جست‌وجوی استاد و پژوهشگر

Nourridine Sieweمشاهده پروفایل

استادیار

• Mathematical Biology
• Disease Dynamics
• Computational Biology
• +۴ مورد دیگر

Nourridine Siewe is an Assistant Professor at the Rochester Institute of Technology (RIT) in the School of Mathematics and Statistics under the College of Science . He specializes in mathematical biology, focusing on modeling diseases like obesity-induced Type 2 Diabetes Mellitus (T2DM), cancer, malaria, and infectious diseases. PhD in Mathematics from Howard University Postdoctoral Research Fellow at the University of British Columbia Postdoctoral Fellow at the National Institute for Mathematical and Biological Synthesis (NIMBioS) His research integrates mathematical modeling, differential equations, and computational analysis to address global health challenges. Recent work includes modeling glucose-insulin dynamics in T2DM and evaluating anti-T2DM drug efficacy. His methodologies have potential applications in cancer, hepatitis B, and chronic wound healing. The 15 most recent articles highlight his focus on mathematical modeling for infectious diseases, metabolic disorders, and ecological systems. These include studies on desert locust dynamics, diabetic wound treatments, and cancer immunotherapy optimization. 2025 NIGMS SuRE–First Award for research linking obesity to T2DM He mentors undergraduate and graduate students at RIT, teaching courses in multivariable calculus and research methodologies. His models are critical for virtual patient simulations and drug evaluation frameworks.

Douglas Baumannمشاهده پروفایل

استاد

• Statistics Education
• Data Visualization
• Statistical Genomics and Epigenomics
• +۴ مورد دیگر

Douglas Baumann is a Professor in the Department of Mathematics & Statistics at the University of Wisconsin-La Crosse. His research focuses on Statistics Education, Data Visualization, and Statistical Genomics, with recent work emphasizing pedagogical innovations in STEM education and interdisciplinary applications in ecology and biology. Education: PhD in Statistics (Purdue University, 2012), MS in Mathematical Statistics (Purdue, 2009), B.A. in Mathematics (St. Olaf College, 2006) His recent publications address improving organic chemistry instruction through generative pedagogies and advancing mathematical biology education via natural resource modeling. He has secured NSF funding for ecological modeling REU programs. Awards include the 2019 NSF REU grant for Mississippi River Basin research initiatives. Teaching: Courses include Statistical Methods, Categorical Data Analysis, and Statistical Computing Collaborations span chemistry education, biology, and environmental science, reflecting his commitment to bridging theory and applied practice.

• Financial Risk Management
• Econometrics
• Algorithmic Contract Standards
• +۳ مورد دیگر

Prof. Henriette Elise Breymann is a Professor in the Department of Finance, Risk Management and Econometrics at the ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW). Her research focuses on financial risk modeling, algorithmic contract standards (ACTUS), big data analytics, and regulatory technology (RegTech). She has led numerous projects, including Data Driven Financial Risk and Regulatory Reporting, Strengthening Swiss Financial SMEs through Reinforcement Learning, and Explainable AI in Credit Risk Management. Her work intersects finance, physics, and computational methods, with contributions to energy economics (e.g., renewable energy systems), high-frequency financial data analysis, and quantum chaos applications. She co-authored Unified Financial Analysis: The Missing Links of Finance (2009) and pioneered the ACTUS framework for standardizing financial contracts. Projects: 12+ leadership roles in applied finance, risk modeling, and energy systems. Key Expertise: Regulatory reporting, stress testing, and data-driven financial systems. ACTUS Standard: Global initiative for granular financial contract representation. Her research spans interdisciplinary collaborations, including the Risk- and Finance-Lab, and has been presented at major conferences like the Society for Economic Measurement. She advises on regulatory innovations and financial resilience strategies for SMEs.

