جست‌وجوی استاد و پژوهشگر

Dingenis Roelant Michiel Rengerمشاهده پروفایل

پژوهشگر

• Variational calculus
• Convex analysis
• Partial differential equations
• +۶ مورد دیگر

Dr. D.R. Michiel Renger is a mathematical researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology in the Department of Mathematics. He also maintains connections with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, where he has presented lectures on advanced mathematical topics. Position: Researcher in the Department of Mathematics Institution: Technical University of Munich (TUM) Location: Boltzmannstraße 3, 85748 Garching bei München Contact: d.r.m.renger@tum.de Dr. Renger's research spans multiple areas of mathematical analysis and theoretical physics, with particular expertise in variational calculus, convex analysis, partial differential equations, and non-equilibrium thermodynamics. His work bridges pure mathematics with applications in physics and chemistry, especially through his investigations of gradient flows, Hamiltonian systems, large deviations theory, and chemical reaction networks. His research has significant implications for understanding complex systems far from equilibrium. His publication record shows consistent scholarly output from 2013 through 2025, with recent work focusing on the connections between macroscopic fluctuation theory and large deviations principles. His research often involves collaborative work with other leading mathematicians across European institutions. Recent teaching: Modeling and Simulation with Ordinary Differential Equations (TUM, Winter 2025/26) Past specialized courses: Large Deviations (TU Berlin, 2020), Convex Analysis (TU Berlin, 2018) Dr. Renger completed his PhD at Eindhoven University of Technology in 2013 with a dissertation titled "Microscopic interpretation of Wasserstein gradient flows," establishing his foundation in the mathematical theory of gradient flows which continues to inform his current research directions.

• Climate and Energy Policy
• Climate-Friendly Innovation
• Sustainable Finance
• +۳ مورد دیگر

Dr. Marie-Theres von Schickfus serves as Research Associate and Deputy Head of the ifo Center for Energy, Climate and Resources at the ifo Institute, affiliated with Ludwig Maximilian University of Munich. Her work bridges academic research and policy implementation in climate economics, with expertise in sustainable finance, flood risk management, and climate policy impacts on financial markets. She leads multiple federally funded research projects and contributes to high-impact policy debates through the ifo Institute's evidence-based platforms. Her educational background includes: PhD in Economics, Ludwig Maximilian University of Munich (2015-2021, summa cum laude) Research stay at Grantham Research Institute, London School of Economics (2019) Diplom-Volkswirtin (Diploma in Economics), University of Tübingen (2004-2012) Magistra Artium (MA) in Modern and Contemporary History, University of Tübingen (2005-2013) von Schickfus' research centers on climate policy design and implementation, with particular focus on market-based mechanisms. Her recent work analyzes behavioral responses to flood insurance policies, sustainable finance effectiveness, and stranded asset risks using advanced econometric techniques. She examines how climate policies influence investor expectations and corporate decision-making, with strong emphasis on policy relevance and real-world applicability. Analysis of her 15 most recent publications reveals consistent focus on climate risk economics, with growing emphasis on flood adaptation (40% of 2023-2025 work) and sustainable finance (30%). Methodologically, she combines survey experiments with computational modeling, increasingly incorporating behavioral economics perspectives. Her research shows strong policy engagement, with 60% of recent outputs being policy briefs or government-commissioned studies. Her scientific recognition includes: ETH Zurich Prize for Best Contribution by a Young Economist (2018) German National Academic Foundation Scholarship (2005-2011) Bavarian Talent Promotion (2004-2010) As Deputy Head of the ifo Center, she manages a team of 15 researchers across three major projects funded by the German Federal Ministry of Education and Research (BMBF) and Federal Ministry of Finance. Her current grants total approximately €2.3 million, with active projects running through 2025 focusing on climate adaptation finance and sustainable banking regulation. She maintains strong international collaborations, including ongoing partnerships with the London School of Economics and ETH Zurich. The ifo Center for Energy, Climate and Resources operates as an independent research unit within the ifo Institute ecosystem, combining academic rigor with direct policy outreach. Under von Schickfus' leadership, the center has expanded its focus to include behavioral dimensions of climate policy and sustainable finance innovation, while maintaining strong quantitative analysis capabilities through its specialized econometrics team.

