Find Academic Supervisors

• Probability and Statistics
• Estimation Theory
• Biostatistics
• +3 more

Georgios Tzavelas is an Associate Professor in Probability and Statistics at the Department of Statistics and Insurance Science, University of Piraeus. His academic career includes roles in teaching and research since 1994, with expertise in parametric distribution theory, biased sampling models, and statistical inference methods. PhD in Mathematical Statistics (University of Maryland, 1994) MA in Mathematical Statistics (University of Maryland, 1991) Bachelor’s in Mathematics (University of Patras, 1984) Research focuses on estimation theory, characterization problems, environmental statistics, and biostatistics. Notable contributions include work on failure rate functions, factor analysis rotation effects, and size-biased sampling methodologies. His publications span over 35 peer-reviewed articles in journals like Metrika, Journal of Statistical Computation and Simulation, and Biometrical Journal. Awards include Excellence in Teaching (1993) and top academic performance recognition during undergraduate studies. Serves as a referee for multiple statistical journals. Teaching responsibilities include courses on sampling methods, biostatistics, actuarial science, and clinical trials. Research interests bridge statistical theory and applications in health and environmental sciences, with active participation in interdisciplinary collaborations.

Xavier RivalView profile

Adjunct Professor

• Static Analysis
• Abstract Interpretation
• Probabilistic Programming
• +4 more

Xavier Rival is a Research Director at INRIA Paris and Adjunct Professor at École Normale Supérieure (ENS) PSL. He serves as Director of the Computer Science Department at ENS, leading a CNRS/ENS/PSL/INRIA laboratory. Previously, he was head of the ANTIQUE (ANalyse staTIQUE) research group at INRIA Paris/DIENS from 2014 to 2024. His academic roles include: Adjunct Professor in Computer Science (ENS/PSL) Director of Computer Science Department at ENS Principal Investigator (PI) of the VeriAMOS ANR Project Education & Academic Background While specific education details are not explicitly stated, his long-term involvement in academic research and leadership roles at ENS/PSL and INRIA indicates a strong academic foundation in computer science and formal methods. Research Interests Rival's work focuses on static analysis and abstract interpretation , particularly in: Verification of semantic program properties Probabilistic program analysis Safety-critical embedded software verification (via the Astrée project) Static analysis of spreadsheet and JavaScript applications Software reliability in safety-critical systems Teaching He contributes to: Semantics and Applications to Verification course at ENS (taught with Jérôme Feret and Josselin Giet) Master Parisien de Recherche en Informatique (MPRI) course on Abstract Interpretation Projects & Labs Key initiatives include: VeriAMOS (ANR Project): Focuses on verified OS services via Domain-Specific Languages (DSLs) Astrée project: Static analysis for safety-critical embedded software MemCAD (ERC Project): Static analysis of memory-constrained systems

Paolo BallariniView profile

Professor

• Stochastic Modeling
• Formal Verification
• Computational Biology
• +3 more

Paolo Ballarini is a Professor specializing in stochastic modeling, formal verification, and computational biology. He leads the Paolo Ballarini Laboratory for Mathematics and Computer Science for Complexity and Systems , focusing on interdisciplinary research at the intersection of computer science and life sciences. His work spans probabilistic systems analysis, statistical model checking, and algorithm design for complex networks. Research interests include stochastic process discovery, parameter estimation in biological systems, and performance evaluation of wireless networks. His contributions to formal methods have advanced applications in healthcare systems, manufacturing processes, and genetic network analysis. Key technical developments include the COSMOS platform for statistical model checking and the HASL formal language for specifying verification properties. His work bridges theoretical foundations with practical tools for analyzing real-world systems under uncertainty. Ballarini’s recent studies emphasize Bayesian methods for biological pathway inference and optimization-based approaches to stochastic process discovery. His collaborations span academia and industry, addressing challenges in network security, distributed systems, and systems biology.

