جستجوی استاد راهنمای دانشگاهی

Alessio Lomuscio is a Professor of Safe Artificial Intelligence at Imperial College London, holding the prestigious Royal Academy of Engineering Chair in Emerging Technologies and recognized as an ACM Distinguished Member. He leads the Safe AI Lab, which focuses on developing methods and tools for the verification of AI systems to ensure their safe and secure deployment in applications of societal importance. His research spans verification and robust learning for neural networks and decision trees, robust machine learning in aviation and finance, monitoring of machine learning systems, assurance for autonomous systems and AI, and verification and validation of neuro-symbolic systems. Lomuscio has made significant contributions to formal verification methods for AI systems, particularly in the context of safety-critical applications. His recent publications demonstrate a strong focus on neural network verification techniques, with applications across multiple domains including finance, aviation, and autonomous systems. His work bridges theoretical advances in formal methods with practical applications in real-world AI systems, addressing critical challenges in AI safety and trustworthiness. Scientific Awards: Royal Academy of Engineering Chair in Emerging Technologies ACM Distinguished Member Lomuscio has served in numerous leadership roles, including as Co-Director (2023-present) and Deputy Director (2019-2023) of the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence. He has also held positions as Director of Strategy and Planning (2017-2020), Member of Management Committee (2013-2020), and Deputy Head of Department (2016-2017). His editorial service includes roles as Associate Editor for Artificial Intelligence Journal and Editorial Board Member for Journal of Artificial Intelligence Research and Journal of Autonomous Agents and Multi-agent Systems. His research group actively mentors students and researchers, with current openings for PhD and postdoctoral positions focused on AI verification and safety. Lomuscio's work has established him as a leading figure in the field of safe and verifiable AI systems, with significant contributions to both theoretical foundations and practical applications.

• Artificial Intelligence
• Machine Learning Verification
• Neural Network Verification
• +۵ مورد دیگر

Professor Simon Godsill MA PhD FIET FIEEE is a University Professor of Statistical Signal Processing in the Department of Engineering at the University of Cambridge. He heads a research team specializing in statistical signal processing, digital audio restoration, and Bayesian inference. His work addresses the processing and analysis of digital speech, audio, tracking systems, and financial datasets, with a focus on probabilistic modeling and computational methods. Research interests include statistical signal processing , degraded signal restoration , and Bayesian computational methods . Recent publications emphasize Gaussian processes, variational inference, and multi-object tracking for applications in audio enhancement and financial data analysis. He co-founded the audio remastering company CEDAR Audio Ltd in 1988. Scientific awards: Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Institute of Electrical and Electronics Engineers (FIEEE) Outside academia, he enjoys singing, cricket, piano/organ playing, and running. His team at Cambridge's Engineering department focuses on robust tracking algorithms and signal enhancement techniques.

• Statistical Signal Processing
• Digital Audio Restoration
• Bayesian Computational Methods
• +۵ مورد دیگر

Carl Henrik Ek is a Professor of Statistical Learning at the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge. He is also a fellow and Director of Studies at Pembroke College, and holds visiting positions at Karolinska Institute in Stockholm and the Royal Institute of Technology. He serves as co-Director for the UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems, a collaboration between Cambridge and Manchester universities, and is involved with the Accelerate Program in the Computer Laboratory. Dr. Ek's educational background includes a MEng degree in Vehicle Engineering from the Royal Institute of Technology in Stockholm, followed by a PhD from Oxford Brookes University. During his PhD, he spent time at the University of Manchester and the University of Sheffield. His PhD supervisors were Professor Neil Lawrence and Professor Phil Torr, and his postdoctoral research was conducted at UC Berkeley with Professor Trevor Darrell and Professor Raquel Urtasun. Professor Ek's research focuses on statistical learning, particularly on developing data-efficient and interpretable machine learning methods. His work spans modeling and inference in machine learning, with special emphasis on Bayesian non-parametric methods and Gaussian processes. He explores how to specify assumptions that allow learning from small amounts of data, bridging theoretical foundations with practical applications in various domains. His recent publications demonstrate a strong trend toward applying machine learning to healthcare, drug discovery, and engineering design. There's significant work on Gaussian processes, reinforcement learning, and generative models, with applications ranging from medical diagnostics to structural engineering. His research shows an increasing interdisciplinary focus, connecting machine learning with fields like cardiology, pharmacology, and computational geometry. Professor Ek has received numerous teaching awards throughout his career: Pilkington Price for Teaching Excellence (2024) Teacher of the year in Computer Science at University of Bristol (2016) Docent in Machine Learning at Royal Institute of Technology (2016) Teacher of the year at Royal Institute of Technology, Sweden (2015) Teacher of the year from Student chapter in Industrial Economics at Royal Institute of Technology (2015) Teacher of the year in Computer Science at Royal Institute of Technology (2012) Professor Ek teaches Advanced Data Science, Advanced topics in machine learning, and Machine Learning and the Physical World. He has supervised PhD students throughout his career but is not currently accepting new PhD students for 2025/26 or 2026/27. His research is supported by various grants, including his role as co-Director of the UKRI AI Centre for Doctoral Training. He is an active member of the ml@cl research group at Cambridge and has previously been involved with research groups at University of Bristol and Royal Institute of Technology. His work connects with several interdisciplinary initiatives, particularly in healthcare AI and engineering applications of machine learning.

