جستجوی استاد راهنمای دانشگاهی

Andrea Vedaldi is a Professor of Computer Vision and Machine Learning at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). He specializes in unsupervised methods for understanding images and videos, focusing on 3D geometry and semantics. His research bridges foundational AI and practical applications, with contributions to generative models, neural fields, and self-supervised learning. Education: PhD in Computer Science (2008), University of California, Los Angeles MSc in Computer Science (2005), UCLA BSc in Information Engineering (2003), University of Padua Research Interests: Unsupervised learning, 3D perception, generative AI, neural rendering, and scalable vision systems. His work emphasizes ethical, responsible AI aligned with ERC-funded projects like UNION (ERC Consolidator Grant). Key Contributions: Co-developer of VLFeat and MatConvNet libraries Leader in 3D reconstruction and diffusion models (e.g., CatFree3D) Recipient of the PAMI Thomas S. Huang Prize and multiple best paper awards Grants & Service: Principal Investigator on £2.3M ERC Consolidator Grant (UNION) Co-organizer of major conferences (ECCV 2020 Program Chair, CVPR 2023 Area Chair) Reviewer for top journals/conferences (PAMI, CVPR, NeurIPS) Labs & Teams: VGG Group at Oxford, collaborating on projects like Meta 3D Gen and Common Objects in 3D (CO3D).

• Computer Vision
• Machine Learning
• Unsupervised Learning
• +۳ مورد دیگر

Prof. Ingmar Posner is a leading figure in applied artificial intelligence at the University of Oxford, where he serves as Principal Investigator for the Applied Artificial Intelligence Lab (A2I) and founding Director of the Oxford Robotics Institute. His work focuses on enabling robots to operate effectively in complex real-world environments through experience-driven learning. Key research areas: robot learning, scene interpretation, data-efficient learning, and transfer learning Applications in manipulation, autonomous driving, logistics, and space exploration His team has produced groundbreaking work in world models, sim-to-real transfer, and constraint-based manipulation systems (e.g., COMBO-Grasp). Notable contributions include the TWIST distillation framework and foundational research in tactile data generation (TactGen). He has received multiple best paper awards at top robotics venues. Publications reveal evolving research themes: 2025 work emphasizes language-conditioned learning (Lumos) and multi-agent decision-making, while 2024 focused on diffusion models for locomotion and differentiable simulators. Earlier work spans from urban scene analysis to physically plausible scene synthesis (RELATE).

• Robotics
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Christopher L. Tucci is a Professor of Digital Strategy & Innovation at Imperial College London's Business School and currently serves as Interim Vice Dean of Education. He is also the Founding Dean of NEOM's College of Business & Innovation in Saudi Arabia. Previously, he held the Chair in Corporate Strategy & Innovation at École Polytechnique Fédérale de Lausanne (EPFL), where he served as Dean of the College of Management from 2013 to 2018. His academic qualifications include a PhD in Management from MIT and multiple degrees from Stanford University: an AB in Music, BS in Mathematical Sciences, and MS in Computer Science and Computer Science. His research focuses on digital strategy, innovation management, open innovation, and business model innovation, with particular emphasis on how firms transition to new technologies and organizational forms. He has pioneered work on crowdsourcing, inter-networking, and AI-driven innovation. His award-winning article 'Crowdsourcing as a Solution to Distant Search' (co-authored with Allan Afuah) has received multiple accolades, including the AMR Decade Award in 2022. Professor Tucci teaches courses such as AI Ventures, Co-Creation in AI & Robotics, and Design Thinking. He actively contributes to academic leadership roles, including serving on the Academy of Management's Board of Governors and leading initiatives like Imperial's I-X (Artificial Intelligence and Digital Innovation hub). His work integrates technology, strategy, and organizational dynamics to address modern business challenges. Educations: PhD in Management, MIT (1992–1997) SM in Technology & Policy/EECS, MIT (1989–1991) BS in Mathematical Sciences, Stanford (1978–1982) AB in Music, Stanford (1979–1983) MS in Computer Science, Stanford (1982–1984) Labs/Teams: Co-Director of Imperial's I-X initiative, focusing on AI, data science, and digital innovation.

