Find Academic Supervisors

• Machine Learning
• Neuroscience
• Biomedical Engineering
• +3 more

Julien Audiffren is a Lecturer and Senior Researcher in the Department of Informatics at the University of Fribourg. His research focuses on machine learning applications in biomedical engineering, human perception, and data analysis. He holds roles in both education and research, contributing to interdisciplinary projects in informatics and health sciences. His research interests span machine learning methodologies, including multi-label classification, adaptive algorithms for perception quantification, and applications in sports science and healthcare. He has published extensively on topics such as postural control, fall prediction, and virtual reality training systems. Recent work emphasizes the development of robust adaptive procedures for threshold estimation in neuroscience experiments and the use of large language models for semi-structured data cleaning. His contributions bridge computational methods with practical applications in health monitoring and sports performance analysis. No scientific awards or grants are explicitly mentioned in the provided information. He is affiliated with the Interfaculty Informatics Department and collaborates on projects involving wearable sensors and AI-driven health solutions.

Xiaoyu ChenView profile

Assistant Professor

• Machine Learning
• Advanced Manufacturing
• Cyber Manufacturing Systems
• +5 more

Xiaoyu Chen serves as an Assistant Professor in the Department of Industrial and Systems Engineering within the School of Engineering and Applied Sciences at the University at Buffalo. His academic foundation includes a PhD in Industrial and Systems Engineering (2021) and an MEng in Computer Science (2021) from Virginia Tech, complemented by a BS in Optical Information Science and Technology from Beijing Institute of Technology (2015). Dr. Chen's research spans machine learning methodology for advanced manufacturing, networked data analytics in cyber manufacturing systems, statistical methods for Bayesian deep learning, smart connected health applications for organ transplantation and perioperative medicine, and human behavior/performance modeling. His work bridges industrial engineering with cutting-edge AI techniques to solve complex problems in manufacturing and healthcare systems. Analysis of recent publications reveals a strong focus on developing interpretable and context-aware AI systems for manufacturing and clinical applications. Key trends include multimodal learning frameworks, distributed data processing for fog manufacturing, human-AI collaboration paradigms, and medical diagnostics through novel data modalities like breath analysis. Dr. Chen actively teaches core engineering courses including Probability & Statistics for Engineers (IE 360/560), Simulation (IE 541), and Predictive Analytics (Deep Learning) (IE 463/663), demonstrating commitment to both foundational and emerging topics in industrial engineering education.

• Bayesian Statistics
• Time Series Analysis
• Econometrics
• +3 more

Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.

Mamoon Al DeyabView profile

Senior Lecturer

• Antimicrobial Stewardship
• Clinical Pharmacy
• Pharmacoepidemiology
• +4 more

Dr. Mamoon Al Deyab is a Senior Lecturer in Clinical Pharmacy and Director of the International Clinical Practice Research Unit at the University of Huddersfield, UK. He holds adjunct roles including Adjunct Associate Professor at Jordan University of Science and Technology and Board Member of the Alliance for the Prudent Use of Antibiotics (APUA). His expertise spans clinical pharmacy, pharmacoepidemiology, and antimicrobial stewardship, with a focus on antibiotic resistance and healthcare policy development. Dr. Al Deyab is a registered pharmacist in the UK and Jordan, and his work has contributed to UN Sustainable Development Goals related to global health and innovation. Education includes a BSc in Pharmacy from Damascus University, MSc in Clinical Pharmacy from Strathclyde University, and a PhD in Clinical Pharmacy from Queen’s University Belfast. He also holds postgraduate certificates in epidemiology, pharmacovigilance, and higher education practice. Research interests include antibiotic use patterns, resistance mechanisms, and policy interventions to combat antimicrobial resistance. His work has led to over 95 peer-reviewed articles, book chapters, and global collaborations. Notable contributions include advising the World Health Organization on antimicrobial stewardship programs in the Middle East and establishing MOUs with institutions like Jordan FDA and EMPHNET. Dr. Al Deyab has received the UK Clinical Pharmacy Association Antimicrobial Management Award for his efforts in optimizing antibiotic use and reducing healthcare-acquired infections. He actively engages in international conferences, editorial roles (e.g., Antibiotics journal), and education initiatives to enhance pharmacy training and public health outcomes.

