جست‌وجوی استاد و پژوهشگر

Wenbin Liمشاهده پروفایل

مدرس ارشد

• Robotics
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Dr. Wenbin Li is a Senior Lecturer (Associate Professor) in Robotics at the University of Bath's Department of Computer Science. He leads the Pering Laboratory (Perceptual Intelligence Laboratory), affiliated with the AI & Machine Learning and Visual Computing groups. Previously, he held postdoctoral positions at Imperial College London (2016-2018) and UCL (2014-2016), and earned his PhD from the University of Bath in 2013, with earlier degrees from Imperial College London (MSc, 2009) and Xidian University (B.Eng, 2008). His research focuses on unified autonomous systems, including multi-sensory localization/mapping, dynamic motion capture, and uncontrolled scene understanding with applications in manufacturing and professional capture. Key areas include Robotics, Computer Vision, Graphics, and Machine Learning. He actively supervises doctoral students in these fields and has funded PhD openings. Dr. Li has been involved in major initiatives such as the My World - Strength in Places Fund (2021–2027), SLAM with Reinforcement Learning (2022–2023), and the CAMERA MC2 Award (2019–2023). His work aligns with UN Sustainable Development Goals, particularly in advancing technology for societal benefit. Recent publications emphasize aerial robotics, autonomous systems, and computer vision applications, including UAV package delivery reviews, Bayesian optimization for balloon station-keeping, and generative models for intrinsic image decomposition.

• Operational Research
• Simulation Methods
• Healthcare Management
• +۳ مورد دیگر

Dr. Katy Hoad is an Associate Professor of Operational Research at the University of Warwick's Warwick Business School (WBS), affiliated with the ISM-Analytics (ISMA) Group. Her work spans operational research methodologies, simulation techniques, and their application in healthcare and education. She currently teaches modules including Business Analytics, Forecasting, Advanced Mathematics, and Maths for Management across various undergraduate and postgraduate programs at WBS. Dr. Hoad's research focuses on simulation-based education strategies, discrete event simulation optimization, and healthcare management modeling. Notable contributions include studies on tuberculosis epidemiology and the integration of system dynamics with discrete event simulation in pedagogical contexts. Her work has been published in journals such as the Journal of the Operational Research Society and Health Care Management Science, alongside conference proceedings on simulation practices. Her technical expertise includes automating simulation output analysis and adapting simulation software for practical applications. Collaborations with colleagues like Currie, Robinson, and O’Brien highlight her involvement in cross-disciplinary research.

• Maintenance Optimization
• Reliability Engineering
• Forecasting Methods
• +۲ مورد دیگر

Dr. Wenjuan Zhang is Associate Professor of Operational Research at Warwick Business School. Her research develops optimization and forecasting methods for reliability engineering, maintenance planning, and renewable energy systems. Work includes evolutionary algorithms for wind farm maintenance optimization and reliability assessment methods for transportation systems. Research has applications in energy infrastructure and healthcare modeling.

• Statistical Learning
• Game Theory
• Decision-Making Algorithms
• +۶ مورد دیگر

David Leslie is a Professor of Statistics and Director of Engagement in the Department of Mathematics and Statistics at Lancaster University. He specializes in statistical learning, decision-making algorithms, and game theory, with applications in real-time website optimization through bandit algorithms. Previously, he served as a Senior Lecturer at the University of Bristol and co-directed a cross-disciplinary decision-making research group. He has led significant projects such as the EPSRC/NERC-funded DSNE initiative and contributed to strategic partnerships like ALADDIN (with BAE Systems and EPSRC) and NG-CDI (with BT). His work emphasizes bridging theoretical research with practical industry solutions. Education details are not explicitly provided, but his career trajectory includes prior roles at prestigious institutions. Research interests focus on statistical methodologies, AI-driven decision systems, and environmental data science. He advises multiple PhD students in areas like Statistical AI and Extreme Value Theory. Notable projects include AI Hub, NABS+, and ProbAI, reflecting his engagement with cutting-edge technologies and interdisciplinary collaboration. David actively participates in research groups such as STOR-i Centre for Doctoral Training and the Statistical Artificial Intelligence group. His contact information includes an office at B73 in the PSC building and a direct email address. No specific scientific awards are listed, but his extensive project leadership and contributions to foundational AI research highlight his academic impact.