Christian HaaseView profile

Professor

• Lattice Polytopes
• Toric Algebra
• Tropical Geometry
• +3 more

Prof. Dr. Christian Haase is a Professor at the Department of Mathematics within the School of Mathematics and Computer Science at Freie Universität Berlin. He leads the Discrete Algebraic Geometry & Math for Teaching Post workgroup. His research focuses on lattice polytopes, toric algebra, tropical geometry, combinatorial commutative algebra, algorithmic algebra, and geometric/topological combinatorics. He has held a permanent academic position since at least 2008, as evidenced by his extensive publication record from that period onward. His work includes collaborations on foundational topics like Ehrhart theory, integer decomposition properties, and convex polytope decompositions. His research contributions span theoretical advances in algebraic geometry and discrete mathematics, with applications to computational algebra and geometric combinatorics. Notable works include studies on the finiteness threshold width of lattice polytopes and the exploration of Gaussian mixture models' mode counts. He maintains an active role in mentoring PhD students and postdoctoral researchers in his workgroup, though specific advisee names are not listed here. His research has been published in top journals such as Advances in Mathematics, Mathematische Zeitschrift, and the Annals of Combinatorics. Haase's academic activities include organizing research projects and conferences, and he serves as a key contributor to the mathematical community through his editorial work and collaborative research initiatives.

Karl WetteView profile

Research Fellow

• Gravitational Waves
• Neutron Stars
• Pulsars
• +3 more

Dr. Karl Wette is a Research Fellow at the Centre for Gravitational Astrophysics at the Australian National University (ANU). He holds a B.Sc. from the University of Auckland and a Ph.D. from ANU. His research focuses on gravitational waves, neutron stars, pulsars, and data analysis theory, with a particular emphasis on detecting and interpreting gravitational wave signals from compact objects like black holes and neutron stars. Dr. Wette is actively involved in the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration, contributing to the analysis of gravitational wave data from the third observing run. His work bridges theoretical astrophysics with experimental data analysis, aiming to uncover insights into extreme astrophysical phenomena. Research Interests: Gravitational waves, neutron star dynamics, pulsar physics, and data-driven methods for gravitational wave astronomy. Dr. Wette's projects include studying postmerger remnants of black hole-neutron star binaries, constraining ultralight vector boson models, and developing advanced search algorithms for continuous gravitational waves. Collaborations: Engaged with international teams to analyze multi-messenger data from gravitational wave observatories and gamma-ray satellites like Fermi-GBM and Swift-BAT. His contributions have advanced techniques for detecting transient gravitational wave signals and interpreting their astrophysical implications. Education: B.Sc. (University of Auckland), Ph.D. (ANU).

• Time Series Analysis
• Seasonality Modeling
• Applied Econometrics
• +2 more

Tomás Del Barrio Castro is a University Professor in the Department of Economics at the University of the Balearic Islands (UIB). He has been a faculty member at UIB since 2007, previously serving as a professor at the University of Barcelona from 1993 to 2007. His academic career includes positions as assistant professor (1993-1995), full professor at university school (1995-2001), and full professor at university (2001-2007) at the University of Barcelona. Dr. Del Barrio Castro was born in Madrid in 1968. He received his Bachelor's degree in Economics and Business Administration from the Autonomous University of Madrid in 1992 and completed his PhD in Economics from the University of Barcelona in 1998. He has also been a visiting researcher at the University of Manchester (UK, 2003) and Banco de Portugal (Portugal, 2013). His primary research interests focus on Time Series Analysis (Econometric), Seasonality Modeling, and Applied Econometrics. Dr. Del Barrio Castro has made significant contributions to the understanding of seasonal unit roots, periodic integration, and time series econometrics. His work has advanced methodologies for analyzing economic data with seasonal patterns and has provided new insights into the properties of various econometric tests under different data generating processes. Dr. Del Barrio Castro's publication record demonstrates a consistent focus on time series econometrics, particularly in the areas of seasonal unit root testing, periodic integration, and the properties of econometric tests in the presence of various data characteristics. His work spans theoretical developments and practical applications, with publications in top econometrics journals including Econometric Theory, Journal of Time Series Analysis, and Econometric Reviews. Among his notable achievements is the Econometric Theory Fine Scripsit Award received in 2014. Additionally, he has earned recognition for 4 research sections by CNEAI. These honors reflect the high quality and impact of his research contributions to the field of econometrics. Dr. Del Barrio Castro has served as principal investigator for Competitive Research Projects since 2007. He is currently the principal investigator of the "Econometría y Ciencia de Datos (ECD)" consolidated R&D group at UIB. His teaching portfolio includes courses such as Macroeconometrics, Time Series Analysis, and Economics Thesis supervision for both undergraduate and graduate programs at UIB. He is actively involved in the "Econometría y Ciencia de Datos (ECD)" research group, where he leads research efforts in econometrics and data science. This group focuses on advancing methodologies in time series analysis, econometric modeling, and the application of these techniques to economic and business problems.