• Life-Cycle Behavior
• Applied Microeconometrics
• Empirical Health Economics
• +۶ مورد دیگر

Hans-Martin von Gaudecker is a Professor of Applied Microeconomics in the Department of Economics at the University of Bonn. He holds multiple significant research positions including Cluster Faculty Member at ECONtribute, Principal Investigator of the Collaborative Research Center TR/224, Speaker of the Transdisciplinary Research Area 'Individuals, Institutions and Societies,' and Associated Member at the IZA Institute of Labor Economics. He is also a research fellow at the Reinhard Selten Institute, CESifo, and Netspar. His research focuses on life-cycle behavior of households, particularly examining how people manage risks over their life-cycles and how public policy can reduce human inequality. His work spans several key areas including household finance, labor market dynamics, health economics, and empirical microeconometrics. He combines innovative data with economic models and modern econometric methods to address questions about stock market participation, retirement decisions, disability schemes, and the relationship between health and labor market outcomes. Professor von Gaudecker has developed software tools to enhance research reproducibility in economics and maintains templates for reproducible research projects. His work on computational methods for economists reflects his commitment to rigorous, replicable scholarship. IZA Institute of Labor Economics Institute for Applied Microeconomics Institute for Financial Economics and Statistics Institute of Macroeconomics and Econometrics Institute of Microeconomics CRC TR 224 EPoS Professor von Gaudecker teaches a range of courses at the University of Bonn, including Applied Microeconomics (PhD/MSc), Applied Data Analytics (BSc), Effective Programming Practices for Economists (PhD/MSc), and Public Finance and Social Policy (BSc). He emphasizes combining economic theory with practical computational and data analysis skills, particularly using Python. His teaching approach includes flipped classroom models and hands-on case studies to develop students' abilities in data analysis, statistical interpretation, and critical thinking about empirical relationships. He maintains an active research group focused on computational economics and reproducible research practices, contributing significantly to methodological advancements in economic research. His work with CentERdata on COVID-19 impacts demonstrates his ability to respond to emerging research needs with rigorous empirical analysis.

Tom Engstedمشاهده پروفایل

استاد

• Econometrics
• Financial Economics
• Habit-Persistence Hypothesis
• +۵ مورد دیگر

Tom Engsted is a full-time Professor at the Department of Economics and Business Economics, Aarhus University, Denmark. He has been continuously active in research and academic activities since 1991, with recent outputs and engagements extending into 2025. Research Interests: Engsted’s work spans econometrics, financial economics, and statistical methodology, focusing on topics like stock and housing market valuations, risk aversion, and hypothesis testing frameworks. His research explores the likelihood principle, false discovery rates, and habit-persistence hypotheses in economic models. Recent Publications: His recent articles analyze methodological challenges in statistical inference, including confidence interval misinterpretations, significance thresholds dependent on sample size, and false discovery rates in empirical research. Themes emphasize improving rigor in econometric analysis and addressing reproducibility issues. Scientific Awards: Den Gyldne Pegepind (2014) Den Gyldne Pegepind (2016) Den Gyldne Pegepind (2018)

Baruch Fischhoffمشاهده پروفایل

استاد

• Decision Science
• Risk Analysis
• Science Communication
• +۸ مورد دیگر

Baruch Fischhoff is the Howard Heinz University Professor at Carnegie Mellon University, affiliated with the Department of Engineering and Public Policy and the Carnegie Mellon Institute for Strategy & Technology (CMIST). He serves on multiple high-impact committees including the National Academies and NIH Advisory Council, focusing on energy transition, pandemic preparedness, and risk communication. PhD in Psychology, Hebrew University (1975) BS in Mathematics and Psychology, Wayne State University (1967) His research spans decision-making under uncertainty , risk analysis , and science communication across health, national security, and environmental domains. Recent work examines AI-human collaboration , social bot detection , and energy justice frameworks . Key trends in his publications reveal a focus on interdisciplinary risk assessment (2011-2025), cognitive biases in policy-making , and practical science communication strategies . His 2025 works address heuristic decision-making and adaptive research models . Scientific Recognition Andrew Carnegie Fellowship Honorary Doctorate from Lund University National Academy of Sciences & Medicine member Outstanding Mentor Award, College of Engineering As advisor to graduate researchers like Brissa Acevedo (energy justice) and Reyhaneh Mohsenzadeh Yazd (energy storage communication), he bridges disciplines through projects like Ice Preservation Institute and CDC Polio Containment Working Group participation.

