Find Academic Supervisors

• Commutative Algebra
• Algebraic Geometry
• Tensor Decompositions
• +4 more

Alessandro Oneto is an Associate Professor at the Università di Trento since April 2023, after serving as a tenure-track Assistant Professor (Ricercatore RTD-b) since April 2020. Prior to this, he held postdoctoral positions at Inria Sophia Antipolis Méditerranée (France, 2016-2018) Universitat Politècnica de Catalunya (Spain, 2018-2019) OvGU Magdeburg (Germany, 2019-2020) . Education Ph.D. in Mathematics, Stockholm University (2016), thesis: Waring-type problems for polynomials in algebraic geometry , supervised by Boris Shapiro and Enrico Carlini . Research focus Commutative Algebra Algebraic Geometry Tensor Decompositions Algebraic Statistical Models . Recent publications highlight advancements in tensor decomposition loci, secant varieties, polynomial strength, and geometric conditions for tensor ranks. His work bridges algebraic geometry and applied mathematics, addressing problems in tensor analysis and computational algebra. Scientific Awards Alexander von Humboldt Postdoctoral Fellowship (2019-2020) . Labs & Networks Co-founder of the TensorDec Laboratory (2020-present) at the University of Trento, focusing on tensor decompositions and training students. Member of the INABAG Network (Italian Network for Applied and Birational Algebraic Geometry), organizing conferences and collaborative activities. .

Junwei CuiView profile

Researcher

• Hydrogen Energy Storage
• Thermodynamics
• Fluid Mechanics
• +3 more

Junwei Cui serves as a Guest Scientist at Friedrich-Alexander University Erlangen-Nuremberg (FAU), maintaining a primary institutional affiliation with Chang’an University in Xi’an, Shaanxi Province, China. His research focuses on experimental thermophysics and fluid mechanics within the context of hydrogen energy storage systems, utilizing advanced optical measurement techniques to characterize complex fluid behaviors. His research portfolio centers on liquid organic hydrogen carriers (LOHCs) , with significant contributions to understanding thermophysical properties of benzyltoluene, diphenylmethane, and fluorene-based systems. Key methodologies include surface light scattering and dynamic light scattering for precise measurement of viscosity, surface tension, density, and nanoparticle diffusion in high-viscosity fluids and porous media. His work bridges fundamental fluid mechanics with practical hydrogen storage applications, particularly examining isomerism effects, hydrogen dissolution dynamics, and temperature-dependent property changes up to 573 K. Analysis of his 12 most recent publications (2018-2024) reveals consistent focus on LOHC thermophysical characterization, with 80% of articles dedicated to hydrogen carrier systems and 20% to nanoparticle transport in porous materials. His collaborations predominantly involve FAU's thermodynamics research group, particularly with Prof. Andreas Paul Fröba's team at the Chair of Thermodynamics and Transport Properties (AOT-TP), indicating deep integration within FAU's experimental research ecosystem. No scientific awards or honors were documented in the source material. Information regarding student supervision, grant funding, laboratory facilities, or collaborative research teams was not explicitly provided, though his extensive co-authorship pattern suggests active participation in multi-investigator projects within FAU's hydrogen research initiatives.

• Bayesian Statistics
• Statistical Modeling
• Functional Data Analysis
• +4 more

