Find Academic Supervisors

• Marketing
• Economics
• Consumer Behavior
• +3 more

Chris Hand is a Professor of Marketing at the Department of Strategy, Marketing and Innovation, Kingston University London, within the Faculty of Business and Social Sciences. He leads the Customer Insights Research Hub and has held academic leadership roles including Course Director for BSc International Business and BBA programs. His academic background includes a PhD in Economics from the University of Portsmouth (2001), with prior research in Television Audience Statistics at Bournemouth Media School and Royal Holloway. Education BA(Hons) Economics MSc Business Economics MA Strategic Marketing Management PhD Economics (Thesis: Empirical Studies of the Demand for Cinema in the UK) PgC Learning and Teaching in Higher Education Research Interests Professor Hand’s work bridges economics and marketing, focusing on quantitative analysis of consumer behavior, entrepreneurship, and multichannel retailing. His research applies econometric methods to study self-employment impacts, digital media effects, and arts consumption patterns. Recent studies explore cyber aggression, random forest accuracy in modeling, and spatial influences on well-being. Publications Trends His 2020s research emphasizes entrepreneurship dynamics (self-employment well-being links), digital consumer behavior (social media compulsion effects), and spatial economics (regional convergence theories). Earlier work includes foundational studies on cinema demand modeling and arts audience behavior. Awards Fellow of the Higher Education Academy Certified Member of the Market Research Society Leadership & Impact He has directed Kingston’s MRes Business and Management doctoral training program and contributed to curriculum development in strategic marketing. His research actively informs policy discussions on entrepreneurship and cultural economics through applied quantitative frameworks. Research Hub As head of the Customer Insights Research Hub, he fosters interdisciplinary work on consumer decision-making and data-driven marketing strategies.

• Biostatistics
• Statistical Methodology
• Medical Statistics
• +3 more

Frank Schaarschmidt is a researcher affiliated with the Department of Biostatistics and the Institute of Cell Biology and Biophysics at Leibniz University Hannover within the Faculty of Natural Sciences. His work focuses on developing and applying advanced statistical methodologies across diverse fields such as biomedicine, ecology, agriculture, and molecular biology. Key research interests include statistical inference techniques, prediction modeling for overdispersed data, and the design of experiments. He has contributed extensively to methodologies for simultaneous confidence intervals, multiple testing adjustments, and applications in clinical trials and environmental risk assessment. Collaborations span disciplines from cell biology to soil science, reflecting a strong interdisciplinary approach. Publications highlight his expertise in statistical methods for medical monitoring, ecological modeling, and molecular biology. Notable contributions include work on hydrogel platforms for stem cell research and studies on pyrimidine metabolism in plants. His research often bridges theoretical statistical developments with practical applications in health sciences and environmental sustainability. Frank Schaarschmidt holds the title of PD Dr. (Privatdozent) and has been actively involved in academic management within the Department of Biostatistics.

Wei LongView profile

Associate Professor

• Economics of Crime
• Financial Econometrics
• Econometrics
• +2 more

Wei Long is an Associate Professor and Director of Undergraduate Studies in the Department of Economics at Tulane University, within the School of Liberal Arts. He holds a MA in Statistics from Columbia University (2010) and a PhD in Economics from Texas A&M University (2015). His research focuses on applied economics with concentrations in the economics of crime and financial econometrics, emphasizing econometric methodologies such as quantile regression, copula models, and panel data analysis. His work has been published in top-tier journals like the Journal of Econometrics and Journal of the American Statistical Association. Key research areas include evaluating police effectiveness, crime deterrence strategies, financial market predictability, and the impact of policy reforms. He has contributed to understanding how oversight mechanisms influence policing outcomes and how socioeconomic factors shape criminal behavior. His recent studies explore machine learning applications in panel data models and privacy-preserving quantile regression techniques for large datasets. Despite not listing explicit scientific awards, his prolific publication record and academic roles reflect significant scholarly contributions. His advising and grants are not detailed here, but his research spans interdisciplinary topics such as income inequality in Latin America, stock market bubbles, and the effects of highly publicized police incidents on community policing strategies.

