High-Dimensional Methods Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۳۶-۷۵۰ از ۶٬۵۷۷ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Gerald Wangمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Dr. Gerald Wang is an Assistant Professor in Civil and Environmental Engineering at Carnegie Mellon University with courtesy appointments in Chemical Engineering and Mechanical Engineering. He leads the M5 Lab (Mechanics of Materials via Molecular and Multiscale Methods), focusing on nanoscale mechanics using computational approaches to solve civil engineering challenges related to water-energy systems, material resilience, and sustainable polymers. Education: Ph.D. in Mechanical Engineering and Computation, MIT (2019) S.M. in Mechanical Engineering, MIT (2015) B.S. in Mechanical Engineering, Mathematics & Physics, Yale University (2013) His research integrates statistical physics, fluid mechanics, and high-performance computing to investigate nanoscale structural and transport phenomena. Key areas include climate-resilient infrastructure, energy-water nexus solutions, and nanoscale thermal transport in materials. Recent work explores molecular-scale separation processes and recyclable polymer design using advanced simulation techniques. Publication trends demonstrate consistent focus on nanoscale transport mechanisms, computational method development, and interdisciplinary applications from materials science to urban systems. Articles frequently bridge molecular dynamics with macro-scale engineering problems. Awards: Scott Institute Seed Grant for clean energy research CMU Celebration of Education Award for teaching excellence Current projects include NSF-funded work on nanoscale slip phenomena and polymer upcycling collaborations. The M5 Lab develops open-source simulation tools and maintains active industry partnerships in advanced materials.

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Rafał Weronمشاهده پروفایل

استاد

Graz University of TechnologyAustria

Rafał Weron is a Full Professor at Wrocław University of Science and Technology, where he has held leadership roles since 2015, including Head of the Department of Operations Research and Business Intelligence and Chairman of the Scientific Discipline Council for Management and Quality Sciences. His expertise spans electricity price forecasting, computational economics, and risk management, with significant contributions to probabilistic forecasting methods. As a globally recognized scholar, he has received prestigious awards such as the Hugo Steinhaus Prize (2018) and the Tao Hong Award (2017). Key Affiliations : Wrocław University of Science and Technology; Polish Academy of Sciences (Statistics and Econometrics Committee); Polish Mathematical Society. Research Trends: Weron's work focuses on electricity price forecasting, leveraging machine learning and statistical models to enhance accuracy and reliability. His publications emphasize probabilistic forecasting frameworks, quantile regression, and hybrid modeling techniques, reflecting a commitment to methodological rigor and practical applications in energy markets. Scientific Awards: Top 1% globally ranked economist (IDEAS/RePEc, 2013-2022) World's Top 2% Most Widely Cited Scientist (2019-2021) 'Hugo Steinhaus' Prize (2018) Tao Hong Award (2017) Emerald Citation of Excellence (2017) Minister of Science & Higher Education Prize (2016) Commission of National Education Medal (2016)

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Luca Pavarinoمشاهده پروفایل

استاد

University of PaviaItaly

Luca Pavarino is a Professor at the Department of Mathematics, University of Pavia. His research focuses on scientific computing and numerical methods, particularly in the context of cardiac electrophysiology and multiphysics systems. He leads the Scientific Computing group, specializing in domain decomposition methods (BDDC/FETI-DP), isogeometric analysis, and parallel algorithms. His work integrates advanced numerical techniques with biomedical applications, including cardiac electromechanical coupling, drug testing on cardiac tissues, and modeling genetic cardiac disorders like LQT8 syndrome. Key contributions include scalable solvers for nonlinear systems, preconditioners for heterogeneous media, and operator learning for ionic dynamics. Research interests span computational cardiology, numerical analysis, and parallel computing, with applications to biophysics and drug discovery. His projects often involve interdisciplinary collaborations between mathematics, engineering, and medicine. Notable contributions include: Development of BDDC/FETI-DP preconditioners for cardiac models Integration of machine learning with cardiac electrophysiology High-performance computing for multiphysics systems (Biot’s consolidation, protein stability) Labs/Teams: Scientific Computing Group at the University of Pavia’s Department of Mathematics.