Haoran Shiمشاهده پروفایل

پژوهشگر

• river hydrodynamics
• environmental fluid mechanics
• physical limnology
• +۳ مورد دیگر

Dr. Haoran Shi is a Postdoctoral Research Officer at the School of Ocean Sciences, Bangor University. He holds a PhD in Civil and Environmental Engineering from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2023). His research focuses on river hydrodynamics, environmental fluid mechanics, and physical limnology, with recent work exploring subsurface heatwaves in lakes, hyperpycnal river plumes, and multivariate extremes in aquatic systems. His methodologies include experimental studies, numerical modeling, and spatio-temporal analysis. Education: PhD in Civil and Environmental Engineering, EPFL (2023); prior degrees not detailed in the text. Research collaborations span international teams addressing coastal sediment dynamics, lake climatology, and vegetation effects on flow patterns. Key contributions include the development of analytical models for vegetated channel hydraulics and improved parameterization of environmental fluid dynamics processes. Dr. Shi’s publications (n=15+) demonstrate expertise in fluid dynamics applications across riverine, lacustrine, and coastal environments. His work bridges fundamental fluid mechanics with real-world challenges like climate impacts on water resources and sediment transport modeling. No awards or grants are explicitly listed in the provided materials. Research activities include laboratory experiments on plume dynamics, field observations of lake thermal regimes, and computational simulations of flow-vegetation interactions. Current projects likely involve integrating remote sensing data with in-situ measurements for environmental monitoring.

• Causal Inference
• Statistical Genetics
• Time Series Analysis
• +۳ مورد دیگر

Andrew Grant is a Lecturer in Biostatistics at the University of Sydney's Sydney School of Public Health within the Faculty of Medicine and Health. Previously, he worked as a Research Associate at the MRC Biostatistics Unit, University of Cambridge, and an Associate Lecturer in Statistics at Macquarie University. He earned his PhD in Statistics from Macquarie University in 2018. His research focuses on causal inference, statistical genetics, and time series analysis, with an emphasis on applying these methods to genetic data to identify disease risk factors. Recent work includes developing robust Mendelian randomization techniques and clustering approaches for genetic association data. He has also contributed to MendelianRandomization , an R package for genetic analysis. Andrew’s early career research spanned medical education, including mentorship strategies, reflective learning practices, and mental health challenges faced by trainee doctors and medical students. His work highlights systemic issues in mental health support within healthcare training programs. In 2023, he secured a grant titled Statistical methods for understanding the causes and mechanisms underlying disease through the University of Sydney’s Faculty of Medicine and Health. His collaborations include global institutions and interdisciplinary teams in both biostatistics and medical education.

Irene Klugkistمشاهده پروفایل

استاد

• Bayesian Statistics
• Circular Data Analysis
• Multivariate Statistics
• +۳ مورد دیگر

Prof. Dr. Irene Klugkist is a Professor of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences, serving as Vice Dean for the Social and Behavioral Sciences. Her research focuses on Bayesian statistics, circular data analysis, and informative hypotheses evaluation, with recent emphasis on Bayesian research synthesis methods for conceptual replications. She holds a PhD in Bayesian Analysis (2005) and has contributed to over 90 peer-reviewed publications. Klugkist's work bridges statistical methodology with applications in psychology, education, and health sciences, emphasizing reproducibility and transparent research practices through preregistration and open data advocacy. She leads the Methodology and Statistics department, guiding interdisciplinary research projects and graduate training. Research Interests: Bayesian inference methods, circular statistics, evidence synthesis, statistical modeling for behavioral and health sciences, and methodological innovations in hypothesis testing. Her work often addresses translational challenges, such as integrating qualitative and quantitative data in mixed methods reviews or applying expert priors in veterinary epidemiology. Recent Contributions: Her 2024 papers advance Bayesian alternatives to meta-analysis and explore student-program matching dynamics. Ongoing work includes developing circular statistical tools for behavioral measurements and improving Bayesian approaches to latent class models in diagnostic testing. Klugkist also advocates for transparent research practices through guidelines on preregistration and data sharing in psychopathology studies.

Augusto Ferranteمشاهده پروفایل

استاد

• Control Theory
• Quantum Computing
• Linear Algebra
• +۵ مورد دیگر

Augusto Ferrante is a Full Professor at the Department of Information Engineering, University of Padova. His research spans control theory, quantum computing, and linear algebra, with a focus on optimal control, spectral estimation, and matrix analysis. University: University of Padova Department: Information Engineering Research Interests: Ferrante's work addresses theoretical and applied problems in control systems, including quantum control, Riccati equations, and covariance matrix estimation. His contributions bridge mathematical rigor with engineering applications, particularly in spectral analysis and optimization. Publications Trends: His recent work (2014-2012) emphasizes quantum channel estimation, entropy-based spectral methods, and generalized Riccati equations, reflecting a synthesis of control theory and quantum information science. Book Chapters: He has co-authored works on modeling, estimation, and control, including a 2007 Modeling, Estimation and Control volume dedicated to Giorgio Picci.

