Find Academic Supervisors

• Wildlife Ecology
• Movement Ecology
• Spatial Ecology
• +3 more

Dr. Kevin Morelle is a Researcher at the Max Planck Institute of Animal Behavior, Department of Migration, located in Radolfzell, Germany. His academic background includes a bio-engineering background in forest and nature management, followed by an MSc in wildlife ecology (focusing on post-fire habitat use of red deer) and a PhD on wild boar movement ecology across scales. He leads the Research Group Safi and has over a decade of experience in wild boar spatial ecology research. Key research interests include African Swine Fever (ASF) epidemiology in wild boar populations, movement ecology, and innovative wildlife monitoring technologies. He co-founded the Euroboar network to advance wild boar ecological knowledge and currently works on the Horizon 2020 VACDIVA project to study ASF spread mechanisms. Collaborating with IT engineers, he develops alternative ear-tagging devices for wild boar monitoring. Publications highlight his work on ASF transmission dynamics, movement syndromes, and spatial risk modeling. His research bridges ecology, technology, and public health, addressing critical issues in wildlife disease control and conservation.

• Signal Processing
• Wavelet Analysis
• Image Processing
• +3 more

Prof. Dr. Ing. Alexandru Isar is a Full Professor at the Department of Communications, Faculty of Electronics and Telecommunications, Politehnica University of Timișoara. He holds academic ranks since 1990 (Adjunct Professor), 1995 (Associate Professor), and 1999 (Full Professor). His research focuses on signal processing, wavelet analysis, and medical imaging. He has advised 5 Ph.D. students and currently supervises 2 doctoral candidates. Isar has published extensively in IEEE journals and authored books on time-frequency representations and network security. **Education**: Ph.D. in Engineering (1993), supervised by Prof. Eugen Pop. Completed postdoctoral research at Telecom Bretagne, France (2003). **Grants**: European Space Agency-funded projects (2013-2017) and national grants (IDEI 2009-2011). **Research Directions**: Introduced time-frequency representations and wavelet theory applications. **Notable Contributions**: Inventions in ultrasonic control systems (1987), development of the Hyperanalytic Wavelet Transform (2008), and SAR/SONAR image denoising algorithms. **Administrative Roles**: Deputy Dean (2001-2004), Department Director (2012-2020). **Collaborations**: European Space Agency projects, international conferences (Brest, Bordeaux), and peer reviews for IEEE journals. His work bridges theoretical signal processing with practical applications in healthcare and telecommunications.

Dimitris FouskakisView profile

Professor

• Bayesian Statistics
• Objective Bayesian Methodology
• Stochastic Optimization
• +3 more

Dimitris Fouskakis is a Professor at the Department of Mathematics within the School of Applied Mathematical and Physical Sciences at the National Technical University of Athens (NTUA), where he also serves as the Director of the Statistics Lab. His research primarily focuses on Bayesian statistics, objective Bayesian methodology, and stochastic optimization. He has developed novel approaches for prior specification, variable selection, and model comparison in statistical modeling. Professor Fouskakis maintains active research interests in: Objective Bayesian methods and prior construction Bayesian variable selection and model averaging Stochastic optimization techniques High-dimensional data analysis Applications in educational statistics and public health His recent publications demonstrate a consistent focus on Bayesian methodology development, particularly in the areas of shrinkage priors, power-expected-posterior methods, and model comparison techniques. The research spans both theoretical developments and practical applications across multiple domains including education, public health, and computational statistics. Professor Fouskakis directs the Statistics Lab at NTUA, which serves as a hub for statistical research and collaboration. While specific grant and student advising information isn't provided in available texts, his leadership role indicates active engagement in research supervision and academic mentorship.

