جست‌وجوی استاد و پژوهشگر

Max Cohenمشاهده پروفایل

پژوهشگر

• Control Systems
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

Max Cohen is a Postdoctoral Scholar Research Associate in the Department of Mechanical and Civil Engineering at the California Institute of Technology (Caltech). His research focuses on safety-critical control systems, adaptive control, and the integration of control barrier functions with reinforcement learning. He explores theoretical frameworks for robust control design in nonlinear systems, emphasizing safety guarantees through formal verification methods. His work addresses challenges in automated vehicle navigation, hybrid systems, and rehabilitation engineering using functional electrical stimulation (FES). Key research directions include uncertainty quantification in adaptive systems, layered control architectures, and safe exploration strategies in model-based reinforcement learning. Publications span topics like control barrier function synthesis, temporal logic-guided learning, and reduced-order modeling for safety-critical applications. Current research trends emphasize bridging formal methods with data-driven control paradigms to ensure safe operation in complex robotic and cyber-physical systems. No scientific awards or grants are explicitly mentioned in the provided information. His office is located in Gates-Thomas Laboratory (Room 300), and he actively contributes to interdisciplinary projects at Caltech's engineering division.

Jessica Erikssonمشاهده پروفایل

دانشیار

• Entrepreneurship
• Innovation Management
• Sustainable Development
• +۴ مورد دیگر

Jessica Eriksson is an Associate Professor in Business Administration at the Umeå School of Business, Economics and Statistics (USBE), specializing in Entrepreneurship. She holds a docent title and is part of the ROBUST research group focusing on sustainable transitions. Her research explores entrepreneurial ecosystems, innovation in cultural/creative sectors, institutional change, and sustainable practices in industries like agriculture and forestry. Education qualifications include recognition as a Distinguished University Teacher. She teaches entrepreneurship and research methods, emphasizing practical integration of theory. Key projects include analyzing collaboration between industrial giants and cultural SMEs, and translating GHG reduction policies into industry practices. Research Focus: Coopetition strategies, regional renewal, low-carbon transitions, and creative industry dynamics Awards: EU award for researcher working conditions (2020) and distinguished teaching honor Grants/Projects: Led a 2018-2021 innovation project in cultural sectors, and contributed to EU-funded sustainability studies Her work bridges academic research with practical industry challenges, emphasizing institutional entrepreneurship and sustainable development frameworks.

Donggyu Sulمشاهده پروفایل

استاد

• Panel Data Econometrics
• Empirical Growth
• International Finance
• +۳ مورد دیگر

Dr. Donggyu Sul is the John F. Kain Professor of Economics at the University of Texas at Dallas (UT Dallas), within the School of Economic, Political and Policy Sciences. His research focuses on panel data econometrics, empirical growth, and international finance. He earned his Ph.D. in Economics from The Ohio State University (1992) and a B.S. in Economics from Korea University (1988). His career includes roles at the University of Auckland and research economist positions in Korea. Key research interests include panel data methodologies, economic growth analysis, and financial econometrics. He has contributed to advancements in factor models, convergence testing, and dynamic panel estimation. Notable publications include work on X-differencing, terrorism drivers, and common factor identification in exchange rates. Professional Preparation: Ph.D., Economics, The Ohio State University, 1992 B.S., Economics, Korea University, 1988 Awards: 2019 Maekyung-KAEA Economist Award for contributions to economic research Professional Activities: General Secretary of Korea-American Economic Association Co-editor of International Economic Journal Past associate editor of New Zealand Economic Papers Grants & Collaborations: Extensive collaboration with Peter C.B. Phillips on econometric theory and panel data methods. Labs/Teams: Active in organizing the Korean Econometric Camp and New Zealand Econometric Study Group. His work bridges theoretical econometrics and applied research, emphasizing robust estimation techniques and policy-relevant insights, particularly in growth disparities and financial markets.