Christian Rohrbeckمشاهده پروفایل

مدرس ارشد

• Extreme Value Analysis
• Spatial Statistics
• Environmental Statistics
• +۳ مورد دیگر

Dr. Christian Rohrbeck is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath. He holds a PhD in Statistics and Operational Research from Lancaster University and leads projects in statistical climatology through the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics and Centre for Climate Adaptation & Environment Research. His research combines extreme value theory, spatial statistics, and Bayesian methods to analyze environmental and financial data. Current projects focus on flood risk modeling, climate extremes standardization, and monotonic regression techniques. Dr. Rohrbeck accepts doctoral students in extreme value analysis, flood risk, and climate adaptation statistics.

Malin Palö Forsströmمشاهده پروفایل

عضو هیئت علمی

• Discrete Probability Theory
• Lattice Gauge Theories
• Mathematical Physics
• +۳ مورد دیگر

Malin Palö Forsström is a Visiting Lecturer at Gothenburg University and lectures at Chalmers University of Technology. She previously held a postdoctoral position at KTH Royal Institute of Technology, focusing on probability theory under supervision of Fredrik Viklund and Jonatan Lenells. Her PhD, completed in 2019, was at Chalmers University of Technology under Jeffrey E. Steif. She has also taught courses at multiple institutions, including Chalmers, KTH, and Gothenburg University. Malin Palö Forsström earned her PhD in 2019 from Chalmers University of Technology, supervised by Jeffrey E. Steif. Prior to that, she pursued academic training leading to her doctoral studies. Her primary research interests lie in discrete probability theory, particularly applying probabilistic methods to study lattice gauge theories and related models. She also investigates properties of Boolean functions, including noise sensitivity, volatility, and total influence. Her work bridges theoretical probability with applications in mathematical physics and statistical mechanics. Her recent publications focus on lattice gauge theories, exploring Wilson loops and Higgs models, as well as the analysis of Boolean functions' sensitivity properties. Her work often combines probabilistic techniques with insights from mathematical physics, contributing to both theoretical frameworks and applied models in statistical mechanics. Scientific Awards and Grants: 2021: Ruth och Nils-Erik Stenbäcks stiftelse 2019: Kungliga vetenskapsakademin, Stiftelsen G S Magnusons fond 2015: Stiftelsen Olle Engkvist byggmästare 2014: SveFuM Malin has received several grants supporting her research, including notable awards from the Ruth och Nils-Erik Stenbäcks Foundation and the Swedish Research Council. She has not listed specific advising roles or grants beyond those mentioned. Her work has been presented at numerous events, including the Nordic Congress of Mathematicians, the Mittag-Leffler Institute, and international conferences like ICMP 2021.

Md Hasinur Rahaman Khanمشاهده پروفایل

استاد

• High-dimensional Data Analysis
• Statistical Genomics
• Bioinformatics
• +۶ مورد دیگر

Dr. Md Hasinur Rahaman Khan is a Professor at the Institute of Statistical Research and Training (ISRT), University of Dhaka, Bangladesh. His academic roles include teaching advanced courses in statistical machine learning, Bayesian statistics, and statistical computing. He holds a PhD in Statistics from the University of Warwick, UK, and has extensive research experience in high-dimensional data analysis, variable selection, and censored data modeling. Education: Ph.D. in Statistics (Warwick, UK), MSc in Statistics (Warwick, UK), MSc in Applied Statistics (Dhaka), BSc in Applied Statistics (Dhaka). Research Interests: High-dimensional data analysis, statistical genomics, bioinformatics, biostatistics, machine learning, public health, Bayesian statistics, and social statistics. His work emphasizes variable selection techniques, survival analysis, and applications in public health. Awards: Multiple Dhaka University Dean’s Honor Awards (2013, 2015, 2019), ISCB Conference Awards for Scientists (2015, 2016), CRiSM scholarship (Warwick), and several grants from UGC and international bodies. Grants: Includes studies on child marriage prediction, healthcare inequities in marginalized communities, poverty vulnerability, and climate change analysis in Bangladesh. He has secured over a dozen research grants. Advising: Supervised numerous PhD and MSc students in survival analysis, machine learning, and statistical modeling. Current students include Shakil Ahmed, Nasrin Sultana, and Shahriar Nayeem. Labs/Teams: Leads the Data Analytics Solution in Bangladesh (DaSiB), a virtual platform for data science. Active in editorial roles for journals like Journal of Statistical Research and PLOS One.