Jefersson Alex dos Santosمشاهده پروفایل

استادیار

• Remote Sensing Image Processing
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Jefersson Alex dos Santos is an Assistant Professor (Lecturer) in Computer Vision at the University of Sheffield, UK. Previously, he served as an Associate Professor at Universidade Federal de Minas Gerais (UFMG), Brazil (2013–2022). He holds a PhD in Computer Science from the University of Campinas (Unicamp) and the University of Cergy-Pontoise, France (2013). His research focuses on remote sensing image processing, computer vision, and machine learning, with applications in geospatial data analysis and medical imaging. He is an IEEE Senior Member and serves as an Associate Editor for IEEE Geoscience and Remote Sensing Letters and Co-Chair of the ISPRS Working Group for AI/ML in Geospatial Data. Education: PhD in Computer Science: University of Campinas (Unicamp) & University of Cergy-Pontoise, 2013 Master's in Computer Science: Unicamp, 2009 Bachelor's in Computer Science: University of Mato Grosso do Sul (UEMS), 2006 Research Interests: Remote sensing image processing, computer vision, machine learning, and geospatial data analysis. His work emphasizes interdisciplinary research, including applications in environmental monitoring, medical imaging, and digital forensics. Grants & Awards: CNPq Productivity Research Scholarship (2016–2022) Serrapilheira Institute Research Grant (2021) Labs & Teams: Founder of the Laboratory of Pattern Recognition and Earth Observation (PATREO) at UFMG's Department of Computer Science.

Luca Manneschiمشاهده پروفایل

مدرس

• Machine Learning
• Neuromorphic Computing
• Biological Networks
• +۳ مورد دیگر

Dr Luca Manneschi is a Lecturer in Machine Learning at the School of Computer Science, University of Sheffield, with an IBM Liaison role. He holds a PhD in Physics from the University of Sheffield (2021) and completed a PostDoc there before his current position since March 2022. His research focuses on designing learning algorithms inspired by biological networks for physically defined systems, emphasizing neuromorphic computing, reservoir computing, and stochastic environments. Education: Bachelor's in Physics: University of Padua (Italy) Master's in Physics: Sapienza University of Rome (Italy) PhD in Physics: University of Sheffield (2021) Research Interests: Dr. Manneschi explores algorithms for physically defined networks, leveraging biological network principles to enhance computation in dynamic environments. His work bridges machine learning with neuromorphic hardware, emphasizing adaptability and energy efficiency. Key areas include reservoir computing, magnetic metamaterials, and multi-timescale learning strategies. Grants & Funding: "Real-time Reservoir Computing on Prosthetic Devices" (2023–2025, Royal Society, PI) "MARCH: Magnetic Architectures for Reservoir Computing Hardware" (2021–2025, EPSRC, Co-PI) "CausalXRL: Causal Explanations in Reinforcement Learning" (2021–2024, EPSRC, Co-PI) "ActiveAI" (2019–2024, EPSRC, Co-PI) Lab/Team Affiliation: Machine Learning Research Group, School of Computer Science.

Jin Zhuمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Machine Learning
• Statistical Software Development
• +۳ مورد دیگر

Jin Zhu is a Researcher in the Department of Statistics at the London School of Economics and Political Science (LSE), working with Prof. Chengchun Shi on reinforcement learning and machine learning. His research focuses on developing algorithms with statistical and computational guarantees, alongside statistical software design to enhance algorithmic applications. Prior to LSE, he earned his PhD in Statistics at Sun Yat-Sen University under Dr. Xueqin Wang and Dr. Na You. Key expertise includes reinforcement learning, machine learning, and computational statistics. His work addresses challenges in off-policy evaluation, robustness in RL, and sparsity-constrained optimization. He has contributed to open-source tools like skscope and abess for efficient statistical computation. Research interests also span causal inference, high-dimensional data analysis, and algorithmic design for complex systems. Notable contributions include methodologies for genetic factor identification, spatial experimental design, and nonparametric statistical inference. Jin’s research bridges theoretical advancements with practical software implementations to address real-world computational and statistical challenges.

Sunil Kumarمشاهده پروفایل

استاد

• Urbanisation
• Housing Policy
• Rural-Urban Migration
• +۴ مورد دیگر

Dr. Sunil Kumar is a Professor in the Department of Social Policy at the London School of Economics (LSE). He holds a PhD from University College London and specializes in urbanization, housing, migration, and poverty in the Global South, particularly India. His research critiques urban policies and proposes inclusive alternatives. He served as Dean of Graduate Studies from 2011 to 2017 and is affiliated with the LSE India Observatory and Appropriate Technology Asia. Dr. Kumar's research focuses on urbanization-construction-migration linkages, informal labor markets, and rental housing dynamics. He has led major DFID-funded projects, including studies on South Asian urbanization and housing policies. His work appears in journals like International Journal of Urban and Regional Research and Urban Studies . He is committed to pedagogy, mentoring students, and fostering discussions on race and decolonization in social policy through initiatives like the 'Race Matters' program. His consultancy work includes projects for DFID, UN-Habitat, and the World Bank.