• Federated Learning
• Machine Learning
• Cybersecurity
• +3 more

Yan Xiao is an Adjunct Professor at the University of Illinois. His research focuses on federated learning, cybersecurity, and distributed systems, with an emphasis on adversarial robustness, privacy-preserving techniques, and optimization in machine learning frameworks. He has contributed to foundational tools like FedML and explores challenges in federated learning security, energy efficiency, and causal mechanisms. Key research interests include federated learning architectures, adversarial defense mechanisms, and the integration of reinforcement learning for automated feature selection. His work spans theoretical advancements (e.g., invariant aggregators for backdoor attacks) and practical applications (e.g., thermal management in HPC systems). Recent publications highlight trends in federated learning security, distributed optimization, and causal discovery. Notable contributions address straggler-free coreset-based learning and Bayesian-optimized energy efficiency in federated setups. No scientific awards or grants are explicitly mentioned in the provided texts. Advising details are not listed, though collaborations with research groups in federated learning and HPC are implied through publication topics.

Siwei LiuView profile

Associate Professor

• Research Methodology
• Developmental Psychology
• Health Psychology
• +4 more

Dr. Siwei Liu is an Associate Professor in the Department of Human Development & Family Studies at the University of California, Davis. Their research focuses on advanced statistical methods for analyzing longitudinal data in developmental and health psychology, with an emphasis on biobehavioral synchrony in close relationships and health disparities. They lead a lab investigating multi-layer contextual factors (e.g., environment, family characteristics) affecting health behaviors, prioritizing diversity in both research subjects and student recruitment. Research Interests : Longitudinal data methodologies: multilevel modeling, time series analysis, functional data analysis Biobehavioral synchrony in parent-child and couples relationships Health disparities, environmental justice, and stress physiology Intensive longitudinal methods in health research Key Projects : GROWell randomized controlled trial for dietary interventions in prenatal/postpartum populations Multimodal assessment of biobehavioral synchrony in autism spectrum disorder Analysis of family risk dynamics and parenting stress through cortisol contagion Labs/Teams : Directs a lab prioritizing underrepresented students, focusing on innovative methods for studying emotions, well-being, and family systems. Collaborates on NIH-funded projects involving wearable devices and ecological momentary assessments.

• Evolutionary Biology
• Genetics
• Bioinformatics
• +3 more

Rui Carlos Pinto Borges is a Lecturer in Statistics at the School of Mathematics and Statistics, University of St Andrews. His research focuses on evolutionary biology, population genetics, and computational methods, with particular expertise in Bayesian statistical modeling and genomic analysis. He contributes to the UN Sustainable Development Goals through his work in these fields. Education details are not explicitly provided in the text, but his academic role suggests advanced qualifications in statistics and genetics. His research interests include cladistics, allele analysis, evolutionary adaptation, and genomic sequencing. Notable contributions include developing Bayesian methodologies for estimating selection pressures (Bait-ER software) and effective population sizes, alongside analyzing mutation patterns in bacterial populations. His work has been published in journals like Methods in Ecology and Evolution and Molecular Biology and Evolution . He collaborates internationally on topics such as phylogenetic signal testing and genomic sequence analysis. Borges has created datasets like 'mutation_models' and 'Bait-ER v1.0' available on Zenodo, highlighting his commitment to open science.

• Bayesian Statistics
• Uncertainty Quantification
• Statistical Theory
• +3 more

Eduard Belitser is an Associate Professor in the Department of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam. His research focuses on Bayesian statistics, uncertainty quantification, and statistical theory, contributing to the UN Sustainable Development Goals. He currently teaches courses such as Experimental Design and Data Analysis , Statistical Models , and Statistics for AI . Belitser is actively involved in academic collaborations and has presented his work at international conferences and institutions. Belitser’s research interests include Bayesian methods, robust estimation, and applications to change point analysis, inverse problems, and variable selection. He explores topics like empirical Bayes procedures, optimality in estimation, and uncertainty quantification, with a focus on mathematical foundations and practical implications in statistical modeling and data analysis. Belitser's recent publications (2022–2025) emphasize Bayesian uncertainty quantification in change point detection, inverse problems, and robust statistical estimation. His work highlights methodological advancements, such as empirical Bayes approaches for sparse quantiles and optimality principles in variable selection and multiple testing frameworks, reflecting a blend of theoretical rigor and applied data analysis. No scientific awards or honors are explicitly mentioned in the provided information. In advising, he has supervised 3 PhD theses, though student names are not listed. His professional activities include editorial roles ( Statistica Neerlandica ), visiting research at North Carolina State University (March–April 2024), and organizing the 34th European Meeting of Statisticians (July 2023). He has also delivered invited lectures on Bayesian UQ and change-point models in 2023 and 2024.

