جستجوی استاد راهنمای دانشگاهی

Oana Lang is a Lecturer in Mathematics at Babeş-Bolyai University and a former STUOD Research Associate at Imperial College London. Her research focuses on stochastic analysis, particularly nonlinear stochastic partial differential equations (SPDEs) and their applications in fluid dynamics and data assimilation. She holds a PhD from Imperial College London (2020), with a thesis on stochastic transport equations and data assimilation. Affiliations: Academic Women in Mathematics, Mathematics of Planet Earth, Stochastic Analysis Research Group at Imperial Education: PhD in Mathematics, Imperial College London (2016–2020) MRes in Mathematics of Planet Earth, Imperial College London (2015–2016) MSc in Applied Mathematics, University of Bucharest (2013–2015) BSc in Mathematics, University of Bucharest (2010–2013) Her research interests emphasize SPDEs driven by transport noise, particularly in ocean and climate modeling. She has developed analytical frameworks for stochastic Euler equations, rotating shallow water models, and their applications in data assimilation. Key contributions include proving well-posedness for transport SPDEs and advancing calibration methods for stochastic fluid models. She organizes the Stochastic Analysis Seminar at Imperial College and the STUOD SPDEs Seminar. Recent work includes invited sessions on stochastic models in fluid dynamics at international conferences and editorial roles for Emergent Scientist . Awards: MRes degree with distinction (Imperial College London, 2016), multiple conference organization roles, and active grant-funded research in stochastic fluid dynamics. Labs/Teams: STUOD research group, Mathematics of Planet Earth Centre for Doctoral Training (MPE CDT), and collaborations with institutions like Reading University and the University of Aachen.

• Stochastic Analysis
• Stochastic Partial Differential Equations (SPDEs)
• Nonlinear Filtering
• +۴ مورد دیگر

Jon Devlin is a Lecturer and Outreach Officer in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. He began his role in February 2022 after completing both his BSc (Hons) and PhD in Mathematics at the same institution. His work spans teaching, student supervision, educational outreach, and research in statistical modeling of biological systems. His educational background includes: BSc (Hons) in Mathematics, University of Strathclyde (awarded 2016) PhD in Mathematics, University of Strathclyde (awarded 2022) PGCert in Learning and Teaching in Higher Education (awarded 2024) Jon Devlin's research focuses on statistical inference in stochastic models, particularly applied to biological phenomena such as cell migration. His work employs Bayesian methods, optimal estimation, and computational statistics to model drift and diffusion processes, especially under measurement error. He has published in high-impact journals such as Physical Review E and Computational Statistics . The recent articles demonstrate a strong trend in applying advanced statistical techniques to real-world biological data, particularly in understanding collective cell behavior and parameter estimation under uncertainty. His scientific awards highlight excellence in teaching and outreach: Fellow of Advance HE (FHEA) Faculty of Science Teaching Excellence Award – Early Career Impact (2023) Faculty of Science Teaching Excellence Award – Team Award (2024) Strathclyde Medal – Team Award (2024) Jon Devlin is actively involved in grant-funded educational initiatives and student mentoring. He supervises final-year Honours and MSc Data Analytics projects and supports pre-sessional mathematics programs. He leads multiple outreach projects, including the Maths Week Scotland Inter-Authority Maths Conference, funded by a £2400 grant, and other internally funded programs like the AGI Tutoring Programme and the Maths Week Scotland Event at Strathclyde. These reflect a strong commitment to widening participation and improving mathematics education at the school and community level. His outreach efforts constitute a major component of his role, with over 267 recorded activities, including workshops at primary and secondary schools, public engagement events, and professional symposia. These activities are designed to showcase the real-world applications of mathematics and statistics and encourage students to pursue STEM subjects at university.

