جست‌وجوی استاد و پژوهشگر

Armin Iskeمشاهده پروفایل

استاد

• Numerical Approximation
• Applied Mathematics
• Kernel Methods
• +۴ مورد دیگر

Prof. Dr. Armin Iske is a Full Professor of Numerical Approximation at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a PhD from the University of Göttingen (1994) and habilitation from TU Munich (2002). His research focuses on numerical approximation, kernel-based methods, computational fluid dynamics, and medical imaging. He has held academic positions globally, including visiting roles at ANU (Australia) and the University of Leicester (UK). Research interests include scattered data approximation, adaptive particle methods for flow simulation, and high-dimensional data analysis. He has authored 118+ publications, including works on kernel interpolation, medical imaging reconstruction, and machine learning applications. He serves on editorial boards for journals like Advances in Computational Mathematics and Sampling Theory . His contributions span interdisciplinary projects, such as SFB/TRR 181 on energy transfer in atmosphere and ocean, and collaborations in nanotechnology for brain interfaces. His work bridges theoretical mathematics with practical applications in engineering and biosciences.

Claudia Nevesمشاهده پروفایل

مدرس ارشد

• Extreme Value Theory
• Spatio-Temporal Processes
• Asymptotic Statistics
• +۳ مورد دیگر

Claudia Neves is a Senior Lecturer in Statistics at King's College London, Department of Mathematics, since 2023, having previously held positions at the University of Reading (2015–2021) and the University of Aveiro, Portugal. She holds a Visiting Associate Professorship at the University of Lisbon (2024–present). Her research focuses on extreme value theory, spatio-temporal processes, and asymptotic statistics, with applications in climate science, environmental hazards, and finance. She leads the EPSRC-funded project on multivariate max-stable processes for hazard forecasting and serves on editorial boards, including REVSTAT-Statistical Journal and the Royal Statistical Society's Environmental Statistics Section. Education: PhD in Statistics and Operational Research (2006, University of Lisbon). Teaching since 1997. Awards: EPSRC Innovation Fellowship (2015–2021). Research highlights include modeling African flood drivers beyond El Niño and developing statistical tools for space-time extreme events. Collaborates with institutions globally, including the Centre for Sustainable Business at King’s and the Net Zero Centre. Active in interdisciplinary initiatives like EPRI Climate READi. Publications emphasize methodological advancements in extreme value analysis and applications to environmental risks. Ongoing work includes advancing spatio-temporal models for climate extremes and fostering sustainable research through King’s Net Zero initiatives.

• Metabolomics
• Mass Spectrometry
• Food Science
• +۵ مورد دیگر

Dr. Jos Hageman is an Assistant Professor affiliated with the Mathematical and Statistical Methods group (Biometris) at Wageningen University & Research. His research focuses on integrating advanced analytical techniques (e.g., mass spectrometry, metabolomics) with statistical modeling to address challenges in food science, plant biology, and environmental systems. He specializes in developing predictive models for food product design, microbial activity analysis, and environmental impact assessment. Education: Not explicitly stated in provided texts. Key research interests include metabolomics-driven food innovation, lipid and protein interaction modeling, and the environmental effects of microplastics. His work combines proteomics, metabolomics, and statistical methods to investigate plant-microbe interactions, crop diversity, and sustainable agriculture. He has collaborated on projects involving statistical analysis of fermentation processes, QSAR modeling for antibacterial compounds, and machine learning for ingredient characterization. Recent publications highlight advancements in QSAR models for food systems, microplastic toxicity assessment, and lipid species analysis via mass spectrometry. His datasets include metabolite profiles of grassland plants and breast milk protein dynamics. As a co-promotor, he oversees PhD projects on kwashiorkor etiology, plant-based protein optimization, and bioreactor transcriptomics. Current active projects focus on improving plant-based food proteins and understanding kwashiorkor pathophysiology through causal learning. His work bridges analytical chemistry, computational methods, and applied biology, contributing to food security and environmental sustainability initiatives.