Francisco BlasquesView profile

Professor

• Econometrics
• Statistics
• Data Science
• +5 more

Francisco Blasques is a Full Professor of Econometrics and Data Science at the Vrije Universiteit Amsterdam (VU) and a Research Fellow at the Tinbergen Institute. He holds leadership roles, including Co-founder of Churned (QuantIQ) and datastuff BV, and served as TI Doctoral Director for the School of Business and Economics. His research focuses on econometrics, statistics, and data science, with expertise in time-series analysis, financial econometrics, and nonlinear dynamics. Teaching includes advanced econometrics courses at VU and Tinbergen Institute, covering topics like time-series modeling and financial econometrics. His PhD supervision spans over 15 students since 2012, focusing on econometric methods and applications. Research contributions emphasize score-driven models, volatility analysis, and spatial econometrics. Key publications address topics such as natural disaster impacts on economies, robust volatility estimation, and fusion of experimental/observational data. Awards include the NWO-VIDI Grant (2019) for methodological innovations in econometrics. Professional activities include ancillary roles as Partner at QuantiQ BV and Director at Metyis, alongside academic duties. His work contributes to UN Sustainable Development Goals through economic resilience and policy analysis.

Jim SmithView profile

Professor

• Bayesian statistics
• Decision theory
• Probabilistic graphical models
• +7 more

Jim Smith is a Professor in the Department of Statistics at the University of Warwick. He actively leads research initiatives in Bayesian statistics, decision theory, and causal modeling with applications to food security, forensic science, and public health. His work bridges theoretical advancements in graphical models with real-world implementations through collaborations with government agencies and industry partners. PhD in Statistics (University of Warwick) Research focus: Bayesian networks, chain event graphs, expert judgment elicitation, causal inference Key collaborations: Martine Barons, Manuelle Leonelli, Ann Nicholson Research Interests Smith's work centers on high-dimensional Bayesian decision analysis, causal discovery in dynamic systems, and integrating expert judgments into formal models. He has developed novel Chain Event Graphs for complex system modeling and focuses on applications in: Food security risk assessment Forensic evidence evaluation Counter-terrorism strategies Medical imaging analysis Crisis management Energy security Recent Publications Trend His recent work demonstrates increasing focus on adversarial risk analysis (2025), secure data modeling for law enforcement (2024), and methodological improvements in Bayesian networks (2023-2024). Key themes include: Polynomial regression-based moment propagation Multi-agent decision frameworks Dynamic graphical model extensions Causal analysis in security contexts Mathematical foundations of Bayesian graphs Scientific Awards Fellow of the Alan Turing Institute (2017-2024) COST European Cooperation award for expert judgment research EPSRC-funded OxWaSP PhD training consortium co-director Grants & Collaborations Currently serving as PI in 6 Alan Turing Institute projects and CI on 7 others. Major grants include: European Food Standards Agency expert elicitation funding EPSRC OxWaSP initiative National Digital Twin Programme research Home Office policy evaluation projects Labs & Teams Co-leads a university research team studying cross-domain expert coherence. Member of the Food Global Research Priority team coordinating UK food security studies. Developed software tools in R/Python for Chain Event Graph analysis.