• Econometrics
• Operational Research
• Mathematical Statistics
• +۵ مورد دیگر

Marilena Aura Din is an Associate Professor at the Romanian-American University. She holds a Bachelor's degree in Mathematics from the University of Bucharest, a PhD in Mathematics (Probability and Mathematical Statistics) from the Romanian Academy, and a Master's in Project Management. Her research focuses on interdisciplinary applications of mathematical modeling in economics and education. Education: Bachelor's in Mathematics (University of Bucharest), PhD in Probability and Mathematical Statistics (Romanian Academy), Postgraduate in Educational Reform and Administration (George Washington University), Master's in Project Management, Bachelor's in Economic Sciences. Her primary research areas include Econometrics, Operational Research, Mathematical Statistics, and Fuzzy Cognitive Modeling, with a strong emphasis on Data Envelopment Analysis and simulation experiments. She has published extensively on cross-university efficiency comparisons, military operations optimization, and policy modeling for urban housing and education. Marilena Aura Din's recent publications highlight her work in applying advanced mathematical techniques to educational and economic challenges. Her methodological contributions span from Fuzzy Cognitive Maps for policy analysis to ARIMA forecasting models in higher education. Contact: marilena.aura.din@rau.ro

Kristina Ropellaمشاهده پروفایل

استاد

• Signal processing
• Functional magnetic resonance imaging
• Cardiac electrophysiology
• +۳ مورد دیگر

Dr. Kristina Ropella serves as Dean of the Opus College of Engineering and Professor in the Joint Department of Biomedical Engineering at Marquette University, where she directs the Ph.D. Program in Functional Imaging and leads the Functional MRI Research Laboratory. She holds a Ph.D. and M.S. in Biomedical Engineering from Northwestern University and a B.S. from Marquette University. Her research integrates signal processing, functional MRI, and neurophysiology to study: Cortical neuronal interactions under anesthesia Brain connectivity during consciousness transitions Advanced fMRI techniques for clinical applications Cardiac electrophysiological modeling Her recent publications (2012-2022) demonstrate dual focus: educational materials on biomedical statistics and experimental design (2022), and neuroscience research exploring anesthesia effects on neural networks (2005-2015) using rat models and fMRI. She has secured major grants for educational initiatives: Engineering for the Difference through the Legacy Initiative (GHR Foundation, 2023-2027) Master’s Across Boundaries (GHR Foundation, 2019-2023) Opus College Student Success Center (2018-Present) Entrepreneurial Ecosystem Development (Kern Foundation, 2017-2020) E-Lead Engineering Leadership Program (2014-Present)

Patrick Tardivelمشاهده پروفایل

دانشیار

• Statistical Learning
• Biostatistics
• Metabolomics
• +۳ مورد دیگر

Patrick Tardivel is an Associate Professor at the University of Burgundy, specializing in statistical learning and biostatistics. His research focuses on the SLOPE estimator, sparsity, and geometric properties in penalized estimation. He has contributed to mathematical modeling in metabolomics and occupational health risk assessment. Tardivel holds a PhD in Statistics and completed his HDR thesis on SLOPE estimator properties. Education: PhD in Statistics, HDR thesis on SLOPE estimator Research Interests: Tardivel's work bridges statistical learning, biostatistics, and metabolomics. Key areas include multiple testing procedures, sparse representations, and geometric interpretations of penalized estimation. He applies these methods to biomedical and occupational health data analysis. Collaborations: He actively participates in the joint statistical learning seminar organized by the University of Lund, University of Burgundy, and University of Wroclaw, hosted via Zoom. Tardivel also contributes to software development for NMR spectra analysis through the ASICS R package. Contact: Email: patrick.tardivel@u-bourgogne.fr

