Find Academic Supervisors

Jonathan D. MosleyView profile

Associate Professor

• Genetics
• Clinical Medicine
• Polygenic Risk Scores
• +7 more

Jonathan D. Mosley, M.D., Ph.D., is an Associate Professor in the Department of Internal Medicine at UT Southwestern Medical Center. He is a clinically trained physician scientist with expertise in epidemiology and molecular genetics. His educational background includes: M.D./Ph.D. from Case Western Reserve University Master's in Chronic Disease Epidemiology from Johns Hopkins Bloomberg School of Public Health Residency in Internal Medicine at Vanderbilt University Medical Center Dr. Mosley's research focuses on translating complex genetics to clinical medicine, with particular emphasis on: Developing and implementing polygenic methods for clinical decision-making Identifying biomarkers through multi-omics approaches (metabolomics, proteomics) Studying how benign genetic variations create vulnerable populations Reducing health inequities through genomic research Applications in prostate cancer, cardiac disease, and pharmacogenomics His publications (2016-2025) demonstrate extensive work in genetic risk prediction, particularly using polygenic scores across diverse clinical areas including cardiology, oncology, and hematology. Recent articles show increasing focus on health disparities and clinical implementation of genetic findings. Awards and Recognition: Fellow of the American Heart Association Research Leadership: He directs the Mosley Lab, which specializes in applying genomic and informatics approaches to improve healthcare delivery and reduce inequities through: Integration of electronic health records with biobank data Development of clinical prediction models Identification of genetically vulnerable populations

• Bayesian non-parametric statistics
• Machine learning
• Statistical privacy
• +5 more

Dr Marco Battiston is a Lecturer (equivalent to Assistant Professor) in Statistics at Lancaster University, UK, based in the School of Mathematical Sciences. His research integrates Bayesian non-parametric methods with machine learning, privacy, and modern data-science challenges. Research Interests Bayesian non-parametric statistics and prior construction Statistical machine learning and graphical models Privacy-preserving data analysis and differential privacy Feature allocation, missing mass, and species sampling Variable selection in high-dimensional regression Across 12 peer-reviewed papers (2017–2025) he has advanced theory and methodology for stochastic block models, disclosure risk assessment, robust variable selection and the estimation of small probabilities in feature-sampling settings. His work appears in top outlets such as Journal of Computational and Graphical Statistics , Statistics and Computing , JMLR , and Annals of Applied Probability . PhD Supervision & Collaboration Battiston is actively supervising doctoral researchers (names not disclosed in the source material) and collaborates with international scholars including F. Ayed, C. Nemeth, S. Favaro, Y. W. Teh, and L. Rimella. Contact Email: m.battiston@lancaster.ac.uk Tel: +44 1524 592850 Office: Fylde College, Lancaster University, LA1 4YF, UK ORCID: 0000-0003-0954-1244

Patrick BahrView profile

Associate Professor

• Type Systems
• Compilers
• Functional Programming
• +4 more

Patrick Bahr is an Associate Professor at the IT University of Copenhagen and Co-Head of Education for the Master of Software Design program. His research focuses on developing programming languages and tools to ensure high-assurance software through formal methods. Education : PhD in Computer Science (2012), University of Copenhagen; MSc in Computational Logic (2009), Dresden & Vienna University of Technology; BSc in Computer Science (2008), Dresden University of Technology Patrick's research spans Type Systems , Compilers , and Functional Reactive Programming (FRP) , emphasizing Modal Types to ensure causality and productivity. His work includes Formal Verification of compiler correctness and Domain-specific Languages for enterprise and financial contracts. Recent publications highlight trends in Graph-Based Compilers , Asynchronous FRP , and Guarded Recursion . His methodologies often integrate Haskell and Agda for verified implementations. Key contributions include Sound-By-Construction Type Systems and Monadic Compiler Calculation . Scientific Awards : Best Contribution to RTA 2010 DIKU Paper of the Year 2012 He actively contributes to program committees (e.g., TFP 2026, ICFP 2024) and has led research projects like Guarded Recursive Types funded by the Danish Independent Research Fund (2015-2019). Collaborations span institutions such as the University of Nottingham and Utrecht University.