• Health Psychology
• Positive Psychology
• Sexual Behavior
• +4 more

Balázs András Varga is a Lecturer at the Department of Personality and Health Psychology, Eötvös Loránd University. He is affiliated with the Institute of Psychology and can be reached at varga.balazs@ppk.elte.hu. His office is Room 512 at 1064 Budapest, Izabella u. 46. His research interests span Health Psychology, Positive Psychology, Sexual Behavior, Adolescent Mental Health, Quantitative Methods, and Cultural Psychology. He focuses on topics such as school climate, well-being assessment, sexual response dynamics, and cultural adaptation across generations. His recent publications (2019–2025) reflect a focus on psychological well-being, sexual health, and cultural studies, with methodological contributions in quantitative research. Notable works include studies on flourishing measurement in Hungarian populations and the psychometric validation of the PERMA Well-Being Profile. No scientific awards are explicitly mentioned in the provided texts. Details on advising students or grants are not available. No specific laboratory or team affiliations are noted.

• Survival Analysis
• Biostatistics
• Copula Models
• +3 more

Antai Wang is an Associate Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT), specializing in biostatistics and survival analysis. His research focuses on developing statistical methodologies for dependent data structures, particularly using Archimedean copula models and frailty models. He has secured funding from the National Science Foundation for projects like 'Analysis of Survival Data Using Copula Models' (2011-2015), which advanced methodologies for analyzing dependent competing risks and semi-competing risks data. Key research areas include survival analysis, copula models, and their applications in medical studies. Wang has contributed to studies on opioid prescribing patterns in trauma patients, breast cancer biomarkers, and post-traumatic seizure prophylaxis. His work bridges statistical theory with clinical applications, addressing real-world health challenges through rigorous quantitative methods. Notable collaborations include studies on tumor proliferation genes in ethnic patient groups and evaluating green tea extract effects on breast cancer biomarkers. Wang’s methodologies have been applied to improve understanding of disease progression and treatment efficacy, with publications in journals like Statistica Sinica and American Surgeon . His grants and projects reflect a commitment to advancing statistical tools for complex survival data, emphasizing identifiability of models and handling dependent censoring. While no formal awards are listed, his extensive publication record and federal funding underscore his impactful contributions to the field.

Andreas Heinrich HamelView profile

Professor

• Convex and variational analysis
• Optimization theory with set-valued objectives
• Mathematical finance (market models with transaction costs)
• +2 more

Andreas Heinrich Hamel is a Tenured Full Professor at the Faculty of Economics and Management , Free University of Bozen-Bolzano, Italy. His research focuses on advanced mathematical fields including convex and variational analysis, set-valued optimization, mathematical finance (market models with transaction costs), mathematical economics (incomplete preferences and game theory), and multivariate statistics (quantiles). Academic Rank: Professor Institution: Free University of Bozen-Bolzano School: Faculty of Economics and Management Professor Hamel co-developed the groundbreaking area of Set Optimization , with applications in mathematical finance, economics, statistics, game theory, and multi-criteria decision making. His work includes inventing set optimization approaches to risk measures for multivariate positions and developing optimization methods for economics and business applications. Recent publications highlight his expertise in set-valued risk measures, variational principles, and duality theories. His research spans topics such as cone distribution functions, multivariate shortfall risk measures, and utility maximization under transaction costs, reflecting interdisciplinary contributions to financial mathematics and optimization.

Maria BurnsView profile

Assistant Professor

• Data Science
• Network Analysis
• Security Studies
• +5 more

Maria Burns is an Assistant Professor in the Department of Construction Management/Political Science at the University of Houston’s College of Technology. Her research focuses on the intersection of data science, security, and logistics, with applications in energy, maritime, and homeland security sectors. She specializes in network analysis, AI-driven risk assessment, and policy analysis for critical infrastructure protection. Her work integrates multidisciplinary approaches including machine learning, time-series forecasting, and data visualization to address complex challenges such as human trafficking, cargo security, and port resilience. Key areas include supply chain vulnerability analysis, maritime security protocols, and the economic/environmental impacts of port activities. Burns has contributed to numerous DoS- and DHS-funded training manuals and courses, including 'Global Supply Chain Security' and 'UN Security Resolutions and WMD.' Her research also explores energy security paradoxes, intermodal transport risks, and crisis management strategies. Recent publications highlight innovative applications of AI in security technology and policy-driven solutions for modern security challenges. Her funded projects emphasize practical outcomes, such as optimizing port operations through 5G connectivity and enhancing border management through strategic convergence of trade and immigration policies. Burns’ work bridges academia and industry, offering actionable insights for public and private sector stakeholders.