• Machine Learning
• Artificial Intelligence
• Bayesian Non-parametric Methods
• +۶ مورد دیگر

Dr. Yongchao Huang is a Lecturer (Assistant Professor) in the School of Natural and Computing Sciences at the University of Aberdeen, where he has been employed since August 2023. He also holds affiliations with the University of Oxford and the University of Cambridge through past postdoctoral and collaborative roles. He is actively involved in research, teaching, and academic service, and is currently accepting PhD students. His educational background includes: DPhil in Engineering Science, University of Oxford (2013–2017) Additional training in Machine Learning at Oxford (2015–2019) Dr. Huang's research focuses on fundamental and physics-informed machine learning, with core interests in Bayesian inference, variational methods, generative modeling (especially score-based), reinforcement learning, and interdisciplinary AI applications in mechanics, biology, energy, climate, and finance. A central theme of his work is the inference and sampling of probability densities, particularly through innovative particle-based and physics-inspired computational frameworks. He founded the Computational and Physical Learning (CPL) lab at Aberdeen in 2023. His recent publications (2020–2025) reflect a strong trend in probabilistic machine learning, with increasing focus on physics-based inference methods such as electrostatics, fluid dynamics, and material point methods. These works bridge machine learning with applied mathematics and physical simulation, demonstrating a unique interdisciplinary approach. Topics span Bayesian neural networks, acoustic wave propagation, mortality modeling, and adversarial cybersecurity. Dr. Huang has received academic recognition through invitations to serve on program committees and editorial roles: Program Committee Member, ECAI 2024 Organizing Committee, Bioinference 2024 Guest Editor, Journal of Theoretical Biology Senior Scientific Advisor to a UK firm He has supervised 57 MSc theses independently and currently supervises one PhD student. He has secured research engagement through collaborations with institutions including Oxford, Cambridge, and industry partners. His teaching includes courses such as Introduction to Software Engineering , Software Process and Management , and Computational Intelligence at Aberdeen, as well as practicals in inference at Cambridge. Dr. Huang leads the Computational and Physical Learning (CPL) lab at the University of Aberdeen, a curiosity-driven research group focused on foundational advances in machine intelligence. Though currently a solo researcher due to limited resources, the lab emphasizes end-to-end research and open collaboration. He encourages student mobility and interdisciplinary exploration.

• Machine Learning
• Bayesian Inference
• Probabilistic Machine Learning
• +۱۷ مورد دیگر

Alexandros Kontogiannis is a research fellow at the University of Cambridge, Department of Engineering, specializing in fluid dynamics and applied mathematics. His work combines Bayesian inference, machine learning, and physics-informed algorithms to solve inverse problems in magnetic resonance velocimetry (MRV) and fluid-structure interaction. EPSRC National Fellow in Fluid Dynamics Member of Energy, Fluids and Turbomachinery Division Research Focus: Development of digital twin frameworks that integrate MRV data with Navier-Stokes equations to reconstruct flowfields, infer rheological parameters in non-Newtonian fluids, and estimate hidden quantities like pressure and wall shear stress. Key innovations include: Physics-informed compressed sensing for sparse MRV data Simultaneous boundary shape and flowfield estimation Bayesian turbulence model parameter learning Scientific Awards: ASME Fluids Engineering Division Graduate Student Scholar (2021) Technical Chamber of Greece (TEE) Award (2018) Limmat Foundation Academic Excellence (2017) Mentzelopoulos Scholarship for international studies (2017) Greek State Scholarships Foundation Award (2012) Key Contributions: Algorithms for 3D flow reconstruction with adaptive discretization, viscous signed distance field regularization, and multi-objective aerodynamic shape optimization. His methodologies enable 27x reductions in MRI scanning time while maintaining diagnostic accuracy.

