جست‌وجوی استاد و پژوهشگر

• Probability Theory
• Stochastic Processes
• Theoretical Population Genetics
• +۷ مورد دیگر

Joseph C. Watkins is a Professor in the Department of Mathematics at the University of Arizona, where he also serves as Director of the Data Sciences Academy. He holds affiliations with the Interdisciplinary Programs in Statistics and Data Science, Applied Mathematics, and Genetics, as well as the BIO5 Institute. His academic training includes a Ph.D. in Mathematics from the University of Wisconsin (1982), and degrees from the University of Tennessee. Department of Mathematics Interdisciplinary Program in Statistics and Data Science Interdisciplinary Program in Applied Mathematics Interdisciplinary Program in Genetics BIO5 Institute His research lies at the intersection of probability theory, stochastic processes, and biological applications. Key areas include theoretical population genetics, statistical genetics, human evolution, and biophysics. He has led collaborative projects on archaic admixture in human populations, coevolution of language and genes in Austronesian societies, and mathematical modeling of biological systems such as bacterial colonies and honey bee swarms. His work integrates deep mathematical theory with empirical genomic data. The 15 most recent publications highlight a sustained focus on human evolutionary genetics, particularly Neanderthal and Denisovan introgression, X chromosome evolution, and population structure in Africa and Oceania. His earlier work includes foundational contributions to stochastic modeling in genetics and biophysics. The research spans theoretical developments in probability and their application to real-world biological questions, demonstrating a consistent trajectory of interdisciplinary innovation. He has advised doctoral students such as Kevin R. Anderson, whose dissertation focused on bacterial colony dynamics. He teaches core graduate courses including Theory of Probability (Math 564) and Theory of Statistics (Math 566), and is actively involved in interdisciplinary training programs supported by the NSF and Flinn Foundation. His service includes roles as Vice Chair of the Graduate Interdisciplinary Program in Statistics and membership on the Executive Committee for the Computational and Mathematical Modeling of Biomedical Systems NIH training grant. He maintains active research collaborations across departments, including Anthropology and Biosciences, and with external institutions like the Carl Hayden Bee Research Center.

Bo Bernhardssonمشاهده پروفایل

استاد

• Automatic Control
• Control Theory
• Modeling and Control of Uncertain Systems
• +۱۷ مورد دیگر

Bo Bernhardsson is a Professor in Automatic Control at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University. He has been a full-time professor at Lund since 2010, following a decade (2001–2010) as an Expert in Mobile System Design and Optimization at Ericsson. He is affiliated with major research initiatives including ELLIIT (Excellence Center in Information Technology), LCCC (Lund Center for Control of Complex Engineering Systems), and WASP-AS (Wallenberg AI, Autonomous Systems and Software research school), where he has played a leadership role since 2016. His research focuses on modeling and control of uncertain and large-scale systems, with applications spanning industrial automation, mobile communications, particle accelerators, biomedical systems, and navigation technologies. He integrates theoretical control methods with practical implementations, particularly under constraints such as communication limitations, noise, and delays. His recent publications reveal a strong trend in networked control, communication-constrained estimation, and optimization-based control design. The works span theoretical advances in signal estimation under SNR constraints, event-based and stochastic control, and practical applications like IMU-radio fusion for navigation and RF field control in particle accelerators. Keywords include Control Theory, Communication Systems, Optimization, Signal Processing, and Networked Control , with subfields such as encoder-decoder co-design, virtual antenna arrays, and dynamic programming for time-delay systems. PhD in Control, Lund University, 1992 Professor in Automatic Control, Lund University, since 1999 Expert, Mobile Systems, Ericsson, 2001–2010 Bo Bernhardsson has supervised over 20 PhD and licentiate students, including Jacob Bergstedt (immune system modeling), Anders Mannesson (navigation and radio), and Erik Johannesson (control under communication constraints). His research has been funded by major entities such as the European Spallation Source and the Wallenberg Foundation. He teaches advanced courses in Linear Systems, Convex Optimization, and Robust Control, and has contributed significantly to both academic and industrial advancements in control engineering. He leads and collaborates on interdisciplinary projects involving real-time control, autonomous systems, and machine learning, often in partnership with industry and international research centers. His work in the RobotLab at LTH and on cloud-based control systems highlights his engagement with emerging technologies.