Juannan ZhouView profile

Professor

• Quantitative Biology
• Evolutionary Biology
• Genotype-Phenotype Mapping
• +3 more

Juannan Zhou is a Professor in the Department of Biology at the University of Florida , where they recently established their research lab. Their work focuses on genotype-phenotype mapping using machine learning , mathematical modeling , and wet lab experiments to understand evolutionary dynamics. Research Lab : Department of Biology, University of Florida Contact : juannanzhou@ufl.edu Key research themes include: Mapping genotype-phenotype relationships in diploid organisms Investigating protein fitness landscapes through unsupervised language models Developing machine-learning assisted directed evolution techniques Studying complex probability distributions over biological sequence spaces Exploring interactions between biochemical constraints and evolutionary outcomes Recent publications demonstrate technical innovations in: Gaussian process priors for sequence-function modeling Field-theoretic density estimation methods Higher-order epistasis analysis Nonparametric Bayesian approaches to biological sequence space Differentiable Smith-Waterman algorithms for sequence alignment The lab actively seeks postdocs, lab technicians, and graduate students interested in machine learning applications in population genetics and evolutionary modeling . Current projects include developing gpmap-tools for genotype-phenotype map visualization and implementing empirical variance component regression frameworks.

Tobias HeilmannView profile

Lecturer

• Statistical Inference
• Change Point Detection
• Differential Privacy
• +5 more

Dr. Tobias Heilmann is a Lecturer at the Department of Humanities, Social and Political Sciences at ETH Zurich. His research focuses on statistical inference, change point detection, differential privacy, high-dimensional data analysis, network analysis, and machine learning. He holds a PhD in Statistics from Fudan University, advised by Zhiliang Ying, and has contributed to software packages such as changepoints , GMPro , and APPLE . His work bridges theoretical statistics and practical applications, emphasizing privacy-preserving techniques, dynamic systems analysis, and algorithmic robustness. Key areas include nonparametric methods, contextual bandits, federated learning, and functional data analysis. Recent contributions address challenges in distributed systems, adversarial robustness, and high-dimensional model selection. He has published extensively in top journals like Annals of Statistics , SIAM Journal on Mathematics of Data Science , and IEEE Transactions on Information Theory . Dr. Heilmann's research also involves interdisciplinary collaborations, including social psychology of groups and biomedical applications. His software tools are widely used for change point analysis and community detection in networks. Current projects explore optimal privacy-utility trade-offs and adaptive algorithms for non-stationary environments.

• Genomics
• Quantitative Genetics
• Animal Breeding
• +2 more

Michael Goddard is an Honorary Professor at the Institute for Molecular Bioscience, University of Queensland, with a distinguished research career spanning statistical genetics, genomics, and animal breeding. His work bridges agricultural applications in livestock and human complex trait genetics, contributing foundational methods for genomic prediction and genome-wide association studies. His primary research interests include: Quantitative Genetics and Genomic Prediction Genome-Wide Association Studies (GWAS) Statistical Methods for Complex Trait Analysis Animal Breeding (Cattle and Sheep Genomics) Human Genetics of Complex Diseases Recent publications (2016-2022) demonstrate sustained innovation in multi-trait genomic prediction, functional annotation of animal genomes (FAANG consortium), and cross-species genetic architecture studies. His work frequently addresses non-additive genetic effects and integrates sequence data to improve prediction accuracy, with applications ranging from livestock improvement to psychiatric disorder risk assessment.