Timothy HavensView profile

Professor

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +6 more

Timothy Havens is a Professor at Michigan Technological University in the Department of Computer Science within the College of Computing. He holds the William and Gloria Jackson Professorship and serves as Executive Director of both the Great Lakes Research Center and the Institute of Computing and Cybersystems. Dr. Havens also directs the PRIME Lab and has been recognized for both teaching and research excellence, including the 2014-15 Professor of the Year award from IEEE Eta Kappa Nu and Best Paper Awards at FUZZ-IEEE 2012 and IEEE SMC 2011. Dr. Havens received his Ph.D. in Electrical and Computer Engineering from the University of Missouri, Columbia in 2010. Prior to joining Michigan Tech, he was an NSF/CRA Computing Innovation Postdoctoral Fellow at Michigan State University under Dr. Anil Jain. Before his Ph.D. work, he was an Associate Technical Staff member at MIT Lincoln Laboratory. His educational background includes an M.S. in Electrical Engineering (2000) and a B.S. in Electrical Engineering (1999), both from Michigan Technological University. His research focuses on pattern recognition and machine learning, signal processing, and sensor fusion, with specific expertise in fuzzy integrals, Choquet integration, community detection in networks, and heterogeneous data mining. Dr. Havens has made significant contributions to explainable AI, particularly through the application of fuzzy integrals to deep learning systems. His recent publications demonstrate strong activity in developing novel regularization techniques for fuzzy Choquet integrals, efficient algorithms for community detection, and similarity measures for interval data. Dr. Havens' research has been consistently funded by prestigious organizations including DARPA, NSF, US Navy, Office of Naval Research, National Geospatial-Intelligence Agency, Ford Motor Company, MIT Lincoln Laboratory, and numerous other government and industry partners. His current projects include significant grants for radar systems in the Great Lakes, generative modeling of satellite imagery, and algorithms for autonomous robot systems. Scientific Awards: 2014-15 Professor of the Year by IEEE Eta Kappa Nu, Beta Gamma Chapter Best Paper Award at FUZZ-IEEE 2012 Best Paper Award at IEEE SMC 2011 Best Student Paper Award Finalist (2018) IEEE Franklin V. Taylor Memorial Best Paper Award (2011) Dr. Havens has successfully mentored numerous graduate students, many of whom appear as co-authors on his publications. His research grants demonstrate strong leadership in coordinating multi-institutional collaborations with substantial funding. Beyond his academic work, Dr. Havens is also an active musician, playing bass in several bands including MUFJAC, FLOTUS, Defenestra, Skills of Ortega, Psylocubik, Triptych, Wheels of Fire, and Odibil libidO.

Hodjat ShiriView profile

Associate Professor

• Offshore Engineering
• Subsea Pipelines
• Arctic Geotechnics
• +2 more

Dr. Hodjat Shiri is an Associate Professor in Civil Engineering at Memorial University of Newfoundland, holding the Wood Group Chair in Arctic and Harsh Environments. His research addresses Arctic offshore challenges, including ice-seabed interactions, subsea pipeline design, and reliability assessment. Shiri integrates machine learning with geotechnical modeling to predict iceberg impacts and pipeline performance. Recent work explores trenching techniques, layered seabed responses, and fatigue in riser systems. Publications emphasize practical solutions for energy infrastructure in climate-sensitive regions, combining numerical simulations with field data validation.

• Biomedical Informatics
• Artificial Intelligence in Healthcare
• Machine Learning for Genomics
• +3 more

Dr. V.B. S. Prasath is a Professor in the Department of Biomedical Informatics at the University of Cincinnati and leads the Prasath Lab at Cincinnati Children's Hospital Medical Center (CCHMC). His research focuses on AI/ML-driven biomedical informatics, integrating genomics, imaging, and clinical data to address pediatric healthcare challenges. The lab collaborates with multiple institutions, including the Center for Pediatric Genomics (CPG) and the National Institutes of Health (NIH). Key research areas include: Medical imaging analysis (histopathology, MRI, ultrasound) Single-cell genomics (ATAC-seq, scRNA-seq, spatial transcriptomics) Multimodal data integration (scTriangulate framework) Antimicrobial resistance prediction (MGS2AMR) Recent work includes developing tools like maxATAC for transcription factor binding prediction and DeepImmuno for immunogenic peptide analysis. The lab has secured grants from NIH, Cystic Fibrosis Foundation, and Crohn's & Colitis Foundation.