Mark Jerrumمشاهده پروفایل

استاد

• Combinatorics
• Computational Complexity
• Theoretical Computer Science
• +۷ مورد دیگر

Mark Jerrum is a Professor of Mathematics at Queen Mary University of London, part of the School of Mathematical Sciences. His research focuses on combinatorics, computational complexity, and stochastic processes, particularly in the design and analysis of randomized algorithms. He explores the mixing times of Markov chains and computational complexity of counting problems, including partition functions and generating functions, often motivated by statistical physics, constraint satisfaction, and graph polynomials. Notable grants include an EPSRC-funded project on Sampling in Hereditary Classes (EP/S016694/1, 2019–2023). He has contributed to teaching, serving as module organiser for MTH4213 (Numbers, Sets and Functions) in 2023–24. His work bridges theoretical computer science and discrete mathematics, with applications in algorithmic design and probabilistic analysis. Research highlights include advancements in perfect sampling algorithms, approximation algorithms for counting problems, and foundational work on the interplay between statistical physics models and computational complexity. He is affiliated with the Centre for Combinatorics, Algebra and Number Theory, reflecting his interdisciplinary approach to combinatorial and algorithmic challenges.

John Voglerمشاهده پروفایل

پژوهشگر

• Geography
• Urbanization
• Landscape Fragmentation
• +۴ مورد دیگر

John Vogler is a Researcher at the Center for Geospatial Analytics, North Carolina State University. His work focuses on geospatial analysis of land change patterns, particularly urbanization, landscape fragmentation, and per capita land consumption. He combines geospatial data, modeling, and cartographic design to inform sustainable development strategies. Vogler also supports research administration, project management, and event coordination, including the Geospatial Forum series. His research interests emphasize spatial modeling of land-use dynamics, floodplain management, and the application of LiDAR and remote sensing technologies. Key areas include FUTURES land-change simulation model development, spatial interaction modeling for urban growth, and integrating citizen science into ecological risk assessment. Vogler's publications analyze topics like regulatory floodplain paradoxes, spatially interactive land-change adaptations, and human footprint trends in the U.S. His work bridges technical geospatial methods with policy-relevant outcomes, aiming to enhance environmental resilience through evidence-based approaches.

• Computational chemistry
• Molecular modeling
• Drug discovery
• +۵ مورد دیگر

Marcus Weber serves as Head of the Computational Molecular Design research group within the Modeling and Simulation of Complex Processes department at the Zuse Institute Berlin (ZIB), which operates in close affiliation with Freie Universität Berlin. His interdisciplinary work spans computational mathematics, molecular modeling, drug discovery, and unexpected connections to Egyptology, demonstrating the broad applicability of mathematical approaches across diverse scientific domains. Dr. Weber's research interests focus on the mathematical foundations of molecular simulation and drug design: Developing advanced Markov state models for complex molecular systems Creating computational methods for efficient drug discovery Applying machine learning techniques to molecular dynamics Modeling pH-dependent receptor-ligand interactions Exploring metastable dynamics in biological systems Bridging mathematical approaches with Egyptological research His recent publications reveal a sophisticated integration of computational mathematics with practical pharmaceutical applications, particularly in opioid receptor research. The work demonstrates how mathematical modeling can identify pH-dependent drug candidates that maintain efficacy while reducing side effects. His research group has developed innovative algorithms like ISOKANN for learning Koopman eigenfunctions and has made significant contributions to understanding molecular transition rates and metastable dynamics. Dr. Weber leads multiple significant research projects including 'Drug Candidates as Pareto Optima in Chemical Space,' 'HPC and ML for Drug Discovery,' and interdisciplinary collaborations connecting mathematical approaches with Egyptology. His work on 'Mathematics and Egyptology' and 'Ancient Egyptian' demonstrates the unexpected breadth of mathematical applications. His research has direct implications for developing safer opioid medications and understanding molecular behavior in complex environments.

