Find Academic Supervisors

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +8 more

Dr Dalia Chakrabarty is a Reader in Statistical Data Science at the Department of Mathematics, University of York. Previously, she held positions as Senior Lecturer at Newcastle University and Lecturer at Lancaster University. Her research focuses on probabilistic methods, Bayesian inference, and machine learning applications in fields such as medicine, astronomy, and materials science. She specializes in kernel methods, random graph analysis, and causal forecasting. Notable contributions include developing methodologies for uncertainty quantification and non-parametric learning. Her academic career includes a Royal Society Dorothy Hodgkin Fellowship and supervision of students like Kane Warrior. Dr Chakrabarty's work bridges theoretical statistics and real-world challenges, with publications spanning journals like Plos One and Artificial Intelligence in Medicine . She also authored the textbook Supervised Learning: Mathematical Foundations & Real-world Applications (2024, CRC Press). Research Highlights: Inter-graph distance metrics for medical data analysis Bayesian state-space modeling of galactic dynamics High-dimensional data applications in oncology and materials science Collaborations: Maintains ties with Brunel Mathematics for PhD supervision and international research networks. Contact: Email dalia.chakrabarty@york.ac.uk , Tel: +44 (0)1904 32 1486

Zhiang ChenView profile

Researcher

• Geophysics
• Robotics
• Machine Learning
• +3 more

Zhiang Chen is a Postdoctoral Scholar Research Associate in Geophysics at the California Institute of Technology (Caltech). His work bridges geophysical research with robotics and machine learning for environmental monitoring. Research interests include: Development of low-cost robotic tools (e.g., Shakebot) for seismic studies Integration of UAVs, lidar, and zero-shot deep learning for geologic mapping Analysis of fault scarps, precariously balanced rocks, and volcanic deformation Optimization of environmental monitoring via decision-theoretic and digital twin frameworks Recent publications highlight interdisciplinary applications of robotics and machine learning in geophysics, focusing on 3D mapping of surface deformations, wildfire-induced geohazards, and autonomous detection of geologic features.

Joshua LoyalView profile

Assistant Professor

• Statistical network analysis
• Bayesian inference
• Machine learning
• +2 more

Joshua Loyal is an Assistant Professor in the Department of Statistics at Florida State University. His research focuses on statistical network analysis, Bayesian inference, machine learning, data science, and statistical computing. Education: Ph.D. in Statistics, University of Illinois at Urbana-Champaign M.S. in Physics, Yale University B.S. in Physics and Mathematics, Duke University His research interests span statistical methods for network analysis and computational statistics, with applications ranging from social networks to environmental science and particle physics. His work develops novel methodologies for analyzing complex, high-dimensional data structures. His publications demonstrate a consistent focus on developing statistical models for network data, with recent work advancing Bayesian nonparametric methods and dimension reduction techniques for dynamic networks. He leads research projects applying statistical network analysis to diverse domains including arctic ecology, pandemic modeling, and particle physics.

Jian CaoView profile

Assistant Professor

• Computational Statistics
• Gaussian Process Regression
• Spatial Statistics
• +4 more

Jian Cao is an Assistant Professor in the Department of Mathematics at the University of Houston, specializing in Computational and Spatial Statistics. He earned his Ph.D. in Statistics from KAUST under Dr. Marc Genton and completed postdoctoral work at Texas A&M University under Dr. Matthias Katzfuss. His research focuses on scalable Gaussian Process (GP) regression methods, including Vecchia approximations, truncated multivariate normal distributions, and applications in climate and agricultural science. Education includes a B.S. in Mathematics from University of Science and Technology of China (2014), a Master in Finance from Shanghai Jiaotong University (2016), and a Ph.D. in Statistics from KAUST (2020). Research interests emphasize computational efficiency in statistical modeling, with contributions to scalable algorithms for high-dimensional data. Key themes include Gaussian processes, multivariate normal probabilities, and low-rank matrix methods. His work bridges theory and application, addressing challenges in environmental and agricultural data science. Notable awards include the Al-Kindi Statistics Student Research Award (2020) and Best Student Paper (2019). His publications span journals like the Journal of the American Statistical Association and conferences such as ICML, focusing on scalable statistical methods and software development (e.g., tlrmvnmvt package). Teaching responsibilities include courses on statistics for the sciences and inferential statistics at the University of Houston. His software contributions prioritize high-performance computing solutions for large-scale geospatial and statistical problems.