• Behavioral Genetics
• Developmental Psychology
• Child Mental Health
• +۴ مورد دیگر

Dr. Jody Ganiban is a Professor specializing in Clinical/Developmental Psychology, focusing on the interplay between genetic factors and environmental influences on child and family outcomes. Her work examines how temperament, parenting behaviors, and externalizing/internalizing symptoms are shaped by both inherited and contextual factors. She directs undergraduate psychology advising and has extensive involvement in the NIH-funded ECHO program, studying diverse cohorts to understand developmental trajectories in health and behavior. Education: Ph.D., University of Rochester (1993). Research emphasizes behavioral genetics, prenatal exposures, family dynamics, and the role of socioeconomic factors in child development. Key projects include investigating autism etiology, obesity trajectories, and mental health disparities using large-scale longitudinal data. Her studies often employ adoption designs to disentangle genetic and environmental contributions to psychopathology and resilience. Recent publications highlight her expertise in maternal-child health, environmental toxins, and pandemic impacts on youth wellbeing. Dr. Ganiban collaborates widely, contributing to policy-relevant research on asthma, nutrition, and social media effects in pediatric populations.

Tomasz R. Bieleckiمشاهده پروفایل

استاد

• Mathematical Finance
• Stochastic Analysis
• Credit Risk
• +۵ مورد دیگر

Tomasz R. Bielecki is a Professor in the Department of Applied Mathematics at Illinois Institute of Technology (IIT). He serves as Director of the Professional Master in Mathematical Finance program at IIT. His research interests span stochastic analysis, stochastic processes, mathematical finance, credit risk, counterparty risk, performance measures, stochastic control, semigroup theory, and functional analysis. He has held editorial roles at journals including SIAM Journal of Financial Mathematics and International Journal of Theoretical and Applied Finance. Bielecki has collaborated with numerous researchers such as Igor Cialenco, Stephane Crepey, and Monique Jeanblanc. His work includes influential books like Structured Dependence between Stochastic Processes and Counterparty Risk and Funding: A Tale of Two Puzzles . His recent publications focus on topics like Hawkes processes, dynamic risk measures, and stochastic control under model uncertainty. He has consulted for firms including Bank of America, Bloomberg, and Merrill Lynch. His research emphasizes applications in financial mathematics, credit risk modeling, and the development of robust frameworks for risk assessment and derivatives pricing.

• Hydrology
• Civil and Hydraulic Engineering
• Flood Forecasting
• +۳ مورد دیگر

Professor Alberto Montanari is a leading academic at the University of Bologna , serving as Full Professor of Hydrology and Water Resources Management and Head of the Department of Civil, Environmental, Chemical and Material Engineering (DICAM) since 2015. His research spans hydrology , climate change impacts , and water resources systems with a focus on flood forecasting , sustainable water management , and uncertainty quantification through methodologies like BLUECAT. Education: Master in Civil and Hydraulic Engineering (University of Parma, 1992); Ph.D. in Hydraulic Engineering (University of Bologna, 1996) His scientific work emphasizes human-water systems interaction , evidenced in the Panta Rhei research initiative and global water emergency assessments. Recent publications analyze climate change extrapolation , groundwater dynamics , and anthropogenic impacts on hydrology across Europe and the USA. Scientific recognition includes: 2019 IAHS Dooge Medal 2018 AGU William Kaula Award 2018 EGU Henry Darcy Medal 2017 AGU Fellow 2017 EGU Union Service Award As Editor-in-Chief of Water Resources Research (2013-2017) and leader in international organizations (EGU President Elect, IAHS Commission President), he has shaped global hydrological science. His supervision of 16+ Ph.D. students and €1M+ research grants, including EU FP7 projects, demonstrate his academic leadership.