• Digital Strategy
• Innovation Management
• Open Innovation
• +۷ مورد دیگر

Professor Andrew Hudson-Smith is the Director of the Bartlett Centre for Advanced Spatial Analysis (CASA) at University College London (UCL), where he holds the title of Professor of Digital Urban Systems. He leads UCL's academic and applied research in smart cities, digital twins, and urban informatics. His roles include Editor-in-Chief of the Future Internet Journal and academic lead for the Smart Queen Elizabeth Park project. He contributes to strategic initiatives such as the Greater London Authority Smart London Board and EPSRC Digital Economy Programme Advisory Board. Education: PhD (2003), MSc (1996) from UCL; BSc in Geography from the University of Plymouth (1992). Research focuses on digital urban systems, IoT applications, sustainable urban planning, and cyberphysical systems. Notable projects include urban heat island monitoring, participatory planning platforms, and metaverse-driven urban design. His work bridges technology, art, and public engagement, exemplified by initiatives like Tales of Things and the Haggle-O-Tron. Recent publications emphasize digital twins, metaverse urbanism, and IoT integration in urban environments. Awards include a Fellowship of the Royal Society of Arts. His work addresses SDGs 3 (Good Health) and 11 (Sustainable Cities), with a focus on equitable smart city development and public space innovation.

• Digital Urban Systems
• Smart Cities
• Internet of Things (IoT)
• +۷ مورد دیگر

Professor Haijiang Li is a Chair in BIM for Smart Engineering at Cardiff University's School of Engineering. His roles include leading the Computational Mechanics and Engineering AI Research Group, directing the BIM for Smart Engineering Centre, and overseeing the BIM MSc programme. He holds editorial roles for journals like Construction Innovation and Automation in Construction , and chairs the European Group of Intelligent Computing in Engineering (EG-ICE). Research focuses on smart computational engineering platforms integrating BIM, AI, and big data for sustainable infrastructure. Key areas include digital twins, disaster management, and resilient urban systems. He has secured £40M in research funding, including £9M as PI, and led over 70 research staff and students. Prof. Li is a Standards Committee Technical Executive at buildingSMART, driving international BIM standards. His work includes co-authoring a book on BIM standards across China, the US, and the UK. Awards include Fellowships from the British Computer Society (FBCS) and the Higher Education Academy (FHEA). His research outputs span over 250 publications, covering topics like AI-driven bridge maintenance, ontology-based decision-making, and energy-efficient urban systems. Collaborations with industry and global partners emphasize practical applications of BIM and smart technologies.

• Civil Engineering
• Structural Engineering
• Computational Engineering
• +۳ مورد دیگر

Theo Damoulas is a Professor of Machine Learning at the University of Warwick with a joint appointment in the Department of Computer Science and Statistics. He is a Turing AI Fellow (2021-2026) through UK Research and Innovation, an ELLIS member, and a Visiting Professor at New York University's Center for Urban Science and Progress (CUSP). He founded and leads the Warwick Machine Learning Group and has directed major projects at The Alan Turing Institute including Project Odysseus and the London Air Quality project. Education includes: PhD in Probabilistic Multiple Kernel Learning (University of Glasgow, 2009) MSc in Informatics (Distinction, University of Edinburgh, 2004) MEng in Mechanical Engineering (1st Class, University of Manchester, 2003) His research focuses on probabilistic machine learning and Bayesian statistics, emphasizing the integration of structural priors, spatiotemporal dependencies, physical laws, and causal relationships. Key applications include Digital Twins, urban science, and computational sustainability. His work advances robust and scalable inference methodologies for complex real-world systems. Publications demonstrate strong emphasis on Bayesian methods, spatiotemporal modeling, and uncertainty quantification, with applications spanning battery modeling, urban mobility, federated learning, and causal inference. Recent work shows increased focus on physics-informed models, federated learning frameworks, and causal abstraction techniques. Major scientific awards: Turing AI Acceleration Fellowship (2021-2026) Best Paper Awards (Wilkes 2024, AISTATS 2022, IEEE ICMLA 2010) ACM SIGMOD Most Reproducible Paper (2017) Dissertation Award (Classification Society 2012) Teaching Excellence nominations (Warwick 2015-2017) He actively advises PhD students and secured significant grants including the £multi-million Turing AI Fellowship. Current doctoral researchers investigate federated learning, causal inference, and spatiotemporal modeling. He leads the Warwick Machine Learning Group, a cross-departmental team developing foundational ML methods for scientific and societal challenges.