Yin Tat Leeمشاهده پروفایل

دانشیار

• Convex Optimization
• Convex Geometry
• Spectral Graph Theory
• +۵ مورد دیگر

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he has been faculty since 2017 (initially as Assistant Professor until 2022). He also holds a position as Senior Principal Researcher at Microsoft AI since 2024, having previously served as Principal Researcher (2022-2024) and Visiting Researcher (2018-2022) at Microsoft Research. His academic career spans prestigious institutions including MIT, where he completed his PhD in Mathematics. 2024-Now: Member of Technical staff / Senior Principal Researcher in Microsoft AI 2022-2024: Principal Researcher in Microsoft Research 2022-Now: Associate Professor in University of Washington 2017-2022: Assistant Professor in University of Washington 2018-2022: Visiting Researcher in Microsoft Research 2016-2017: Postdoc in Microsoft Research Dr. Lee received his PhD in Mathematics from MIT (2012-2016) and his undergraduate degree in Mathematics from the Chinese University of Hong Kong (2008-2012). His exceptional academic journey was recognized with the MIT Presidential Fellowship and the Charles W. and Jennifer C. Johnson Prize. Lee's research fundamentally advances algorithms across multiple domains, particularly in convex optimization, convex geometry, spectral graph theory, and online algorithms. His work bridges continuous and discrete mathematics to develop state-of-the-art algorithms for fundamental problems in computer science and optimization. Notably, he has developed breakthrough approaches for linear programming, maximum flow problems, and optimization in high-dimensional spaces. His research has evolved from foundational theoretical work to more applied areas including differential privacy and connections to machine learning. Analysis of his recent publications reveals a strong trajectory toward practical applications of theoretical optimization, with significant contributions to differentially private machine learning, efficient sampling methods, and connections between optimization theory and deep learning. His work consistently demonstrates how deep theoretical insights can yield practical algorithmic improvements across computer science. Lee's exceptional contributions have been recognized with numerous prestigious awards including the Packard Fellowship, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, A.W. Tucker Prize, and multiple Best Paper Awards at top theoretical computer science conferences (FOCS, SODA, NeurIPS). He has also received the NSF CAREER Award and MIT's Sprowls Award for his doctoral thesis. Packard Fellowship (2020) Sloan Research Fellowship (2020) Microsoft Research Faculty Fellowship (2019) Best Paper Awards at FOCS, SODA, and NeurIPS A.W. Tucker Prize NSF CAREER Award As an advisor, Lee has mentored PhD students including Haotian Jiang, whose work earned a Best Student Paper award at SODA. His research has been supported by significant grants from NSF and Microsoft Research. Lee actively contributes to the academic community through service on program committees for FOCS, SODA, and other major conferences, as well as organizing workshops on continuous approaches to discrete optimization. He has also taught graduate courses including Theory of Optimization and Continuous Algorithms and undergraduate courses on algorithms. Lee's work bridges theoretical computer science and practical applications, with his recent research expanding into differential privacy for machine learning and connections between optimization theory and deep learning. His collaborative work spans institutions including MIT, Microsoft Research, and the University of Washington, reflecting his position at the intersection of theoretical and applied computer science.

• Time Series Analysis
• Financial Econometrics
• Volatility Modeling
• +۵ مورد دیگر