• Mathematics Education
• Outreach in STEM
• Bayesian Inference
• +۷ مورد دیگر

Marco De Angelis is a Lecturer at the Centre for Intelligent Infrastructure within the Department of Civil and Environmental Engineering at the University of Strathclyde's Faculty of Engineering. His work focuses on computational methods for handling uncertainty in engineering systems, with applications in structural reliability and health monitoring. Education: PhD in Risk and Uncertainty (2015) from University of Liverpool's Institute for Risk and Uncertainty Master of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Bachelor of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Dr. De Angelis specializes in computing with imprecision, developing methods to propagate uncertainty through models using interval analysis, probability bounds, and other mathematical frameworks. His research enables rigorous inference with scarce empirical data and builds trust in simulation for structural reliability assessment. His work intersects civil engineering, computer science, and statistics, with particular emphasis on practical applications in infrastructure monitoring and risk assessment. His recent publications demonstrate a strong focus on high-dimensional uncertainty analysis, optimization under uncertainty, and verified computational methods for reliability engineering. The research shows increasing sophistication in handling complex uncertainty representations while maintaining computational tractability for real-world engineering problems. Scientific Awards: Best student paper (June 18, 2025) The NASA and DNV Challenge on Optimization under Uncertainty (June 17, 2025) Bronze poster award (July 27, 2021) Teaching and Learning Award (May 17, 2017) ISIPTA-IJAR Young Researcher Award (August 2015) Dr. De Angelis teaches structural engineering theory, computer programming, interval computation, probability theory, and machine learning to undergraduate students. He has developed teaching materials from scratch for advanced dynamics courses. He serves as Co-investigator on the REUN project (Reduction of Uncertainties in risk assessment of structures and infrastructures against Natural hazards) funded by the Royal Society of Edinburgh, running from April 2025 to March 2027. His professional activities include conference participation, journal peer review, and invited talks in his specialty areas. He is actively involved with the Centre for Intelligent Infrastructure, where he contributes to research on digital twins and computational methods for infrastructure monitoring and assessment.

• Failure probability
• Interval computation
• Statistics for imprecise data
• +۱۰ مورد دیگر

Professor V. Radu Craiu is a distinguished faculty member in the Department of Statistical Sciences within the Faculty of Arts and Science at the University of Toronto. He has served as Chair of the Department for 5 years (2018-2022 and 2023-2024) after joining as an Assistant Professor in 2001, being promoted to Associate Professor in 2006 and to Full Professor in 2013. Ph.D. in Statistics (2001) - University of Chicago M.S. in Mathematics (1996) - University of Bucharest B.S. in Mathematics (1995) - University of Bucharest Professor Craiu's research spans multiple domains of statistics with particular expertise in computational methods. His work has evolved from foundational research on Markov chain Monte Carlo samplers to broader applications in Bayesian statistics, copula models, statistical genetics, and more recently, astronomy. His research demonstrates both theoretical depth and practical applications across diverse fields including genetics, ecology, and astrophysics. His recent publications show a strong focus on advancing computational methodologies while addressing complex real-world problems. The research trends reveal increasing interdisciplinary collaboration, particularly with astronomers working on radio transients and stellar flares, while maintaining strong contributions to core statistical methodology in areas like copula modeling, MCMC algorithms, and dimension reduction. Fellow of the American Statistical Association (2022) Fellow of the Institute of Mathematical Statistics (2020) Faculty Affiliate of the Vector Institute (2020) CJS Award for 'Likelihood Inflating Sampling Algorithm' (2019) CRM-SSC prize from Centre de Recherches Mathematiques and Statistical Society of Canada (2016) Elected Member of the International Statistical Institute (2015) Professor Craiu has supervised numerous doctoral students whose work spans statistical genetics, computational methods, and copula modeling. His editorial service includes positions as Contributing Editor for the IMS Bulletin and Associate Editor for multiple prestigious journals including Harvard Data Science Review, Journal of Computational and Graphical Statistics, Statistics Surveys, The Canadian Journal of Statistics, and Statistical Methods and Applications. His research has been supported by various grants that have enabled extensive collaborations across disciplines.