• Probabilistic Machine Learning
• Bayesian Statistics
• Digital Twins
• +۵ مورد دیگر

Virginia Leavell serves as Assistant Professor in Organisational Theory and Information Systems at Cambridge Judge Business School, University of Cambridge. Her research investigates how organisations anticipate technological change through digital prediction systems, AI, and infrastructure transformation, drawing on her decade-long background in political organising for non-profits and labour groups across the US and Thailand. Her academic credentials include: BA in Interdisciplinary Studies, Georgetown University MA in Sociology, University of California Santa Barbara PhD in Technology Management, University of California Santa Barbara Leavell's scholarly work centers on organisational anticipation of technological futures, employing ethnographic and social network analysis methods to examine digital transformation in critical infrastructure contexts. She explores how predictive technologies reshape organisational structures and actions before technological change fully materializes, with particular focus on water systems, urban planning, and labor industries where digital twins and simulation technologies create new governance challenges. Analysis of her 2017-2024 publications reveals consistent examination of prediction performativity across sectors, demonstrating how digital models influence physical realities in infrastructure management. Her research trajectory shows increasing emphasis on sociomaterial dimensions of anticipatory organising, with recent work interrogating the equalization of human and technological agency in digital transformation processes. No scientific awards were specified in source materials. Leavell actively contributes to the Organisational Theory and Information Systems subject group at Cambridge Judge Business School, which engages cross-disciplinary leadership themes. Information regarding student advising and research grants remains unspecified in available documentation.

• Organisational Anticipation
• Digital Prediction Technologies
• AI and Autonomous Systems
• +۴ مورد دیگر

Sebastian Schemm is a Heisenberg Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, a position regarded as equivalent to a non-permanent Associate Professor. He leads research within the Atmosphere-Ocean Dynamics group and previously held an ERC Starting Grant-funded Assistant Professorship (without tenure track) at ETH Zurich. Education and Career Path PhD (2013) and MSc (2010), ETH Zurich, Switzerland Postdoctoral researcher, University of Bergen, Norway (2014–2017) Postdoctoral researcher, Laboratoire de Météorologie Dynamique, ENS Paris (2017–2018) Assistant Professor (ERC Starting Grant), ETH Zurich (2020–2024) Heisenberg Fellow, DAMTP, University of Cambridge (2025–present) Research Focus Schemm’s work centres on atmospheric and climate dynamics, spanning turbulence to planetary scales. Core themes include the physics of extratropical cyclone life cycles, jet-stream and storm-track dynamics, Rossby waves and teleconnection patterns, high-resolution atmospheric modelling, and the integration of machine-learning techniques for parameter estimation, data assimilation, and kilometre-scale global simulations. He also contributes to large-scale initiatives such as ECMWF’s WeatherGenerator. Scientific Awards and Editorial Service DFG Heisenberg Fellowship (2025) ERC Starting Grant (2020–2024) European Meteorological Society Young Researcher Medal (2019) Co-Editor, Weather and Climate Dynamics (EGU) Co-Editor, Quarterly Journal of the Royal Meteorological Society PhD Supervision & Funding He currently supervises PhD students at both Cambridge and ETH Zurich, with funding streams including the Cambridge CREATES Doctoral Training Partnership and Swiss/EU grants. Ongoing students explore reinforcement-learning parameterisations, jet-stream–storm-track relationships, mid-latitude eddy energetics, machine-learning ensemble forecasting, and Bayesian parameter estimation in LES. Active Projects EU Horizon project WeatherGenerator (led by ECMWF) PASC HiRAD-Gen : High-Resolution Atmospheric Downscaling Using Generative Models

