Find Academic Supervisors

• Information Theory
• Coding Theory
• Network Coding
• +7 more

Sidharth Jaggi is a Professor in the School of Mathematics at the University of Bristol, specializing in Information Theory and Coding Theory with applications in secure data systems. His work provides unconditional information-theoretic security guarantees for communication/storage systems under malicious attacks. Education: B.Tech M.Phil PhD Research Focus: Professor Jaggi develops theoretical frameworks for adversarial communication, covert channels, and sparse signal estimation. His CAN-DO-IT research team (Codes, Algorithms, Networks – Design and Optimization for Information Theory) bridges abstract mathematics with real-world applications in distributed data storage, secure computing, and epidemic testing via group-testing methodologies. Key innovations include fundamental limits for stealthy communication and robust coding against jamming adversaries. Publication Trends: Recent works analyze density-dependent group testing structures, causality benefits in security against limited-view adversaries, and hybrid coding strategies for jamming channels. These publications demonstrate convergence of information theory, high-dimensional geometry, and optimization for next-generation secure data systems. Research Leadership: Principal Investigator for "Information Theory for Interactive Distributed AI" (2024-2029) Research Group: Leads the CAN-DO-IT team focusing on theoretical foundations for secure, robust information systems with tangible industrial applications in data processing infrastructure.

• Uncertainty Quantification
• Predictive Modeling
• Computational Mathematics
• +5 more

Nicholas Zabaras is a Professor of Uncertainty Quantification and the Director of the Warwick Centre for Predictive Modelling at the University of Warwick. He is a Hans Fischer Senior Fellow at the TUM Institute for Advanced Study (TUM-IAS), hosted by Phaedon-Stelios Koutsourelakis. His research focuses on advancing computational methods for uncertainty quantification, predictive modeling, and multiscale/multiphysics systems. Key areas include Bayesian methods, stochastic modeling, and data-driven approaches for complex materials systems. Education: He holds a diploma in Mechanical Engineering from the National Technical University of Athens (1982), an M.Sc. in Materials Science and Engineering from the University of Rochester (1983), and a Ph.D. in Theoretical and Applied Mechanics from Cornell University (1987). He has held academic positions at the University of Minnesota, Cornell University, and the University of Warwick, where he now leads the Warwick Centre for Predictive Modelling. Research Interests: His work integrates computational mathematics, statistics, and scientific computing to address challenges in materials science, computational physics, and engineering systems. Specific themes include Bayesian uncertainty quantification, high-dimensional modeling, information-theoretic coarse graining, and stochastic model reduction. Awards: He has received the Royal Society Wolfson Research Merit Award (2014), the Michael Tien’72 College of Engineering Teaching Award (2009), and is a Fellow of the American Society of Mechanical Engineers (2006). Labs/Teams: Director of the Warwick Centre for Predictive Modelling, and leader of the Scientific Computing and Artificial Intelligence (SCAI) Laboratory at the University of Notre Dame, focusing on interdisciplinary research in AI-driven predictive modeling and uncertainty quantification.

• Statistics
• Econometrics
• Machine Learning
• +7 more

Marcel Scharth is a Lecturer in Business Analytics at the University of Sydney Business School and affiliated with the Centre for Translational Data Science. He holds a Ph.D. from VU University Amsterdam (2012) and has conducted postdoctoral research at the University of New South Wales. His expertise spans Bayesian methods, computational statistics, machine learning, and financial econometrics. Research focuses include high-dimensional stochastic volatility models, Monte Carlo methods, and Bayesian machine learning applied to time series and longitudinal data. He teaches courses like QBUS2810 (Statistical Modelling), QBUS2820 (Predictive Analytics), and QBUS5001 (Quantitative Methods for Business), emphasizing statistical reasoning and computational skills. His work has been published in top journals such as the Journal of Econometrics and the Review of Economics and Statistics. He actively engages in AI discourse, contributing to media articles on prompt engineering for generative AI and the limitations of models like ChatGPT. Professional activities include Effective Altruism collaborations and open-source contributions via GitHub repositories like forecasting and Machine Learning for Business .

• Algorithmic Sequential Decision-Making
• Robust AI
• Safe AI
• +11 more

Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.