Zhen LiuView profile

Adjunct Associate Professor

• Geotechnical Engineering
• Intelligent Infrastructure
• Multiphysics Modeling
• +3 more

Zhen Liu is an Adjunct Associate Professor in the Department of Civil, Environmental, and Geospatial Engineering at Michigan Technological University. His research integrates multiphysics modeling, AI, and geotechnical engineering for intelligent infrastructure and system resilience. Teaching includes soil mechanics, foundation engineering, AI applications, and numerical simulations. Recent publications focus on machine learning in geosystems, pavement management, and computational methods.

Krzysztof PytkaView profile

Assistant Professor

• Macroeconomics
• Consumption
• Search Theory
• +2 more

Krzysztof Pytka is an Assistant Professor of Quantitative Macroeconomics at the University of Mannheim's Department of Economics since 2018. He holds a Ph.D. in Economics from the European University Institute (2017). His research focuses on household consumption dynamics, search theory, and computational economics, leveraging large microeconomic datasets and machine learning techniques. Current projects examine retail market frictions, post-job displacement earnings losses, and the macroeconomic implications of consumer behavior. Education: Ph.D. in Economics (2017) - European University Institute, Florence; Earlier degrees not specified. Research Areas: Macroeconomic modeling of heterogeneous agents Consumption patterns using scanner data Machine learning applications in structural econometrics Price dispersion mechanisms Papers emphasize empirical validation of theoretical models using novel datasets. Recent work on Covid-19 labor market impacts has been published in CEPR's policy-oriented journal. Teaching includes Ph.D.-level courses on quantitative macroeconomics and machine learning applications. Advising/Grants: No formal advisees listed in current materials. Research collaborations with Daniel Runge and Andreas Gulyas.

Sastry PamidiView profile

Professor

• Superconductivity
• Power Systems Engineering
• Applied Cryogenics
• +6 more

Sastry Pamidi is the Chair Professor of Electrical & Computer Engineering at Florida State University (FSU), serving as Associate Director of the Center for Advanced Power Systems (CAPS). He holds an MBA in Financial Management from FSU and a Ph.D. in Materials Chemistry from the University of Bombay. His research focuses on superconducting power systems, applied cryogenics, and advanced electrical insulation systems. Pamidi leads a multidisciplinary team at CAPS, collaborating with industry partners to develop superconducting devices and cryogenic technologies. His academic career spans over three decades, including roles at the National High Magnetic Field Laboratory and the University of Aberdeen. Pamidi is a Senior Member of IEEE and holds a Project Management Professional (PMP) certification. He has published 150 peer-reviewed papers and contributed to book chapters on superconducting power grid applications. Key projects include gaseous helium-cooled superconducting cables, fault current limiters, and cryogenically insulated power systems for electric aircraft and ships. Research interests emphasize AC loss measurements, novel insulation materials, and cryogenic thermal management. Pamidi’s work addresses challenges in electric transportation, energy storage, and high-power systems. His team explores hybrid cryogenic systems, additive manufacturing for insulation components, and fault detection using machine learning. Honors include sustained leadership in applied superconductivity through editorial roles and professional development initiatives. Current projects aim to enhance superconducting cable resilience, optimize cryostat designs, and develop HTS-based solutions for all-electric naval vessels and aviation systems.

• Machine Learning
• Bioinformatics
• Data Mining
• +3 more

Vincent Botta is a researcher at the University of Liège, Belgium, specializing in bioinformatics and machine learning. His work bridges computational methods with genetic studies, particularly focusing on Genome-Wide Association Studies (GWAS) using Random Forest algorithms. He holds a PhD in Computer Science (2008-2013), a Master of Advanced Studies in Computer Science (2006-2008), and a Master in Computer Science (2002-2006) from the same institution. Education : PhD in Computer Science, Master of Advanced Studies, and Master in Computer Science from the University of Liège. Experience : Data Scientist at Kensu.io (2016-present), Cytomine.be (2014-present), University of Liège (2006-present), and former roles at Diagenode (2015-2016) and Antibody-Adviser.org (2011-present). His research applies machine learning to genetic data, with publications analyzing SNP correlations, haplotype blocks, and supervised learning for disease risk prediction. Later works extend to pharmacological applications in asthma models. His technical skills include Python, C++, and data mining, while personal interests span web technologies, photography, and visual communication. Vincent contributes to open-source projects like Cytomine.be and co-founded Antibody-Adviser.org. His publications highlight interdisciplinary work between computer science and biomedical research, though no scientific awards are explicitly mentioned.