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Ayesha Aliمشاهده پروفایل

استاد

University of GuelphCanada

Ayesha Ali is a Professor of Statistics and Director of the Master of Data Science program at the University of Guelph. She holds a PhD in Statistics from the University of Washington (2002) and has expertise in statistical methods for complex high-dimensional systems, including ecological networks, causal inference, and bioinformatics. Her research integrates graphical Markov models, machine learning, and statistical computing to address challenges in plant-pollinator networks, livestock genetics, and disease risk modeling. Education: B.Sc. Honours in Statistics and Actuarial Science, University of Western Ontario (1996) M.Sc. in Statistics, University of Toronto (1998) Ph.D. in Statistics, University of Washington (2002) Research Interests: Graphical Markov models and ecological networks Causal inference and longitudinal data analysis Machine learning and high-dimensional predictive modeling Statistical methods for livestock genetics and animal health Computational statistics and bioinformatics Articles Trends: Her recent work spans interdisciplinary applications, including veterinary oncology biomarker discovery, remote sensing for agricultural suitability, and pipeline development for cross-species transcriptomics. She emphasizes graphical structure exploitation in regression and predictive modeling, with contributions to both theoretical and applied statistical methodologies. Awards: Canadian Journal of Statistics Award (2020) for groundbreaking work on doubly sparse regression NSERC Discovery Grant (2018) NSERC Collaborative Research and Development Grant (2015) Advising & Grants: She has supervised numerous graduate and undergraduate students on projects ranging from plant-pollinator network analysis to bioinformatics. Her grants include NSERC-funded research on milk fatty acid genetics and statistical methods for clustered data. Labs/Teams: Involved in the Bioinformatics program at the University of Guelph, contributing to interdisciplinary research collaborations in ecology and animal science.

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Tresa Pollockمشاهده پروفایل

استاد

University of California , Santa Barbara (UCSB)United States

Tresa Pollock is the ALCOA Professor of High Tech Materials in the Materials Department at the University of California, Santa Barbara (UCSB), part of the College of Engineering. Her research focuses on structural materials, high-temperature processing, ultrafast laser interactions, alloy design, and 3D characterization techniques. She holds a Ph.D. from MIT and a B.S. from Purdue University. Research Interests: Her work addresses extreme environment materials performance, thermal barrier coatings, cobalt-base superalloys, hypersonic flight materials, femtosecond laser tomography, and computational materials engineering. Recent projects include developing refractory alloys and advanced characterization methods like the TriBeam system. Awards: She is a Fellow of TMS (The Minerals, Metals & Materials Society) and received the 2023 Acta Materialia Gold Medal. Grants & Collaborations: Supported by agencies like ONR, NSF, AFOSR, and industry partners including GE, Boeing, and Rolls-Royce. Her lab includes advanced facilities at UCSB’s Microscopy and Microanalysis Facility. Labs & Teams: Leads a research group with senior scientists like Chris Torbet. Labs are located in Engineering II and Elings Hall, focusing on 3D tomography, laser-material interactions, and high-temperature alloy development.

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Mark Steelمشاهده پروفایل

استاد

University of WarwickUnited Kingdom

Mark Steel is a Professor of Statistics at the University of Warwick, Department of Statistics. He previously held Chairs in Economics at the University of Edinburgh (1998-2000) and in Statistics at the University of Kent (2000-2003). His research focuses on Bayesian statistics, including theoretical and applied work in distribution theory, Bayesian model averaging, spatial statistics, survival analysis, and stochastic volatility models. He has extensive editorial experience, serving as Editor-in-Chief of Bayesian Analysis (2022-2025) and as Head of the Statistics Department at Warwick (2014-2018). He is currently Chair-Elect of the Objective Bayes section of the International Society for Bayesian Analysis (ISBA). His research interests span Bayesian methodology, econometrics, and statistical theory, with applications to economics, survival analysis, and spatial data. He is affiliated with the Centre for Research in Statistical Methodology at Warwick and actively contributes to academic conferences. His work has been recognized through highly cited publications, including influential papers on Bayesian model averaging and non-Gaussian statistical models. Professional activities include editorial roles in top journals such as the Journal of Econometrics , Journal of the Royal Statistical Society , and Journal of Productivity Analysis . He collaborates with researchers globally and maintains an active profile on academic platforms like Google Scholar and ResearchGate.

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Daolang Huangمشاهده پروفایل

پژوهشگر

Aalto UniversityFinland

Daolang Huang is a Doctoral Researcher and Student in the Department of Computer Science at the School of Science, affiliated with Professor Samuel Kaski's group. He holds a Bachelor's degree in Engineering and Technology from Jinan University (2020). His research focuses on advanced machine learning techniques, including Bayesian inference, robust statistical modeling, and simulation-based methods. Key areas include experimental design optimization, neural processes, and equivariance in deep learning. Recent work emphasizes decision-aware algorithms and cost-effective simulation frameworks. Huang has collaborated internationally, with publications in top venues like NeurIPS. Despite no listed awards, his work demonstrates significant contributions to probabilistic modeling and optimization. Education : Bachelor's degree in Engineering and Technology, Jinan University (2020) Research Interests : Bayesian methods and amortized inference Robust statistics under model misspecification Continuous control and neural process architectures Optimization algorithms with decision-theoretic foundations Recent Research Trends : His articles (2020–2025) emphasize Bayesian experimental design, preference-based optimization, and equivariant neural networks. Themes include balancing statistical rigor with computational efficiency, particularly in high-dimensional decision-making contexts. Labs/Teams : Active member of Samuel Kaski’s research group, focusing on interdisciplinary applications of machine learning.