• Atmospheric Sciences
• Extratropical Cyclone Dynamics
• Jet Stream Physics
• +۷ مورد دیگر

Professor Ruchi Choudhary is a Professor in Architectural Engineering at the University of Cambridge, Department of Engineering, within the School of Technology. She leads the Energy Efficient Cities initiative (EECi), a cross-disciplinary research project focused on strengthening the UK's capacity to address energy demand reduction and environmental impact in cities through research in building and transport technologies, district power systems, and urban planning. Her research interests span urban energy systems, building energy modeling, sustainable cities, geothermal energy systems, and data-driven energy modeling . She has pioneered work in digital twins for energy systems, urban subsurface thermal modeling, and building-integrated agriculture. Her research group develops numerical tools to improve energy efficiency of cities, with particular focus on modeling energy consumption of large building sets at multiple time and spatial resolutions. Analysis of her recent publications reveals a strong trend toward integrating machine learning with physics-based modeling for energy systems, with increasing emphasis on uncertainty quantification, digital twins, and value of information analysis for decision-making. Her work bridges the gap between theoretical modeling and practical urban implementation, with significant focus on city-scale geothermal potential, underground climate change impacts, and energy equity considerations. Professor Choudhary has supervised numerous PhD students who have gone on to prominent positions at institutions including UCL, University of Cambridge, BEIS, Arup, and various international universities. Her research group includes faculty members, research associates, graduate students, and international collaborators from institutions worldwide. Her current research focuses on two parallel investigations: one on using multi-scale multidisciplinary models to address energy use questions in the built environment, and second, on quantifying uncertainties in model outcomes. Current projects include integration of food production in urban environments, analysis of underground transport systems as energy sources, large-scale integration of ground source heat pumps, and distributed energy networks.

• Urban Energy Systems
• Building Energy Modeling
• Sustainable Cities
• +۷ مورد دیگر

Dr. Pavlos Tafidis is a Lecturer in Transport (Systems) Engineering at the School of Engineering, University of Edinburgh. His work integrates interdisciplinary approaches to advance transport planning and engineering, with a focus on smart and sustainable mobility solutions. PhD in Transport Engineering, Hasselt University (2022) M.Sc in Transport Planning, Aristotle University of Thessaloniki (2015) M.Eng in Transportation, Aristotle University of Thessaloniki (2013) He leads projects like "BikeHood" (Science Foundation of Ireland), developing Ireland’s first cycling neighborhood, and contributes to initiatives such as "REALLOCATE" (Horizon 2020) and "CISMOB" (Interreg Europe). His research emphasizes accessible mobility solutions, equity in transport infrastructure, and urban livability. Recent publications analyze cyclist crash hotspots using machine learning, electric bike route preferences via GPS data, and traffic-emission correlations in Dublin. His expertise spans digital twins, virtual reality, and geospatial analysis. Dr. Tafidis teaches courses including Transport Engineering 3, Transport and Society, and Multi-Scale Energy Demand, affiliated with the School of Engineering and Edinburgh Future Institute.

• Transport Planning
• Smart Mobility
• Sustainable Transportation
• +۷ مورد دیگر