• Fluid Dynamics
• Applied Mathematics
• Bayesian Inference
• +۱۰ مورد دیگر

Nathan Judd is a Research Fellow in Statistics at the School of Mathematics, University of Birmingham. His research focuses on Bayesian non-parametric methods applied to modern slavery data and stochastic process modeling. He earned his PhD in Statistics from the University of Warwick (2024), MSc in Statistics from Lancaster University (2019), and BSc in Mathematics from the University of Kent (2018). Education: PhD in Statistics, University of Warwick (2024) MSc in Statistics, Lancaster University (2019) BSc in Mathematics, University of Kent (2018) Research themes include: Construction of non-diffusive Wright-Fisher processes Bayesian non-parametric models for crime-linkage analysis Testing methodologies for jumps in discretely observed stochastic processes Application of statistical models to socio-political challenges Recent publications demonstrate expertise in predictive modeling during disruptions, including the 2025 paper on zero-inflated mixed effects models for foodservice sales forecasting. Contact: n.a.judd@bham.ac.uk

• Bayesian non-parametric methods
• Modern slavery data analysis
• Non-diffusive Wright-Fisher processes
• +۲ مورد دیگر

Ludovic Renou is a Professor of Economics at Queen Mary University of London, affiliated with the School of Economics and Finance. He serves as a CEPR Fellow and Associate Editor at Games and Economic Behavior . His research focuses on economic theory, particularly game theory, information design, and strategic interactions. He has contributed to topics including repeated games, persuasion mechanisms, and networked economic systems. Recent work explores themes like human-machine interaction in decision-making, cross-verification in communication networks, and contracting under persistent information. His 2020 paper in the American Economic Review advanced revealed preference analysis under risk and uncertainty. Awards: CEPR Fellowship Renou advises on research projects but no formal advisees are listed. His professional activities include serving on the REF2021 Economics sub-panel. Outside academia, he is an avid runner with personal bests in multiple distances, emphasizing training regimens and collaborative running ethics outlined in his 'Four Axioms for Collaboration.'

• Economic Theory
• Game Theory
• Information Design
• +۳ مورد دیگر

Professor Jian Zhang is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on non-parametric and high-dimensional statistics, bioinformatics, computational biology, statistical genetics, neuroimaging methods, and Bayesian modeling. He has advised students including Jie Li and Tong Wang. His work spans theoretical advancements and applied methodologies across diverse fields such as genomics, neuroimaging, and biomedical data analysis. Publications highlight contributions to Bayesian inference, neuroimaging techniques, and statistical genetics. Notable collaborations include studies on mixture models for genetic association analysis and beamforming methods for functional connectivity. He holds an ORCID iD and is based at Canterbury Campus, University of Kent.

• Non-parametric statistics
• High-dimensional statistics
• Bioinformatics
• +۴ مورد دیگر

Professor Dan Crisan is a Professor of Mathematics at Imperial College London and Director of the EPSRC Centre for Doctoral Training in the Mathematics of Planet Earth. He holds a PhD in Mathematics from the University of Edinburgh and an MSci in Mathematics from the University of Bucharest. His research focuses on Stochastic Analysis, Stochastic PDEs, Fluid Dynamics, Nonlinear Filtering, and Data Assimilation. He leads the Stochastic Transport in Upper Ocean Dynamics (STUOD) project, supported by an ERC Synergy Grant, exploring stochastic models in geophysical fluid dynamics. His academic roles include directing the MPE CDT and teaching advanced courses like Stochastic Calculus with Applications to Non-Linear Filtering. He collaborates extensively with researchers globally on topics including stochastic fluid dynamics, particle methods, and Bayesian inference. His work bridges theoretical advancements with applications in climate modeling and environmental systems. Education: PhD (University of Edinburgh, 1996), MSci (University of Bucharest, 1992). Professional roles include Director of MPE CDT (2013–present), Professor at Imperial College (2011–present), and prior academic appointments at the University of Cambridge and Imperial College. His research emphasizes stochastic processes in fluid dynamics, with projects funded by EPSRC and EU grants. Current PhD supervision opportunities are available in areas like stochastic fluid dynamics and data assimilation. Key achievements include foundational contributions to particle filtering, numerical methods for stochastic PDEs, and stochastic transport models. His work on the Camassa-Holm equation and rotating shallow water models demonstrates expertise in nonlinear wave dynamics and stochastic parametrization. He actively engages in international collaborations and serves on editorial boards for leading journals in stochastic analysis.