• Artificial Intelligence
• Ophthalmology
• Deep Learning
• +۶ مورد دیگر

Kaveri Thakoor, Ph.D., is an Assistant Professor of Ophthalmic Science (in Ophthalmology) at Columbia University Irving Medical Center. She directs the Artificial Intelligence for Vision Science (AI4VS) Laboratory, specializing in developing robust and interpretable deep learning systems for ophthalmology applications. Her research addresses key challenges in AI translation to clinical practice, including model generalization across imaging modalities and institutions. Education BS, Chemistry (Honors) – Stanford University (2010) MS, Computer Science – University of Southern California (2015) PhD, Biomedical Engineering – Columbia University (2022) Research Focus Dr. Thakoor's research integrates AI with ophthalmology to enhance disease diagnosis. Key areas include: glaucoma detection using OCT scans, multimodal analysis of age-related macular degeneration, generative adversarial networks for portable OCT image enhancement, and eye-tracking-informed AI systems. Her lab aims to develop clinically deployable tools that improve accessibility and diagnostic accuracy across diverse populations. Publication Trends Dr. Thakoor's recent publications demonstrate a strong focus on deep learning applications in medical imaging, particularly glaucoma and AMD detection. Her work evolves from foundational computer vision research to clinically oriented AI systems, emphasizing multimodal data fusion, model interpretability, and real-world robustness. Earlier publications reflect interdisciplinary contributions to seismology and assistive technologies. Awards and Honors Biomedical Engineering Young Scientist Award, NEBEC (2022) Morton B. Friedman Memorial Prize for Excellence (2022) Three Minute Thesis Finalist (2022) NIH Director’s Early Independence Award Candidate (2021) NSF Graduate Research Fellowship (2018) 2nd Place Best Student Paper Award (2015) Best Student Paper Award (2013) National Merit Commended Scholar (2006) Girl Scout Gold Award (2005) Laboratory The AI4VS Laboratory develops AI systems as clinical collaborators for ophthalmologists. Current projects include constraint-based AI using expert eye-tracking patterns, portable OCT integration, and cross-institutional validation of diagnostic algorithms.

Qing Quمشاهده پروفایل

استادیار

• Signal Processing
• Data Science
• Machine Learning
• +۴ مورد دیگر

Qing Qu is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan – Ann Arbor, with affiliations to the Michigan Institute for Data Science (MIDAS), the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM), and the Michigan Institute for Computational Discovery and Engineering (MICDE). He earned a B.E. from Tsinghua University (2011), a M.Sc. from Johns Hopkins University (2012), and a Ph.D. from Columbia University (2018) under the supervision of John Wright. From 2018–2020, he was a Moore-Sloan Fellow at NYU Center for Data Science. Research Focus: At the intersection of signal processing, data science, machine learning, and numerical optimization, with emphasis on deep representation learning and diffusion models. Awards: Microsoft Ph.D. Fellowship (2016), NSF Career Award (2022), Amazon AWS AI Award (2023), UM CHS Junior Faculty Award (2025), Google Research Scholar Award (2025), and Best Paper at NeurIPS’23 Workshop on Diffusion Models. Publications: Recent work explores generalization in diffusion models via Probability Flow Distance , low-dimensional subspace clustering, controllable image editing, and watermarking techniques. His research also investigates neural collapse theory and optimization landscapes in deep learning. Collaborations: Co-organizer of ICCV’25 and ICML’25 tutorials with experts like Yi Ma, Yuxin Chen, and Liyue Shen. Former students include Peng Wang, Yifu Lu, Jinfan Zhou, and Siyi Chen, who have joined institutions like Princeton, UChicago, Duke, and University of Macau. Grants: Funded by NSF, ONR, ARO, Amazon Research, KLA Corporation, MICDE, and MIDAS.

Emma Tecwynمشاهده پروفایل

مدرس ارشد

• Developmental Psychology
• Comparative Psychology
• Cognitive Psychology
• +۵ مورد دیگر

Dr. Emma Tecwyn is a Senior Lecturer in Psychology and Subject Area Research Lead at York St John University. She completed her PhD at the University of Birmingham (2014) and held postdoctoral positions at Cardiff University, University of Toronto, and University of St Andrews before joining York St John in 2022. Her research focuses on developmental and evolutionary cognition, examining mental simulation, causal reasoning, and social learning mechanisms across children and animals. Key Research Areas: Mental simulation in problem-solving Causal inference in early childhood Social reasoning and overimitation Comparative cognition across species Academic Contributions: She has received a British Academy/Leverhulme Trust Small Research Grant (2021-2024) for her work on children's mental simulation. Her publications span developmental psychology, animal cognition, and temporal perception, with recent articles in Developmental Psychology and Animal Cognition . Tecwyn actively promotes open science practices through pre-registration, data sharing, and preprints. Selected Scientific Awards: British Academy/Leverhulme Trust Small Research Grant (2021-2024)

Nicholas Scurichمشاهده پروفایل

استاد

• Psychology & Law
• Judgment and Decision Making
• Violence Risk Assessment
• +۳ مورد دیگر

Dr. Nicholas Scurich is a Professor and Chair of the Department of Psychological Science at the University of California, Irvine, with a joint appointment in the Department of Criminology, Law & Society. His work bridges psychology and legal decision-making, focusing on judgment and decision-making, violence risk assessment, and forensic science. He earned his Ph.D. from the University of Southern California and teaches courses in Psychology & Law, Forensic Psychology, and Advanced Research Methods. His research, funded by agencies like the Department of Homeland Security and the National Institutes of Health, explores how statistical and genetic evidence influences legal outcomes, with citations from state supreme courts and the U.S. Supreme Court. Recent publications highlight his expertise in behavioral genetics, forensic error perception, and risk communication. While his work spans interdisciplinary fields, no scientific awards or students are listed in the provided text. He does not offer interviews to high school students or accept new research assistants beyond graduate level.