• Applied Statistics
• Biostatistics
• Machine Learning
• +۳ مورد دیگر

Dr Emmanuel Ogundimu is an Associate Professor in the Department of Mathematical Sciences at Durham University and a Fellow of the Wolfson Research Institute for Health and Wellbeing. His research focuses on Applied Statistics, Biostatistics, Machine Learning, and Causal Inference, with emphasis on Missing Data Methodology and Models for Rare Events. His work bridges statistical theory and practical applications in healthcare, finance, and social sciences. Key contributions include methodological advancements in sample selection models, penalized regression techniques, and handling non-random samples in credit scoring. He has published extensively in top-tier journals and co-edited the book Developments in Statistical Modelling (2024). Dr Ogundimu collaborates across disciplines, leading studies such as the Understanding Repetitive Behaviours clinical trial (2025) and the NED APRIQOT colonoscopy performance trial (2024). His research emphasizes robust statistical methods for improving healthcare outcomes and predictive modeling accuracy in rare event scenarios. He advises three graduate students: Adam Iqbal, Adam Stone, and Duaa Nadhrah. His work has been featured in journals like Statistical Modelling , Statistics in Medicine , and Clinical Gastroenterology and Hepatology .

• Econometrics
• Time Series Analysis
• High-Dimensional Data Analysis
• +۳ مورد دیگر

Anna Bykhovskaya is an Assistant Professor of Economics at Duke University, affiliated with the Trinity College of Arts & Sciences. She holds a Ph.D. in Economics from Yale University (2019), an MA in Economics from the New Economic School, and BS/MS in Mathematics from Moscow State University. Her research focuses on econometric methodologies for analyzing high-dimensional time series data, including cointegration in large VAR models and network evolution dynamics. She teaches advanced econometrics courses such as Time Series Econometrics and Special Topics in Econometrics. Education: Ph.D. in Economics, Yale University, 2019 MA in Economics, New Economic School BS/MS in Mathematics, Moscow State University Research Interests: Anna combines mathematical rigor with real-world economic challenges, particularly in analyzing long-run dependencies in stock prices, evolving networks of relationships, and macroeconomic time series. Her work emphasizes methodological innovations for high-dimensional datasets and nonlinear econometric models. Publications Trends: Her recent work addresses cointegration in large-scale VAR models, dynamic Tobit models for persistent data, and time series approaches to network evolution. These studies contribute to both econometric theory and applied macroeconomic analysis. Awards: No specific awards listed, though her research has been highlighted in Duke Economics news features. Teaching & Advising: Teaches graduate econometrics courses at Duke. No advising/mentoring details explicitly listed.

Matt Springhamمشاهده پروفایل

دانشیار

• Sports Science
• Exercise Physiology
• Strength and Conditioning
• +۵ مورد دیگر