Gabriela Jaramilloمشاهده پروفایل

دانشیار

• Nonlinear Partial Differential Equations
• Integro-Differential Equations
• Pattern Formation in Biological Systems
• +۴ مورد دیگر

Gabriela Jaramillo is an Associate Professor in the Department of Mathematics at the University of Houston. Her research focuses on nonlinear partial differential equations, integro-differential equations, and pattern formation in biological and physical systems. She investigates how nonlocal diffusion mechanisms influence pattern formation, particularly in oscillatory media, and explores defects and impurities in such systems. Her teaching roles include advanced mathematics courses such as Partial Differential Equations, Theory of Differential Equations, and Multivariable Calculus at the University of Houston, as well as prior teaching at the University of Arizona and University of Minnesota. She leads research programs such as the Summer REU on pattern formation, supervising students like Sameel Siddiqi, Kevin Sony, and Jared Solomon. Her work emphasizes nonlocal effects in biological systems, including studies on spiral waves, target patterns, and the impact of mutations on ecological models. Recent publications explore topics like spiral wave dynamics in nonlocal oscillatory media and numerical methods for diffusive nonlocal operators. Her research bridges theoretical analysis with computational simulations, addressing challenges in pattern formation across disciplines.

Hong Liمشاهده پروفایل

استاد

• Actuarial Science
• Insurance Economics
• Data Analytics
• +۶ مورد دیگر

Hong Li is a Professor in the Department of Economics and Finance at the Gordon S. Lang School of Business and Economics , University of Guelph (ranked #1 in Canada for Actuarial Science). He also holds an Adjunct Associate Professor position at the University of Waterloo's Department of Statistics and Actuarial Science. His research focuses on actuarial science, insurance economics, and data analytics applied to risk management challenges in longevity, climate, and agricultural sectors. Education : PhD in Econometrics & Operations Research, Tilburg University (2015) MPhil in Economics, Tilburg University (2012) B.A. in Finance, Xiamen University (2010) Research Interests include: Machine learning for insurance underwriting and pricing Mortality forecasting and longevity risk transfer Catastrophe bond design and climate risk modeling Optimization of index-based agricultural insurance Dynamic hedging strategies for financial institutions Awards : 2018 Bob Alting von Geusau Prize (best ASTIN Bulletin paper) University Research Award (2022-2023) Grants & Funding include: SSHRC Insight Development Grant ($69,623 CAD) NSERC Discovery Grant ($127,500 CAD) CAS Individual Grant ($18,000 USD) Teaching includes courses on risk management, actuarial economics, and financial econometrics at both undergraduate and graduate levels. Professional Service : Editorial Board Member: Annals of Actuarial Science and Journal of Risk and Insurance Member, Canadian Institute of Actuaries Academic Research Committee

• Machine Learning
• Medical Data Analysis
• Time-Series Data Analysis
• +۳ مورد دیگر

Dr. Weitong Chen is a Senior Lecturer at the School of Computer and Mathematical Sciences, University of Adelaide, and an ARC EC Industry Fellow at the Australian Institute for Machine Learning (AIML). He holds a PhD from the University of Queensland (2020), with prior roles as a Post-Doc Research Fellow and Associate Lecturer there. His research focuses on machine learning applications in medical data, particularly time-series analysis, semi-supervised learning, and IoT. He collaborates widely across academia, industry, and government, supported by multiple grants. His work emphasizes healthcare applications, adversarial robustness, federated learning, and unlearning mechanisms. Education: PhD in Machine Learning, University of Queensland (2020) Master's Degree, University of Queensland Bachelor's Degree, Griffith University Research Interests: Medical Data Analysis (e.g., EHRs, radiology) Time-Series Modeling (healthcare IoT, irregular data) Adversarial Machine Learning (backdoor attacks, robustness) Federated Learning (modality incompleteness, clustered frameworks) Data Privacy (unlearning, compliance) Grants & Collaborations: ARC EC Industry Fellowship Industry partnerships in healthcare and IoT Labs/Teams: Australian Institute for Machine Learning (AIML) Cross-disciplinary health tech collaborations

Jon Helgheim Holteمشاهده پروفایل

دانشیار

• Healthcare Economics
• Health Policy Analysis
• Public Health
• +۳ مورد دیگر

Jon Helgheim Holte is an Associate Professor in the Department of Health Management and Health Economics at the University of Oslo. His work focuses on healthcare economics, policy analysis, and chronic disease management. He has conducted extensive research on healthcare utilization patterns, cost drivers in chronic conditions, and physician workforce dynamics. His recent projects include the INOREG initiative analyzing care pathways using registry data. Holte holds an affiliation with the Institute of Education (as a student, though specific details are unclear). Research interests include: healthcare resource allocation, policy impacts on provider behavior, socioeconomic determinants of healthcare use, and registry-based methodologies. His studies often combine epidemiological approaches with economic modeling to address public health challenges. Publications highlight analysis of COPD care pathways, musculoskeletal disorder economics, and physician preference modeling. Articles consistently emphasize translational research with clear policy implications. No academic awards are listed, though his work demonstrates strong methodological rigor. Advising/grants sections remain unspecified in available texts. Collaboration with cross-disciplinary teams is evident through co-authorship patterns.