Nadja KleinView profile

Professor

• Bayesian learning
• Machine Learning
• Deep Learning
• +5 more

Nadja Klein is a Professor at the Scientific Computing Center (SCC) at Karlsruhe Institute of Technology (KIT), where she leads the Methods for Big Data research group. She holds the position of Emmy Noether Research Group Leader and is a member of prestigious organizations including AcademiaNet and Die Junge Akademie. Her academic journey includes doctoral studies in Mathematics, a postdoctoral position at the University of Melbourne as a Feodor-Lynen fellow by the Alexander von Humboldt Foundation, and a professorship in Statistics and Data Science at Humboldt-Universität zu Berlin before joining KIT. Her research focuses on Bayesian learning methods, which allow for the incorporation of prior knowledge, quantification of uncertainties, and bringing clarity to the "black boxes" of machine learning. The Methods for Big Data lab under her leadership develops innovative, reliable, and generalizable methods to handle massive datasets across diverse fields including biomedical data analysis, weather prediction, and autonomous driving technologies. Her work spans theoretical analysis, method development, and practical applications of statistical and machine learning techniques. Dr. Klein's recent publications demonstrate a strong trend toward integrating Bayesian principles with deep learning approaches to address challenges in uncertainty quantification, data efficiency, and model interpretability. Her research shows particular strength in distributional regression frameworks, copula modeling, and developing robust methods for real-world applications where uncertainty matters. She has been recognized with the prestigious Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award for her outstanding contributions to statistical methodology. Her work bridges theoretical statistics with practical machine learning applications, making significant contributions to both academic research and industry collaborations. Dr. Klein actively collaborates with industry partners such as Continental Automotive GmbH on cutting-edge research in autonomous driving technologies. Her lab maintains a strong focus on both theoretical foundations and practical implementations of statistical learning methods, with particular emphasis on making machine learning more reliable, interpretable, and data-efficient through Bayesian approaches.

• Bayesian Statistics
• Category Theory
• Compiler Construction
• +4 more

Matthijs Vákár is an Assistant Professor at the Department of Information and Computing Sciences , Utrecht University , focusing on the software foundations of machine learning . He actively develops the Stan language , a state-of-the-art probabilistic programming language. Areas of Expertise: Bayesian Statistics, Category Theory, Compiler Construction, Machine Learning, Programming Languages. Research Themes: Probabilistic programming languages, differential programming languages, separation of modeling from algorithmic tasks, automatic differentiation in ML-family languages. His research bridges theoretical computer science and practical machine learning, emphasizing logical relations, monoidal structures, and efficient compiler designs for differentiable programming. His recent publications explore correctness proofs, category-theoretic models, and optimization techniques in automatic differentiation. Scientific Awards: ERC Starting Grant NWO Veni Fellowship Marie Sklodowska-Curie Fellowship Academic Journey: PhD from the University of Oxford (dependent types and computational effects), MSc in Pure Mathematics (University of Cambridge), and BScs in Mathematics and Physics (Utrecht University). Prior roles include postdoctoral research at Columbia University and the University of Oxford. Industrial Experience: Build systems at Microsoft Research, NLP at Linguamatics, and compiler design for Stan.

Xiang ZhouView profile

Professor

• Statistical Genetics
• Genomics
• Biostatistics
• +3 more

Xiang Zhou is a Professor of Statistics and Data Science at Yale University and an Adjunct Professor of Biostatistics at the University of Michigan. He holds a Ph.D. in Neurobiology and an M.S. in Statistics from Duke University (2010), and a B.S. in Biology from Peking University (2004). His research spans statistical genetics, genomics, and machine learning, with a focus on developing computational methods for high-dimensional genetic data. Research Interests: Dr. Zhou specializes in Bayesian methods, spatial statistics, and deep learning for genomic applications. His work includes genome-wide association studies (GWAS), single-cell RNA sequencing, spatial transcriptomics, and integrative modeling of multi-omics data. Key areas include genetic prediction, causal inference, and scalable algorithms for large-scale genomic datasets. Publication Trends: His recent articles emphasize spatial transcriptomics, Mendelian randomization, and non-parametric modeling, published in high-impact journals like Nature Biotechnology and Nature Methods . These works advance methodologies for spatial gene expression analysis, cell-type deconvolution, and genetic architecture inference. Labs & Teams: He leads the Zhou Lab at Yale, which focuses on genomic data science and develops open-source software (e.g., GEMMA, SPARK) for the research community.