Professor Shelton Peiris is an Associate Professor at the School of Mathematics and Statistics , University of Sydney , where he has been since 1990. He holds visiting appointments at institutions worldwide, including University of Waterloo , University of Manitoba , and University of Malaya . Currently, he serves as Sub Dean (Student Affairs) in the Faculty of Science and coordinates interdisciplinary teaching with the School of IT for MIT/MDS degrees. His research focuses on time series analysis, financial econometrics, and technology integration in statistics education. Education: PhD in Statistics, Monash University , 1987 Shelton's research interests include statistical analysis of stationary and non-stationary time series, theory and applications of estimating functions, financial time series modeling, saddlepoint and Edgeworth approximations, and exploring technology's role in statistics education. He is a member of the Statistics Research Group at the University of Sydney and leads projects in financial econometrics, generalized autoregressive models, and nonlinear time series analysis. Recent publication trends highlight his work in financial econometrics, particularly volatility and duration modeling, stochastic processes, and hybrid forecasting methods combining traditional statistical techniques with machine learning advancements like GANs and neural networks. His collaborations span Australia, Canada, Malaysia, and Indonesia, with notable grants including ARC Linkage and University of Malaya Research Grants . Scientific Awards: 2012: Faculty of Science Teaching Citation 2011: Bronze Medal, University Putra Malaysia 2007: Bronze Medal, University Putra Malaysia 1983: Monash University Graduate Scholarship Elected Member, International Statistical Institute (ISI) Fellow, Royal Statistical Society (FRSS) Honorary Fellow, Institute of Applied Statistics, Sri Lanka (FIASSL) Current Research Students: Leonard Mushunje - High-Dimensional Financial Functional Time Series Data Grants: ARC Linkage Grant (2005-2007): Modelling Stock Market Liquidity ARC Bridging Grant (2012-2014): Financial Duration Modeling University of Malaya Research Grants (2011-2015): Volatility Models, GARCH MOHE Malaysia Grant (2013-2015): Robust Control Charts Professor Peiris contributes to editorial boards such as the Journal of Statistical Computation & Simulation and Sri Lankan Journal of Applied Statistics . His teaching roles include MATH1015 (Statistics for Life Science) , MATH1905 (Statistics Advanced) , and advanced honors courses in time series analysis.

Feliks Nüskeمشاهده پروفایل

استاد پژوهشی

• Data-driven modeling
• Molecular simulation
• Koopman operator theory
• +۶ مورد دیگر

Dr. Feliks Nüske is a Max Planck Group Leader at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, leading the Data-driven Modeling of Complex Physical Systems research group. He also holds a position as Guest Professor at Freie Universität Berlin (2023-2024). His research bridges applied mathematics, data science, and molecular simulation to develop novel algorithms for understanding complex physical systems at the molecular level. Dr. Nüske's educational background includes: Ph.D. in Mathematics from Freie Universität Berlin (2012-2017) Postdoctoral research at Universität Paderborn (2019-2022) and Rice University (2017-2019) His research focuses on developing data-driven methods that combine physical insights with machine learning to extract meaningful information from molecular simulation data. Key areas include Koopman operator theory for analyzing nonlinear dynamical systems, dimensionality reduction techniques, tensor methods for efficient computation, and kinetically consistent coarse-graining approaches. His work enables more efficient modeling of complex molecular processes that would otherwise be computationally prohibitive. Dr. Nüske's publication record shows a consistent trajectory of advancing both the theoretical foundations and practical applications of data-driven modeling in molecular science. His recent work emphasizes error analysis for data-driven models, control of stochastic systems, tensor-based dimensionality reduction, and methods to preserve kinetic properties in coarse-grained models. These contributions address critical challenges in scaling molecular simulations to biologically relevant timescales and system sizes. Dr. Nüske actively collaborates with researchers worldwide and has established partnerships with leading institutions including Freie Universität Berlin, Rice University, and TU Ilmenau. His collaborative network spans multiple disciplines, connecting mathematicians, chemists, and computational scientists. Dr. Nüske advises several Ph.D. students at the Max Planck Institute, including Vahid Nateghi, Lei Guo, Minakshi Verma, and Hauke Sprink. He has organized workshops on Uncertainty Quantification for molecular systems and regularly presents at major international conferences including SIAM MS, MTNS, and IMSI workshops. His research group continues to push the boundaries of what's possible in computational molecular science through innovative mathematical approaches.

Dan Schonfeldمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Video Processing
• +۷ مورد دیگر

Dan Schonfeld is a Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago . His research spans signal, image, and video processing, with interdisciplinary applications in genomic signal processing and multimedia systems. Education: Ph.D. and M.Sc. in Electrical and Computer Engineering from The Johns Hopkins University (1990, 1988), B.Sc. in Electrical Engineering and Computer Science from UC Berkeley (1986). Research Interests: Schonfeld's work focuses on video communications, retrieval, and networks, integrating computer vision, pattern recognition, and stochastic optimization. His contributions include mathematical morphology for image processing and statistical methods for real-time scene change detection. Article Trends: His recent publications emphasize particle filtering for video tracking, hidden Markov models for activity recognition, and multi-camera systems for pose estimation. Applications in genomic signal processing and crowded scene tracking highlight his interdisciplinary impact. Scientific Leadership: He has been a Senior Member of IEEE since 2005 and received multiple Best Student Paper Awards at IEEE ICIP (2006, 2007) and SPIE VCIP (2006). Editorial Contributions: Schonfeld has served as Guest Editor for IEEE journals on video and genomic signal processing and as Associate Editor for key IEEE Transactions since the 1990s.

