جستجوی استاد راهنمای دانشگاهی

Yarin Galمشاهده پروفایل

دانشیار

• Bayesian Deep Learning
• Machine Learning
• AI Safety
• +۴ مورد دیگر

Yarin Gal is an Associate Professor of Machine Learning at the University of Oxford's Department of Computer Science and a Tutorial Fellow at Christ Church College. He is also a Turing AI Fellow at the Alan Turing Institute and Director of Research at the UK Government’s AI Safety Institute. He leads the Oxford Applied and Theoretical Machine Learning (OATML) Research Group, focusing on Bayesian deep learning, AI safety, and uncertainty quantification. Education PhD in Uncertainty in Deep Learning (2016) Research Interests His research integrates Bayesian methods with deep learning to address challenges in AI safety , uncertainty quantification , and robustness . Key areas include: Bayesian neural networks and approximate inference Uncertainty estimation in deep learning AI safety and interpretability Applications in autonomous driving, medical imaging, and NLP Publications & Trends His recent work spans Bayesian optimization , adversarial robustness , and continual learning . Notable contributions include Targeted Dropout for model pruning, Uncertainty in Autonomous Driving , and theoretical studies on adversarial examples in Bayesian networks. Awards & Honors Turing AI Fellow Teaching & Supervision He has taught Advanced Machine Learning , Uncertainty in Deep Learning , and contributed to NASA's Frontier Development Lab. His current students include Kelsey Doerksen, Gunshi Gupta, and Shreshth Malik. Labs & Teams He leads the OATML Group , a multidisciplinary team advancing theoretical and applied machine learning, with a focus on safety and interpretability in AI systems.

Michael Ben-Gadمشاهده پروفایل

استاد

• Macroeconomics
• Public Finance
• Economics of Immigration
• +۳ مورد دیگر

Professor Michael Ben-Gad is a Professor of Economics at City, University of London, where he has served since 2009. He previously held roles as Head of the Department of Economics (2010–2013) and Deputy Head (2008–2010). His research focuses on dynamic macroeconomics, taxation, public debt, immigration economics, and national security, with notable contributions to endogenous growth models and compositional data econometrics. He holds a PhD from the University of Chicago (1996) and has taught at institutions including the University of Houston and University of Haifa. Ben-Gad served on the U.S. National Academy of Sciences panel on immigration's economic and fiscal impacts (2014–2016) and received a 2017 Fellowship from the Royal Society of Arts (FRSA). His work spans academic journals (Economic Modelling, Journal of Economic Dynamics and Control) and media outlets (BBC, CNBC, Al Jazeera), addressing topics like Brexit economics, fiscal policy, and geopolitical challenges. He co-convenes the City Branch of Academics for Academic Freedom and is a founding member of London Universities' Council for Academic Freedom. His research grants include funding from HM Treasury/ESRC on fiscal policy and immigration (2011), the Israel Science Foundation (2007–2008), and the Samuel Ne'eman Foundation (2004). Professional roles include editorial board membership for Economic Affairs and membership of the ESRC Grant Assessment Panel (2021). He is proficient in Hebrew and contributes to policy debates on taxation, macroprudential frameworks, and defense economics.

• Reinforcement Learning
• Multi-Agent Systems
• Deep Learning
• +۲ مورد دیگر

Shimon Whiteson is Professor of Computer Science at the University of Oxford, leading the Whiteson Research Lab focused on reinforcement learning, multi-agent systems, and deep learning. His research develops algorithms for efficient learning in complex environments. Current work explores meta-reinforcement learning frameworks that enable agents to rapidly adapt to new tasks, with applications in autonomous driving simulation and robotics. Recent innovations include novel methods for offline reinforcement learning, multi-agent coordination, and morphology-aware control. Publications demonstrate advances in: Meta-RL algorithm design for few-shot adaptation Multi-agent reinforcement learning environments and benchmarks Imitation learning in autonomous driving Bayesian methods for sample-efficient learning Research outputs include widely used benchmarks and tools including JaxMARL for accelerated multi-agent RL research. Current doctoral supervision focuses on temporal abstraction in RL, multi-agent coordination, and reinforcement learning theory.