Zhigen ZhaoView profile

Associate Professor

• Bayesian/empirical Bayesian statistics
• High-dimensional data analysis
• Multiple comparison
• +5 more

Zhigen Zhao is an Associate Professor and Beury Research Fellow at the Fox School of Business and Management, Temple University, within the Department of Statistics, Operations, and Data Science. He holds a Ph.D. from Cornell University (2009) and specializes in Bayesian/empirical Bayesian statistics, high-dimensional data analysis, and bioinformatics. His research is supported by the National Science Foundation. Dr. Zhao’s research focuses on developing statistical methodologies for high-dimensional problems, including selective inference, multiple comparisons, and sufficient dimension reduction. His work bridges theoretical advancements with practical applications in healthcare, genomics, and predictive analytics. Key contributions include Bayesian hierarchical models for electronic health records and novel techniques for controlling false discovery rates in genomic studies. He teaches courses such as Intermediate Statistics, Regression and Predictive Analytics, and Statistical Methods for Business Research at both undergraduate and graduate levels. His recent publications appear in top-tier journals like the Journal of the American Statistical Association and Journal of the Royal Statistical Society, Series B. Dr. Zhao’s grants include NSF funding for high-dimensional statistical research. He advises on methodological challenges in data science and collaborates on projects involving healthcare analytics, genomics, and machine learning applications.

• Kernel Methods
• Gaussian Processes
• Machine Learning
• +3 more

Motonobu Kanagawa is an Assistant Professor (Maitre de Conférences) in the Data Science Department at EURECOM since September 2019. Previously, he was a researcher at the University of Tuebingen and the Max Planck Institute for Intelligent Systems in Germany under Prof. Philipp Hennig (2017–2019), and completed his PhD at the Institute of Statistical Mathematics in Tokyo under Prof. Kenji Fukumizu (2013–2016). Education: Doctorate in Statistical Science (2016), Graduate University for Advanced Studies / Institute of Statistical Mathematics, Japan Master in Computer Science (2013), Nara Institute of Science and Technology, Japan Research Interests: His work focuses on kernel methods, Gaussian processes, and machine learning for enhancing computer simulations. Key areas include statistical validation of simulation models, applications in climate, disaster, economic, and financial analysis, and interpretable distribution comparison. He emphasizes bridging theory and practice in probabilistic numerics and Bayesian optimization. Publications Trends: His recent work addresses challenges in Bayesian quadrature, Gaussian process scaling, covariate shift adaptation, and pension system risk analysis. He frequently explores kernel-based approaches for computational efficiency and interpretability. Awards: Chris Daykin Prize (2022) from the International Actuarial Association for work on pension system risk analysis Grants & Leadership: He chairs a position at 3IA Cote d'Azur (2021) and organizes the ProbNum 2025 conference. His advisory roles include editorial board membership at the Journal of Machine Learning Research (JMLR) and reviewing for top venues like TPAMI, NeurIPS, and ICML. Labs & Collaboration: Active in the Data Science Department at EURECOM,他曾领导Max Planck的Probabilistic Numerical Computing Group，并与EDHEC等机构合作研究养老金系统风险。他的研究团队通过贝叶斯优化和核方法推进跨领域应用。

• Statistics
• Machine Learning
• Simulation
• +5 more

Motonobu Kanagawa is an Assistant Professor (Maitre de Conférences) in the Data Science Department at EURECOM in France since 2019. Previously, he held research positions at the University of Tübingen and the Max Planck Institute for Intelligent Systems in Germany under Prof. Philipp Hennig. He earned his PhD in 2016 from the Institute of Statistical Mathematics in Tokyo under Prof. Kenji Fukumizu. His research focuses on statistical methodologies for complex systems simulation, including reliability validation of simulators, Gaussian processes, Bayesian optimization, and kernel methods. Recent work explores variable selection in distribution comparison and covariate shift adaptation. Key achievements include organizing the inaugural ProbNum 2025 conference, receiving the Chris Daykin Prize (2023) for pension system analysis with Bayesian optimization, and securing a chair position at the 3IA Côte d'Azur AI institute (2021). He reviews for leading journals like JMLR and conferences such as NeurIPS and ICML. Publications span topics like k-NN regression optimization, Gaussian process scale parameter estimation, and counterfactual mean embeddings. His work bridges theoretical statistics and applied machine learning, emphasizing computational efficiency and uncertainty quantification.