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Davi De Castro Silvaمشاهده پروفایل

پژوهشگر

University of CambridgeUnited Kingdom

Davi De Castro Silva is a Researcher at the University of Cambridge, affiliated with the Department of Computer Science and Technology and the Centre for Quantum Information and Foundations. His current work is advised by Tom Gur and Sergii Strelchuk. Previously, he was a postdoc at CWI (Amsterdam) in QuSoft, advised by Jop Briët, and completed his PhD in Applied Mathematics at the University of Cologne under Frank Vallentin and Fernando de Oliveira Filho. He holds a Master's from IMPA (Brazil) under Roberto Imbuzeiro Oliveira and a BSc/MSc from École Polytechnique (France). His research focuses on theoretical computer science, quantum computing, and combinatorics, with recent emphasis on quantum speedups' structural foundations, such as symmetry's role. Key areas include additive combinatorics (e.g., higher-order Fourier analysis), computational complexity (lower bounds), combinatorial optimization (semidefinite programming), and quantum information theory. Notable contributions include studies on quasirandomness in additive groups, quantum algorithms' limitations, and tensor analysis. His work bridges combinatorial methods with quantum computing, exploring algorithmic efficiency and structural properties. He has published in journals like Discrete Analysis , Combinatorica , and Forum of Mathematics, Sigma , with preprints addressing quantum computation symmetry, Goldreich-Levin algorithms, and hypergraph quasirandomness. His research highlights interdisciplinary approaches to foundational questions in computing and mathematics.

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Martin Andersenمشاهده پروفایل

دانشیار

University of New South WalesAustralia

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.

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Jan van den Brakelمشاهده پروفایل

استاد

Maastricht UniversityNetherlands

Jan van den Brakel is an Extraordinary Professor of Survey Methodology at Maastricht University and a Senior Statistician at Statistics Netherlands. He holds a position in the Department of Quantitative Economics within the School of Business and Economics. His primary affiliation is with the methodology department at Statistics Netherlands, where he focuses on advancing statistical methods for official statistics. **Research Interests:** Professor van den Brakel specializes in survey methodology, with a focus on mixed-mode surveys, time series modeling, small area estimation, and discontinuity analysis. He explores topics such as sampling techniques, variance estimation, and the integration of non-probability data sources (e.g., social media) into official statistics. His work addresses challenges in labor force surveys, health surveys, and the impact of technological changes like tablet-based data collection. **Key Projects:** Current projects include mixed-frequency time series modeling for short-term statistics, accuracy assessments of non-probability web panels, and modeling discontinuities in official surveys. He has contributed to high-impact studies, such as analyzing mobility patterns during the pandemic and improving unemployment rate estimation using Bayesian models. **Awards:** His 2013 paper on factorial designs in probability samples was honored as the best journal paper by Survey Methodology in 2014. He is a member of the Advisory Committee on Statistical Methods at Statistics Canada, reflecting his international leadership in statistical methodology. **Professional Roles:** In addition to his academic role, he collaborates extensively with national statistical offices, advising on methodological challenges. His work bridges theory and practice, ensuring statistical methods are robust and applicable to real-world data collection and analysis.

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Kenneth R. Jacksonمشاهده پروفایل

استاد

University of TorontoCanada

Kenneth R. Jackson is a Full Professor of Computer Science at the University of Toronto, where he has been active since 1981. He retired in 2020 but continues research in numerical computation, computational finance, and scientific computing. Born in Montreal and raised in Toronto, Jackson earned all his degrees from the University of Toronto: BSc (1973), MSc (1974), and PhD (1978). He held roles as Gibbs Instructor and Visiting Assistant Professor at Yale University before returning to Toronto. Recognized as an NSERC University Research Fellow, he also served as Associate Chair for Graduate Studies (2002–2005) and President of the Canadian Applied and Industrial Mathematics Society (2001–2003). His research focuses on numerical methods for ODEs, parallel computation, validated solutions, and applications in finance, medical imaging, and climate modeling. He has advised over 30 graduate students, many of whom contributed to groundbreaking work in computational finance and scientific computing. Jackson organized the 2001–02 Thematic Year on Numerical Challenges in Science at the Fields Institute and currently advises YetiWare. He teaches advanced courses in numerical methods, optimization, and computational finance, and has published extensively on topics ranging from high-dimensional ODE systems to GPU-accelerated financial modeling.