Marcel Prokopczukمشاهده پروفایل

استاد

• Commodity Markets
• Capital Market Research
• Derivatives
• +۳ مورد دیگر

Prof. Dr. Marcel Prokopczuk is a full Professor of Finance and Commodity Markets at Leibniz University Hannover, serving as Dean of the Faculty of Economics and Management since 2024. He holds a Ph.D. from the University of Mannheim and is a CFA Charterholder. His research focuses on commodity markets, capital market dynamics, derivatives, risk management, and household financial decisions. Affiliations: Institute of Finance and Commodity Markets, Leibniz Research Centre Energy 2050, Hannover Center of Finance and Insurance (HCFI). Roles: Editor of the Journal of Commodity Markets , Associate Editor of the Journal of Banking and Finance , and Board Member of CEMA. Research Interests: Commodity markets, derivatives pricing, energy finance, risk management, and the impact of historical factors on financial development. He has published extensively in top journals like Review of Economic Studies and Journal of Banking and Finance . Awards: Includes the Deutsche Bundesbank Prize (2024), FIRN Annual Conference Award (2017), and Best Teaching Award (2015). Grants & Advisory: Member of the German Business Foundation and involved in energy policy initiatives. His work bridges academic research with practical applications in financial markets and commodity pricing.

Ammar Mianمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Machine Learning
• Riemannian Geometry
• +۴ مورد دیگر

Ammar Mian is an Associate Professor at Université Savoie Mont Blanc, affiliated with the LISTIC lab and Polytech Annecy-Chambéry. He holds a PhD from CentraleSupélec (2016-2019) and conducted postdoctoral research at Aalto University (2019-2020). His research focuses on statistical signal processing, machine learning, and Riemannian geometry with applications in remote sensing and frugal computations. He leads the Qanat project, an experiment tracking tool for reproducible research. Research interests include covariance-based methods for SAR image analysis, robust detection algorithms for sonar and GPR systems, and optimization on Riemannian manifolds. His work emphasizes reproducibility in ML and efficient computational techniques for resource-constrained environments. Key contributions include real-time SAR time-series change detection, robust classification using second-order deep learning models, and novel methods for handling missing data in EEG signals. His recent articles (2023-2025) explore reproducibility frameworks, GPR-based object classification, and Riemannian geometry applications. No awards listed, but maintains active collaborations through LISTIC and industry partnerships. Advises students via internship programs (e.g., Federated ML energy cost analysis). Lab work involves developing open-source tools like Qanat for experiment management and reproducibility.

• Bayesian Statistics
• Cluster Analysis
• Data Science
• +۵ مورد دیگر

Bettina Grün is an Associate Professor and Deputy Head of the Institute for Statistics and Mathematics at Vienna University of Economics and Business (WU). Her research focuses on Bayesian mixture models, cluster analysis, and applying statistical methods to sustainability, tourism, and environmental studies. She has led multiple research projects, including studies on environmental behavior in tourism and advanced text modeling in economics. Grün holds a PhD in Technical Mathematics from TU Wien (2006) and a Habilitation in Statistics from Johannes Kepler University Linz (2012). She has authored over 150 publications in top journals like Journal of Environmental Management and Expert Systems with Applications . Her work emphasizes practical applications, such as reducing hotel waste through behavioral interventions and developing R packages like movMF and circlus for statistical clustering. Grün has received awards including the AIEST Best Contribution Award (2019) and the MRS Silver Medal (2016). Grün teaches courses on statistical modeling and leads projects like Analysis of Central Bank Communication (2022–2026) and Environmentally Friendly Behavior in Tourism (2019–2024).

• Clinical Child and Family Studies
• Adolescent Development
• Parent-Adolescent Relationships
• +۱۲ مورد دیگر

Loes Keijsers is a Full Professor in Clinical Child and Family Studies at the Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam. Her work focuses on adolescent development, parent-adolescent relationships, and the application of advanced methodological approaches such as Experience Sampling Methods to study daily emotional and behavioral dynamics in teens. Her research interests include: Clinical Child and Family Studies Adolescent mental health and well-being Parenting dynamics and autonomy support Statistical modeling and longitudinal analysis Digital interventions and e-health tools for youth Her recent publications highlight a strong trend in using intensive longitudinal data and mobile technology to understand heterogeneity in adolescent development and family processes. She emphasizes preregistered research and robust statistical models to enhance scientific rigor. Her work increasingly integrates digital tools such as smartphone apps and serious games (e.g., the Grow It! App) to promote adolescent mental health. Notable contributions include: Developing and evaluating e-health applications for adolescent depression Advancing ecological momentary assessment in family research Translating psychological research into public impact through TEDx talks and the book Waarom tieners zo irritant kunnen zijn Contributing to UN Sustainable Development Goals related to well-being and mental health She actively supervises research and has contributed to multiple datasets in the areas of social media use, adolescent well-being, and intensive longitudinal methods. Her collaborative network spans interdisciplinary teams working on youth mental health innovation. Loes leads the research project PROJECT ADAPT (www.projectadapt.nl), which aims to develop and implement personalized digital tools to support adolescent mental health and family functioning. She is also active on social media (Twitter: EUR_ADAPT) and engages in public science communication through platforms like TEDx and Universiteit van Nederland.