Milica TodorovicView profile

Visiting Professor

• Machine Learning
• Materials Science
• Quantum Mechanics
• +6 more

Milica Todorovic is a Visiting Professor at Aalto University's Department of Applied Physics , leading machine learning research within the Computational Electronic Structure Theory (CEST) group. Her work bridges quantum mechanical simulations and machine learning algorithms to optimize material functionality, particularly for solar cell components and organic-inorganic interfaces . Research Interests include: Data-driven materials science Active learning for molecular datasets Bayesian optimization in atomic structure prediction Quantum simulations of surfaces and adsorbates Thermodynamic property modeling for atmospheric molecules Article Trends (2017-2025) show focus on machine learning applied to materials science , with subfields spanning bayesian optimization , conformational analysis , and density functional theory . Key collaborations include Patrick Rinke and Hanna Vehkamäki . Activities include organizing workshops like the Young Researcher’s Workshop on Machine Learning for Materials Science (2019) and International Workshop on Machine Learning for Materials Science (2018).

Victor ChernozhukovView profile

Professor

• High-Dimensional Statistics
• Econometrics
• Quantile Regression
• +3 more

Victor Chernozhukov is a Professor at the Department of Economics and Center for Statistics at the Massachusetts Institute of Technology (MIT) . He also holds international fellowships at the University College London's CEMMAP and is a Professor by Courtesy at the New Economic School in Russia. His research spans econometrics, high-dimensional statistics, quantile regression, and causal inference. Education: Ph.D. in Economics from Stanford University (2000), M.S. in Statistics from University of Illinois at Urbana-Champaign (1997) His work focuses on developing statistical methods for high-dimensional data, including central limit theorems , post-selection inference , and quantile regression . He has contributed to partial identification , Bayesian inference , and extreme value analysis , with applications to economic policy evaluation. Recent publications emphasize high-dimensional causal inference , adaptive confidence bands , and set estimation . His research has been supported by the National Science Foundation and recognized with the Alfred P. Sloan Research Fellowship . Scientific Awards: Alfred P. Sloan Research Fellowship (2005-2007) Castle-Krob Career Development Chair (2004-2007) Grants include long-term support from the National Science Foundation since 2001 and software development collaborations for Stata and MATLAB implementations.

Hervé CardotView profile

Professor

• Functional Data Analysis
• Stochastic Algorithms
• Survey Sampling
• +10 more

Hervé Cardot is a Professor of Statistics and team leader of the Statistics, Probability, Optimization and Control (SPOC) group at the Institut de Mathématiques de Bourgogne (IMB) , University of Burgundy. His research spans functional data analysis, stochastic algorithms for robust estimation, survey sampling methodology, and applied statistics across diverse fields. Research Interests : Functional data analysis, online principal component analysis, zero-inflated regression, survey sampling, nonparametric estimation Key Applications : Agriculture, climatology, energy consumption, food science, remote sensing, medical imaging Recent Work includes: Developing statistical frameworks for analyzing categorical trajectories using multivariate functional PCA (2025) Advancing bias-robust estimation for functional data in survey sampling (2020) Creating online stochastic algorithms for high-dimensional robust estimation (2017) Applying semi-Markov models to sensory analysis (2018) Teaching at the University of Burgundy includes courses on statistical modeling, big data analysis, and applied statistics for economics and psychology. He supervises PhD students in statistics and co-edits Statistics and Probability Letters .

Yarema OkhrinView profile

Professor

• Statistics
• Data Science
• Machine Learning
• +7 more

Prof. Dr. Yarema Okhrin serves as Full Professor at the Department of Statistics and Data Science within the Faculty of Economics at the University of Augsburg. He leads the Chair of Statistics and Data Science, which is part of the Business Analytics & Operations and Finance, Accounting, Controlling & Taxation clusters. His research group includes Dr. Sebastian Heiden, Dr. Rui Ren, and several research assistants working on advanced statistical methods and their applications in business and economics. Professor Okhrin's research spans mathematical and statistical models for data-driven problems in business and economics, with particular expertise in statistics, data science, and machine learning. His work focuses on developing methodologies for time series analysis, portfolio optimization, risk management, and statistical process control. Recent research has expanded into high-dimensional statistics, image analysis, and network monitoring applications. Analysis of his recent publications (2023-2025) reveals a strong trend toward interdisciplinary applications of statistical methods, particularly in finance and process monitoring. His work demonstrates increasing integration of machine learning techniques with traditional statistical approaches, especially in portfolio optimization and risk assessment. A notable pattern is the development of specialized statistical methods for high-dimensional data analysis across multiple domains. Professor Okhrin supervises bachelor's and master's theses at the Department of Statistics, with research topics including data mining, time series modeling, forecasting methods, regression analysis, and statistical data analysis. His department offers thesis opportunities in specialized areas such as asymmetric dependencies in financial markets, forecasting, investor sentiment analysis, medical statistics, volatility modeling, multivariate distributions, sustainable investing, and portfolio optimization. The Chair of Statistics maintains active research collaborations and offers academic consulting on statistical issues and data analysis. The department hosts regular research seminars to facilitate exchange of ongoing research projects and maintains connections with industry partners for practical applications of statistical methods.