• Non-Invasive Neurostimulation Therapies
• Neurobiology of Psychiatric Disorders
• Depression Treatment
• +3 more

Dr. Fidel Vila-Rodriguez is an Assistant Professor in the Department of Psychiatry at the University of British Columbia, affiliated with the Faculty of Medicine and the School of Biomedical Engineering. He serves as Director of the Non-Invasive Neurostimulation Therapies (NINET) Laboratory and the Schizophrenia Program. His research focuses on translational applications of non-invasive neurostimulation therapies (NINETs), particularly in severe psychiatric disorders like schizophrenia and treatment-resistant depression. The NINET Lab is uniquely equipped with simultaneous TMS-fMRI and tDCS-fMRI capabilities, enabling advanced investigation of neurobiological mechanisms and biomarkers of neuromodulation. Research interests include optimizing TMS protocols, understanding neuroplasticity mechanisms, and developing biomarkers to predict treatment responses. Key areas of exploration involve the neurobiology of psychiatric disorders and bridging clinical practice with neuroscientific findings. Dr. Vila-Rodriguez’s work emphasizes improving therapeutic outcomes for patients with refractory mental illnesses through innovative neuromodulation approaches. His recent studies highlight advancements in single-day TMS regimens, theta-burst stimulation effects on depression subtypes, and the role of sleep and neuroplasticity in treatment efficacy. As part of the ENIGMA-Neuromodulation Working Group, he collaborates globally to standardize neuromodulation research methodologies. Dr. Vila-Rodriguez’s lab integrates clinical practice with cutting-edge neuroimaging and computational tools to address unmet needs in psychiatric care, aiming to enhance personalized treatment strategies for severe mental health conditions.

• Bayesian Methodology
• Statistical Machine Learning
• Variational Bayes
• +3 more

Dr. Minh-Ngoc Tran is an Associate Professor in Business Analytics at the University of Sydney Business School and an Associate Investigator in the ARC Centre of Excellence for Mathematical and Statistical Frontiers. His research develops Bayesian and machine learning methods for complex models with big data. His methodological innovations include quantum-enhanced variational Bayes, manifold optimization techniques, and subsampling MCMC algorithms. Current research focuses on time series modeling for financial markets, quantum computing applications in statistics, and variational inference for models with intractable likelihoods. Dr. Tran teaches quantitative methods, data mining, and predictive analytics. He currently supervises PhD students in deep learning for financial time series, explainable AI, and quantum machine learning. Recent research grants support quantum computation in business analytics and deep learning-based financial forecasting. His work has been recognized through awards including the University of Sydney Business School Emerging Scholar Research Fellowship and designation as Australia's top researcher in Probability and Statistics with Applications in 2021.

Naomi MazzilliView profile

Lecturer

• Hydrogeology
• Karst Aquifers
• Groundwater Modeling
• +3 more

Naomi Mazzilli is a Lecturer and Hydrogeologist at Avignon Université, affiliated with the Research and Innovation Support Department (DARI) within the Sciences and Agrosciences laboratories. Her research focuses on the characterization and modeling of karst aquifers, particularly in the Fontaine de Vaucluse region. She specializes in groundwater dynamics, monitoring techniques, and climate change impacts on karst systems. Key projects include automated measurement stations at the Rustrel Low Noise Underground Laboratory, collaborations with INRA and the National Karst Observation Service, and tracer studies in karst networks. Mazzilli emphasizes the importance of observational data and interdisciplinary approaches in advancing hydrogeological understanding. She is also involved in educational outreach, teaching hydrogeology and mentoring students through collaborative research projects. Her work integrates hydrogeophysical methods like Surface Nuclear Magnetic Resonance (SNMR) and superconducting gravimetry to study unsaturated zone processes. Recent initiatives include developing the KarstMod modeling platform and the KarstID application for analyzing spring discharge data. These tools enhance the assessment of karst system functioning and vulnerability to anthropogenic pressures. Mazzilli’s research contributes to sustainable water resource management in karst regions, addressing challenges such as climate variability and groundwater contamination. Collaborations span international projects, including studies in Peru’s Amazon-Andes karst systems and interdisciplinary efforts with geophysicists and environmental engineers. Her contributions to open science include publications on fluorescence-based tracing and Bayesian modeling frameworks, reflecting her commitment to advancing both theoretical and applied hydrogeology.