• Computational Statistics
• Markov Chain Monte Carlo
• Bayesian Inference
• +۵ مورد دیگر

Rocco Caprio is a Postdoctoral Research Fellow in the Department of Statistics at the University of Warwick, specializing in computational statistics, applied probability, optimization, and the mathematical foundations of machine learning algorithms. His research investigates theoretical convergence properties and error bounds for statistical methods, leveraging functional inequalities such as logarithmic Sobolev and Poincaré inequalities to analyze algorithms including Expectation Maximization, particle gradient descent, and Markov chain Monte Carlo variants. This work bridges rigorous mathematical analysis with practical machine learning applications. Recent publications reveal a concentrated focus on algorithmic theory, with recurring themes in stochastic optimization convergence, sampling method efficiency, and inequality-based error quantification. His output demonstrates strong interdisciplinary connections between statistics, theoretical computer science, and applied mathematics, primarily targeting high-impact venues in statistical theory and machine learning.

• computational statistics
• applied probability
• optimization
• +۱ مورد دیگر

Joe Watson is a Postdoctoral Researcher at the University of Oxford within the Applied Artificial Intelligence Lab at the Oxford Robotics Institute . He earned his PhD in 2024 from the Technical University of Darmstadt , supervised by Prof. Jan Peters, and holds an MEng in Information & Computer Engineering from University of Cambridge . His research focuses on statistical methods for robot learning , particularly the duality between entropy-regularized optimization and Bayesian inference. Key interests include uncertainty quantification , policy optimization , and incorporating structural knowledge into robotics. Scientific contributions include Coherent Soft Imitation Learning (NeurIPS 2023, spotlight) and Monte Carlo Posterior Policy Iteration (CoRL 2022, oral). Recent publications address safe foundation models and tensor motion planning in 2025. 2022 : R:SS Pioneer for robotics research 2016 : Charles Babbage Senior Scholarship at Cambridge He has contributed to open-source projects like i2c and monte-carlo-posterior-policy-iteration , with 534 GitHub contributions in the last year.

• Robot Learning
• Machine Learning
• Bayesian Inference
• +۵ مورد دیگر

Dr. Nikolas Kantas is a Reader in Statistics at Imperial College London's Department of Mathematics. He completed his undergraduate studies and PhD at the University of Cambridge's Signal Processing Group. His research focuses on developing numerical methods for complex problems in inference, optimisation, filtering, and control. Research Interests: Kantas specializes in computational statistics and stochastic processes, with expertise in particle filtering, Sequential Monte Carlo, and Markov Chain Monte Carlo methods. His work bridges theoretical foundations with applications in data assimilation, optimization under uncertainty, and high-dimensional statistical modeling. Publication Trends: Recent work (2022-2025) demonstrates strong focus on optimization algorithms, stochastic differential equations, and Monte Carlo methods. Key themes include multi-objective optimization, privacy-preserving algorithms, Langevin dynamics, and distributed computing. Methodological innovations frequently address high-dimensional and real-time computational challenges. Student Advising & Grants: Currently supervises 4 PhD students and has graduated 9 doctoral candidates. Research funding includes JP Morgan AI Faculty Research Awards and support from the National Physical Laboratory (NPL). Academic Leadership: Co-organizes the annual Greek Stochastics workshop on Statistics and Applied Probability. Coordinates PhD programs through the Mathematics Research program, MFC CDT, and Statistics and Machine Learning CDT.

• Computational Statistics
• Stochastic Processes
• Numerical Methods
• +۷ مورد دیگر

Professor Andrzej Murawski is a Tutorial Fellow at Worcester College , affiliated with the University of Oxford Department of Computer Science . His research spans the semantics of programming languages , automata theory , and software verification , integrating game semantics and probabilistic computation for modeling complex systems. Education: D.Phil., University of Oxford (2001) His work addresses higher-order recursion , concurrent programs , and probabilistic verification , including differential privacy and machine learning applications. Recent publications focus on reachability analysis , contextual equivalence , and automata over infinite alphabets . Key activities include organizing SIGLOG (Vice-Chair), editing SIGLOG News , and serving on LICS , POPL , and CONCUR program committees. Awards include the POPL 2025 Distinguished Paper Award . Students : Current advisees include Benedict Bunting and Haoxuan Yin; past students include Fabian Zaiser and David Hopkins.