• Colloid Science
• Biophysics
• Nanotechnology
• +5 more

Peter van Oostrum is a Privatdozent (Priv.Doz.Dr.) at the Institute of Colloid and Biointerface Science , Department of Biotechnology and Food Science, University of Natural Resources and Life Sciences Vienna (BOKU). His research integrates advanced optical techniques and biophysical modeling to study colloidal systems. Projects : Led key initiatives like AI-supported Holographic Environmental Water Monitoring (2025–2029) and Self-folding particle chains (2015–2020), funded by Austrian Science Fund, City of Vienna, and European Commission. Research Focus : Self-assembly of colloids, holographic microscopy, microfluidics, and force-modulated multivalent binding in bacterial systems. His work bridges nanotechnology, biophysics, and environmental monitoring. Publications : 32 peer-reviewed works, emphasizing colloidal interactions, inverse patchy particles, and holographic cytometry applications. Community Services : Active reviewer for journals like Physical Review Letters , Chemistry of Materials , and Soft Matter . Theses Supervised : Guided Bernhard Pichler’s diploma thesis on polymer-functionalized nanopores (2020).

Borzoo BonakdarpourView profile

Associate Professor

• Security/Privacy
• Cyber-physical Systems
• Distributed Computing
• +5 more

Prof. Borzoo Bonakdarpour is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. He directs the Trustworthy and Reliable Technologies Lab (TART), focusing on formal methods in distributed systems, cyber-physical systems, and security/privacy. Research Interests Security/Privacy Cyber-physical Systems Distributed Computing Formal Methods His recent work involves runtime verification, hyperproperty analysis, and fault-tolerant systems. He has received multiple awards, including the Withrow Excellence Award in Teaching (2022), Best Paper Awards at IEEE SRDS (2017), SSS (2014), and ACM SIES (2010), and was recognized as Best Professor by the McMaster Software Engineering Club in 2017. Professional Activities: Serve as PC member for ICCPS'24, CAV'24, RV'23, and General Chair for SRDS'21.

• Operator Theory
• Complex Variables
• One and Several Complex Variables
• +3 more

John E. McCarthy is the Spencer T. Olin Professor of Mathematics at Washington University in St. Louis, with a courtesy affiliation as Professor of Statistics and Data Science. He holds a PhD from the University of California at Berkeley and maintains an active research program spanning multiple mathematical disciplines. His research interests focus on Operator Theory, Complex Variables, and their interaction , with particular emphasis on One and Several Complex Variables. Professor McCarthy believes that pure mathematicians can bring valuable perspectives to many areas of science and actively seeks collaborations with scientists open to mathematical approaches. His scholarly output shows consistent engagement with fundamental questions in functional analysis and complex variables, with recent work extending into applications including ultrasound signal interpretation and mathematical perspectives on public health issues like the COVID-19 pandemic. His publications reveal a trajectory from deep theoretical work in operator theory and interpolation problems toward increasingly interdisciplinary applications while maintaining rigorous mathematical foundations. Faculty member in the Neuroscience Program of the Division of Biology and Biomedical Sciences (DBBS) Founder/editor of the Journal of Xenomathematics, exploring alternative mathematical systems Author of multiple influential books including Pick Interpolation and Hilbert Function Spaces and Operator Analysis: Hilbert Space Methods In Complex Analysis Professor McCarthy is married to Suzanne Langlois, founder of Kaldi's Coffee and interim CEO of Meds and Food for Kids, indicating his connection to entrepreneurial and humanitarian efforts beyond academia.

Quentin ChangeatView profile

Assistant Professor

• Exoplanets
• Atmospheric Modeling
• Telescope Observations
• +3 more

Quentin Changeat is an Assistant Professor at the Kapteyn Astronomical Institute, University of Groningen, and an Honorary Research Fellow in the Department of Physics and Astronomy at University College London. His research bridges exoplanet atmospheric studies and computational astrophysics, with affiliations spanning multiple international institutions. Research Interests: Dr. Changeat focuses on the physics and chemistry of exoplanet atmospheres, leveraging data from the James Webb Space Telescope (JWST), Hubble Space Telescope (HST), and Spitzer. His work emphasizes atmospheric retrieval techniques, machine learning applications, and the development of open-source tools like TauREx for spectral analysis. He is actively involved in the ESA Ariel mission, which aims to survey 1000 exoplanets by 2029. Publication Trends: His 15 most recent articles (2019–2024) reveal a strong emphasis on JWST/HST data analysis, atmospheric retrievals of hot Jupiters, and population-level studies. Key themes include thermal inversions, phase-curve degeneracies, and the development of statistical frameworks for large-scale exoplanet characterization. Machine learning and interdisciplinary collaborations feature prominently in his methodology. Awards: Honorary Research Fellow, University College London Academic Contributions: He leads educational initiatives like the ARES Summer School and ORBYTS program, mentors PhD students in exoplanet research, and develops datasets for community use (e.g., Ariel Data Challenge). His lab focuses on atmospheric retrieval algorithms and collaborates with teams at UCL, Flatiron Institute, and ESA.