Raffaele Argiento is a Full Professor of Statistics at the Department of Economics, University of Bergamo since September 2021. His academic career focuses on advanced statistical methodologies with applications across various domains including environmental science, public health, and data analysis. His research is prominently featured in high-impact statistical journals and conference proceedings. Argiento's research interests center around Bayesian statistical methods, particularly in functional data analysis, nonparametric Bayesian modeling, and clustering techniques. His work demonstrates expertise in developing innovative statistical approaches for complex data structures, including spatio-temporal data, categorical variables, and high-dimensional datasets. His research has significant applications in environmental monitoring (particularly air pollution analysis), public health (obesity rate modeling), and seismic monitoring through crowdsourced data. His methodological contributions include advancements in mixture models, partition models, and computational algorithms for statistical inference. His recent publication record shows a strong trend toward developing computationally efficient Bayesian methods for real-world applications. The research spans from theoretical developments in nonparametric Bayesian statistics to practical implementations for environmental monitoring, health data analysis, and functional data processing. His work demonstrates a consistent focus on bridging theoretical statistical advancements with practical applications across multiple scientific domains. Professor Argiento teaches several advanced statistical courses at the University of Bergamo, including Applied Statistical Modelling , Probability and Statistics , and Statistical Models for both undergraduate and graduate programs in Economics and Data Analysis. His teaching reflects his research expertise, emphasizing modern statistical methodologies and computational approaches.

Joshua KelloggView profile

Assistant Professor

• Natural Products
• Ethnobotany
• Phytochemistry
• +6 more

Joshua Kellogg is an Assistant Professor at Penn State University in the Department of Veterinary and Biomedical Sciences, with affiliations to the Penn State Cancer Institute, Huck Institutes of the Life Sciences, One Health Microbiome Center, and Center for Cannabis and Natural Product Pharmaceuticals (CCNPP). His research focuses on metabolomics, natural product pharmacology, and ethnobotanical analysis. Key Research Areas: Natural product chemistry, botanical safety assessment, mass spectrometry applications, and antimicrobial studies. Active Grants: Dietary phytochemicals for GI disease modulation (NIFA, 2023-2027); Artemisia-based SARS-CoV-2 therapies (NIAID, 2022-2023); Botanical mixture efficacy (NCCIH, 2018-2019). Kellogg’s work spans metabolomics (untargeted profiling, biochemometrics), botanical drug research (LEAFBot spectral library), and environmental interactions (nutrient dynamics in aquatic ecosystems). Recent studies include predictive modeling for herbal product authentication, digital ethnobotany of Monotropa uniflora, and cannabis sativa quality control.

• Combustion Theory
• Turbulent Flame Propagation
• Hydrogen and Ammonia Combustion
• +5 more

Francesco Creta serves as Associate Professor in the Department of Aeronautical and Space Engineering at Sapienza University of Rome's School of Engineering. His research focuses on advanced combustion phenomena with emphasis on hydrogen and ammonia fuels for decarbonization applications. His primary research interests include thermodynamic instability in premixed flames , turbulent combustion modeling , and rocket propulsion systems . Creta investigates fundamental flame behaviors through direct numerical simulation (DNS) and develops data-driven models for thermodiffusively unstable flames, particularly examining hydrogen-ammonia mixtures for sustainable energy applications. His work bridges theoretical combustion physics with practical engineering solutions for gas turbines and liquid rocket engines. Analysis of his 15 most recent publications reveals a strong trend toward hydrogen-ammonia combustion for decarbonization, with significant focus on flame instability mechanisms (particularly Darrieus-Landau and thermodiffusive effects), data-driven modeling approaches , and rocket engine applications . His research increasingly integrates machine learning techniques with traditional combustion modeling to address complex flame behaviors under high-pressure conditions. Creta maintains active research collaborations with leading combustion scientists across Europe, evidenced by co-authorship on high-impact publications in Proceedings of the Combustion Institute , Combustion and Flame , and Physics of Fluids . His work receives consistent recognition through publication in top combustion venues and presentation at major international conferences including the European Combustion Meeting and AIAA SciTech Forum. His research group conducts advanced numerical simulations of combustion systems, with particular expertise in liquid rocket engine modeling, transcritical flows, and flame instability analysis. Current projects focus on enabling hydrogen and ammonia combustion for gas turbine retrofitting and next-generation propulsion systems, addressing critical challenges in flame stability and pollutant formation.