• Statistics
• High-Dimensional Data Analysis
• Statistical Genetics
• +۷ مورد دیگر

Aaron J Molstad is an Assistant Professor in the Department of Statistics at the University of Minnesota – Twin Cities, within the College of Science and Engineering. His research lies at the intersection of statistical methodology and genomic data science, with a focus on developing rigorous and scalable methods for modern high-dimensional datasets. His research interests include high-dimensional statistics, covariance and precision matrix estimation, regression modeling with structured responses, variable selection, and integrative analysis of omics data. He develops methods tailored for compositional data, multivariate responses, and ancestry-specific genetic association studies, contributing to both theoretical statistics and public health applications. The recent publications and funded projects highlight a strong trend in developing objective, reliable, and heterogeneous-aware statistical frameworks for genomics and biomedicine. His work emphasizes methodological innovation with direct applicability to complex biological data, particularly in diverse populations and multi-omics integration. Awarded grants from the National Science Foundation and the National Institutes of Health demonstrate recognition of his research’s significance and impact. These include projects on inference from omics data, new regression models for categorical responses, and integrative genomics in African American populations. Objective and reliable methods for inference from modern omics data (NSF, 2024–2027) Collaborative Research: New Regression Models for Multiple Categorical Responses (NSF, 2024–2025) Integrative Genomics into Genetic Association Studies of Blood Pressure and Stroke in African Americans (NIH/Fred Hutchinson, 2023–2024) Dr. Molstad advises and collaborates on major genomic studies involving protein expression, blood pressure, stroke, and ancestry-specific effects. While specific PhD students are not listed, his role as Principal Investigator on multiple grants indicates mentorship of graduate researchers and postdoctoral scholars. He is also active in the broader statistical community, with publications in top-tier journals such as Biometrika , Biometrics , and Genome Biology .

Sivan Sabatoمشاهده پروفایل

دانشیار

• Interactive machine learning
• Machine learning theory
• Fairness in machine learning
• +۳ مورد دیگر

Sivan Sabato is an Associate Professor at McMaster University's Department of Computing and Software , a Canada CIFAR AI Chair, and faculty member at the Vector Institute of Artificial Intelligence . She holds a joint appointment at Ben-Gurion University's Department of Computer Science while on leave. Her research focuses on machine learning theory, active learning algorithms , and fairness in machine learning . Education: PhD in Computer Science, Hebrew University of Jerusalem Postdoctoral Fellowship, Microsoft Research New England Her theoretical work develops interactive learning frameworks that optimize information costs through algorithmic interaction patterns. Recent publications emphasize differential privacy and discriminative feature analysis with applications to healthcare and social data. She serves as Action Editor for Journal of Machine Learning Research and organizes conference tracks including ICML 2022-2023 and ALT 2021 . Awards include the Alon Scholarship and Google Anita Borg Memorial Scholarship . Advising: Actively supervises Computer Science PhD and MSc students through McMaster's Faculty of Engineering. Research interns can apply via the Vector Institute program with Summer 2026 opportunities.