Yu ChengView profile

Assistant Professor

• Machine Learning
• Optimization
• Game Theory
• +4 more

Yu Cheng is an Assistant Professor in the Department of Computer Science at Brown University. He holds a Ph.D. from the University of Southern California, advised by Shang-Hua Teng. Prior to his current role, he was a postdoc at Duke University, a visiting member at the Institute for Advanced Study, and an Assistant Professor at the University of Illinois at Chicago. His research focuses on machine learning, optimization, and game theory, with recent emphasis on robust algorithms for machine learning, including high-dimensional robust statistics and non-convex optimization. He teaches advanced courses such as CSCI2952Q: Robust Algorithms for Machine Learning and CSCI1520: Algorithmic Aspects of Machine Learning. His work has been recognized with the Best Paper Award at WINE 2018. Yu advises Ph.D. students like Binhao Chen and Xing Gao (co-advised with Lev Reyzin), and has mentored undergraduate researchers including Tianle Jiang and Haichen Dong. His algorithmic contributions span spectral graph theory, mechanism design, and strategic classification. Recent publications address challenges in robust matrix sensing, outlier-robust estimation, and efficient algorithms for participation-constrained planning. Affiliations: Brown University, University of Illinois at Chicago (former), Duke University (former postdoc) Research Areas: Robust machine learning, non-convex optimization, algorithmic game theory Key Projects: Robust algorithms for high-dimensional data, strategic classification systems, spectral graph sparsification

• Network Science
• Graphical Models
• Causality
• +3 more

Veronica Vinciotti is an Associate Professor in the Department of Mathematics at the University of Trento. She holds a PhD in Statistics from Imperial College London (2002) and a First Degree in Mathematics from the University of Perugia (1999). Her academic career includes roles at Brunel University London as Lecturer (2007-2012), Senior Lecturer (2012-2017), and Reader (2017-2020) before joining the University of Trento in 2020. Her research focuses on Network Science, Graphical Models, Causality, and Biostatistics. Key contributions include advancements in sparse inference, causal generalized linear models, and applications to microbiome interactions and healthcare quality evaluation. She leads the PRIN 2022 project and has coordinated initiatives like COSTNET (2016-2020). Editorial roles include Editor of Statistica Neerlandica and guest editorships for special issues on Network Science. She has advised six PhD students and contributed to R packages like cglasso and sglasso . Her work bridges statistical methodology with applications in biology, healthcare, and sociology. Recent grants include funding from the Research Council of Finland and Swedish Research Council. She is a member of the Bernoulli Society and Royal Statistical Society, and has organized conferences such as the International Workshop on Statistical Modelling (2024) and sessions at the Isaac Newton Institute (2016).

Ozan ÖzdenizciView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Robustness and Safety in ML
• +4 more

Assoc. Prof. Ozan Özdenizci is a faculty member at the Institute of Machine Learning and Neural Computation, TU Graz, Austria. His research focuses on robustness, safety, and efficiency in machine learning, particularly in adversarial robustness, spiking neural networks, and privacy-aware learning. He holds a PhD from Northeastern University (2016), and MSc/BSc degrees from Sabancı University (2010/2008). Previously, he served as a postdoc at TU Graz (2016–2020) and a research group leader at Montanuniversität Leoben (2020–2023). Affiliations: TU Graz (2023–present), Montanuniversität Leoben (2020–2023), TU Graz Postdoc (2016–2020) Education: PhD (Northeastern University, 2016), MSc/BSc (Sabancı University, 2010/2008) Research Interests: Developing ML systems with robustness guarantees, efficient deep learning (e.g., spiking networks), and privacy-aware mechanisms. Key areas include adversarial defense mechanisms, sparse network design, and neuromorphic computing applications. His work bridges theoretical foundations with practical implementations in computer vision and autonomous systems. Recent Contributions: Advances in privacy-aware lifelong learning (ICLR 2025), robust spiking networks (TMLR 2024), and weather-resistant vision models (TPAMI 2023). His research emphasizes trade-offs between model efficiency, robustness, and scalability. Awards: Top Reviewer at NeurIPS 2023, Outstanding Reviewer at ICML 2022/ICLR 2022 Labs/Teams: Member of Graz Center for Machine Learning (GraML) and ELLIS Unit Graz

