جستجوی استاد راهنمای دانشگاهی

Martin Herdegenمشاهده پروفایل

دانشیار

• Mathematical Finance
• Probability Theory
• Stochastic Processes
• +۵ مورد دیگر

Martin Herdegen is a Reader in Financial Mathematics at the Department of Statistics, University of Warwick. He previously served as a postdoc at ETH Zürich under Johannes Muhle-Karbe and holds a PhD in Mathematics from ETH Zürich (2014) under Martin Schweizer. His research focuses on equilibrium theory, utility maximization, stochastic processes, and risk measures, with applications to financial bubbles and market microstructure. Herdegen’s academic career includes supervising multiple PhD students (e.g., Florian Gutekunst, Andreea Popescu) and postdocs (e.g., Nazem Khan). His research group explores topics like ρ-arbitrage, recursive utility, and liquidity provision under adverse conditions. He has contributed to foundational work on strict local martingales and their implications for financial markets. Publications span leading journals such as Finance and Stochastics , Mathematical Finance , and Annals of Applied Probability , addressing equilibrium models with transaction costs, optimal investment strategies, and risk measurement techniques. His work frequently integrates stochastic analysis and control theory to solve practical finance problems. Herdegen’s research also intersects with reinforcement learning applications in trading, as seen in the Mbt-gym framework for limit order book simulations. His contributions emphasize rigorous mathematical foundations while addressing real-world market frictions and liquidity dynamics.

Matthew Thorpeمشاهده پروفایل

دانشیار

• Machine Learning
• Data Science
• Applied Mathematics
• +۴ مورد دیگر

Matthew Thorpe is an Associate Professor in the Department of Statistics at the University of Warwick and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). His research focuses on applying methods from applied analysis—including partial differential equations (PDEs), calculus of variations, and optimal transport—to machine learning and data science challenges. He has organized workshops such as the 'Machine Learning in Infinite Dimensions' at ETH Zurich and the 'LMS-Bath Symposium on Inverse Problems and Artificial Intelligence in Medicine.' Thorpe currently seeks PhD students for his research projects. His work bridges theoretical mathematics with practical applications in data-driven fields. Thorpe's research interests include manifold learning in Wasserstein space, PDE-based approaches to data science, and convergence analysis of graph-based learning algorithms. He has contributed to understanding the impact of imputation quality on machine learning models and developed novel transportation distances for pattern recognition. His interdisciplinary approach integrates mathematical rigor with advancements in artificial intelligence. He has co-organized the One World Seminar Series on the Mathematics of Machine Learning and remains active in promoting collaborative research initiatives. Despite no explicitly listed awards, his prolific publication record reflects sustained academic impact. Thorpe’s advising focuses on training students in the intersection of applied mathematics and modern data science techniques.

• Machine Learning
• Biomedical Engineering
• Medical Imaging
• +۵ مورد دیگر

Professor Anil Bharath holds the role of Professor of Biologically-Inspired Computation & Inference in the Department of Bioengineering at Imperial College London. He serves as Academic Director of Imperial Global: Singapore and co-leads the IN-CYPHER research program on AI-driven healthcare security. His research focuses on machine learning, deep networks, and biomedical applications. He earned his BEng from UCL and PhD from Imperial College, followed by roles including President of the City and Guilds College Association (2022-2024). Research Interests: Machine learning, neural networks, medical imaging, and biologically-inspired computation. Notable contributions include 2D steerable filters for shape detection (1998), Bayesian marginalisation in early computer vision, and deep learning for cardiac MRI analysis. He co-founded Cortexica Vision Systems (acquired by Zebra Technologies in 2019), applying biological neuron models to visual search technology. Labs & Affiliations: Director of the BICI Lab, affiliated with the Data Science Institute, Centre for Neurotechnology, and multiple healthcare networks. His work spans interdisciplinary projects in AI for healthcare, cardiovascular engineering, and medical device innovation. Publications: Recent work emphasizes AI in cardiology (e.g., MRI analysis for mitral regurgitation, aortic stenosis detection) and synthetic data privacy. His research bridges computational models with clinical interpretability, addressing challenges in medical imaging efficiency and diagnostic accuracy.