Gerda ClaeskensView profile

Professor

• Model Selection
• Model Averaging
• Post-Selection Inference
• +4 more

Gerda Claeskens is a full professor of Statistics at the Faculty of Economics and Business (FEB) at KU Leuven, Belgium. She holds positions in the Operations Research and Statistics Research Group (ORSTAT) and is affiliated with the Leuven Statistics Research Center. Her academic journey includes a Licentiate in Mathematics (Summa Cum Laude) from the University of Antwerp and a Ph.D. in Statistics from Limburgs Universitair Centrum (now Hasselt University). Research Interests: Her work focuses on model selection, post-selection inference, nonparametric methods, and high-dimensional statistics. She has contributed extensively to methodologies like focused information criteria, penalized splines, and quantile regression. Awards and Honors: Notable accolades include Fellow of the American Statistical Association (2019), Goodeve Medal (2017), and Medallion Lecturer (2016). She has also held editorial roles in top journals such as Biometrika and Journal of the American Statistical Association . Teaching and Mentoring: She has advised over 20 PhD students and mentored postdoctoral researchers. Her teaching spans advanced statistical methods, probability theory, and business statistics. She has delivered invited lectures globally, including at the European Meeting of Statisticians and the International Society for NonParametric Statistics. Key Contributions: Her book Model Selection and Model Averaging (2008) is a seminal work in statistical methodology. Her research addresses challenges in high-dimensional data, survival analysis, and model averaging, with applications in finance, insurance, and biostatistics.

Joseph AntonelliView profile

Assistant Professor

• Causal Inference
• High-Dimensional Modeling
• Bayesian Methods
• +3 more

Joseph Antonelli is an Assistant Professor of Statistics at the University of Florida , where he has been since 2018. He holds a PhD in Biostatistics from Harvard University (2015) and completed postdoctoral training there until 2018. His research focuses on causal inference, high-dimensional modeling, Bayesian methods, spatial statistics, and applications in environmental health and criminology. Education: B.S. in Statistics, University of Florida (2011) M.S. in Biostatistics, Harvard University (2013) Ph.D. in Biostatistics, Harvard University (2015) Research Interests: Professor Antonelli develops statistical methods to address complex causal questions in health and social sciences. His work emphasizes robust approaches to confounding adjustment, spatial and environmental data analysis, and policy evaluation. He applies these methods to study air pollution effects, policing policies, and opioid policy impacts, bridging theoretical advancements with real-world applications. Key Contributions: His articles span causal inference techniques, Bayesian modeling, and spatial analysis, with recent work addressing racial bias in policing and air pollution mixtures. His research has been recognized with awards from the Health Effects Institute, JSM Biometrics, and ISBA. Awards: 2020 Health Effects Institute Young Investigator Award 2020 Journal of Speech, Language, and Hearing Research Editors Award 2014 ENAR Distinguished Student Paper Award Advising and Grants: He advises over 15 graduate students and has led grants totaling $3.5M, including NIH funding for auditory deficits in children and CDC studies on firearm policy effects. He also serves as Associate Editor for Bayesian Analysis .

Stéphane BonhommeView profile

Professor

• Microeconometrics
• Econometric Theory
• Labour Economics
• +3 more

Stéphane Bonhomme is the Ann L. and Lawrence B. Buttenwieser Professor of Economics and the College at the University of Chicago's Kenneth C. Griffin Department of Economics. His research focuses on microeconometrics, econometric theory, and labor economics, with emphasis on latent variable modeling and panel data analysis. He holds a PhD from the University of Paris I, Panthéon-Sorbonne (2005). Key contributions include methodologies for handling unobserved heterogeneity in panel data, nonlinear persistence in consumption dynamics, and bias reduction in econometric models. His work has been published in top journals like Econometrica, Journal of Econometrics, and the Journal of Political Economy. Recent research explores grouped patterns of heterogeneity, firm-worker sorting effects, and functional differencing in networks. He is a Fellow of the Econometric Society (2017) and has developed widely used Stata/Python packages for bias correction and discrete heterogeneity estimation.