Dionysis KalogeriasView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Optimization
• +5 more

Dionysis Kalogerias is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. He holds a PhD from Rutgers University and previously served as an Assistant Professor at Michigan State University, with postdoctoral positions at the University of Pennsylvania and Princeton University. His research focuses on machine learning, optimization, risk-aware decision-making, and their applications in autonomous systems, wireless communications, and financial risk management. Education: PhD in Electrical & Computer Engineering, Rutgers University MEng/MSc in Electrical & Computer Engineering, University of Patras, Greece Research Interests: Dr. Kalogerias explores mathematical optimization, statistical learning, and decision-making under uncertainty, with emphasis on applications in resource allocation, autonomous systems, and financial risk management. His work bridges theory and practice, addressing challenges in wireless networks, robust control, and algorithmic trading. Awards: ICASSP Best Paper Award (2020) Rutgers SOE Outstanding Graduate Student Award (2017) Rutgers ECE Graduate Program Academic Achievement Award (2017) Advising & Grants: Recipient of NSF grant for reliable wireless autonomous networks Advising PhD student Baturay Saglam at Yale Collaborations: Active involvement in federated learning marketplaces (FEDSTR) and decentralized AI protocols.

Jean-Christophe Olivo-MarinView profile

Professor

• Bioimage Analysis
• Computational Biology
• Machine Learning
• +13 more

Jean-Christophe Olivo-Marin is the Head of the Biological Image Analysis Unit and Director of the Carnot Pasteur Institute for Microbes and Health at the Institut Pasteur in Paris, France. He previously served as Director of the Department of Cell Biology and Infection (2010–2014) and continues to lead impactful research at the intersection of biology, computation, and imaging. His research focuses on bioimage informatics , developing computational methods in Machine learning and deep learning for image analysis Bayesian tracking and optical flow algorithms Statistical modeling of spatial patterns in biological systems Mathematical imaging and computational cell biophysics Digital pathology and cancer microenvironment analysis His work enables rigorous quantitative analysis of complex biological phenomena such as cell motility, host-pathogen interactions, and neural dynamics. His recent publications (2024–2025) reflect a strong trend toward AI integration in bioimaging , with advances in deep learning-based segmentation (e.g., Deep ContourFlow), large-scale image annotation (SAMJ), and frameworks for evaluating neuron tracking. There is also a focus on spatial analysis in disease contexts , particularly in cancer and neurodevelopment, combining deep learning with spatial statistics. He has been awarded research funding for projects including Next-generation Structured Illumination Microscopy (SIM) Machine learning for cancer detection using FFOCT Compressive sensing in biological imaging Statistical analysis of spatial coupling in bioimaging (SODA) Olivo-Marin actively mentors students and postdoctoral researchers and contributes to open science through the development and maintenance of Icy , a widely used open-source bioimage analysis platform. He regularly participates in international conferences such as QBI, ICPR, and NEUBIAS, and leads advanced training courses, including the upcoming Advanced Bioimage Analysis with Artificial Intelligence (AI) course at Institut Pasteur in 2026. He leads a dynamic research unit with PhD students, postdocs, and engineers working on cutting-edge image analysis challenges. The team fosters collaboration across disciplines and institutions, promoting open science and community-driven software development.