Elnaz Bajooriمشاهده پروفایل

مدرس ارشد

• Game Theory
• Auction Theory
• Delegated Search
• +۲ مورد دیگر

Elnaz Bajoori is Senior Lecturer in Economics at University of Bath, specializing in game theory, auction design, and decision-making under uncertainty. Joined Bath in 2013 after PhD from Maastricht University. Leads econometric research group supporting graduate students. Research develops theoretical frameworks for auction mechanisms including security-bid auctions with risk-averse bidders and delegated search problems. Experimental work examines bidder behavior under cash-equity tradeoffs. Current projects include mathematical modeling of renewable energy auctions for efficient allocation systems. Secured funding for social housing quality research during cost-of-living crisis. Coordinates academic workshops on auction mechanisms through SWDTP grant.

Richard Romanoمشاهده پروفایل

استاد مهمان

• Driving Simulation
• Autonomous Vehicles
• Human-Machine Interaction
• +۱ مورد دیگر

Richard Romano is Visiting Professor at the University of Leeds' Institute for Transport Studies and Staff Researcher at General Motors. His interdisciplinary work bridges engineering and behavioral science to optimize human-automation interaction. Research develops simulation frameworks for testing autonomous vehicle systems, with focus on pedestrian interaction models and motion cueing algorithms. Current projects analyze how drivers evaluate automated driving styles and manage control transitions. Over 143 publications advance real-time simulation methodologies for vehicle dynamics. He directs projects on human-centered AV design funded by EPSRC and leads doctoral training in human factors engineering.

• Quantum Cognition
• Cognitive Psychology
• Decision Making
• +۷ مورد دیگر

Emmanuel M. Pothos is a Professor in the Department of Psychology at City, University of London, within the School of Arts and Social Sciences. He has held prior academic positions as Senior Lecturer at City, Reader at Swansea University, and Lecturer at both the University of Edinburgh and Bangor University, reflecting a sustained and progressive academic career in cognitive psychology. PhD in Psychology, University of Oxford (1995–1998) BSc in Physics, Imperial College London (1992–1995) His research primarily focuses on quantum cognition, where he applies the mathematical formalism of quantum theory to model cognitive phenomena such as decision-making, judgment, and similarity perception. He investigates violations of classical probability in human cognition, context effects, and the role of ambiguity and conflict in choices. His interdisciplinary work draws from psychology, physics, and computational modeling. The recent trend in his publications reveals a strong emphasis on formal modeling of cognitive processes using quantum probability, with applications extending into behavioral health, financial systems, and foundational questions in decision theory. His work frequently appears in high-impact journals across psychology, physics, and interdisciplinary sciences, indicating a broad scholarly impact. While no formal awards are listed in the provided data, his extensive publication record in top-tier journals and frequent peer-review activities for leading outlets such as Nature , Psychological Review , and PNAS reflect significant academic recognition and service. Dr. Pothos actively collaborates with researchers internationally and has contributed to theoretical advances in understanding how quantum-like structures can explain human cognitive biases and complexities. Although no direct mention of student supervision is made, his role as a professor and prolific researcher implies involvement in mentoring graduate students and leading research initiatives. He has not indicated any lab or research group name, but his work clearly involves collaborative projects in cognitive modeling and decision science.

• Motion Capture
• Robotics
• Machine Learning
• +۵ مورد دیگر

Xiangren Shi is a researcher at Bournemouth University , contributing to fields such as motion capture, robotics, and machine learning. His work focuses on enhancing inertial motion capture systems using transformers and optimizing 3D point-cloud compression with convolutional neural networks. Research Interests : Motion capture algorithms, sensor calibration, machine learning applications in robotics, point-cloud processing, teleoperation systems, and sensor fusion. Recent publications highlight advancements in dynamic on-body IMU calibration for motion capture and real-time humanoid teleoperation systems. His work integrates neural networks for sensor optimization and 3D motion analysis. Key trends include wearable sensor technologies, signal processing, and 3D reconstruction techniques.