Dr. Li Wan is an Associate Professor in Urban Planning and Development at the Department of Land Economy, University of Cambridge, and a Fellow of Gonville and Caius College. He serves as Director for the MPhil in Planning, Growth and Regeneration and is a co-investigator of the Centre for Smart Infrastructure and Construction. Dr. Wan is also a Trustee of the IJURR Foundation and leads a research group focused on understanding urban land and transport development through applied modeling and data analytics. Dr. Wan holds a BArch, MPhil, and PhD from the University of Cambridge. Prior to joining Land Economy, he worked in Architecture and Engineering Departments, giving him a highly interdisciplinary perspective. His research focuses on spatial economic modeling of urban land use and transport systems, with recent interests including strategic planning at city/regional levels, impact studies of flexible working, micromobility, and electric vehicles. Dr. Wan's research group has produced significant work in city digital twins, as evidenced by his 2023 book Digital Twins for Smart Cities: Conceptualisation, challenges and practices . His publications span urban analytics, transport emissions, spatial equilibrium modeling, and economic geography, demonstrating the interdisciplinary nature of his work. Recent research trends show increasing focus on post-pandemic urban dynamics, digital governance, and sustainable mobility solutions. His research group has secured funding from initiatives like AI@Cam for projects examining how local authorities use AI for urban decision-making. Dr. Wan actively supervises PhD and MPhil students, with his group comprising researchers working on diverse urban topics from land-use efficiency to micromobility impacts. As an educator, Dr. Wan teaches courses including Tripos Paper 10 - The Built Environment, PGR01 - Urban and Environmental Planning, PGR02 - Urban and Housing Policy, PGR05 - Place-based Policy, and RM03 – Spatial Analysis and Modelling. His group has presented at major international conferences including CUPUM and ICML, reflecting his active engagement in the academic community.

• Spatial Economic Modelling
• Urban Land Use
• Transport Systems
• +۱۲ مورد دیگر

Ian Ewart is an Associate Professor at the University of Reading's School of the Built Environment, serving as Head of Construction and Engineering Management and Research Group Lead for Organisation, People and Technology. He chairs the Research Ethics Committee since 2016 and supervises undergraduate/postgraduate dissertations. His academic journey spans engineering and anthropology: DPhil Social and Cultural Anthropology, University of Oxford, St Hugh's College (2007-2012) MSc Material Anthropology and Museum Ethnography, University of Oxford, St Hugh's College (2006-2007) BA (Hons) Archaeology and Anthropology, University of Oxford, Harris Manchester College (2003-2006) Diploma in Management Studies, University of the West of England (1990-1994) BEng (Hons) Mechanical Engineering, Staffordshire University (1983-1987) Ewart's research integrates ethnographic methods with digital technology studies, examining human-technology interactions in construction and domestic settings. His work bridges engineering practice and social anthropology, focusing on skill transmission, sustainable design, and multisensory experiences in virtual environments. Publications from 2025-2013 reveal a dominant trajectory in digital twins for socio-ecological sustainability, VR-based occupant behavior prediction, and HBIM for heritage conservation. The corpus demonstrates consistent cross-disciplinary innovation, merging archaeological reconstructions with healthcare applications while maintaining anthropological rigor. Key recognition: ESRC Future Research Leader fellowship (2013) for Designing Healthy Homes project He supervises PhD candidates like Afolabi Dania (Nigerian sustainable construction) and Joanna Hull (Heritage BIM), leveraging ESRC funding for ethnography-VR health studies. His grants emphasize participatory design and real-world impact assessment in built environments. Leaders the Organisation, People and Technology research group, developing multisensory Roman town reconstructions with sound/smell integration to advance archaeological and architectural experience modeling.

• Anthropology of Technology
• Built Environment
• Ethnographic Research
• +۷ مورد دیگر

Dr. Muhammad Imran is a Reader and Lecturer in Mechanical, Biomedical & Design Engineering at Aston University, UK. He is affiliated with the Energy and Bioproducts Research Institute (EBRI) and the College of Engineering and Physical Sciences. His research focuses on energy efficiency, waste heat recovery, and low-temperature power cycles such as Organic Rankine Cycle (ORC) and Supercritical CO₂ systems. He has contributed to the commercialization of ORC systems and collaborates internationally on hybrid energy systems, solar-thermal integration, and district heating networks. Dr. Imran holds a PhD in Energy System Engineering (2016), MSc in Thermal Power Engineering (2012), and BEng in Mechanical Engineering (2009). He has held academic roles at institutions in Pakistan, South Korea, and Denmark, including a Marie Curie Fellowship at the Technical University of Denmark. His awards include the Marie Curie Fellowship (EU), Innovation Award (South Asia Triple Helix), and multiple Research Excellence Awards from South Korea. He leads funded projects on hybrid energy systems for agriculture, waste heat recovery in industries, and sustainable energy solutions in developing countries. His editorial roles include associate editorships in Frontiers in Thermal Engineering and Resources, Environment and Sustainability . He supervises PhD students in renewable energy and low-temperature thermodynamic systems, with ongoing projects on solid-state heat pumps and advanced ORC control strategies. Dr. Imran’s work bridges engineering, data science, and environmental science to address energy challenges. Notable collaborations include projects in Ethiopia, Kenya, Nigeria, and Sudan, focusing on off-grid cold storage, smart irrigation, and biomass energy systems. His research outputs include over 130 peer-reviewed articles, patents, and contributions to international conferences.