Yann Pontyمشاهده پروفایل

پژوهشگر

• RNA Folding
• RNA Design
• RNA Evolution
• +۹ مورد دیگر

Yann Ponty is a tenured CNRS Researcher at the Computer Science Department (LIX) of École Polytechnique (Institut Polytechnique de Paris, France). He leads the AMIBio team and serves as Deputy Director of LIX. His work focuses on developing bioinformatics methods at the intersection of computer science, mathematics, and molecular biology, particularly for RNA structure prediction, design, and evolution. He holds leadership roles in the ISCB Board of Directors (2025-2027) and the HDR referent for the IDIA department (CS&Interactions) at IP Paris. Research Interests: RNA folding/design/evolution, RNA-RNA/RNA-protein interactions, random generation, enumerative combinatorics, discrete algorithms, parameterized complexity, RNA visualization Key Contributions: Developed algorithms for RNA inverse folding, pseudoknot modeling, and dynamic programming optimization Collaborations: Partnerships with institutions like Simon Fraser University, Boston College, and Université Paris-Saclay His recent publications (15 most recent) span RNA structure prediction, pseudoknot partition functions, linear-time inverse folding algorithms, and parameterized sampling techniques. The work emphasizes dynamic programming, combinatorial approaches, and integration of experimental data for improving RNA modeling. Scientific awards include election to the ISCB Board of Directors (2025-2027) and leadership roles in academic networks like GdR BIM. He actively contributes to software development (VARNA, RNANR, SPARCS, IncaRNAtion, RNARedPrint) and serves as Associate Editor for Bioinformatics (OUP). Teaching engagements include graduate-level courses in combinatorial optimization, RNA bioinformatics, and algorithms at Université Paris-Saclay and École Polytechnique.

Vahab Khoshdelمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Computer Vision
• +۶ مورد دیگر

Vahab Khoshdel is an Assistant Professor in the Department of Electrical and Computer Engineering at the Price Faculty of Engineering, University of Manitoba. His research bridges machine learning, deep learning, computer vision, robotics, and medical imaging, with applications in rehabilitation robotics, microwave/ultrasound imaging, stored grain monitoring, and precision agriculture. Education: 2021, Ph.D. Biomedical Engineering, University of Manitoba 2017, Ph.D. Mechanical Engineering, Ferdowsi University of Mashhad 2013, M.Sc. Mechatronic Engineering, University of Shahrood 2011, B.Sc. Robotics Engineering, University of Shahrood Research Interests: Khoshdel specializes in applying generative AI, neural networks, and optimization techniques to medical imaging and robotics. His work includes microwave/ultrasound breast imaging, impedance control for rehabilitation robots, and AI-driven agricultural monitoring systems. Publication Trends: His recent articles emphasize machine learning workflows for medical diagnostics, deep learning in multimodal imaging, and neural networks in rehabilitation robotics. Key subfields include 3D imaging, inverse scattering, tissue classification, and sEMG signal analysis. Contact: Vahab.Khoshdel@umanitoba.ca

Xingye Qiaoمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Causal Inference
• +۳ مورد دیگر

Xingye Qiao is a Professor and Chair in the Department of Mathematics and Statistics at Binghamton University, State University of New York . He serves as Chair of the Data Science Transdisciplinary Area of Excellence steering committee and has been affiliated with Binghamton since 2010. Education : Ph.D. in Statistics (2010), University of North Carolina at Chapel Hill M.S. in Statistics (2007), University of North Carolina at Chapel Hill B.S. in Mathematics and Applied Mathematics (2005), Fudan University His research focuses on Statistics, Machine Learning, and Causal Inference , with recent work exploring conformal prediction methods, treatment effect estimation, and set-valued classification techniques. His publications span journals like Transactions on Machine Learning Research , NeurIPS , and AAAI . His 15 most recent articles (2025-2020) demonstrate expertise in areas including: bandit feedback systems, treatment effect heterogeneity analysis, spectral clustering for neuroscience, and conformal prediction under distribution shifts. These works blend statistical theory with real-world applications in healthcare, ecology, and data science. He mentors Ph.D. students in mathematical sciences and has supervised research topics such as: machine learning in precision medicine, goodness-of-fit tests for spatial processes, and high-dimensional data analysis. Courses he teaches include Math 605: Theory of Machine Learning and Data 501: Predictive & Inferential Analytics .

• Multilevel Modeling
• Single-Case Experimental Design (SCED)
• Bayesian Analysis
• +۵ مورد دیگر

EUNKYENG BAEK is an Associate Professor in the Department of Educational Psychology at Texas A&M University. She specializes in multilevel modeling (MLM) for analyzing educational and psychological data, with a focus on longitudinal and time-series data such as single-case experimental design (SCED) data. Her research bridges methodological advancements and applied studies, addressing gaps in data analysis techniques for SCED and educational datasets. **Education**: Ph.D., Educational Research, Measurement and Statistics, University of South Florida (2015) M.A., Psychometrics, Korea University (2006) B.A., Psychology, Sungshin Women University (2003) **Research Interests**: Dr. Baek’s work centers on Bayesian analysis, meta-analysis of single-case studies, and the integration of machine learning techniques in educational contexts. She explores statistical methods to handle autocorrelation, heterogeneity, and variance in SCED datasets, contributing to robust methodologies for synthesizing research findings. **Grants & Leadership**: She has led or co-led multiple grants, including projects on developing effect size toolkits for SCED meta-analyses and studying online learning readiness. She serves as an Action Editor for Behavior Research Methods and holds leadership roles in academic organizations like EREL. **Teaching**: She teaches courses in educational measurement, statistical analysis, and SCED data techniques, such as EPSY622 and EPSY661.