Jürgen FrankeView profile

Professor

• Nonlinear Time Series Analysis
• Stochastic Processes
• Statistical Methodology
• +5 more

Prof. Dr. Jürgen Franke is a renowned academic in statistics and applied mathematics. He held the position of Professor C4 (Chair) for Applied Mathematical Statistics at Technische Universität Kaiserslautern from 1988 to 2017. Since 2017, he has been a Consultant Researcher at the Fraunhofer Institute for Industrial Mathematics (ITWM) in Kaiserslautern. His research focuses on nonlinear time series analysis, stochastic processes, and their applications in finance, risk management, and biomedical data analysis. Education: - Diplom in Mathematics, Goethe-Universität Frankfurt a.M., 1974. - PhD (Dr. phil.nat.) in Mathematics, Goethe-Universität Frankfurt a.M., 1980. - Habilitation in Mathematics, Goethe-Universität Frankfurt a.M., 1985. Research Interests: Franke's research emphasizes the development of statistical methodologies for nonlinear time series, spatial data, and stochastic processes. Key areas include local smoothing techniques (kernel and wavelet-based estimates), functional data analysis, resampling methods (bootstrap), and nonparametric approaches in machine learning. His applied work addresses challenges in finance, risk management, and biomedical data analysis where traditional methods may be insufficient. Advising and Grants: While specific grants are not detailed, Franke has collaborated extensively with researchers in applied mathematics and statistics, contributing to numerous projects. His work often intersects with interdisciplinary applications, reflecting a commitment to bridging theoretical and practical domains. Labs/Teams: Affiliated with the Department of Mathematics at TU Kaiserslautern and the Fraunhofer Institute for Industrial Mathematics (ITWM), where he focuses on applied statistical research and collaboration with industry.

Jörn SassView profile

Professor

• Mathematical Finance
• Stochastic Control
• Financial Mathematics
• +3 more

Prof. Dr. Jörn Sass is a Professor at the Department of Mathematics, University of Kaiserslautern since 2008, supported by a DFG-funded Heisenberg professorship. His research focuses on mathematical finance, stochastic control, and statistical methodologies, particularly in portfolio optimization under constraints such as transaction costs, partial information, and risk limits. He also works on option pricing and parameter estimation in financial models. Education: Ph.D. in Applied Mathematics (2001), University of Kiel M.Sc. Mathematics and M.Ed. in Mathematics/Physics (1998), University of Kiel Key Research Interests: Portfolio optimization with constraints, stochastic volatility models, hidden Markov models, robust utility maximization, and risk measures. His work bridges theoretical mathematics and practical applications in finance and engineering. Publications: Over 30 peer-reviewed articles in leading journals such as Finance and Stochastics , Mathematics and Financial Economics , and Quantitative Finance . Recent work emphasizes robust strategies and model uncertainty in financial markets. Professional Activities: Previously held positions at UBC Vancouver (Postdoc, 2001–2003), RICAM (Senior PostDoc, 2003–2008), and JKU Linz (External Lecturer). Active in academic leadership roles, including the Financial Mathematics Group at RPTU.

Colleen ReidView profile

Associate Professor

• Environmental Health
• Public Health
• Climate Change
• +3 more

Colleen Reid is an Associate Professor of Geography at the University of Colorado Boulder, specializing in the health impacts of environmental change. Her research focuses on interactions between environmental and social exposures, particularly wildfire smoke, extreme heat, and urban vegetation. She holds a PhD in Environmental Health Sciences from UC Berkeley and a MPH from the same institution, with a BS from Brown University. She has received grants from the EPA, NSF, CDC, and foundations like the Robert Wood Johnson Foundation. Education: PhD (UC Berkeley, 2014), MPH (UC Berkeley, 2007), BS (Brown University, 2000) Her research explores topics such as wildfire smoke effects on children’s health, health impacts of the Marshall Fire, and the mental health benefits of green spaces. Notable awards include the Provost’s Faculty Achievement Award (2021) and the AAG Emerging Scholar Award (2020). Current Projects: Indoor air quality post-wildfires, green space mental health linkages, and climate change health impacts Reid teaches courses on health and medical geography and global public health. She advises four graduate students and has held roles at Harvard’s Chan School of Public Health as a postdoctoral fellow.