• Energy Efficiency
• Waste Heat Recovery
• Organic Rankine Cycle (ORC)
• +۷ مورد دیگر

Professor Shaomin Wu is a faculty member at the University of Kent's Kent Business School, where he holds the academic rank of Professor of Business/Applied Statistics. He earned an MSc and PhD in applied statistics and has extensive industry experience, including a five-and-a-half-year stint at a global manufacturer in Shanghai before moving to the UK in 2001. He has held roles as a postdoctoral researcher and lecturer before joining Cranfield University and later the University of Kent. His research focuses on recurrent event data analysis, machine learning, and reliability mathematics, with funding from the EPSRC and ESRC. His research projects include managing risk in warranty servicing policies, smart data analytics for local government, and sustainable supply chain demand forecasting. He teaches modules such as risk analysis, reliability engineering, and machine learning. Currently supervising PhD students in time series forecasting, explainable AI, and recurrent event data analysis, he also serves as a co-chair of international conferences, editorial board member, and external examiner for doctoral degrees. Notably, he ranks among the top 2% of global scientists by Stanford University. His work integrates machine learning with business analytics, resilience engineering, and environmental sustainability. Key contributions include IoT-driven resilience methodologies for smart grids and unmanned systems, as well as frameworks for corporate carbon disclosure and maintenance optimization under uncertainty.

• Machine Learning
• Business Analytics
• Risk Management
• +۵ مورد دیگر

Stephen Marshall is Professor of Urban Morphology and Urban Design at The Bartlett School of Planning, University College London. He also served as Visiting Professor at the Department of Architecture and Urban Studies, Politecnico di Milano, Italy from 2019 to 2021. With over twenty-five years of experience in the built environment fields, initially in consultancy and subsequently in academia, Professor Marshall has established himself as a leading expert in urban morphology and design. His educational background includes a Doctor of Philosophy from University College London (2001), a Postgraduate Diploma from Edinburgh College of Art (1995), a Master of Science from the University of Leeds (1989), and a Bachelor of Engineering from the University of Glasgow (1988). Professor Marshall's principal research focuses on urban morphology and street layout, examining their relationships with urban formative processes including urban design, coding and planning. His work bridges urban design theory with practical applications, exploring how cities evolve through complex interactions of physical form, social processes, and planning interventions. He has written or edited several influential books including 'Streets and Patterns' (2005), 'Cities, Design and Evolution' (2009), and 'Urban Coding and Planning' (2011). His recent publications reveal a growing interest in applying complexity science to urban morphology, with particular attention to biological analogies for understanding self-organizing cities. He has pioneered research on digital participation methods in urban planning, exploring how online platforms can enhance public engagement in urban space design. His work consistently bridges theoretical urban morphology with practical applications for contemporary urban challenges like pandemic adaptation and sustainable transport. Professor Marshall has served as Chair of the Editorial Board of Urban Design and Planning from its launch to 2012, and is now co-editor of Built Environment journal. His editorial work has significantly shaped scholarly discourse in urban planning and design. He leads several significant research initiatives including the Incubators of Public Spaces project, which explores digital platforms for co-creating urban spaces, and the Self-Organising Built Environment project, which investigates biological analogies in urbanism. These projects reflect his interdisciplinary approach to understanding and shaping urban environments, connecting with Sustainable Development Goal 11 (Sustainable Cities and Communities).

• Urban morphology
• Urban design
• Street layout and network structure
• +۵ مورد دیگر