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Melvyn Weeksمشاهده پروفایل

مدرس ارشد

University of CambridgeUnited Kingdom

Dr Melvyn Weeks serves as a University Senior Lecturer, Director of Teaching, and Director of Undergraduate Studies at the Faculty of Economics, University of Cambridge. His academic leadership spans curriculum development and student supervision within one of the world's leading economics departments. Specializing in Microeconometrics , Causal Inference , and Machine Learning , Weeks' research bridges advanced statistical methodology with real-world economic applications. His work prominently features in energy economics (analyzing consumer behavior in prepayment energy systems), development economics (examining microcredit impacts and religious institution effects in India), and economic growth theory (addressing model uncertainty in convergence clubs). Publication trends reveal a consistent integration of machine learning techniques with traditional econometric frameworks, particularly in causal tree estimation for heterogeneous treatment effects and robust model averaging approaches. His recent work demonstrates growing emphasis on machine learning applications in economics , causal inference methodologies , and distributional analysis across diverse economic contexts. As a doctoral supervisor, Weeks mentors PhD candidates including Christian Tien (focusing on causal inference and machine learning applications) and Cheuk Fai Ng (specializing in cluster-robust inference for high-dimensional regression). His teaching portfolio spans foundational statistical inference through advanced topics including machine learning in economics, causal inference, and research computing. Based at Clare College with office in Room 60, Weeks maintains active research collaboration through the Faculty of Economics' Econometrics Research Group, contributing to Cambridge's reputation in quantitative economic analysis.

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Iason Papaioannouمشاهده پروفایل

استاد مدعو

Technical University of MunichGermany

Iason Papaioannou is an Adjunct Professor in the area of Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the Engineering Risk Analysis Group. He holds a habilitation from the TUM School of Engineering and Design and has been tenured since 2021 as an Akademischer Rat. His academic journey includes a Ph.D. in Civil Engineering from TUM (2012), an M.Sc. in Computational Mechanics (2007), and a Diploma in Civil Engineering from the National Technical University of Athens (2005). His research focuses on uncertainty quantification , reliability assessment , and Bayesian updating of engineering systems. Key areas include probabilistic modeling, machine learning applications, spatial variability analysis, and geotechnical reliability. He has pioneered methods for system reliability analysis, adaptive subset simulation, and cross-entropy-based importance sampling. Teaching responsibilities include courses such as Stochastic Finite Element Methods, Structural Reliability Methods, and Elements of Machine Learning. His work integrates advanced computational techniques with practical engineering challenges, emphasizing high-dimensional uncertainty analysis and data-driven model updating.

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Chuanping Sunمشاهده پروفایل

مدرس

City, University of LondonUnited Kingdom

Chuanping Sun is a Lecturer in Finance at Bayes Business School, City, University of London. He earned his PhD in Economics from Queen Mary University of London and has held visiting positions at New York University and FGV Sao Paulo. His research bridges machine learning and empirical finance, with a focus on robust factor selection in asset pricing models. His research interests include: Empirical Asset Pricing Financial Econometrics Machine Learning in Finance Portfolio Choice Cross-Sectional Asset Returns Factor Models His recent work investigates the impact of factor correlations on model robustness, proposing a correlation-robust machine learning approach (using OWL shrinkage) to identify key drivers of asset returns, including the market, liquidity, momentum, and profitability factors. His research shows that traditional methods like LASSO and Fama-MacBeth often fail to detect the market factor due to high correlations, while his approach maintains robustness and delivers superior out-of-sample portfolio performance. His two recent publications in the Journal of Empirical Finance (2024, 2022) demonstrate his focus on methodological innovation in high-dimensional financial datasets, particularly in handling correlated factors and exploiting stock return correlations. He has received internal research funding from City University of London's Pump Priming Fund, supporting his ongoing projects in machine learning applications in finance. While no formal advisees are listed, his role as a lecturer and research supervisor (as indicated by his CRediT contribution) suggests involvement in student mentorship. Chuanping Sun is affiliated with the Finance department at Bayes Business School, where he contributes to research and teaching in quantitative finance and econometrics.

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Gregory Wheelerمشاهده پروفایل

استاد

Ludwig Maximilian University of MunichGermany

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.

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