• High-Frequency and Algorithmic Trading
• Stochastic Games between Takers and Makers of Liquidity
• Mathematical Finance
• +۲ مورد دیگر

Prof. Leandro Sanchez Betancourt is a faculty member at the University of Oxford , affiliated with the Mathematical Institute . His work bridges Mathematical Finance and Stochastic Games , with a focus on high-frequency trading and market microstructure. He has been recognized with prestigious awards, including the Bruti Liberati Prize for Quantitative Finance research and the Gabino Barreda medal from UNAM. Education Doctor of Philosophy (DPhil) Master of Science (MSc) Bachelor of Science (BSc) Research Contributions Prof. Sanchez Betancourt’s research addresses critical challenges in algorithmic trading , latency impacts , and toxic order flow . His recent publications explore Nash Equilibrium in broker-trader dynamics, Mean Field Games for informed trading, and innovative applications of Lévy-Ito Processes and Quantum Measurement in financial modeling. These works highlight his expertise in integrating advanced mathematics with real-world trading strategies. Scientific Awards Bruti Liberati Prize for best PhD thesis in Quantitative Finance Best overall performance student award from King’s College London Gabino Barreda medal from Universidad Nacional Autónoma de México Labs and Teams He is a member of the Mathematical and Computational Finance research group at the University of Oxford, collaborating with leading experts like Álvaro Cartea, Sebastian Jaimungal, and others.

Wenfei Fanمشاهده پروفایل

استاد

• Database Theory
• Data Science
• Information Visualization
• +۹ مورد دیگر

Wenfei Fan holds the Personal Chair in Web Data Management at the School of Informatics, University of Edinburgh . He is affiliated with the Database Group , LFCS (Laboratory for Foundations of Computer Science), Bell Labs , and the International Research Center on Big Data at Beihang University. His research spans database theory, data science, and big data systems. PhD in Computer and Information Science, University of Pennsylvania, USA MS in Computer Science, Peking University, China BS in Computer Science, Peking University, China His research interests focus on database theory and systems , including big data, data quality, data integration, database security, distributed query processing, query languages, social networks, and XML. He has pioneered techniques for graph pattern matching, data dependencies, and scalable algorithms. Recent publications emphasize graph data management , rule-based systems , and ML-data quality integration . Key themes include incremental computation, GPU-accelerated graph cleaning, entity linking, and logic-driven ML. Articles like 'Rock' and 'MiniClean' demonstrate hybrid approaches for real-world data challenges. Fellow of the Royal Academy of Engineering (FREng), 2023 Foreign Member, Chinese Academy of Sciences (ForMemCAS), 2019 Fellow of the Royal Society (FRS), 2018 ERC Advanced Fellowship, 2015 ACM Fellow (FACM), 2012 Roger Needham Award, 2008 He leads the ERC project: Resource-Bounded Graph Query Answering and contributes to systems like GRAPE and GraphScope , which parallelize sequential algorithms and process large graphs. He mentors students and collaborators globally, with a focus on data quality and graph analytics.

• Glaucoma Research
• Visual Field Testing
• Age-Related Macular Degeneration
• +۷ مورد دیگر

Professor David P. Crabb is a leading researcher in the Department of Optometry and Visual Science within the School of Health Sciences at City, University of London. With over 180 publications spanning from 2010 to 2025, his work has significantly contributed to the fields of glaucoma research, age-related macular degeneration, and visual field testing methodologies. He is particularly known for his involvement in the MACUSTAR study, a major international research initiative focused on developing clinical endpoints for intermediate age-related macular degeneration. Crabb's research interests span a wide range of ophthalmic topics with particular emphasis on visual field progression in glaucoma, structure-function relationships in retinal diseases, and patient-centered outcomes in eye care. His work has consistently bridged the gap between clinical practice and research methodology, developing novel approaches to visual field testing and analysis. He has made substantial contributions to understanding eye movements in patients with visual field loss and their implications for daily functioning. His recent publications (2024-2025) demonstrate continued leadership in the field, with significant contributions to MACUSTAR study reports, glaucoma progression analysis, and patient-centered research in geographic atrophy. These works showcase his dual focus on technical innovation in visual assessment and the practical implications for patient care and quality of life. Professor Crabb has collaborated extensively with international research groups across Europe and has contributed to major multicenter studies including the United Kingdom Glaucoma Treatment Study (UKGTS) and the LiGHT trial. His work frequently appears in top ophthalmology journals including Ophthalmology, Investigative Ophthalmology & Visual Science, and American Journal of Ophthalmology. His research methodology often combines traditional clinical approaches with advanced data analysis techniques, including machine learning applications for predicting visual field progression and optimizing testing protocols. This interdisciplinary approach has positioned him as a key contributor to evidence-based practice in ophthalmology.