• Logics
• Databases
• Formal languages
• +5 more

Gabriele Puppis is an Associate Professor at the Department of Mathematics, Computer Science, and Physics of Udine University since 2022. Previously, he held a tenure-track position at the same university (2019-2022) and was a tenured CNRS researcher at LaBRI, Bordeaux (2012-2019). He completed postdoctoral research at Oxford University (2009-2012) and Udine University (2006-2008). His academic journey began with a PhD in Computer Science at Udine University (2002-2006). Education: PhD in Computer Science, Udine University (2006) Research Interests: Logics and formal languages String transducers and automata theory XML schema validation and database repairability Interval temporal logic and spatial reasoning Higher-order query languages Academic Contributions: His work focuses on the decidability and complexity of problems in automata theory, particularly for finite-valued transducers and their equivalence. He has advanced understanding of bounded repairability for tree and word languages, dynamic data structures for timed automata, and resynchronizability in transducer models. His recent publications explore decomposition theorems for streaming string transducers and their equivalence to two-way models. Teaching and Leadership: He teaches courses in Distributed Systems, Algorithms and Data Structures, and Verification techniques in AI and Cybersecurity. He has organized events like the GandALF 2023 summer school and participated in workshops on unambiguity in automata theory and regular cost functions.

Eunsook KimView profile

Professor

• Psychometrics
• Structural Equation Modeling
• Multilevel Modeling
• +4 more

Dr. Eunsook Kim is a Professor in the College of Education at the University of South Florida (USF Tampa campus). Her research specializes in advanced quantitative methodologies for educational and psychological measurement. Research Focus: Dr. Kim's work centers on psychometric innovations, including multilevel modeling, structural equation modeling, factor mixture approaches, and rigorous testing of measurement invariance in complex datasets. She frequently employs Monte Carlo simulations to evaluate methodological robustness in cross-cultural, longitudinal, and hierarchical research designs. Publication Trends: Her recent articles (2011-2017) demonstrate consistent focus on improving statistical methods for detecting measurement bias, validating instruments across populations, and developing reporting standards for multilevel analyses. Collaborative work features prominently, often addressing methodological challenges in large-scale educational and psychological assessment.

• Statistics
• Randomized Response Techniques
• Survey Sampling
• +5 more

Pier Francesco Perri is a Full Professor of Statistics (SSD STAT-01/A) at the Department of Economics, Statistics, and Finance 'Giovanni Anania' (DESF) of the University of Calabria (Unical). He has held academic positions since 2002 and coordinates institutional activities such as the National Plan for Scientific Degrees (L-41, Statistics) and editorial roles in journals like Metron and Model Assisted Statistics and Applications . His research focuses on survey methodology, sensitive data estimation, and statistical modeling. Education: Ph.D. in Methodological Statistics (University of Trento, 2002), Honors Degree in Statistical and Actuarial Sciences (University of Calabria, 1997) Perri’s research spans randomized response techniques for sensitive populations, copula modeling for financial data, and inequality index linearization. He explores indirect questioning methods to detect truthfulness and improve survey accuracy, particularly for stigmatized behaviors like abortion, migration, and non-heterosexual identities. His work integrates auxiliary information and complex sampling designs to enhance estimation efficiency. Recent publications emphasize innovations in sample survey traditions, including the 2025 editorial on methodological advancements and 2024 studies on adjusting self-protective behaviors in prevalence estimation. His 2023–2021 research includes logistic regression extensions for randomized response models, prevalence estimates for health behaviors, and crossed model applications for sensitive topics. Earlier work (2020–2000) covers item sum techniques, financial copula modeling, and environmental sensitivity analysis. He serves on editorial boards and institutional committees, including the Italian Statistical Society’s Council and the Ph.D. Teaching Board at Unical. The Laboratorio Statistico Informatico supports his research in statistical computing and survey design.