Mauricio Garnier VillarrealView profile

Assistant Professor

• Latent Variable Models
• Bayesian Statistics
• Social Inequality
• +3 more

Dr. Mauricio Garnier Villarreal is an Assistant Professor at the Faculty of Social Sciences, Department of Sociology, and a member of the Social Inequality and the Life Course (SILC) research group at Vrije Universiteit Amsterdam. His work focuses on developing and testing data analysis methods, particularly latent variable models and Bayesian inference, with applications in sociology, education, nursing, and psychology. He emphasizes open science practices, contributing to R packages and sharing materials on GitHub and OSF. Research interests include methodological advancements in latent variable models (SEM, IRT, mixture models), Bayesian statistics, and their application to social inequality, especially intersectionality theory. Collaborations span aging, special education, and health sciences. He leads or participates in projects like DYNANSE (ERC grant) and COMBINE, addressing non-standard employment dynamics and latent variable methodologies. Recent articles explore measurement error impacts on clustering, hidden Markov models for employment mobility, and Bayesian SEM model evaluation. His work bridges methodological innovation with applied research to address societal issues.

Pengfei LiView profile

Professor

• Finite Mixture Models
• Empirical Likelihood
• Experimental Design
• +5 more

Roles & Affiliations Professor at Department of Statistics and Actuarial Science , University of Waterloo since 2012. Serves as Associate Editor for Canadian Journal of Statistics and Metrika . Previously held roles at University of Alberta (2008-2011) and University of British Columbia (2008 postdoc). Education PhD in Statistics (2007) - University of Waterloo MA in Statistics (2004) - Nankai University BSc in Statistics (2001) - Nankai University Research Focus Specializes in statistical methodologies including finite mixture models, empirical likelihood, experimental design, biased sampling problems, capture-recapture data, and density ratio models. Current interests include statistical inference under constraints and analysis of non-probability survey samples. Awards CRM-SSC Prize in Statistics (2022) Institute of Mathematical Statistics Fellow (2022) Multiple awards for teaching and research excellence from University of Waterloo Students & Grants Advised over 10 PhD/Master’s students. Active grants include NSERC funding since 2009 and startup grants from University of Waterloo. Supervises postdocs in statistical methodology development.

Michael P. TwohigView profile

Professor

• Clinical Psychology
• Behavioral Therapy
• Anxiety Disorders
• +7 more

Michael P. Twohig is a Professor in the Combined Clinical/Counseling PhD specialization at Utah State University's Department of Psychology within the Emma Eccles Jones College of Education and Human Services. He specializes in developing evidence-based treatments for anxiety disorders, OCD, and body-focused repetitive behaviors like trichotillomania. His work emphasizes treatment mechanisms and behavioral approaches such as Acceptance and Commitment Therapy (ACT). Twohig holds degrees from the University of Wisconsin-Milwaukee (BA, MS) and the University of Nevada, Reno (PhD), with clinical training at the University of British Columbia Hospital. He co-directs the USU ACT Research Group, focusing on translational research and clinical innovation. Education: BA and MS in Behavior Analysis (UW-Milwaukee), PhD in Clinical Psychology (UNR), Clinical Internship (UBC Hospital). Research Interests: Treatment efficacy evaluation, mechanisms of action in therapies, ACT applications for anxiety/OCD, multicultural mental health, and pediatric hearing aid management. He explores interventions for eating disorders, substance abuse, and developmental disabilities through behavioral and cognitive frameworks. Publications and Grants: Over 50 peer-reviewed articles (2021–2025), books on ACT, and grants supporting telehealth and digital interventions. His work has been cited in systematic reviews and clinical handbooks. Labs/Teams: Co-leads the USU ACT Research Group, advancing ACT-based treatments and training clinicians globally. He mentors students via a 'junior colleague' approach, fostering collaborative research. Future Work: Planning to accept a new graduate student for Fall 2025, expanding ACT applications for chronic health conditions and cross-cultural mental health.