Matt Springham serves as Associate Professor in Strength and Conditioning Science and Exercise Physiology at St Mary's University, Twickenham, where he co-leads the Performance and Practice research theme within the Centre of Applied Science. Concurrently, he holds the position of Consultant Research, Development and Innovation Coordinator at Arsenal Football Club Academy in London and delivers guest lectures in Sports Medicine at University College London and Queen Mary University of London. Undergraduate Degree: Sport and Exercise Science, University of Hertfordshire Master's Degree: Human Performance Science, Brunel University PhD: Edith Cowan University (2021), investigating training/match load interrelationships with physical performance and immunoendocrine responses in elite football players His research centers on football science with specialized expertise in physical performance optimization, training/match load quantification, and fatigue monitoring systems. Springham employs advanced methodologies including neuromuscular testing, immunoendocrine profiling, and wearable technology to develop evidence-based practices for elite athlete development. His work bridges theoretical exercise physiology with practical application in high-performance environments, particularly focusing on youth football pathways. Analysis of his 15 most recent publications reveals a dominant focus on English Premier League academy football, with 12 of 15 articles examining neuromuscular performance, load monitoring, and fatigue responses in U-18 players. Collaborative work with UEFA and professional clubs drives his research agenda, while secondary streams explore ballet science and pandemic impacts on sports organizations. His methodological approaches increasingly incorporate multivariate statistics and technology validation. UKSCA accredited Strength and Conditioning Coach BASES Accredited Sport and Exercise Scientist (Physiology) Chartered Scientist Fellow of the Higher Education Academy Springham actively supervises postgraduate research and has secured over £1.1 million in external research funding through consultancy and collaborative grants. His partnerships with Arsenal FC, UEFA, and academic institutions facilitate translational research from laboratory to pitch. Current projects include longitudinal monitoring of immunoendocrine markers across football seasons and validation of novel performance assessment technologies. He directs research activities within St Mary's Centre of Applied Science Performance and Practice theme while maintaining operational integration with Arsenal FC's Academy infrastructure, creating a unique applied research ecosystem for football science innovation.

• Statistical Climatology
• Statistical Anthropology
• Spatial Statistics
• +۴ مورد دیگر

Jeff Babb is an Associate Professor in the Department of Mathematics & Statistics at the University of Winnipeg. He has held academic roles since 2000, including serving as Department Chair from 2004-2009. Prior to his academic career, Babb worked as a Biometrician at the Canadian Grain Commission for 16 years and as a Principal Consultant for the University of Manitoba’s Statistical Advisory Service. His expertise spans statistical climatology, anthropology, spatial statistics, and multivariate analysis, with notable contributions to Mesolithic burial chronology and paleoclimatic studies. Education: M.Sc., Mathematical Statistics, University of Alberta (Thesis: Autocorrelated Differences in Paired Samples) B.Sc., Mathematics, University of Winnipeg Research focuses on statistical applications in climatology, demography, and archaeology. Collaborations with anthropologists and geographers have explored Mesolithic burial practices and European prehistoric populations. Recent work addresses teacher well-being during the pandemic, blending statistical methods with educational policy analysis. Publications span over 20 peer-reviewed articles, including studies in Journal of Human Evolution and Proceedings University of Bristol Spelaeological Society , plus contributions to books on gifted education and agricultural science. Technical reports include statistical analyses for food safety programs and crop quality assessment. Awards: None explicitly listed. Consulting experience includes work with industry, government, and academia on experimental design, survey analysis, and statistical audits. Labs/Teams: Active in interdisciplinary collaborations, particularly with archaeologists and climatologists. Engages in policy-related statistical consulting for education and agricultural sectors.

Teng Wah LEOمشاهده پروفایل

دانشیار

• Applied Microeconomics
• Applied Econometrics
• Welfare Economics
• +۴ مورد دیگر

Teng Wah LEO is an Associate Professor in the Department of Economics at St. Francis Xavier University since 2014. He holds a Ph.D. in Economics from the University of Toronto (2006), an M.Soc.Sc. from the National University of Singapore (2001), and a B.A. in Economics & Statistics from the same institution (1994). His research focuses on applied microeconomics, applied econometrics, and welfare economics, with particular emphasis on economic inequality, policy impact analysis, and stochastic dominance methodologies. Key research areas include measuring changes in social justice, analyzing the implications of China's one-child policy, and evaluating the effects of custody laws on child outcomes. He has held visiting positions at National Taiwan University (2018) and the University of Guelph (2012-2013). Recipient of multiple awards including the University Research/Publication/Teaching Award (2009, 2012-2014, 2016, 2018, 2022), he has published extensively in journals like Journal of Economic Growth and Economics Letters. His work bridges econometric rigor with real-world policy applications. Active in academic service, he has served on editorial boards (Advances in Decision Sciences), grant review committees (Hong Kong RGC, NWO), and university committees. Teaching responsibilities span microeconomics, econometrics, and health economics.