Yee Whye Tehمشاهده پروفایل

استاد

• Statistical Machine Learning
• Probabilistic Learning
• Deep Learning
• +۴ مورد دیگر

Yee Whye Teh is a Professor at the Department of Statistics, University of Oxford, and a research scientist at DeepMind. His work focuses on statistical machine learning, including probabilistic learning, Bayesian nonparametrics, deep learning, and Monte Carlo methods. He co-directs the ELLIS programme on Robust Machine Learning and has held roles such as Programme Co-chair for ICML 2017. Teh has delivered keynotes at UAI 2019, an IMS Medallion Lecture at JSM 2019, and the Breiman Lecture in 2017. His research emphasizes scalable inference algorithms, hierarchical models, and applications in genetics and natural language processing. Teh's educational background includes a PhD from the University of Toronto (2003) and a Master's from the same institution (2000). He has contributed to widely used software tools like the Sequence Memoizer and has been recognized for his work through prestigious lectureships. Research interests span Bayesian nonparametric models, MCMC methods, and their applications in genetics and data compression. His lab collaborates on projects like fragmentation-coagulation processes for genetic variation modeling and Mondrian forests for online learning. Teh advises students through Oxford's graduate programs, though he notes high demand for mentorship. His work often bridges theory and practice, addressing challenges in big data learning and small data problems.

Adrian Wellerمشاهده پروفایل

استاد پژوهش

• Artificial Intelligence
• Machine Learning
• Explainability
• +۹ مورد دیگر

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

• Complex Systems
• Financial Mathematics
• Statistical Physics
• +۶ مورد دیگر

Tomaso Aste is a Professor of Complexity Science at the Department of Computer Science, University College London (UCL). He founded the Financial Computing and Analytics group and co-founded the UCL Centre for Blockchain Technologies. His work bridges complex systems, data science, and finance, with applications in blockchain, fintech, and market modeling. Education: PhD in Physics (Politecnico di Milano, 1994); Laurea in Physics (University of Genoa, 1990) Prior Appointments: Reader at University of Kent's School of Physics; Associate Professor at Australian National University's Applied Mathematics His research focuses on data-driven modeling of complex systems , particularly financial systems, complex networks, and statistical physics. He has pioneered information filtering networks and topological machine learning methods for financial applications, including portfolio optimization, risk assessment, and cryptocurrency analysis. Recent publications highlight his expertise in financial time-series analysis, blockchain technology, and AI-driven modeling. Articles explore topics like limit order books, cryptocurrency market fragility, and topological neural networks. His work has influenced regulatory technology (RegTech) frameworks and digital economy strategies. Scientific Awards : Marie Curie Individual Fellowship University of Genoa graduate study specialization Fellowship Bacheflor Boncompagni-Ludovisi Foundation Fellowship Awarded fellowships from European Commission and academic foundations support his interdisciplinary research. He has held editorial roles at journals like Philosophical Magazine and Granular Matter , and contributed to professional societies including American Physical Society and Australian Research Council panels. Teaching & Academic Leadership : Co-created four UCL Master's programs: Financial Risk Management, Computational Finance, Financial Technologies, Emerging Digital Technologies Coordinates executive training on AI, blockchain, fintech, and regtech for regulators and private firms Teaches graduate-level courses in Data-Driven Modeling, Data Science, and Advanced AI

Roger Giner-Sorollaمشاهده پروفایل

استاد

• Moral Psychology
• Social Psychology
• Emotion Research
• +۷ مورد دیگر

Roger Giner-Sorolla is Professor of Social Psychology at the University of Kent's School of Psychology, where he has been a faculty member since 2001 after completing his PhD at New York University in 1996. He served as Editor-in-Chief of the Journal of Experimental Social Psychology from 2016-2022 and currently leads a Leverhulme Foundation grant examining moral heroism across professions. His academic journey includes a postdoctoral fellowship at the University of Virginia and has established him as a leading figure in social and moral psychology. Professor Giner-Sorolla's research focuses on the social role of emotions, particularly moral emotions including self-condemning emotions (guilt and shame) and other-condemning emotions (anger, contempt, and disgust). His work extends to intergroup apologies, dehumanization, emotionally driven prejudice, and collective moral roles, with a notable side interest in the ironic enjoyment of music and aesthetic experiences. He is a strong advocate for transparency in scientific reporting, having written extensively on improved reporting guidelines, pre-registration, and open science practices. Analysis of his recent publications reveals a consistent focus on moral psychology with expanding interdisciplinary reach. His work bridges traditional social psychology with emerging areas including cross-cultural emotion research, replication science, and historical memory. The trend shows increasing attention to methodological rigor while maintaining core interests in emotion and morality, with growing emphasis on practical applications in conflict resolution and intergroup relations. Fellow of the Association for Psychological Science Member of Rising Stars selection committee (2023-2024) Promising Scholars Award (University of Kent, 2006) Vacation Research Scholarship (Wellcome Trust, 2005) Multiple ESRC grants totaling over £400,000 European Science Foundation Workshop Award (2004) Professor Giner-Sorolla has supervised an impressive number of PhD students, with 17 doctoral candidates listed in the documentation including both current and past students. His grant portfolio demonstrates significant research impact with funding from major organizations including ESRC, Wellcome Trust, and Leverhulme Foundation. His supervision style appears to encourage students to pursue research that aligns with his core interests in moral emotions while allowing exploration of novel applications and contexts. While the documentation doesn't specify laboratory or research team structures, his extensive collaborative work suggests leadership of research groups focused on moral psychology and emotion research. His numerous international collaborations and invited workshops indicate an active research program with global reach, likely involving multiple research teams working on interconnected projects related to his core interests in moral psychology and scientific methodology.