Jeffrey S. RosenthalView profile

Professor

• Probability theory
• Stochastic processes
• Markov chain Monte Carlo
• +4 more

Jeffrey S. Rosenthal is a Professor in the Department of Statistical Sciences at the University of Toronto, Faculty of Arts and Science. He holds a PhD in Mathematics from Harvard University and a BSc from the University of Toronto. PhD, Mathematics, Harvard University BSc, University of Toronto His research centers on probability theory , stochastic processes , and statistical computation , with a particular focus on Markov chain Monte Carlo (MCMC) algorithms . His work spans theoretical foundations and practical applications, including random walks on groups and interdisciplinary modeling. He is also known for his public engagement in statistics through bestselling books and media appearances. The recent publications reflect a sustained focus on the theoretical underpinnings and convergence properties of MCMC methods, including adaptive and non-reversible algorithms. His work also extends into data science applications, such as analyzing streaks in online chess and the long-term impact of the COVID-19 pandemic on mortality. The keywords span probability, statistics, computational mathematics, and machine learning, indicating a blend of theoretical rigor and applied relevance. Scientific Awards and Honors: CRM-SSC Prize in Statistics COPSS Presidents' Award SSC Gold Medal Fellow of the Royal Society of Canada Fellow of the Institute of Mathematical Statistics Alumnus of Influence, University College Pierre Robillard Award SSC Student Research Presentation Award Savage Award Finalist Academic Supervision and Grants: Professor Rosenthal has supervised a large and diverse group of students, including numerous PhD candidates, MSc students, and post-doctoral fellows, many of whom have gone on to successful academic careers. His research is supported by ongoing publications and collaborations, indicating active grant funding and a vibrant research program. He maintains a well-documented research team and provides extensive resources for students and collaborators. Research Teams and Labs: He leads an active research group in probability and computational statistics, with a documented team of current and past post-doctoral fellows, PhD students, and research assistants. The group maintains a collaborative environment, as evidenced by joint publications and team photos, and focuses on advanced topics in MCMC theory and applications.

Chris HaufeView profile

Professor

• History of Philosophy
• Philosophy of Science
• History of Science
• +3 more

Chris Haufe is the Elizabeth M. and William C. Treuhaft Professor of the Humanities and Chair of the Department of Philosophy at Case Western Reserve University. His research focuses on the history of philosophy and science, particularly exploring how scientific practices evolve and the role of the humanities in knowledge production. He holds a PhD from Columbia University (2006). His notable works include How Knowledge Grows (MIT Press, 2022), Do the Humanities Create Knowledge? (Cambridge, 2023), and Illusory Riches: The False Promise of Evolutionary Psychology (Oxford, 2025). These publications critically examine scientific methodologies, interdisciplinary knowledge creation, and ethical implications of scientific claims. Haufe has been recognized with prestigious awards, including the Treuhaft Professorship and the Past Beamer-Schneider Professorship in Ethics. His research also addresses environmental ethics, evolutionary theory, and the philosophy of biology, reflecting a broad engagement with both historical and contemporary scientific debates.

Seokhyun ChungView profile

Assistant Professor

• Internet of Things (IoT)
• Data-Driven Analytics
• Federated Learning
• +4 more

Seokhyun Chung is an Assistant Professor at the University of Virginia, located in Lab 257 of Olsson Hall. His research focuses on IoT-enabled systems, leveraging data-driven methods such as federated learning, multi-task learning, and Bayesian probabilistic modeling to address challenges in statistical heterogeneity, scalability, and personalization in connected systems. Applications span smart healthcare and manufacturing systems, emphasizing reliability and efficiency. Research Interests: Chung's work integrates IoT, machine learning, and systems engineering to develop collaborative analytics frameworks. He explores federated learning architectures for distributed data fusion, probabilistic modeling for uncertainty-aware predictions, and optimization techniques for resource allocation in edge computing environments. His healthcare focus includes gait analysis and load estimation for wearable devices, while manufacturing applications involve job scheduling and additive manufacturing quality control. Key Contributions: Recent work highlights include federated multi-output Gaussian processes for heterogeneous systems, real-time adaptation of time-series predictions, and fairness-aware machine learning for load carriage tasks. His interdisciplinary projects address both technical and operational challenges, such as emergency department layout optimization and toxic gas monitoring networks. Labs & Infrastructure: Chung's research is conducted in Olsson Hall's advanced labs, leveraging IoT sensor networks, edge computing setups, and collaborative simulation environments to prototype innovative smart systems solutions.