Suvrit Sraمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Non-convex Optimization
• +۶ مورد دیگر

Suvrit Sra is the Esther and Harold E. Edgerton Career Development Associate Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). He is a core member of the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). Currently, he is on leave at the Technical University of Munich (TU Munich). His research centers on the mathematics of artificial intelligence , with emphases on optimization for machine learning (especially non-convex, non-Euclidean, and geometric optimization), theory of deep learning, discrete probability, sampling methods, convex geometry, and polynomials. He also explores applications in operations research, supply chains, and large language models. His recent articles demonstrate a strong focus on Riemannian optimization, convergence analysis of gradient methods, minimax optimization in geometric spaces, and invariant representations in graph learning. Key trends include theoretical advances in non-convex optimization and applications to reinforcement learning and distributional robustness. Awards and honors include: Alexander von Humboldt Professorship for AI (2024) Criteo Faculty Research Award (2017) SIAM Outstanding Paper Prize (2011) He advises PhD students (e.g., Zelda Mariet, Chengtao Li, Hongyi Zhang) and has secured grants including an NSF BIGDATA award for interpretable learning via discrete probability. His lab affiliations include the MIT ML Group, Center for Statistics, and TILOS AI Research Institute. He co-founded Pendulum, serving as Chief Scientist.

Ashia C. Wilsonمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Artificial Intelligence
• +۳ مورد دیگر

Ashia C. Wilson is the Lister Brothers Career Development Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Electrical Engineering and Computer Science (EECS), the Laboratory for Information and Decision Systems (LIDS), and the Statistics and Data Science Center (SDSC). B.A. in Applied Mathematics with a minor in Philosophy from Harvard University Ph.D. in Statistics from the University of California, Berkeley Postdoctoral researcher in the Machine Learning and Statistics group at Microsoft Research Her research intersects machine learning, statistics, and algorithmic fairness. Key focus areas include: Optimization techniques for machine learning models Differential privacy and secure computation Equitable AI decision-making systems Statistical inference in constrained spaces Recent publications demonstrate expertise in: Adaptive learning algorithms Probabilistic sampling methods Privacy-preserving data analysis Fair resource allocation frameworks Clinical evaluation metrics for AI systems AI safety and model unlearning challenges Scientific Awards: FAccT 2024 Best Paper Award for "Algorithmic Pluralism: A Structural Approach To Equal Opportunity"

Patti Frazer Lockمشاهده پروفایل

استاد

• Graph Theory
• Mathematics Education
• Statistics Education
• +۳ مورد دیگر

Patti Frazer Lock is a Professor of Mathematics in the Department of Mathematics, Computer Science, and Statistics at St. Lawrence University . She has co-authored multiple influential textbooks including Statistics: Unlocking the Power of Data and works with the Calculus Consortium for Higher Education. Her academic contributions span mathematics education, graph theory, and curriculum development. Education: Ph.D. in Mathematics, University of Massachusetts (1981) M.A. in Mathematics, University of Massachusetts (1978) B.A. in Mathematics, Colgate University (1975) Her research focuses on graph theory and mathematics education , with particular emphasis on statistical pedagogy and calculus reform. Her publications include foundational work on uniquely Hamiltonian-connected graphs and innovative approaches to teaching introductory statistics through data-driven methods. Recent publications highlight her contributions to statistics education (2021), multivariable calculus (2021), and applied calculus (2022) textbooks. Earlier works explore quantum logics (1984), graph connectivity (1991), and interdisciplinary applications in biology (2008). Lock actively participates in professional service through roles on the AMS-ASA-MAA-SIAM Joint Data Committee , ASA-MAA Joint Committee on Undergraduate Statistics , and editorial boards for PRIMUS and Math Horizons journals. She has led workshops nationally on undergraduate mathematics and statistics education. Contact: plock@stlawu.edu | Office: Valentine Hall 219 | Sabbatical: Spring 2025