Francis L. Huangمشاهده پروفایل

استاد

• Quantitative Methods
• Multilevel Modeling
• School Climate
• +۴ مورد دیگر

Dr. Francis L. Huang is a Professor in the Department of Educational, School, and Counseling Psychology at the University of Missouri-Columbia 's College of Education and Human Development. An applied quantitative methodologist , he teaches courses in program evaluation, multilevel modeling, and data management while researching school climate, bullying prevention, and large-scale educational data analysis. PhD in Research, Statistics, and Evaluation (University of Virginia) MA in Instructional Technology and Media (Teachers College, Columbia University) BS in Legal Management (Ateneo de Manila University) His methodological expertise spans clustered data analysis , plausible values modeling , and robust standard error estimation . He develops tools like the MLMusingR package for multilevel modeling in education research. Recent work focuses on 2025 grant-funded studies about data weighting in international assessments and 2024 open-access replication frameworks for nonexperimental datasets. He advocates for rigorous causal inference and equitable discipline policy analysis through projects like the National Center for Rural School Mental Health (funded by Institute of Education Sciences). Dr. Huang contributes to Missouri Prevention Science Institute as Methodology Co-Director and collaborates with interdisciplinary teams on school violence prevention and behavioral threat assessment systems. His 2023-2025 publications demonstrate technical innovations in three-level cluster-robust errors and missing data handling for large-scale assessments.

• Granular Mechanics
• Mechanical Engineering
• Neutron Diffraction
• +۵ مورد دیگر

Associate Professor Christopher Wensrich is a faculty member in the School of Engineering at the University of Newcastle, Australia, specializing in Mechanical Engineering. He has a strong background in applied mechanics from both computational and experimental perspectives, with significant expertise in granular mechanics, neutron diffraction strain measurement, and Bragg-edge transmission strain tomography. Education: PhD, University of Newcastle Bachelor of Mathematics, University of Newcastle Bachelor of Engineering, University of Newcastle Professor Wensrich's research focuses on several interconnected areas within mechanical engineering and materials science. His primary expertise lies in granular mechanics, spanning from micromechanics and homogenization of granular systems to analytical modeling of granular dynamics (particularly the silo quaking problem) and computational modeling using the Discrete Element Method (DEM). He is also a pioneer in applying neutron diffraction strain scanning techniques to granular systems. In the broader field of applied mechanics, he has made significant contributions to neutron diffraction-based strain measurement, including breakthroughs in Bragg-edge Transmission Strain Tomography, where he demonstrated the world's first practical application outside of simple axisymmetric systems. His publication record demonstrates a consistent focus on developing and applying advanced techniques for strain measurement and reconstruction in granular and composite materials. His recent work has centered on tomographic reconstruction methods using neutron diffraction, with particular emphasis on Bragg-edge techniques for 2D and 3D strain field reconstruction. His research bridges theoretical mathematics, computational methods, and experimental validation, creating a robust framework for non-destructive stress measurement in complex materials. Professional Recognition: President of the Australian Neutron Beam User Group (ANBUG) since December 2022 Member of the ACNS Program Advisory Team at ANSTO (Australian Nuclear Science and Technology Organisation) since March 2019 Visiting Fellow at Clare Hall College, Cambridge University (January-June 2023) Visiting Researcher at Isaac Newton Institute for Mathematical Sciences (January-June 2023) Professor Wensrich has secured substantial research funding, with a total of $5,478,793 across 42 grants. His funding portfolio includes projects from the Australian Research Council (ARC), ANSTO, and international partners like Oakridge National Laboratory and Japan Proton Accelerator Research Complex. He has successfully supervised 11 PhD and Masters students to completion, with research topics spanning granular mechanics, conveyor systems, and neutron strain tomography. His current research involves collaborations with institutions worldwide, focusing on advanced strain measurement techniques and their application to complex material systems.