• Landslide Susceptibility
• Geomorphology
• Natural Hazards
• +۳ مورد دیگر

Chiara Martinello is a Researcher in Earth and Marine Sciences at the University of Palermo, Italy, with office hours held Tuesdays 3:00-5:00 PM at Via Archirafi, 20 - 4th floor. Her work focuses on advanced geomorphological analysis of landslide and coastal processes using statistical and machine learning methodologies. Her research spans landslide susceptibility mapping, coastal erosion vulnerability, and earthquake/tropical storm-induced geohazards. She addresses critical methodological challenges including incomplete landslide inventories, cell-size effects in modeling, and integration of preparatory/triggering factors. Fieldwork concentrates on Sicily (Italy) and El Salvador, with emphasis on practical risk assessment applications. Analysis of her 15 most recent publications reveals consistent innovation in statistical landslide modeling, particularly regarding inventory completeness validation and multi-typology susceptibility mapping. Recent work expands into coastal vulnerability indices and anthropogenic impacts on fluvial systems, demonstrating interdisciplinary integration of geospatial data, bathymetry, and hydrological modeling. No scientific awards are documented in available sources. Dr. Martinello supervises Master's theses as indicated by her 'Edited theses' role, though specific student names aren't provided. Research grant details remain undisclosed in public materials, though her extensive publication record suggests active project involvement. She operates within the Earth and Marine Sciences Department's research infrastructure at the University of Palermo, contributing to regional risk assessment initiatives through field data collection, statistical modeling, and validation frameworks for geohazard prediction.

Nima Rabieiمشاهده پروفایل

استادیار

• Computational Mathematics
• Numerical Analysis
• Optimal Control
• +۴ مورد دیگر

Nima Rabiei is an Assistant Professor at the International University of Sarajevo in the Department of Applied Mathematics . His research focuses on computational mathematics, numerical analysis, and their applications to engineering and environmental systems. Education: PhD in Applied Mathematics (2014) from Universitat Politècnica de Catalunya-BarcelonaTech (UPC) His work spans population balance modeling , non-linear optimal control , integro-differential equations , and limit analysis decomposition methods . He has developed numerical schemes for crystallizers and explored statistical methods for hyper-ellipsoids. Recent publications highlight his contributions to adjoint methods , Monte Carlo techniques , and meshless computational approaches .

Diane Donovanمشاهده پروفایل

استاد

• Combinatorics
• Design theory
• Latin squares
• +۶ مورد دیگر

Diane Donovan is an Affiliate Professor in the School of Mathematics, Faculty of Science at the University of Queensland. Her research spans combinatorics, design theory, and their interdisciplinary applications to cryptography, coding theory, and biological systems. Her work demonstrates strong theoretical foundations while increasingly incorporating real-world applications across diverse scientific domains. Professor Donovan's research interests focus on combinatorial structures, particularly Latin squares, orthogonal arrays, and design theory. Her work explores critical sets in Latin squares, biembeddings of combinatorial structures, and applications to coding theory and cryptography. She has made significant contributions to the understanding of partial Latin squares, Heffter arrays, and their connections to graph theory and finite geometry. Her recent work shows an expanding focus on interdisciplinary applications, particularly in biological systems and energy modeling. Analysis of her recent publications reveals a trajectory from purely theoretical combinatorial mathematics toward increasingly interdisciplinary applications. While maintaining strong theoretical contributions to design theory and combinatorics, her work now frequently intersects with biological modeling, medical research, and energy systems. This evolution demonstrates how fundamental mathematical structures can provide powerful tools for addressing complex problems in diverse scientific fields, from cancer modeling to gene expression analysis and petroleum engineering. Professor Donovan maintains an active research profile with numerous collaborations across disciplines, particularly with researchers in mathematics, biology, and engineering. Her publication record shows consistent productivity with significant contributions to top journals in combinatorics and discrete mathematics. Her work demonstrates the power of combinatorial methods to address both theoretical questions and practical applications across scientific domains.