• Econometric Methods
• Machine Learning
• Causal Inference
• +4 more

Jeremy L'HOUR is a quantitative research specialist at Capital Fund Management (CFM) in Paris, France, and an affiliated researcher in the Economics department of CREST (Center for Research in Economics and Statistics, Université Paris-Saclay). His work focuses on econometric methods and the application of machine learning to causal inference.

Jennifer CastleView profile

Associate Professor

• Climate Econometrics
• Econometric Modeling
• Model Selection
• +7 more

Jennifer Castle is the Director of Climate Econometrics at the University of Oxford and a Titular Associate Professor and Tutorial Fellow in Economics at Magdalen College. Her work bridges econometrics and climate-economic modeling, focusing on robust forecasting methodologies and empirical analysis of macroeconomic variables like inflation, unemployment, and climate impacts. Affiliation: University of Oxford, Magdalen College Research Themes: Climate econometrics, structural break analysis, general-to-specific modeling, and non-linear time series Research Interests center on disentangling complex climate-economic relationships and improving forecasting accuracy through rigorous model evaluation. She applies these methods to inflation dynamics, energy policy, and long-term climate transition scenarios. Recent Publications highlight her work on UK inflation forecasting challenges, cryptocurrency market stability, income inequality projections, and climate policy intervention points. Her research emphasizes model robustness in the face of structural shifts and data uncertainty.

Luis SecoView profile

Professor

• Harmonic Analysis
• Mathematical Physics
• Mathematical Finance
• +3 more

Luis Seco is a Professor in the Department of Mathematics at the University of Toronto, with a focus on Mathematical and Computational Sciences. He is based at the Bahen Centre for Information Technology (Room BA-7262) and maintains a presence at the University of Toronto Mississauga (UTM) campus. He serves as Director of the Mathematical Finance Program and is affiliated with RiskLab Toronto, housed at the Bahen Centre. Dr. Seco's research spans multiple disciplines at the intersection of mathematics and finance: Harmonic Analysis Mathematical Physics Mathematical Finance Quantitative Risk Management His scholarly work demonstrates a sophisticated integration of advanced mathematical techniques with practical financial applications. Recent publications (2022-2025) reveal a strong emphasis on machine learning approaches to financial problems, with particular focus on algorithmic trading, risk modeling, and ESG (Environmental, Social, and Governance) investing. His research has evolved from theoretical mathematical foundations to cutting-edge applications in quantitative finance. Dr. Seco's publication record shows significant scholarly productivity with a clear trajectory from pure mathematics toward increasingly applied financial mathematics. His work bridges theoretical rigor with practical financial applications, making substantial contributions to both academic research and industry practices. As Director of the Mathematical Finance Program, Dr. Seco provides leadership in shaping education and research in quantitative finance at the University of Toronto. His RiskLab affiliation indicates active engagement with real-world risk management challenges, connecting academic theory with industry applications.

Spiridon PenevView profile

Professor

• Wavelet Methods in Non-Parametric Curve Estimation
• Edgeworth Expansions and Saddlepoint Approximation Methods
• Structural Equation Models
• +4 more

Professor Spiridon Penev is a faculty member at the School of Mathematics & Statistics , University of New South Wales (UNSW), Sydney. After completing his PhD in Mathematical Statistics at Humboldt University, Berlin, he worked at Technical University of Sofia (Bulgaria) for 10 years, becoming Associate Professor in 1991. He joined UNSW in 1992, progressing from Lecturer to Professor in 2019. His teaching focuses on Statistical Inference , Multivariate Analysis , Longitudinal Data Analysis , and related advanced courses. Research Interests: Wavelet Methods in Non-Parametric Curve Estimation, Edgeworth Expansions, Saddlepoint Approximation, Structural Equation Models, Inference in Semiparametric Models, and Stochastic Risk Modelling. Article Trends: His work spans from foundational wavelet methods (pre-2010) to modern applications in climate model ensembles , portfolio optimization , marine engineering , and machine learning . Keywords include Statistics, Finance, Climate Science, Structural Health Monitoring, and Optimization. Awards: DAAD Award, Elected Member of the International Statistical Institute (ISI). Grants: Led ARC Discovery Project (2016–2018), ARC Linkage Project (2018–2022), and industry collaborations like SCA water quality analysis (2014–2019). Location: School of Mathematics and Statistics, UNSW Sydney, Room 1038, The Red Centre.

