stochastic homogenization. He has organized conferences such as the ICM satellite conference on Probability and Mathematical Physics. His work bridges pure and applied mathematics, with implications for materials science and fluid dynamics. Education: Diplom (1990), PhD (1993) from Bonn University. Postdoctoral roles at Bonn, Courant Institute, and CMU. Tenured faculty roles at UCSB (1998–2010) and Bonn (1999–2010). Research interests span stochastic PDEs, calculus of variations, and the mathematical theory of materials. Grants include BMBF and DFG projects on microstructure modeling and homogenization.

Jason Millerمشاهده پروفایل

دانشیار

• Probability Theory
• Schramm-Loewner Evolution
• Gaussian Free Field
• +۴ مورد دیگر

Jason Miller is a Reader in Probability at the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics at the University of Cambridge. His research focuses on advanced topics in probability theory and mathematical physics, particularly exploring the deep connections between random geometry, conformal invariance, and quantum gravity. Miller's research interests span a sophisticated range of topics in modern probability, with particular emphasis on Schramm-Loewner evolution (SLE), Gaussian free field, Liouville quantum gravity, random planar maps, and random walks. His work sits at the intersection of probability theory, complex analysis, and mathematical physics, developing rigorous mathematical frameworks for understanding two-dimensional random structures that arise in statistical mechanics and quantum gravity. He has made significant contributions to establishing the connections between discrete random structures and their continuum limits, particularly in the context of Liouville quantum gravity and the Brownian map. Analysis of Miller's recent publications reveals a consistent research program focused on establishing the deep connections between various mathematical objects in two-dimensional random geometry. His work demonstrates how Liouville quantum gravity serves as a universal scaling limit for random planar maps, how Schramm-Loewner evolution describes the continuum limits of critical interfaces, and how these objects relate to the Brownian map through various characterization theorems. The mathematical techniques employed span conformal field theory, metric geometry, stochastic analysis, and complex analysis. As a member of the Statistical Laboratory research group within DPMMS, Miller collaborates extensively with leading researchers in probability theory, including Scott Sheffield, Ewain Gwynne, and Wendelin Werner. His work has been published in the most prestigious mathematics journals including Acta Mathematica, Inventiones Mathematicae, and the Annals of Probability, reflecting the significance and rigor of his contributions to the field.

Dimitris Karlisمشاهده پروفایل

استاد

• Computational Statistics
• Mixture Models
• EM Algorithm
• +۱۲ مورد دیگر

Dimitris Karlis is a Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), within the School of Information Sciences and Technology. He has been a key academic figure since earning his BSc and PhD in Statistics from AUEB in 1992 and 1999, respectively, and was promoted to Associate Professor in 2012 before advancing to full Professor. Education: BSc in Statistics, AUEB (1992) PhD in Applied Statistics, AUEB (1999) His research spans computational statistics, mixture models, EM algorithms, copulas, multivariate discrete data, and applications in sports, insurance, and seismicity. He has published extensively in top-tier statistical journals such as the Journal of the Royal Statistical Society and Statistics in Medicine . The 15 most recent publications reveal a strong focus on multivariate count data, integer-valued time series, model-based clustering using copulas, and applications in actuarial science and health. His work frequently involves mixture models, Bayesian inference, and innovative extensions of Poisson-based frameworks. Scientific Service and Recognition: Associate Editor: Metron, Communications in Statistics, IMA Journal of Management Mathematics, Stochastic Environmental Research and Risk Assessment Editor: Biometrics Bulletin of IBS Member: American Statistical Society, International Statistical Institute, International Association of Statistical Computing, Hellenic Statistical Institute Publicity Officer: Eastern Mediterranean Region, International Biometric Society Advising and Grants: He has supervised 4 completed PhDs and 18 Master’s theses, with several more in progress. He has led and participated in research projects funded by the European Union and EUROSTAT, particularly in official statistics. His advising spans methodological and applied topics in statistics. Labs and Teams: While no formal lab is named, he collaborates extensively with researchers in actuarial science, transportation, biostatistics, and environmental risk, often through joint projects and publications.