• Marketing Research
• Consumer Behavior
• E-commerce
• +۷ مورد دیگر

Prof. Tammo H.A. Bijmolt is a Full Professor and Chairman of the Department of Marketing at the University of Groningen's Faculty of Economics and Business. He holds the title of Director of the Groningen Digital Business Center and has served as a Visiting Professor at the University of Hamburg (part-time). His research focuses on consumer decision making, e-commerce, advertising, retailing, loyalty programs, and meta-analysis. He has authored over 130 peer-reviewed articles, including in top-tier journals like the Journal of Marketing Research and Journal of Consumer Research , earning multiple best paper awards. Education: PhD (cum laude) from University of Groningen (1996), MSc in Quantitative Business Economics (cum laude, 1991). Leadership Roles: Former Director of Research Institute SOM (2009–2015), current President of EMAC (European Marketing Academy), and board member of EIASM. His research interests span empirical generalizations in marketing, with emphasis on loyalty program effectiveness, multi-unit promotions, and digital marketing dynamics. Notable contributions include frameworks for analyzing customer behavior in omni-channel environments and meta-analyses on marketing strategy impacts. He advises companies like DVJ Insights and has contributed to legal cases involving market research. Teaching focuses on advanced marketing research methods, multivariate analysis, and customer analytics at both graduate and executive levels. He has been honored with awards for teaching excellence, including the 1998 Best Teacher Award at Tilburg University.

Vardayani Rattiمشاهده پروفایل

پژوهشگر

• Mathematical Biology
• Ecology
• Infectious Diseases
• +۳ مورد دیگر

Vardayani Ratti is a Researcher at Dartmouth College's Department of Mathematics, holding the position of John Wesley Young Research Instructor since July 2016. Her research focuses on Mathematical Biology with applications in ecology, infectious diseases, pollination biology, and agriculture. She completed her Ph.D. in Mathematics at the University of Guelph under Hermann J. Eberl, followed by a postdoctoral fellowship there before joining Dartmouth. Her work emphasizes disease dynamics in honeybee colonies (Varroa destructor and viruses), mosquito population control, and climate change impacts on vector-borne diseases like Lyme disease. She has presented at leading institutions including Arizona State University, Rochester Institute of Technology, and the Society for Mathematical Biology conferences. Ratti's teaching spans advanced mathematics courses like Linear Algebra, Differential Equations, and Multivariable Calculus at Dartmouth, along with extensive graduate teaching and mentoring at the University of Guelph. Her research outputs include peer-reviewed articles in Mathematical Medicine and Biology , Bulletin of Mathematical Biology , and contributed chapters in In Silico Bees .

Richard Peterمشاهده پروفایل

استاد

• Decision-making under risk and uncertainty
• Risk management and insurance
• Microeconomic theory
• +۷ مورد دیگر