• Stochastic Analysis
• Fluid Dynamics
• Stochastic Partial Differential Equations (SPDEs)
• +۶ مورد دیگر

Tom Fincham Haines is a Senior Lecturer in the Department of Computer Science at the University of Bath. He is affiliated with the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI, the Centre for Mathematics and Algorithms for Data (MAD), and multiple interdisciplinary institutes including Visual Computing, IAAPS, the Bath Institute for the Augmented Human, and the Institute for Digital Security and Behaviour (IDSB). He is actively accepting doctoral students and maintains a strong research presence in machine learning and its applications. Research Interests: Dr. Haines applies machine learning to computer vision and graphics, with a focus on graphical models, Bayesian non-parametric models, directional statistics, and active learning. His current interests include developing tools for artists, machine learning for education, online/real-time machine learning, causality, and scaling non-parametric methods for big data. His work contributes to several UN Sustainable Development Goals, particularly in health and security domains. Recent Research Trends: His recent publications (2024–2025) reflect a strong interdisciplinary focus, combining machine learning with applications in sonar imaging, drug detection, and creative media. He leverages deep learning and hybrid spectroscopic techniques for portable forensic devices and explores tangible interfaces for virtual film production, demonstrating a blend of technical innovation and societal impact. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Dr. Haines supervises doctoral students and has contributed to 8 supervised works. He is actively involved in multiple funded research projects, including as Principal Investigator (PI) on a Defence Science and Technology Laboratory project for synthetic opioid detection, and as Co-Investigator on EPSRC, MRC, Innovate UK, and National Police Chiefs' Council projects related to healthcare, chronic pain, and radicalization. His grant portfolio reflects strong collaboration across engineering, health, and security sectors. Labs and Teams: He is embedded in several research centers at the University of Bath, including the Centre for Mathematics and Algorithms for Data (MAD), the Institute for the Augmented Human, and the Institute for Digital Security and Behaviour (IDSB), where he contributes to AI, machine learning, and visual computing initiatives. His work spans collaborative teams in healthcare technology, digital forensics, and creative computing.

• Machine Learning
• Computer Vision
• Computer Graphics
• +۱۵ مورد دیگر

Professor Camila Caiado is affiliated with Durham University as a Professor in the Faculty of Science, Department of Mathematical Sciences . Her research spans Bayesian Statistics, Parametric Inference, Information Theory , and Stochastic Processes , with interdisciplinary applications in Health Informatics and Geophysics . Key Research Themes : Uncertainty Quantification, Computational Statistics, Decision Theory, and Applications of Bayesian Methods in Complex Systems Notable Collaborations : Atom Bank, Healthcare Institutions, Petroleum Engineering Projects Recent Publications focus on digital twin banking models , healthcare utilization during pandemics , and geochronological uncertainty analysis . She employs agent-based modeling and machine learning for pandemic planning and medical diagnostics. Academic Leadership : Active in Durham Research Methods Centre and Institute for Data Science. No formal awards or supervisees are explicitly mentioned in available data.

• Bayesian Statistics
• Parametric Inference
• Information Theory
• +۷ مورد دیگر

Massimiliano Tamborrino is an Associate Professor in the Department of Statistics at the University of Warwick since August 2024. He holds a WIHEA Fellowship (2023-2026) and has organized the One World ABC Seminar (2020–present), focusing on approximate Bayesian computation (ABC) and simulation-based inference. He co-organizes the BioInference conference series, which explores mathematical modeling in biological systems. His research integrates stochastic processes, numerical methods, and statistical inference, with applications in neuroscience, biology, and parallel-in-time algorithms. Education: PhD in Probability Theory and Statistics from the University of Copenhagen (2013), supervised by Prof. Susanne Ditlevsen. Previously held roles include University Research Assistant at JKU Linz (2014–2019) and Postdoc at the University of Copenhagen (2012–2014). Research interests include stochastic processes (diffusions, point processes), parallel-in-time numerical schemes (PinT), and ABC methods. His work bridges stochastic numerics with computational statistics, particularly in neuroscience and biological systems. He has developed R packages for exact simulation of non-Gaussian processes (e.g., shot noise, OU processes). Awards: WIHEA Fellow (2023–2026). Active in grants, including leadership of the EPSRC-funded project on AI-informed decision-making using Decision Field Theory. Teaching responsibilities include ST232/ST233: Introduction to Mathematical Statistics for undergraduate students.