Shan GeView profile

Assistant Professor

• Finance
• Corporate Finance
• Insurance
• +10 more

Shan Ge is an Assistant Professor of Finance at the Leonard N. Stern School of Business, New York University, where she has been a faculty member since 2018. Her research lies at the intersection of finance, insurance, and climate change, with a focus on corporate finance, risk management, and institutional investor behavior. Ph.D. in Finance, The Ohio State University M.A. in Economics, The Ohio State University B.A. in German and Business Administration, Renmin University of China Her research explores three primary areas: (1) corporate finance issues using the insurance sector as a setting; (2) the impact of health and flood insurance on households and firms; and (3) the role of insurers as financial intermediaries in capital markets, particularly in hedging climate-related risks. Her work employs rigorous empirical methods, often leveraging exogenous shocks such as weather events to establish causality. Shan Ge’s publications appear in top journals including The Journal of Finance , Review of Financial Studies , and Journal of Financial Economics . Her recent work analyzes climate risk transmission to mortgage markets, health insurance effects across income groups, opioid prescribing under provider financial stress, and insurers’ portfolio shifts under financial constraints. These studies reveal how financial conditions shape real economic outcomes across sectors. Research cited by the U.S. Senate Featured in The New York Times , Wall Street Journal , Financial Times , ABC News , and National Observer Public commentary on rising insurance costs post-Hurricane Helene and climate-driven housing instability She advises PhD students such as Ammon Lam (PhD ’24) and has received research grants and institutional support for her work on climate finance and insurance markets. Her research has policy relevance for financial regulation, housing stability, and climate risk management. She is actively involved in academic and public discourse, frequently contributing insights on the financial implications of climate change. Shan Ge is a key contributor to the emerging field of climate finance, leading research that bridges corporate finance, insurance, and macroeconomic stability. Her work is conducted through collaborations with scholars at NYU and beyond, and she is affiliated with research centers focused on risk and sustainability. Future research directions include expanding climate risk metrics, analyzing insurer resilience under stress scenarios, and exploring behavioral responses to environmental and financial shocks.

Anestis TouloumisView profile

Senior Lecturer

• Biostatistics
• Categorical Data Analysis
• High-Dimensional Statistics
• +13 more

Anestis Touloumis is a Principal Lecturer in the School of Architecture, Technology and Engineering at the University of Brighton, affiliated with the Computing and Mathematical Sciences Research Excellence Group. He holds a PhD in Statistics from the University of Florida and has held postdoctoral positions at the European Bioinformatics Institute and the University of Cambridge before joining Brighton in 2015. Education: PhD in Statistics, University of Florida (2006–2011) MSc in Applied Statistics, University of Piraeus (2003–2006) BSc in Mathematics, Aristotle University of Thessaloniki (1998–2003) His research centers on biostatistics, high-dimensional statistics, and statistical genetics, with a focus on developing methods for correlated responses, covariance matrix estimation, and hypothesis testing in high-dimensional settings. His work is applied in omics, health studies, and social sciences, and he actively disseminates methods through R packages on CRAN and Bioconductor. He teaches a range of statistical modules using R and SAS, emphasizing conceptual understanding and critical thinking. The recent research articles reflect strong trends in biostatistical methodology, particularly in high-dimensional inference, longitudinal and correlated data analysis, and statistical computing. There is a consistent emphasis on developing robust, open-source tools for real-world applications in healthcare and data science. Scientific Awards and Professional Service: Treasurer, British and Irish Region of the International Biometric Society (2020–present) Committee Member, British and Irish Region of the International Biometric Society (2019–present) External Examiner, MSc in Applied Statistics, Birkbeck University of London (2020–present) Member, International Biometric Society He has been active in research supervision and mentoring, with a stated interest in supervising postgraduate students in machine learning, categorical data, and high-dimensional statistics, often involving software development. He has contributed to research grants, notably as a Co-Investigator on the ERDF-funded Digital Research & Innovation Value Accelerator (DRIVA) project (2018–2021). He is involved in multiple research collaborations and has delivered invited talks and peer reviews for leading journals. His work bridges theoretical statistics and practical applications across disciplines.