Stefanie LiebeView profile

Physician Scientist

• Neuroscience
• Clinical Neuroscience
• Artificial Intelligence in Healthcare
• +3 more

Dr. Stefanie Liebe is a Principal Investigator at the Universitätsklinikum Tübingen and a Clinician Scientist affiliated with the Cluster of Excellence ‘Machine Learning for Science’. Her work bridges clinical and computational neuroscience, focusing on neural mechanisms of memory, perception, and epilepsy diagnostics using AI. She leads projects on theta-gamma oscillations in sequence memory, seizure behavior mapping, and spatial hearing modeling. Her team includes PhD students Meghal Dani, Muthu Jeyanthi Prakash, and Mohammed Raees MK. Collaborations span institutions like the Max Planck Institute and the University of Bonn. She holds grants from Else Kröner, DFG, and the MSCA CherISH network. Education: Psychology (TU Dresden), Neuroscience PhD (Max Planck), Medical training (Tübingen/Bonn). Her research integrates invasive neurophysiological recordings, recurrent neural networks, and clinical data analysis to advance diagnostic tools for epilepsy and cognitive neuroscience. Key Projects: Multimodal seizure analysis, spatial hearing computational models, neural oscillations in MTL. Funding: Else Kröner Graduierten Kolleg, DFG Priority Program ‘Sensing LOOPS’, MSCA CherISH.

Bagkavos DimitriosView profile

Associate Professor

• Mathematical Statistics
• Survival Analysis
• Probability Theory Applications
• +7 more

Bagkavos Dimitrios is an Associate Professor in the Department of Mathematics at the School of Science, University of Ioannina. His research focuses on Mathematical Statistics, Survival Analysis, and Probability Theory Applications, with expertise in nonparametric estimation, statistical inference, and kernel methods. He holds a B.Sc. in Mathematics from the University of Ioannina and an M.Sc. in Mathematics (Statistics) from the University of Birmingham, UK. His work emphasizes hypothesis testing, computational statistics, and applications of martingales and Edgeworth expansions. He has developed R packages such as NPHazardRate and asymmetry.measures , and his research has been published in top journals like Journal of Multivariate Analysis and Computational Statistics and Data Analysis . He actively contributes to the academic community by refereeing for journals including Test , Journal of Statistical Planning and Inference , and Communications in Statistics .

• Multivariate Geostatistics
• Environmental Data Analysis
• Space-Time Covariance Modeling
• +7 more

Monica Palma is an Associate Professor in Statistics at the University of Salento's Department of Economics. She holds a PhD in Statistics from the University of Chieti (2003) and a Degree in Economics from the University of Lecce (1996). Her research focuses on multivariate geostatistics for environmental data, space-time covariance modeling, stochastic simulation, and GIS/WebGIS applications for big data analysis. She is affiliated with the Centro Ecotekne laboratory and serves as a referee for journals like Stochastic Environmental Research & Risk Assessment and Environmental Science & Technology. Education: PhD in Statistics, University of Chieti, 2003 Master's in Economics, University of Lecce, 1996 Her work bridges statistical methodologies with environmental and socio-economic applications. She has authored 18 journal articles, 16 books, and numerous book chapters. Her research emphasizes interdisciplinary approaches to spatial-temporal data analysis and geostatistical modeling.

• Transportation Systems
• Behavioral Modeling
• Machine Learning
• +5 more

Rico Krueger is an Associate Professor at the Department of Technology, Management and Economics within the Transport Division at the Technical University of Denmark (DTU). His research focuses on developing models and technologies at the intersection of behavioral modeling, machine learning, and simulation to improve human-centric and sustainable transport systems. Key areas include travel demand forecasting, multimodal systems, and emerging technologies like virtual reality for understanding human decision-making. Education: Ph.D. in Civil and Environmental Engineering from UNSW Sydney (Australia), followed by postdoctoral research at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds a prestigious European Research Council Starting Grant (2024–2029) for his project IMMERSION , exploring human decision-making through choice and process data integration. Research emphasizes interdisciplinary approaches, combining data science with behavioral theories to address challenges in urban mobility, sustainability, and public health. His work bridges theoretical advancements with practical applications, such as optimizing policy interventions during pandemics and enhancing ride-sourcing systems. Awards: ERC Starting Grant for IMMERSION project. Grants: Focus on behavioral modeling and pandemic-related transport policies. Labs/Teams: Active in DTU’s Intelligent Transport Systems Section, leading interdisciplinary projects on mobility and health.