Russ SchumacherView profile

Professor

• Mesoscale Meteorology
• Extreme Precipitation
• Flash Floods
• +5 more

Russ Schumacher is a Professor in the Department of Atmospheric Science at Colorado State University (CSU) and serves as Colorado State Climatologist and director of the Colorado Climate Center. He holds a B.S. in Meteorology and Humanities from Valparaiso University (2001), and M.S. (2003) and Ph.D. (2008) in Atmospheric Science from CSU. His career includes a postdoctoral fellowship at NCAR (2008–09), an Assistant Professorship at Texas A&M University (2009–11), and promotion to Professor at CSU in July 2022. He currently edits the Monthly Weather Review and leads research on mesoscale meteorology, precipitation extremes, and societal impacts of weather. Research focuses on mesoscale convective systems, flash floods, and climate change in Colorado. His work is funded by NSF, NASA, NOAA, and COMET. Awards include the NSF CAREER Award (2010), AMS Clarence Leroy Meisinger Award (2020), and the 2012 Outstanding Professor of the Year. He advises numerous students, including Ph.D. recipients Allie Mazurek (2024), Eric James (2023), and Casey Zoellick (2025), and leads the Precipitation Systems Research Group. His team develops tools like the CSU-Machine Learning Probabilities (MLP) forecasting system, highlighted for operational weather prediction advancements. As State Climatologist, Schumacher oversees climate data for Colorado, including record-keeping and public outreach. His lab collaborations span international field campaigns (e.g., RELAMPAGO in Argentina) and interdisciplinary projects on wildfire meteorology (WE-CAN). Teaching interests include mesoscale meteorology, severe weather, and numerical forecasting.

Georg MayrView profile

Associate Professor

• Atmospheric Dynamics
• Lightning Prediction
• Climate Modeling
• +3 more

Dr. Georg Mayr is an Associate Professor in the Department of Atmospheric and Cryospheric Sciences (ACINN) at the University of Innsbruck. His research focuses on atmospheric dynamics, lightning prediction, foehn wind patterns, and climate modeling. He leads projects such as LightningPredict and Profcast , addressing environmental risks like upward lightning at wind turbines and atmospheric deserts' impact on extreme weather events. Mayr's work integrates machine learning and statistical methods for probabilistic forecasting, including lightning processes and thunderstorm environments. He has co-authored over 50 peer-reviewed articles since 2016, emphasizing interdisciplinary approaches to climate and meteorological challenges. His team collaborates on tools like foehnix for scalable diagnostics and Cholesky-based regression models for multivariate data analysis. Key contributions include long-term foehn wind reconstructions, spatial lightning climatologies, and risk assessments for tall structures. Mayr's research supports practical applications in renewable energy safety and airport low-visibility forecasts. His lab, ACINN, fosters collaboration across atmospheric science and cryospheric studies.