Andrew Stephenمشاهده پروفایل

استاد

• Digital Marketing
• Social Media Strategy
• Consumer Behavior
• +۵ مورد دیگر

Andrew Stephen is the L’Oréal Professor of Marketing and Deputy Dean for Faculty and Research at Saïd Business School, University of Oxford. He leads the Marketing faculty and directs the Oxford Future of Marketing Initiative, focusing on emerging technologies like AI and data-driven strategies in marketing, media, and advertising. His research explores digital marketing, consumer behavior, and the impact of new technologies, earning him recognition as a top global marketing scholar. His academic contributions span leading journals such as the Journal of Consumer Research and Harvard Business Review. He serves as editor of the Journal of Consumer Research, co-editor of the International Journal of Research in Marketing, and actively engages with industry through councils like the Mobile Marketing Association’s Brand Safety Council. Teaching responsibilities include marketing curriculum oversight and courses across MBA, Executive MBA, and DPhil programs, focusing on digital transformation, marketing analytics, and the future of marketing. His work bridges academic rigor with industry relevance, addressing challenges in tech-enabled marketing and global brand strategies. Awards: Top marketing academic in the UK (AMA), featured in Poets & Quants’ top 40 under 40, multiple research excellence awards. Labs/Initiatives: Oxford Future of Marketing Initiative, fostering collaborations with leading companies to tackle industry challenges. Grants/Advising: Extensive industry partnerships and academic grants not explicitly detailed in the text.

• Macroeconomic Theory
• Endogenous Growth
• Endogenous Fluctuations
• +۵ مورد دیگر

Mauro Bambi is an Associate Professor at Durham University Business School and an Associate Fellow at the Institute of Advanced Studies. His research focuses on Macroeconomic Theory, Endogenous Growth, Habit Formation, and Behavioral Economics. He earned his PhD from the European University Institute (2007) and held positions at ETH Zurich and the University of York. He directs the Centre for Macroeconomic Policy (CEMAP) and has been recognized with the 2008 Italian Association of Applied Mathematics award for his PhD thesis. Education: PhD in Economics (European University Institute, 2007); Postdoctoral Fellowships at Université Catholique de Louvain (Belgium) and ETH Zurich (Switzerland). Research Interests: Macroeconomic Theory and Policy Design Endogenous Growth and Fluctuations Habit Formation in Economic Models Behavioral Economic Preferences Mathematical Methods in Macroeconomics Key Contributions: His work on habit formation models, time-to-build frameworks, and policy design has influenced macroeconomic theory. Recent studies analyze post-COVID demand shifts and pandemic economic impacts. Awards: 2008 Graduate Prize for Best PhD Thesis (Italian Association of Applied Mathematics). Advising and Leadership: Supervised PhD students like Federico Bertoni and Xinyi Xu. Directed CEMAP from 2019–2022, fostering macroeconomic policy research. Active in interdisciplinary collaborations at the Institute of Advanced Studies. Labs/Teams: Leader of CEMAP, collaborating with global institutions on macroeconomic policy analysis.