Fraser A. DalyView profile

Associate Professor

• Applied Probability
• Random Graphs and Networks
• Markov Processes
• +4 more

Fraser A. Daly serves as Associate Professor in the Department of Actuarial Mathematics & Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh. His academic appointment includes leadership roles as undergraduate admissions tutor and instructor for statistical modeling and programming courses. From 2022-2024, he directed the Scottish Mathematical Sciences Training Centre (SMSTC), a consortium serving nine Scottish universities, and previously held positions as MSc Financial Mathematics programme director and Royal Statistical Society Applied Probability section vice chair. Research interests center on applied probability with emphasis on distributional approximation techniques. His work develops theoretical frameworks for approximating complex probability distributions using tractable models like Gaussian or Poisson distributions, with explicit error bounds. Key application areas include random graphs and networks , Markov processes , and actuarial models . The research fingerprint reveals strong focus on approximations (100%), randomness (53%), distribution theory (51%), and Poisson approximation (41%), with significant contributions to Gaussian distribution analysis and graph theory. Analysis of his 15 most recent publications (2021-2025) shows consistent focus on Stein's method applications, distributional approximations across diverse contexts (asymmetric Laplace, geometric-type, negative binomial), and network theory applications. The work demonstrates interdisciplinary reach spanning probability theory, statistical inference, epidemiology modeling, and actuarial science, with increasing emphasis on random graph asymptotics and optimal control in stochastic systems. Professional service includes editorial contributions such as the 2019 Stochastic Networks special issue introduction, reflecting leadership in the probability community. His role in directing SMSTC from 2022-2024 demonstrates commitment to graduate mathematical education across Scotland. Actively supervising PhD students, Daly welcomes projects in random graphs, Markov processes, and actuarial models. His research program shows sustained productivity with 21 total publications including multiple high-impact articles in journals like Scandinavian Actuarial Journal and Journal of Statistical Physics , with growing citation impact (29 Scopus citations for his 2016 Conway-Maxwell-Poisson work).

Matthieu Marbac-LourdelleView profile

Associate Professor

• Biostatistics
• Model-based Clustering
• Computational Statistics
• +2 more

Matthieu Marbac-Lourdelle is an Associate Professor in Statistics at ENSAI/CREST (Bruz, France) since September 2023. Previously, he served as an Assistant Professor at the same institution from September 2017 to September 2023. He is also an external collaborator of the PreMedical team, a joint team between Inria and Inserm, since September 2022. He completed his PhD in October 2014 at the University of Lille 1 under the supervision of Christophe Biernacki and Vincent Vandewalle. In 2022, he defended his habilitation, the French qualification required to supervise PhD students. Dr. Marbac-Lourdelle's research focuses on Biostatistics , Model-based Clustering , Computational Statistics , Empirical Likelihood , and Mixture Models . His work bridges theoretical statistical methods with practical applications in various domains including epidemiology, sports science, and genomics. His recent publications reveal a strong emphasis on developing advanced clustering techniques, particularly for complex data structures including functional data, mixed data types, and data with missing values. His research has significant applications in health monitoring, epidemiological studies, and sports analytics. Dr. Marbac-Lourdelle serves as an Associate Editor for Computational Statistics & Data Analysis (since 2021) and Econometric and Statistics (since 2023). He currently supervises five PhD students working on diverse topics including nonparametric mixture models, soccer game analysis, swimming technique monitoring, arrhythmia detection in racehorses, and probabilistic substitution models. His collaborations span multiple institutions including CREST, Inria, Inserm, and international partners at HEC Montreal and the University of McMaster.

• Applied Probability
• Risk Models
• Stochastic Arrangements and Dependence
• +7 more

Michael V. Boutsikas is an Associate Professor in the Department of Statistics and Insurance Science at the University of Piraeus , Greece. Since 2000 he has held progressively senior academic posts, beginning as Lecturer and becoming tenured Assistant Professor before his promotion to Associate Professor in 2016. He earned his Diploma (1995), M.Sc. (1998) and Ph.D. (2000) in Mathematics from the University of Athens, all awarded with distinction. His research lies at the intersection of applied probability, actuarial science and reliability theory . Core interests include risk models, stochastic dependence, probability metrics, extreme value theory, scan statistics, urn models and compound Poisson approximation . These themes are unified by the aim of quantifying and bounding the behaviour of complex stochastic systems. Across more than twenty-five peer-reviewed articles published in leading journals such as The Annals of Applied Probability , Journal of Applied Probability , Bernoulli , Insurance: Mathematics and Economics and Naval Research Logistics , a clear trajectory emerges: developing sharp approximations and limit theorems for rare events, system reliability, and aggregate claims in risk processes. While the text does not list formal awards, it notes that his work has attracted at least 150 citations from international journals and monographs and that he maintains an h-index of 9 . He is an active referee for more than twenty journals and a reviewer for Mathematical Reviews . Teaching duties span undergraduate courses ( Risk Management, Stochastic Processes, Simulation ) and postgraduate offerings ( Simulation Methods, Extreme Price Theory, Stochastic Financial Models ). He has also authored sixteen sets of detailed teaching notes and lecture notes covering reliability, risk management and statistical software. No specific doctoral students or grant details are provided, but his long-standing presence and prolific output indicate sustained supervisory and research funding activity within the Department of Statistics and Insurance Science.