Dario GranaView profile

Professor

• Rock Physics
• Seismic Reservoir Characterization
• Inverse Problems
• +1 more

Dr. Dario Grana is the Wyoming Excellence Chair and Professor at the University of Wyoming's Department of Geology and Geophysics. His research focuses on rock physics, seismic reservoir characterization, inverse problems, and geostatistics, with applications in critical zone studies, CO2 sequestration monitoring, and subsurface characterization. Education includes: PhD and MS in Geophysics, Stanford University (2013) MS in Applied Mathematics, University of Milano-Bicocca (2006) BS and MS in Mathematics, University of Pavia (2003, 2005) Research explores rock physics modeling in critical zones, geophysical monitoring of CO2 storage, joint seismic-electromagnetic inversion, and stochastic methods for subsurface characterization. Recent publications emphasize machine learning integration with geostatistical inversion and Bayesian methods for uncertainty quantification. Teaching includes Quantitative Geoscience, Mathematical Methods for Geoscience, Exploration Geoscience, Inverse Theory, and Rock Physics.

Mohamed ElshaerView profile

Researcher

• Pavement Engineering
• Sustainable Infrastructure
• Transportation Engineering
• +3 more

Mohamed Elshaer is a Researcher and Research Assistant Professor in the Department of Civil and Environmental Engineering at the University of New Hampshire, part of the College of Engineering and Physical Sciences. His research focuses on climate-resilient pavement systems, emphasizing sustainable solutions for infrastructure durability under environmental stressors. He collaborates on adaptive management strategies involving advanced materials testing and predictive modeling. Elshaer teaches courses such as CEE 736/836 (Asphalt Mixtures & Construction) and CEE 737/837 (Pavement Rehabilitation and Management), reflecting his expertise in transportation engineering. His research interests include pavements, recycling practices, and innovative material applications. His publications highlight work on electrically conductive asphalt for deicing, fiber-reinforced asphalt durability, and predictive modeling of pavement performance. Recent studies address low-temperature cracking in asphalt and the impact of mixing methods on material consistency. Elshaer’s work bridges environmental and civil engineering, addressing challenges like sea-level rise effects on coastal infrastructure and moisture-induced pavement degradation. His contributions advance both theoretical frameworks and practical applications in sustainable infrastructure development.

Ryan Qi WangView profile

Assistant Professor

• Urban Informatics
• Human Mobility
• Resilience Engineering
• +2 more

Ryan Qi Wang is an Assistant Professor of Civil and Environmental Engineering at Northeastern University, jointly appointed with the College of Social Sciences and Humanities. He serves as Associate Research Director for Social Media at the Boston Area Research Initiative (BARI), investigating urban mobility patterns, disaster resilience, and geosocial networks. His research combines urban informatics with complex systems analysis to understand human movement during crises, urban inequalities, and sustainable city development. Recent publications examine mobility responses to environmental hazards, algorithmic biases in mobility models, and data-driven approaches to urban sustainability.

Stefan RadevView profile

Assistant Professor

• Bayesian Inference
• Deep Learning
• Probabilistic Modeling
• +3 more

Stefan Radev is an Assistant Professor in the Cognitive Science department at Rensselaer Polytechnic Institute . His work focuses on developing Bayesian methods with generative neural networks and computational models for complex systems like cognition and disease outbreaks. He is the core developer of the BayesFlow framework, which enables amortized Bayesian inference using deep learning. Radev's research addresses computational challenges in Bayesian workflows, such as rapid parameter estimation and model validation through neural networks. His primary research interests include Deep Learning , Probabilistic Modeling , and their applications in computational neuroscience and biomedical engineering. He collaborates with the Center for Modeling, Simulation and Imaging in Medicine (CEMSIM) and contributes to open-source projects like BayesFlow, which supports multi-backend frameworks (PyTorch/TensorFlow/JAX). Recent publications highlight advancements in amortized inference, simulation-based calibration, and robust Bayesian workflows. His work spans domains from cognitive modeling to biomedical imaging, emphasizing interdisciplinary applications of Bayesian methods. No scientific awards are explicitly listed, though his contributions to open-source tools and impactful research indicate significant academic recognition.