Gregory Wheelerمشاهده پروفایل

استاد

• Formal Epistemology
• Philosophy of Science
• Probability Theory
• +۴ مورد دیگر

Gregory Wheeler is a Professor of Philosophy and Computer Science at the Frankfurt School of Finance and Management, where he serves as Head of the Computational Science & Philosophy Department and Academic Director of the Master of Applied Data Science program. He received a joint PhD in Philosophy and Computer Science from the University of Rochester in 2002 and has held faculty positions at LMU Munich, Carnegie Mellon University, and Nova University Lisbon, with visiting roles at TU Dresden AI Institute and Max Planck Institute for Human Cognition in Berlin. His research focuses on Formal Epistemology , Bayesian Statistics , and Machine Learning , particularly Imprecise Probability Theory and Probabilistic Models of Coherence . His recent work explores intersections between logic, probability, and computational methods in philosophical inquiry. From his publications, key trends emerge in Uncertainty Quantification , Probabilistic Reasoning , and AI Applications across philosophy and statistics. Topics include peer disagreement resolution, causal modeling, and foundational issues in imprecise probability. Gregory co-founded Exaloan AG in 2019, serving as CTO and Head of AI & Machine Learning. He has contributed to academic leadership through editorial roles and organizing symposia, including the International Symposium on Imprecise Probabilities.

Wenlong Mouمشاهده پروفایل

استادیار

• Statistical Learning
• Reinforcement Learning
• Optimization
• +۸ مورد دیگر

Wenlong Mou is an Assistant Professor at the University of Toronto's Department of Statistical Sciences, with additional affiliation at the Vector Institute for AI. His research develops optimal statistical methods and efficient algorithms for data-driven decision-making, focusing on reinforcement learning, stochastic approximation, and causal estimation. He teaches advanced courses in theoretical statistics (STA3000) and stochastic processes (STA447/2006). Education: Ph.D. in EECS, University of California, Berkeley (2023) B.Sc. in Computer Science and Economics, Peking University Research Focus: Mou's work bridges statistical theory with machine learning practice. Key areas include: Theoretical foundations of reinforcement learning (e.g., Bellman equations, continuous-time systems) Efficient algorithms for semi-parametric estimation and debiasing Non-asymptotic analysis of stochastic optimization and MCMC methods High-dimensional statistical inference and causal modeling Publication Trends: Recent articles (2023–2025) emphasize reinforcement learning theory (policy evaluation, adaptive interpolation), causal inference (debiased estimators, propensity scores), and statistical computing (diffusion processes, Langevin algorithms). Methodological rigor and non-asymptotic guarantees characterize his work. Awards: INFORMS APS Student Paper Competition Finalist (2022) Advising and Labs: Actively recruiting PhD students with backgrounds in mathematics or deep learning. Students access GPU clusters via the Vector Institute. Grants unspecified in sources.

David Renfrewمشاهده پروفایل

دانشیار

• Probability
• Random Matrix Theory
• Mathematical Physics
• +۳ مورد دیگر

David Renfrew is an Associate Professor in the Department of Mathematics and Statistics at SUNY Binghamton. His research focuses on probability theory, random matrix theory, and their applications to mathematical physics and biological systems. Primary research areas: Probability, Random Matrix Theory, Mathematical Physics Special interest in non-Hermitian matrices and free probability interplay Current work explores dynamics of coupled systems and spectral properties of structured matrices Recent publications analyze singularity degrees of random matrices (2025), fractional free convolutions (2024), and universality phenomena in elliptic random matrices (2023). His methodological work bridges abstract probability theory with concrete applications in network dynamics and differential equations.

Jordan Cotlerمشاهده پروفایل

استادیار

• Quantum Information
• Quantum Computation
• Quantum Gravity
• +۵ مورد دیگر

Jordan Cotler is an Assistant Professor of Physics at Harvard University, affiliated with the Department of Physics within the Faculty of Arts and Sciences. He holds a BS in physics and mathematics from MIT (2015) and a PhD in physics from Stanford University (2020). Before joining Harvard's faculty, he served as a Junior Fellow at the Harvard Society of Fellows from 2020 to 2024. His research focuses on the intersection of quantum information, computation, and spacetime physics. Key interests include quantum algorithms for analyzing many-body and quantum gravitational systems, information-theoretic frameworks for chaotic dynamics, and non-perturbative methods in quantum cosmology and field theory. Cotler's work has advanced quantum algorithm design for experimental platforms and contributed to understanding black hole microstructure and cosmological spacetimes. He has been recognized with prestigious early-career awards, including his Harvard Society of Fellows Junior Fellowship. His publications span foundational topics such as quantum gravity, holography, computational complexity, and quantum chaos, reflecting a multidisciplinary approach to theoretical physics.