Peter Michael RobinsonView profile

Professor

• Econometrics
• time series analysis
• spatial analysis
• +2 more

Peter Michael Robinson holds the Tooke Professorship of Economic Science and Statistics at the London School of Economics and Political Science (LSE), where he maintains an active research profile in advanced econometric theory. His institutional affiliation centers on LSE's Department of Economics, though the departmental structure is not explicitly detailed in available materials. Robinson's research program focuses on cutting-edge econometric methodologies, particularly long-memory processes, spatial dependence structures, and nonstationary time series analysis. His work bridges theoretical rigor with practical applications in panel data and spatial econometrics, emphasizing semiparametric and nonparametric approaches for complex dependency patterns. Key innovations include refined inference techniques for spatial autocorrelation and fractional integration models. Analysis of his recent publications (2012-2018) reveals a sustained emphasis on spatial econometrics and time-series methodology, with significant contributions to panel data modeling, long-range dependence theory, and nonparametric estimation under cross-sectional dependence. His work consistently advances asymptotic theory while addressing real-world data challenges in economics and statistics. No scientific awards or honors were explicitly documented in the source materials. Available records contain no information regarding doctoral students, grant funding, or academic advising activities. Research infrastructure details such as laboratories or collaborative teams were not specified in the provided documentation.

• Statistics
• Epidemiology
• Genetics
• +7 more

Maria De Iorio is a Professor at the Yong Loo Lin School of Medicine and holds a joint appointment at the Faculty of Science , both at the National University of Singapore. Her work bridges statistics, epidemiology, and clinical sciences through advanced Bayesian modeling and computational methods. Key research areas: Statistics, Epidemiology, Genetics, Clinical sciences, Medical biochemistry Primary affiliations: Paediatrics department (YLLSoM) and interdisciplinary projects in Science faculty Recent publications focus on extreme value theory, digital numismatics, metabolomic simulations, and maternal-child health Her statistical innovations include: Repulsive mixture models for interpretable clustering Bayesian semi-parametric approaches for longitudinal studies Novel prior distributions for graphical models Application domains span: Pediatric oncology (ALL treatment response modeling) Metabolic syndrome analysis (obesity, diabetes, bone health) Digital humanities (coin distribution patterns, ceramic artifact analysis) Financial forecasting (discrete survival methods for invoice prediction)

Sara MostafaviView profile

Associate Professor

• Computational Biology
• Machine Learning
• Artificial Intelligence
• +4 more

Sara Mostafavi is an Associate Professor at the Paul Allen School of Computer Science & Engineering at the University of Washington (UW), with a focus on Artificial Intelligence and Computational Biology. She is currently on leave (2024-2025) leading the Computational Biology and Translation Organization at Genentech. Mostafavi previously held faculty roles at the University of British Columbia (UBC) and the Vector Institute, where she received prestigious Canada Research Chair (CRC II) and Canada CIFAR Chair in AI (CIFAR-AI) awards. PhD in Computer Science, University of Toronto (2011) Postdoctoral Researcher, Stanford University Her research develops machine learning and statistical methods to study gene regulation, disease susceptibility, and molecular networks, with applications in immunology, genetics, and neuroscience. She has pioneered sequence-to-function models for understanding gene regulatory grammar and predicting disease mechanisms. Her publications highlight expertise in genomic data integration, interpretable AI, and cross-modality learning. Recent work includes analyzing mosaic chromosome loss in aging microglia, immune cell differentiation, and Alzheimer's disease networks. Scientific Awards Canada Research Chair (CRC II) in Computational Biology (2015–2020) Canada CIFAR Chair in Artificial Intelligence (CIFAR-AI) Co-founder of the Machine Learning for Computational Biology (MLCB) Conference Mostafavi has mentored numerous PhD and Master’s students, including Xinming Tu, Anna Spiro, Gherman Novakovsky (PhD 2023), and Elijah Willie (MSc 2020). Her lab collaborates with institutions like the Immunological Genome (ImmGen) Consortium and Canadian Institute for Advanced Research (CIFAR). She leads the Mostafavi Lab, which specializes in combining association evidence across genomics datasets and modeling biological pathways to disentangle spurious correlations. Her team develops tools like CEWAS, AI-TAC, and Brain xQTL web server.