Valerie KingView profile

Professor

• Graph Algorithms
• Randomized Algorithms
• Probabilistic Analysis
• +3 more

Valerie King is a Professor in the Department of Computer Science at the University of Victoria, Canada. Her research focuses on graph algorithms, randomized algorithms, probabilistic analysis, and fault-tolerant distributed computing. She holds a PhD from UC Berkeley. Her work emphasizes scalable and secure distributed systems, with contributions to consensus protocols, network algorithms, and adversarial resilience. Dr. King's research bridges theoretical foundations and practical applications in computing systems. Education: PhD in Computer Science from UC Berkeley. Research Interests: Graph algorithms, randomized algorithms, fault-tolerant distributed computing, and algorithmic defense mechanisms against cyberattacks. Her work often addresses challenges in network security, consensus protocols, and efficient distributed algorithms. Key research trends in her articles include Byzantine agreement protocols, communication-efficient distributed algorithms, and sublinear-time graph algorithms. She explores geometric network models, secure multi-party computation, and energy-efficient communication strategies. Her contributions span algorithm design, complexity analysis, and practical implementations in adversarial environments. Advising and Grants: While specific student names or grant details are not listed here, her extensive publication record reflects sustained research activity in distributed systems and algorithms. She contributes to foundational research in theoretical computer science with applications to real-world distributed networks. Labs/Teams: Affiliated with the Computer Science department at UVic, collaborating on projects related to distributed computing and algorithmic security. Her work often involves interdisciplinary approaches to network resilience and computational efficiency.

Konrad AbramowiczView profile

Associate Professor

• Functional Data Analysis
• Statistical Methods in Biomechanics
• Climate Research
• +4 more

Konrad Abramowicz is an Associate Professor and Head of the Department of Mathematics and Mathematical Statistics at Umeå University. His research focuses on functional data analysis, classification, and local inference procedures in the presence of misalignment, with applications in biomechanics, climate research, and forest growth analysis. He teaches stochastic simulations and parametric modeling methods, emphasizing student development and curiosity-driven learning. Education: University of Wrocław (Poland), with Erasmus exchange to Umeå University Research: Functional data analysis, interdisciplinary collaborations in biomechanics and climate science Awards: Teaching awards for dedication to student mentorship Internationalization: Postdoctoral work in Australia; collaborations with Italy and India; faculty-level international contact Konrad's research spans functional data analysis applied to knee kinematics after ACL injuries, climate reconstruction using sediment data, and statistical methods for forest growth analysis. He developed computational tools like the fiberLD R package for fiber length determination in forestry studies. His recent publications highlight interdisciplinary applications of statistical methods, with a focus on movement pattern analysis, climate modeling, and non-destructive forest assessment. Konrad advocates for international collaboration and adaptive academic practices to address global challenges. Konrad has received teaching awards and is recognized for his supportive approach to students. He actively participates in university initiatives for international student integration and served as international coordinator for academic exchange programs. As a key member of the IceLab interdisciplinary research hub, Konrad bridges statistical methods with applications in biomechanics and environmental science. His work emphasizes the importance of mathematical abstraction in solving real-world problems.

Mélina MailhotView profile

Associate Professor

• Actuarial Science
• Risk Theory
• Dependence Modeling
• +2 more

Mélina Mailhot serves as an Associate Professor in the Department of Mathematics and Statistics at Concordia University, specializing in quantitative risk analysis for insurance and financial applications. Her expertise bridges actuarial science, statistics, and climate-related risk modeling. Education: Ph.D., Université Laval, Canada (2012) Research Interests: Professor Mailhot's work focuses on Actuarial Science, Risk Theory, Dependence Modeling, Risk Measures, and Optimization. She develops advanced methodologies for multivariate risk assessment, particularly for extreme events like natural catastrophes. Her research integrates copula theory, extreme value analysis, and machine learning to model complex dependencies in insurance portfolios, with growing emphasis on climate-driven risks such as wildfires and extreme precipitation. Publication Trends: Her recent publications (2021-2025) demonstrate concentrated innovation in dynamic risk measurement and spatial modeling. Key contributions include Bayesian approaches for model uncertainty quantification, multivariate tail-value-at-risk frameworks, and machine learning applications (e.g., Random Forests) for wildfire risk assessment. A significant trend involves translating climate science into actuarial practice, evidenced by spatial interpolation models for extreme rainfall and surge prediction systems using sparse data. Advising and Professional Engagement: She actively mentors graduate researchers including: C. Araiza I. (Tweedie double GLM loss triangles) N. Beck (multivariate extreme expectiles and spatial modeling) B. Kchouk (reciprocal reinsurance treaties) Professor Mailhot maintains strong academic visibility through conferences like the Statistical Society of Canada meetings and international actuarial forums, while also engaging public discourse via Radio Canada appearances on climate-insurance intersections and STEM diversity panels.