Courtney Chrimesمشاهده پروفایل

مدرس

• Digital Fashion Marketing
• Metaverse Retail
• Customer Experience Analysis
• +۵ مورد دیگر

Courtney Chrimes is a Lecturer in Digital Fashion Marketing at The University of Manchester, Department of Fashion Business Technology. She holds an External Examiner role for the BA (Hons) Fashion Promotion and Communication Programme at Buckinghamshire New University (2021-2026). Her academic background includes a Bachelor of Science in Fashion Buying and Merchandising from The University of Manchester (2017) and a PhD focused on product page design impacts on clothing fit appraisal (2021). Her research interests span digital fashion marketing innovations, metaverse retail experiences, sustainable supply chain practices, and consumer behavior analysis. Notable areas include blockchain applications in circular economies, body shape impacts on garment fit satisfaction, and AI-driven marketing strategies. She actively contributes to the UN Sustainable Development Goals related to responsible consumption and production. Dr. Chrimes has published 12 peer-reviewed articles and chapters, with recent focus on metaverse retailing (2025), AI-generated advertising (2024), and blockchain transparency (2023). She received four awards, including the 2024 Teaching Excellence Award for inclusive pedagogy and 2019 recognition for research excellence. Her advisory work includes supervising PhD research on digital fashion marketing and collaborating with industry partners on omnichannel strategies. Current projects explore immersive technologies in fashion retail and digital tools for sustainable product development.

Alessandro De Rosisمشاهده پروفایل

مدرس ارشد

• Multiphysics Modelling
• Lattice Boltzmann Method
• Fluid-Structure Interaction
• +۳ مورد دیگر

Alessandro De Rosis is a Senior Lecturer in Virtual Engineering at the University of Manchester’s Mechanical and Aerospace Engineering department. His research focuses on multiphysics modeling using the lattice Boltzmann method (LBM), with applications in fluid-structure interaction, magnetohydrodynamics, and biomedical engineering. He holds a PhD in Structural and Hydraulic Engineering from the University of Bologna and a Master’s in Civil Engineering from the University of Calabria. His postdoctoral work included a PRESTIGE grant-funded period at the Laboratoire de Mécanique des Fluides et Acoustique, developing LBM algorithms for high-Reynolds magnetohydrodynamic flows. Key research areas include computational fluid dynamics (CFD) modeling of cardiovascular devices, such as LVAD outflow graft positioning to reduce aortic regurgitation, and coupled finite-volume/LBM methods for internal flows. He has contributed to over 60 peer-reviewed publications, with recent work spanning biomedical engineering, plasma physics, and environmental fluid dynamics. Collaborations include the Fluids Research Group at the University of Manchester and institutions like the Technion-Israel Institute of Technology. Education: PhD in Structural and Hydraulic Engineering (2013), University of Bologna Master of Engineering in Civil Engineering (2009), University of Calabria His research bridges theoretical physics and engineering applications, emphasizing multiphase flows, CFD-driven surgical optimization, and high-performance numerical simulations. Current projects address challenges in mechanical circulatory support devices, fluid-structure interaction in biomedical systems, and magnetohydrodynamic turbulence modeling.

• Machine Learning
• Artificial Intelligence
• Probabilistic Modelling
• +۱۰ مورد دیگر

Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. He holds a PhD from the Machine Learning and Robotics Lab (now Learning and Intelligent Systems Lab) in Stuttgart/Berlin, Germany, and completed a postdoctoral position at the Digital and Cognitive Musicology Lab at EPFL, Switzerland. His research focuses on interdisciplinary applications of machine learning and artificial intelligence, with particular emphasis on cognitive modelling, music cognition, and ethical AI. Education: PhD in Machine Learning and Robotics, Stuttgart/Berlin, Germany (2012–2017) MSc Physics and Philosophy, Freie Universität Berlin Research Interests: Probabilistic Modelling (Bayesian inference, graphical models) Neuro-Symbolic Modelling (differentiable parsing algorithms) Structure Learning (feature discovery, hierarchical systems) Applications in music analysis, medical imaging, and autonomous systems Ethical implications of AI in policy and legislation Publications: Recent work includes advancements in deep reinforcement learning for diabetes management, recursive Bayesian networks, and computational models of musical expectancy. His research bridges theoretical AI with practical applications in musicology and healthcare. Students: Supervising four postgraduate students: Ishaq Ibrahim, Megan Finch, Ningxiang Xie, Xiaotang Zhang Labs: Active contributor to the Digital and Cognitive Musicology Lab (EPFL) and Durham's Computer Science research groups.