Villó CsiszárView profile

Assistant Professor

• Probability Theory
• Statistics
• Information Theory
• +5 more

Villó Csiszár is an Assistant Professor at the Department of Probability Theory and Statistics , Faculty of Science , Eötvös Loránd University . Her research focuses on Probability Theory , Statistics , Information Theory , and Markov Chains . Education: PhD in Mathematics (2009, Eötvös Loránd University) MSc in Mathematics (2000, Eötvös Loránd University) Erasmus scholarship at the University of Sheffield (1998-1999) Positions: Assistant Professor (2010-present, ELTE Faculty of Economics) Teaching Assistant (2002-2010, ELTE Faculty of Economics) Visiting Researcher (2007-2008, Rényi Institute of Mathematics) Her work spans random permutations , conditional independence models , hierarchical models , and probabilistic inequalities , with applications in environmental science and interdisciplinary research . Publications include studies on Chebyshev-type inequalities , Markov bases , and statistical inference . She has presented at international conferences such as the 25th EMS European Meeting of Statisticians (Oslo, 2005) , Workshop on Polytopes and Algebraic Statistics (Stockholm, 2008) , and Prague Stochastics (2010) .

• Environmental Economics
• Causal Inference
• Data Science
• +3 more

Vincent Bagilet is a Postdoctoral Fellow at ENS Lyon in the Department of Economics and the Center for Economic Research on Governance, Inequality and Conflict (CERGIC). He completed his PhD in Sustainable Development at Columbia University in 2024. Research Interests: Individual and collective environmental attitudes Unstructured data analysis Causal inference and data visualization Short-term health effects of air pollution His work includes retrospective power analysis of causal inference literature and projects like 'Causal Exaggeration: Unconfounded but Inflated Causal Effects' and 'Estimating Small Effects: Air Pollution and Health'. Technical implementations are available on his GitHub portfolio. Contact: vincent.bagilet@ens-lyon.fr | Personal Website | GitHub

• Latent Variable Models
• Mixture Models
• Rank Data Models
• +7 more

Claire Gormley is an Associate Professor of Statistics at University College Dublin (UCD) and a Funded Investigator at the Insight Centre for Data Analytics. She joined UCD in 2006 as an Assistant Lecturer, was promoted to Assistant Professor in 2008, and to Associate Professor in 2017. Her research group specializes in Machine Learning & Statistics with applications spanning genetics, agriculture, and medical sciences. Education PhD in Statistics, Trinity College Dublin (2007) Visiting Scholar, Department of Statistics/Centre for Statistics and the Social Sciences, University of Washington Seattle (2006) Research Expertise Professor Gormley develops advanced statistical methodologies including latent variable models, mixture models, and Bayesian approaches for high-dimensional data analysis. Her work addresses dimensionality challenges in diverse fields such as metabolomics, orthopaedics, and social science, with current projects focusing on model-based clustering for mixed-type high-dimensional datasets. Methodological innovations are consistently applied to real-world problems involving large-scale biological and agricultural data. Publication Trends Her recent publications (2023-2025) demonstrate strong interdisciplinary impact across genetics (tuberculosis, dairy cattle), agricultural science (nitrogen utilization), and medical research (neonatal sepsis). Methodologically, she pioneers mixture models and latent variable frameworks for complex data structures, particularly in multi-omics integration and network analysis. A clear trajectory shows increasing focus on computationally intensive solutions for biological big data, with applications directly addressing food security and healthcare challenges. Research Leadership As a Funded Investigator at the Insight Centre, Dr. Gormley leads a dynamic research group securing competitive grants for interdisciplinary projects. Her team collaborates with veterinary scientists, geneticists, and medical researchers on grant-funded initiatives including tuberculosis genomics and metabolomics data analysis. The group maintains strong industry partnerships in agricultural technology and healthcare analytics, with recent projects developing predictive models for dairy cattle health monitoring and clinical diagnostics.