Simon Kingمشاهده پروفایل

استاد

• Speech Synthesis
• Speech Processing
• Acoustic Modeling
• +۴ مورد دیگر

Simon King is a Professor of Speech Processing at the University of Edinburgh , affiliated with the School of Philosophy, Psychology and Language Sciences . He serves as Director of the Centre for Speech Technology Research (CSTR) and teaches courses like Speech Processing and Speech Synthesis , while directing the MSc in Speech and Language Processing . Research Interests His research focuses on: Developing new acoustic models (e.g., Linear Dynamical Models, factorial-HMMs) for speech recognition Advancing unit selection and HMM-based speech synthesis Integrating articulatory measurement data for enhanced modeling Exploring perceptual measures in synthesis criteria Building multilingual speech systems to identify universal speech building blocks Publication Trends Simon's recent work emphasizes deep learning (DNNs, LSTMs) in speech synthesis, multilingual frameworks , and articulatory-acoustic feature integration . His studies often bridge grapheme-based modeling , perceptual error reduction , and noise-robust synthesis . Scientific Awards EPSRC Advanced Research Fellowship (2005-2009) Students & Collaborations He has supervised numerous PhD students including Rasmus Dall, Tom Merritt, and Srikanth Ronanki. Current research fellows like Mirjam Wester and Zhizheng Wu contribute to projects such as Natural Speech Technology (NST) and Simple4All .

Michael A Osborneمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +۳ مورد دیگر

Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML

Po-Ling Lohمشاهده پروفایل

استاد

• Statistical Learning
• Robust Statistics
• High-Dimensional Statistics
• +۳ مورد دیگر

Professor Po-Ling Loh is a faculty member at the University of Cambridge, affiliated with the Statistical Laboratory within the Faculty of Mathematics. Her research focuses on statistical theory and methodology, with applications in machine learning, robust statistics, and medical imaging. She holds a professorship position and contributes to advancing computational and theoretical frameworks for high-dimensional data analysis. Loh’s work addresses challenges such as robust regression, differential privacy, and efficient algorithms for complex models. Her educational background includes studies at Cambridge and further academic pursuits, though specific degree details are not provided here. Research interests span statistical learning, adversarial machine learning, and the mathematical foundations of robust algorithms. She actively publishes in top-tier journals and conferences, addressing topics like neural network regularization, privacy-preserving synthetic data, and network analysis. Notably, Loh collaborates on projects involving medical image analysis (e.g., bone age estimation via BAE-ViT) and has contributed to methodological advancements in hypothesis testing and privacy-constrained inference. Her research often bridges theory and practice, emphasizing computational efficiency and statistical rigor. While no specific grants or awards are listed, her prolific publication record reflects sustained academic impact in statistical and machine learning domains. Loh is associated with the Statistical Laboratory, contributing to its research initiatives and possibly advising students in high-dimensional statistics and related fields. Her work frequently intersects with interdisciplinary applications, such as medical imaging and network science, underscoring the practical relevance of her theoretical contributions.

Alessio Lomuscioمشاهده پروفایل

استاد

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

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.

Paul Goldbergمشاهده پروفایل

استاد

• Algorithmic Game Theory
• Computational Complexity Theory
• Machine Learning
• +۳ مورد دیگر

Paul Goldberg is a Professor of Computer Science and Director of the MSc in Mathematics and Foundations of Computer Science (MFoCS) at the University of Oxford. He holds a BA in Mathematics from Oxford University and a PhD in Computer Science from the University of Edinburgh. His research focuses on algorithmic game theory, computational complexity, and machine learning, with notable contributions to equilibrium computation, complexity classes of total search problems, and decentralized systems. Affiliations: Department of Computer Science, Oxford; Editorial Board of ACM Transactions on Economics and Computation. Education: PhD in Computer Science (1993), University of Edinburgh MSc in Computer Systems Engineering (1989), University of Edinburgh and Université Paris-Sud BA in Mathematics (1988), Oxford University Research Interests: Algorithmic game theory, computational complexity (especially total search problems like CLS and PPAD), decentralized computation of equilibria, and applications in machine learning and AI. His work bridges theoretical computer science and economics, with a focus on algorithm design and complexity analysis. Publications and Awards: Over 120 papers, including influential work on Nash equilibrium complexity (2009), gradient descent (2023), and fair division algorithms. Notable awards include the ACM SIGecom Test of Time Award (2022) and a SIAM Outstanding Paper Prize (2011). Grants and Students: Leads EPSRC-funded projects on game theory and machine learning. Supervised 11 PhD graduates and currently advises Giannis Tyrovolas and others. Active in mentoring MSc and undergraduate projects. Labs/Teams: Part of the Algorithms and Complexity Theory group at Oxford, contributing to research on optimization, equilibrium dynamics, and fair division.

Andreas Vlachosمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Dialogue Modelling
• +۱۰ مورد دیگر

Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.