Miaolan Xieمشاهده پروفایل

استادیار

• Operations Research
• Stochastic Optimization
• Data Science
• +۴ مورد دیگر

Miaolan Xie is an Assistant Professor at Purdue University's Edwardson School of Industrial Engineering, joining in 2025. Currently, she is a postdoctoral Principal Researcher at the University of Chicago Booth School of Business, affiliated with the Healthcare Initiative. Her academic journey includes a Ph.D. from Cornell University (2019–2024) under Katya Scheinberg and a Master's and Bachelor's in Combinatorics & Optimization from the University of Waterloo. Prior to academia, she worked as a data scientist at Alibaba, Baidu, and PwC Consulting, and as a Givens Associate at Argonne National Laboratory. Education: Ph.D., Operations Research and Information Engineering, Cornell University (2019–2024) Master of Math, Combinatorics & Optimization, University of Waterloo (2014–2016) Bachelor of Math, Pure Math and Combinatorics & Optimization, University of Waterloo (2010–2014) Research Interests: Focuses on stochastic optimization, data science, nonlinear optimization, and machine learning applications in healthcare. Develops algorithms with rigorous theoretical guarantees for messy data scenarios. Current work emphasizes adaptive optimization methods with high probability complexity bounds, leveraging tools from statistics and stochastic processes. Awards: Second Place, 2023 Student Paper Prize (INFORMS Optimization Society) Second Place, Flash Talk Competition (YinzOR Student Conference) NSF Research Internship Funding (2022) Advising & Grants: Seeking PhD students for Fall 2025. Active in grant-funded research through NSF and institutional collaborations. Prior industry experience informs applied healthcare optimization projects. Labs & Teams: Leads a research group at Purdue, collaborating with healthcare and optimization experts. Affiliated with the Healthcare Initiative at the University of Chicago Booth School of Business.

Youssef Marzoukمشاهده پروفایل

استاد

• Uncertainty Quantification
• Bayesian Computation
• Inverse Problems
• +۴ مورد دیگر

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics

Jason Swansonمشاهده پروفایل

دانشیار

• Stochastic Differential Equations
• Probability Theory
• Mathematical Logic
• +۲ مورد دیگر

Jason Swanson is an Associate Professor in the Department of Mathematics at the University of Central Florida, specializing in stochastic processes and probability theory. His research encompasses stochastic differential equations, fractional Brownian motion, and the foundations of probability. Recent work includes developing the iterated Dirichlet process for Bayesian inference and extending representations for row-exchangeable arrays. He maintains an active research program connecting mathematical logic with probability theory. Teaching responsibilities include graduate courses in Measure and Probability (MAA 6238) and undergraduate probability (MAP 4113). His lecture notes on measure-theoretic probability are publicly available.

Cory Simonمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Optimization
• Computational Materials Science
• +۳ مورد دیگر

Cory Simon serves as Associate Professor in the Department of Chemical, Biological, and Environmental Engineering within Oregon State University's College of Engineering. His research integrates machine learning, optimization, and chemical engineering to advance materials discovery and environmental sensing systems. His academic foundation includes a Ph.D. in Chemical Engineering from the University of California, Berkeley and a B.S. in Chemical Engineering from The University of Akron. Simon's work centers on Bayesian methodologies for scientific challenges, featuring: Bayesian optimization for adaptive materials synthesis Statistical inversion of physical systems with uncertainty quantification Computational design of nanoporous sensor arrays Stochastic algorithms for robotic environmental monitoring Recent publications demonstrate accelerating focus on multi-fidelity optimization for molecular design and atmospheric water harvesting, bridging chemical engineering with computational science through data-driven approaches. Leading The Simon Ensemble research group, Simon champions a versatile 'buffet-style' research philosophy—drawing from mathematics, statistical mechanics, and machine learning to address interdisciplinary problems across chemistry, materials science, and environmental engineering.

• Theoretical Physics
• Quantum Gravity
• Quantum Information
• +۳ مورد دیگر

Anastopoulos Charis is an Associate Professor in the Department of Physics at the University of Patras. He holds dual appointments in the Theoretical and Mathematical Physics Division and the Technical Institute of Patras (Optics and Optometry). His career includes postdoctoral research at Imperial College London, University of Barcelona, University of Maryland, Utrecht University, and extended research roles funded by Marie Curie Fellowships and national grants. Education: B.Sc. (University of Patras), M.Sc. and Ph.D. (Imperial College London). Research focuses on gravity theory (black hole thermodynamics, gravitational quantum physics), quantum gravity (time in quantum mechanics, quantum measurement), and quantum information (entanglement dynamics, relativistic quantum information). Notable contributions include a Princeton University Press book on quantum concepts and over 50 journal articles. His work bridges foundational quantum theory with gravitational physics, emphasizing quantum-classical transitions and spacetime structure. Awards include journal highlights (Class. Quant. Grav. 2008/2013) and Scientific American Book Club recognition. Research spans quantum decoherence, gravitational cat states, and relativistic measurement theory, with recent focus on deep space quantum experiments and holographic thermodynamics.