• Stochastic Processes
• Approximate Bayesian Computation (ABC)
• Parallel-in-Time Numerical Methods
• +۴ مورد دیگر

Zexun Chen is a Lecturer in Predictive Analytics at the University of Edinburgh Business School , specializing in probabilistic machine modeling and non-parametric Bayesian predictive methods. He is affiliated with the Edinburgh Centre for Financial Innovations and holds memberships in the Royal Statistical Society (Fellow), Institute of Mathematics and its Applications (MIMA), London Mathematical Society (LMS), and International Statistical Institute.

• Probabilistic Machine Modelling
• Non-parametric Bayesian Methods
• Gaussian Process Modelling
• +۵ مورد دیگر

Michele Berardi is an Associate Professor (Reader) in Economics at the University of Manchester, serving as Director of Postgraduate Research for the School of Social Sciences. He holds a PhD from the Sant'Anna School of Advanced Studies in Pisa, Italy, and has been at the University of Manchester since 2006. His research focuses on expectations, learning, and information dynamics in economic systems, particularly exploring deviations from rational expectations within the framework of bounded rationality. His work contributes to understanding how economic agents process information and form beliefs, impacting policy design and market stability. Key research areas include Bayesian econometrics, asset pricing under uncertainty, and the implications of bounded rationality on macroeconomic outcomes. His recent articles analyze topics like price stability in cobweb models, sentiment-driven asset price fluctuations, and the role of learning algorithms in economic dynamics. Berardi’s studies often emphasize methodological rigor, combining theoretical models with empirical analysis. He has supervised 9 academic works, contributing to the next generation of researchers in economics. While no specific grants or awards are highlighted, his prolific publications (54 outputs, including 28 working papers and 23 peer-reviewed articles) reflect sustained scholarly engagement. His research aligns with UN Sustainable Development Goals, particularly those addressing economic stability and policy effectiveness.

• Economics
• Econometrics
• Macroeconomics
• +۳ مورد دیگر

Dominik Piehlmaier is an Associate Professor in Marketing (Strategy and Marketing) at the University of Sussex Business School and a Visiting Fellow at the University of Cambridge Judge Business School. He holds a Ph.D. in Consumer Behavior from the University of Wisconsin-Madison and has held prestigious fellowships at Stanford University and the University of Oxford’s Bennett Institute for Applied Data Science. His academic journey reflects a strong interdisciplinary foundation in consumer science, quantitative methods, and behavioral economics. His research centers on overconfidence, a cognitive bias influencing decision-making in finance, health, and consumer contexts. He employs advanced quantitative techniques, including Bayesian inference and quasi-experimental modeling, often using large-scale datasets such as electronic health records. His work spans behavioral finance, public health, and marketing analytics, with a focus on how cognitive biases affect real-world outcomes. Dominik’s recent publications, including in The Lancet Diabetes & Endocrinology , JAMA Psychiatry , and the Journal of Consumer Research , reflect a growing trend toward interdisciplinary research that bridges marketing with public health and data science. His studies often analyze national health data to understand behavioral patterns during crises like the COVID-19 pandemic, demonstrating the societal relevance of his work. He has been recognized with the 2023 Research Excellence Award for Emerging Scholars and is an inducted member of Phi Kappa Phi, the oldest and most selective interdisciplinary honor society in the U.S. His research has received extensive media coverage from major outlets such as the Wall Street Journal, BBC, Sky News, and Cosmopolitan, amplifying its public impact. Dominik has secured competitive grants from the Medical Research Council (MRC) and the British Academy to support his research on overconfidence and public health. He actively supervises postgraduate students and teaches advanced marketing analytics, having developed the module Advanced Marketing Analytics at Sussex. His teaching emphasizes hands-on data collection and statistical coding using tools like Qualtrics and Stata. He is involved in professional service, including membership on the American Council on Consumer Interests Membership Committee. He also contributes to public engagement through media interviews and impact narratives on topics such as financial overconfidence, panic buying, and youth risk behavior.

• Consumer Behavior
• Overconfidence
• Behavioral Finance
• +۱۱ مورد دیگر