• 5G Technologies
• Unmanned Aerial Vehicles (UAVs)
• Internet of Things (IoT)
• +۶ مورد دیگر

Dr. Shahriar Al-Ahmed is a Lecturer and researcher at the University of the West of Scotland's School of Computing, Engineering and Physical Sciences. His work focuses on sustainable technological solutions leveraging 5G, UAVs, IoT, and AI. He teaches modules including Network Security Issues and Object-Oriented Programming, emphasizing accessible education. Research interests include UAV-based network optimization, AI-driven IoT applications in agriculture/healthcare/energy, and cybersecurity. He contributed to the 5GIR project and developed prototypes like a Pycom-based remote monitoring system. His PhD research was funded by UWS. He supervises PhD students exploring 5G, UAVs in wireless networks, and IoT-AI applications. Current projects address smart cities, precision farming, and energy efficiency. He advocates for UN SDGs through tech innovation, particularly in agriculture and environmental monitoring.

• Tidal Energy
• Hydrodynamics
• Renewable Energy Systems

Brian Sellar is a Reader at the University of Edinburgh's School of Engineering, focusing on tidal energy, hydrodynamics, and turbine performance. He holds a PhD and accepts PhD students. His research includes validating tidal turbine models, multi-scale coastal ocean modeling, and turbulence analysis for renewable energy sites. He received the MDPI Energies Editor’s Choice Award in 2021. His work contributes to optimizing tidal energy systems and advancing ocean engineering methodologies.

Joseph Robsonمشاهده پروفایل

استاد

• Physical Metallurgy
• Alloys for Extreme Environments
• Microstructural Modeling
• +۸ مورد دیگر

Prof. Joseph Robson holds the RAEng-DSTL Chair in Alloys for Extreme Environments and is a Professor of Physical Metallurgy at the University of Manchester. His research focuses on microstructural evolution in industrial alloys, particularly aluminum, magnesium, and zirconium, using advanced modeling techniques like Calphad-based thermodynamic and kinetic models. He leads the Light Alloy Processing group and collaborates with institutions like the Henry Royce Institute. Key projects include optimizing microstructures for aerospace, automotive, and nuclear applications through processes like friction stir welding and thermomechanical treatment. Education: BSc in Natural Sciences (Cambridge, 1993), PhD in Metallurgy (Cambridge, 1996). Professional memberships include the Institute of Materials, Minerals and Mining (Fellow, Light Metals Division board member). Research interests span dynamic precipitation behavior, irradiation effects on zirconium alloys, and magnesium alloy strengthening. He has pioneered studies on discontinuous precipitation and its impact on material properties. Awards include the Hume Rothery Award (2015), Grunfeld Medal (2011), and Champion H. Matthewson Award (2017). Advising and grants: Leads major projects like LightForm (EPSRC) and NEWAM (innovative manufacturing). His work addresses challenges in alloy design for extreme environments, with a focus on sustainable materials and advanced processing techniques. Labs/Teams: Active in the Materials Performance Centre and collaborates with industry partners such as Magnesium Elektron and Westinghouse on nuclear materials research.

Abhirup Ghoshمشاهده پروفایل

استادیار

• Distributed Machine Learning
• Mobility Analysis
• Mobile Health
• +۳ مورد دیگر

Abhirup Ghosh is an Assistant Professor at the School of Computer Science, University of Birmingham, and a visiting researcher at the Mobile Systems Research Lab, University of Cambridge. His research focuses on distributed machine learning, particularly Federated Learning and Gossip Learning, applied to mobile health and mobility analysis. He holds a PhD from the University of Edinburgh and has held roles at Imperial College London and Intel Inc. Education: PhD in Computer Science, University of Edinburgh (2019) M.Tech in Computer Science, Indian Institute of Technology Bombay (2011) Bachelor in Information Technology, Jadavpur University (2009) Research Interests: Distributed Machine Learning, Privacy-Preserving Algorithms, Mobile Health, and Mobility Analysis. His work emphasizes collaborative learning on edge devices while addressing resource constraints and privacy concerns. Recent projects include early Alzheimer’s detection using mobility data and federated learning for health diagnostics. Publications Trends: His work spans theoretical advancements (e.g., Gossip Learning convergence) and applied healthcare solutions (e.g., Alzheimer’s detection via outdoor mobility). Key areas include federated learning optimizations, privacy techniques, and domain generalization in activity recognition. Awards: Best Publication of the Year (2022) from University of Cambridge’s Department of Computer Science Lab & Collaborations: Collaborates with the Mobile Systems Research Lab at Cambridge on projects like MEDEA (Wellcome Trust-funded Alzheimer’s detection initiative). Leads efforts in cross-device learning and health data privacy.