• Evolutionary Multiobjective Optimization
• Search-Based Software Engineering
• Semantic Web
• +4 more

Vitor Basto Fernandes is an Associate Professor with tenure at Iscte - University Institute of Lisbon, affiliated with the Department of Information Science and Technology (ISTA) and ISTAR-Iscte research center. His career spans academic, research, and industry roles across institutions like University of Minho, University of Trás-os-Montes, and Polytechnic Institute of Leiria. PhD in Informatics (2006), University of Minho Postgraduate in Mobile Computing (2005), University of Minho Postgraduate in Distributed Systems (1997), University of Minho BSc in Information Systems Management (1995), University of Minho His research focuses on Evolutionary Multiobjective Optimization , Search-Based Software Engineering , and Cyber Security , with applications in healthcare systems, cloud computing, and anti-spam technologies. He has coordinated academic programs, led international research projects, and served as principal investigator for FCT-funded initiatives. Recent publications highlight expertise in cybersecurity risk analysis , ontological knowledge management , and multiobjective evolutionary algorithms . He organizes international conferences and mentors graduate students in fields like cloud infrastructure and data science . As an Integrated Researcher at ISTAR-Iscte, he contributes to projects involving semantic web technologies , enterprise application integration , and dimensionality reduction for spam filtering.

• Numerical Optimization
• Mixed-Integer Nonlinear Programming
• Global Optimization
• +14 more

Ruth Misener is a Professor in the Department of Computing at Imperial College London, where she leads the Computational Optimization Group and holds the BASF/RAEng Research Chair in Data-Driven Optimization (2022–2027). She is affiliated with the Faculty of Engineering and contributes to interdisciplinary research institutes including the Data Science Institute, the Institute for Molecular Science and Engineering, and the Sargent Centre for Process Systems Engineering. Her research lies at the intersection of numerical optimization, operations research, and machine learning, with applications in chemical engineering, bioprocess optimization, energy systems, and industrial scheduling. She develops global optimization algorithms for mixed-integer nonlinear programs (MINLP), focusing on real-world challenges such as heat recovery network design, petrochemical process optimization, and robust bioreactor operation. A key innovation is her work on optimizing over machine learning surrogates, including tree ensembles and neural networks, enabling data-driven decision-making under uncertainty. Her recent publications demonstrate a strong trend toward integrating Bayesian optimization with active learning, explainable AI, and industrial applications, particularly in collaboration with BASF, Royal Mail, and Eli Lilly. She develops and maintains open-source optimization tools such as ROmodel, OMLT, and ENTMOOT, which are publicly available on GitHub. STEM for Britain acceptance Runner-Up Presentation Award at PSE@ResearchDayUK Best Quality Poster to Simon Olofsson 1st Poster Prize at UK/Ireland Annual Meeting of the Society for Industrial & Applied Mathematics (2018) 2nd Poster Prize at Centre for Process Systems Engineering Industrial Consortium Meeting (2017) 1st Poster Prize at 2nd PSE@ResearchDayUK (2017) 2nd Presentation Prize at Department of Computing Research Associate Symposium (2017) Runner-Up for May Hicks Award (via student Natasha Page) Ruth supervises a dynamic research team and has examined and mentored numerous PhD students, including Jean Kossaifi, Robert Walecki, Alexander Thebelt, and Toby Boyne. She leads major research grants, including the BASF/RAEng Research Chair and the IConIC Prosperity Partnership, and collaborates with industry partners to advance continuous manufacturing and data-driven process optimization. Her team actively disseminates work through open-access publications, video presentations, and social media.

Denis BelomestnyView profile

Professor

• Statistics of Stochastic Processes
• Financial Mathematics
• Econometrics
• +5 more

Denis Belomestny is a Professor of Applied Stochastics at the Department of Mathematics, University of Duisburg-Essen. His academic journey includes a PhD from Lomonosov Moscow State University (2002), postdoctoral work at the University of Bonn, and research positions at WIAS Berlin and Humboldt University Berlin. He currently leads research at the intersection of stochastic processes and financial mathematics. PhD in Mathematics, Lomonosov Moscow State University (2002) W3 Professorship in Applied Stochastics, University of Duisburg-Essen (2011–present) His research focuses on statistics of stochastic processes , optimal stopping/control , and Monte Carlo methods , with applications in financial mathematics and machine learning. Key collaborations include work with John Schoenmakers on multilevel approximation algorithms and Alexey Naumov on variance reduction techniques. Recent publications (2025–2023) explore deep neural networks for SDEs , generative adversarial networks , and nonparametric estimation in complex stochastic models. His work spans stochastic differential equations , financial derivatives pricing , and machine learning-driven statistical inference . He supervises doctoral students, including Sascha Nolte (research: robust optimal stopping without reference models). Current projects involve McKean-Vlasov SDEs , gamma-driven processes , and reinforced optimal control .