• Programming Languages
• Automata Theory
• Software Verification
• +۹ مورد دیگر

Professor István Z. Kiss is a Professor of Network Science at the Network Science Institute, Northeastern University London. His research bridges network science, dynamical systems, and stochastic processes with applications in epidemic modeling, computational neuroscience, and complex systems analysis. He leads the Systems Processes and Networks Lab (SPAN Lab) and maintains active collaborations across multiple institutions globally. Current position: Professor of Network Science Institution: Northeastern University London Research unit: Network Science Institute (SPAN Lab) Key collaborators: Péter L. Simon (Eötvös Loránd University), Joel C. Miller (La Trobe University), Gregory A. Rempała (Ohio State University) His research focuses on theoretical and data-driven problems at the intersection of network science and dynamical processes. Key areas include network inference, exactness of mean-field models, temporal and higher-order networks, adaptive/dynamic networks, and resilience of power networks. He has made significant contributions to understanding epidemic dynamics on complex networks, developing mathematical frameworks that connect approximate models with rigorous counterparts. Recent work emphasizes higher-order network structures, network inference from system-level data, and applications to public health and infrastructure resilience. His publication record demonstrates consistent output in top journals including Journal of Mathematical Biology, Bulletin of Mathematical Biology, and Physical Review E. The research shows a clear progression toward increasingly complex network structures, with recent publications focusing on higher-order interactions, temporal dynamics, and practical applications in epidemiology and infrastructure networks. His work bridges theoretical mathematics with real-world applications in disease control and network resilience. Professor Kiss actively supervises postdoctoral researchers and PhD students, with current advisees including Federico Cosimo Malizia (higher-order network contagion), Kevin Teo (shipping networks), and Yan Li (trade dynamics). His supervision spans mathematical theory, computational modeling, and data analysis across multiple domains. Leverhulme Trust grant RPG-2017-370: Bayesian Inference and Approximations in High-dimensional Network Models Network Science Institute Boston funding for higher-order network research EPSRC grant EP/H001085/1 for information diffusion modeling He leads the Systems Processes and Networks Lab (SPAN Lab), which develops mathematical frameworks for complex networked systems. The lab focuses on both theoretical advancements in network science and practical applications to epidemiology, neuroscience, and infrastructure networks. Current projects include developing tools for sequential temporal network analysis, understanding failure prediction in utility networks, and disentangling contact network structure effects on system-level outputs.

• Network Science
• Dynamical Systems
• Stochastic Processes
• +۶ مورد دیگر

Professor Nick Chater serves as Professor of Behavioural Science within the Behavioural Sciences Group at Warwick Business School (WBS), University of Warwick. His academic profile spans cognitive science, behavioral economics, and decision-making psychology, with significant contributions to understanding the fundamental mechanisms of human thought and behavior. Chater's research interests encompass cognitive science, behavioral economics, and the psychology of decision-making. He has pioneered work challenging traditional views of deep, unconscious mental processes, most notably through his influential 2018 book "The Mind is Flat: The Remarkable Shallowness of the Improvising Brain." His research program investigates how people process information, make decisions under uncertainty, and develop language and social coordination mechanisms. Key themes include the role of noise in cognition, Bayesian approaches to modeling human thought, and the application of behavioral insights to public policy design. Chater's extensive publication record reveals a strong trend toward interdisciplinary integration of computational modeling with empirical research. His recent work demonstrates consistent engagement with cutting-edge developments across cognitive science, particularly in Bayesian cognitive modeling and behavioral public policy. The S-Frame agenda for behavioral public policy research represents a systematic approach to translating behavioral insights into policy design. His collaborative network spans psychology, economics, linguistics, and artificial intelligence, reflecting the interdisciplinary nature of modern cognitive science. As an educator, Chater teaches "Behavioural Sciences for the Manager" for Executive MBA programs and "Judgement and Decision Making" for various MSc programs at Warwick Business School. His teaching focuses on applying behavioral science principles to business contexts, helping students understand how cognitive biases and heuristics influence managerial decision-making. His work on coordination in dynamic interactions and moral cognition provides valuable insights for understanding organizational behavior and team dynamics.

• Cognitive Science
• Behavioral Economics
• Decision Making
• +۴ مورد دیگر