Richard Peter is a Professor of Finance and the Nationwide Professor at the Tippie College of Business, University of Iowa, with a joint affiliation as Associate Professor in the Economics department. His research focuses on decision-making under risk and uncertainty, particularly in the context of insurance and risk management, using tools from microeconomic theory. His research interests include: Decision-making under risk and uncertainty Risk management and insurance Behavioral and health economics Precautionary behavior and prevention Ambiguity aversion and insurance design Bargaining and incentive mechanisms Dr. Peter’s recent publications span top journals such as Management Science , Journal of Risk and Insurance , and Health Economics . His work often explores how individuals and institutions respond to uncertainty, with themes including probability weighting, vaccination decisions, optimal prevention, and the impact of genetic testing on insurance. A recurring focus is the intersection of behavioral insights with formal economic modeling. His scientific awards highlight excellence in both research and teaching: David and Lois Gardner Faculty Award for Mid-Career Excellence (2024) Early Career Scholarly Achievement Award (2020) Excellence in Reviewing Award, Journal of Risk and Insurance (2019) Diversity, Equity and Inclusion Award (2019) Ernst-Meyer Prize, The Geneva Association (2013) Dr. Peter has been actively involved in advising and research mentorship, contributing to the academic development of students and junior scholars. He has received multiple teaching and faculty excellence awards, underscoring his commitment to education. His research has been supported through fellowships such as the TriStar Research Fellow and the Nationwide Professorship. He is a member of leading professional organizations including the American Risk and Insurance Association and the European Group of Risk and Insurance Economists. He is affiliated with research initiatives in behavioral economics and risk theory at the University of Iowa, contributing to seminars and collaborative projects in insurance economics and decision science.

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

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur , Nice, France, and holds a Chair in Artificial Intelligence. He serves as Director of the Institut 3IA Côte d’Azur and leads the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. He is an associate editor for The Annals of Applied Statistics and founded the Statlearn workshops . Research Interests: Statistical learning in high dimensions Learning on networks and functional data Deep latent variable models Adaptive learning with uncertain labels Applications in Medicine, Image Analysis, Astrophysics, and Humanities Notable Contributions: Developed multiple R packages including HDclassif , FisherEM , and FunLBM . Created the Linkage.fr platform for network analysis with textual edges. PhD Students: Current: Seydina Niang (Deep Generative Models), Kilian Burgi (Marine Diversity Monitoring), Baptiste Pouthier (Multimodal Learning) Former: Giulia Marchello (Dynamic Networks), Rémi Boutin (Network Analysis), Dingge Liang (Recommender Systems), Nicolas Jouvin (Latent Variable Models), Alexandre Saint-Dizier (Image Aggregation), Warith Harchaoui (Optimal Transport), Pierre-Alexandre Mattei (Sparse Clustering), Rawya Zreik (Temporal Networks), Anastasios Bellas (Anomaly Detection), Camille Brunet (Sparse Clustering) Contact: Email: charles.bouveyron@univ-cotedazur.fr / charles.bouveyron@inria.fr Postal: Equipe Maasai, Inria Sophia Antipolis, 2004 route des Lucioles, 06902 France

Elina Robevaمشاهده پروفایل

استادیار

• Mathematical Statistics
• Machine Learning
• Causal Inference
• +۶ مورد دیگر

Elina Robeva is an Assistant Professor in the Department of Mathematics at the University of British Columbia (UBC), effective until July 2025, when she will be promoted to Associate Professor. She is affiliated with the Institute of Applied Mathematics, CAIDA, and holds the titles of AMII Fellow and Canada CIFAR AI Chair. Her research focuses on the intersection of mathematical statistics, machine learning, combinatorics, and applied algebraic geometry, with an emphasis on causal inference, graphical models, and tensor decomposition. Education: She earned her PhD in Mathematics from UC Berkeley (2016), advised by Bernd Sturmfels, and a BS with Honors in Mathematics from Stanford University (2011). Prior to UBC, she was an NSF Postdoctoral Fellow and instructor at MIT (2016–2019). Research Interests: Her work bridges algebraic methods with statistical problems, including causal discovery in linear models, tensor decomposition applications in machine learning, and super-resolution imaging. She employs algebraic geometry and combinatorics to uncover structural insights in statistical problems. Awards: Recipient of the Andre Aisenstadt Prize (2024), Bernard Friedman Memorial Prize (2016), and Vanier Canada Graduate Scholarship (for student Pardis Semnani). She is also a Canada CIFAR AI Chair and AMII Fellow. Advising & Mentorship: She has mentored numerous students, including PhD candidates (e.g., Pardis Semnani), MSc students, and undergraduates through programs like NSERC USRA and MITACS. Notable collaborations include work on causal inference, log-concave density estimation, and tensor networks. Labs/Teams: Active in interdisciplinary research groups at UBC and collaborates internationally through programs like the Institute for Mathematical and Statistical Innovation (IMSI).