Sigrunn Holbek Sørbyeمشاهده پروفایل

استاد

• Bayesian Statistics
• Time Series Analysis
• Spatial Data Modeling
• +۳ مورد دیگر

Sigrunn Holbek Sørbye is a Professor at the Department of Mathematics and Statistics, UiT The Arctic University of Norway. Her research focuses on Bayesian statistics, time series analysis, and spatial data modeling with applications in climatology, ecological statistics, and computational statistics. Institution: UiT The Arctic University of Norway Department: Department of Mathematics and Statistics Research Interests: Dr. Sørbye specializes in Bayesian computation using integrated nested Laplace approximation (INLA), statistical modeling of long-range dependent processes, and applications to climate systems. Her work includes modeling cosmic dust detection rates, analyzing metabolic risk factors, and studying population dynamics through capture-recapture data. Selected Publications Trends: Recent research spans dietary pattern analysis, solar dust modeling, climate sensitivity studies, and ecological monitoring. Key methodologies involve Bayesian hierarchical modeling (2025, 2023), INLA applications (2022, 2020), and long-memory stochastic processes (2020-2019). Collaborative Networks: Dr. Sørbye collaborates on interdisciplinary projects including "Modellering av komplekse systemer" (Complex Systems Modeling) and "Intermittent fluctuations in physical systems." She also contributes to "Transforming ocean surveying by the power of DL and statistical methods." Contact: Tromsø, Norway. sigrunn.sorbye@uit.no | +47 77 64 55 04

Zifan Wangمشاهده پروفایل

پژوهشگر

• Decision-making under uncertainty
• Machine Learning
• Optimal Transport
• +۳ مورد دیگر

Zifan Wang is a doctoral student and researcher at the Division of Decision and Control Systems (DCS), School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology. He is jointly advised by Prof. Karl H. Johansson and Prof. Michael M. Zavlanos at Duke University. He holds a Master's and Bachelor's degree from the Honors School of Harbin Institute of Technology. His research focuses on decision-making under uncertainty, leveraging tools from Machine Learning , Optimal Transport , Game Theory , and Control Theory , with a special interest in generative models and risk-averse optimization . Recent publications highlight his work on risk-averse learning in online convex games, constrained optimization with decision-dependent distributions, and distributional reinforcement learning for LQR systems. His methodological contributions include zeroth-order gradient estimation, one-point sampling strategies, and residual feedback for variance reduction. Honors include a 2024 Travel scholarship from Björns Foundation and a 2023 Travel grant from Karl Engvers Foundation . He actively participates in peer review for top conferences (NeurIPS, ICLR, L4DC, CDC, ACC) and journals (IEEE Transactions on Automatic Control, Automatica).

• 3D Computer Vision
• Geometric Deep Learning
• Topological Deep Learning
• +۱۰ مورد دیگر

Tolga Birdal is an Assistant Professor (Lecturer) and UKRI Future Leaders Fellow in the Department of Computing at Imperial College London. As the Principal Investigator (PI) of the CIRCLE group , his research focuses on topological deep learning, geometric machine learning, and 3D computer vision, with theoretical interests in non-Euclidean inference and deep learning principles. Education: PhD and MSc in Computer Vision from Technical University of Munich (2018), BSc in Computer Science from Sabancı University (2008). Projects: PI for UKRI-EPSRC's UNTOLD (Topological Deep Learning), Royal Society's drug discovery initiative, and EPSRC's GNOMON (Generative Models in non-Euclidean Spaces). Leadership: Area Chair for CVPR, ICCV, and 3DV 2025 Publication Chair. His work bridges differential geometry, algebraic topology, and deep neural networks, with applications in quantum computer vision, 3D/4D generative priors, and medical imaging. Key contributions include novel frameworks for rotation forecasting, graph generation, and topological generalization bounds. Scientific Awards: UKRI Future Leaders Fellowship EMVA Young Professional Award