Phoebe Barracloughمشاهده پروفایل

مدرس

• Cybersecurity
• Artificial Intelligence
• Fuzzy Systems
• +۳ مورد دیگر

Phoebe Barraclough is a Lecturer (Teaching and Scholarship) in the Department of Computer Science at the University of York. She holds the role of Chair of the Equality, Diversity and Inclusion Committee. Her research focuses on cybersecurity, artificial intelligence, and fuzzy systems with applications in phishing detection and intrusion prevention. Key contributions include adaptive neuro-fuzzy systems for online security and real-time phishing toolbar development. Research interests span cyber threat detection, machine learning models for fraud prevention, and network security protocols. Her work emphasizes practical solutions for online transaction protection and user safety through innovative algorithm design. Publications from 2013–2021 highlight advancements in phishing detection mechanisms, fuzzy logic applications, and intrusion detection systems. No scientific awards are explicitly listed in the provided materials. No advising records or grant information are available in the current dataset. No lab affiliations or collaborative teams are mentioned.

Abheek Ghoshمشاهده پروفایل

پژوهشگر

• Economics and Computation
• Computational Complexity Theory
• Contest Theory
• +۴ مورد دیگر

Abheek Ghosh is a postdoctoral researcher at the University of Oxford , affiliated with the Department of Computer Science . He completed his Ph.D. at Oxford under the supervision of Profs. Edith Elkind and Paul W. Goldberg, with a thesis titled "Contests: Equilibrium Analysis, Design, and Learning." Prior to his Ph.D., he earned his undergraduate degree from IIT-Guwahati and a master's from UT-Austin. Research Interests : Economics and computation, computational complexity theory, contest theory, algorithmic game theory, multi-agent systems, and learning dynamics in games. Collaborations : Worked with Prof. Milind Tambe at Google Research on restless multi-armed bandits; collaborated with Prof. Umang Bhaskar at TIFR on voting theory. His recent work explores the computational complexity of contests, coalition formation, and bandit problems. Publications in top venues like AAMAS, AAAI, SAGT, and ICML highlight his contributions to game theory and artificial intelligence. He received the Departmental Teaching Award (2023) for his teaching roles in courses such as Continuous Mathematics and Computational Game Theory. Scientific Awards : Departmental Teaching Award (2023) Abheek has served as a teaching assistant for multiple courses at Oxford and UT-Austin, and as a reviewer for journals like Journal of the ACM and Games and Economic Behavior , as well as conferences including EC, WINE, NeurIPS, and AAAI.

• Artificial Intelligence
• Machine Learning
• Causal Inference
• +۳ مورد دیگر

Mengyan Zhang is a Researcher at the Department of Computer Science, University of Oxford. Their work focuses on advancing artificial intelligence and machine learning techniques, particularly in applications such as epidemiological modeling, causal inference, and optimization. Mengyan's research bridges theoretical foundations with practical challenges in public health, remote sensing, and synthetic biology. Key research interests include developing AI-driven frameworks for disease surveillance, Bayesian optimization methods with integrated feedback, and uncertainty-aware regression for socio-economic estimation. Their contributions span graph-based algorithms, transformer neural processes, and adaptive recommendation systems with bandit feedback mechanisms. Mengyan’s publications explore cutting-edge topics like causal Bayesian optimization, Gaussian process bandits, and personalized news recommendation. Their work emphasizes interdisciplinary applications, from healthcare analytics to genetic sequence design. While no specific awards or grants are listed, Mengyan’s research demonstrates a strong focus on addressing real-world challenges through innovative machine learning approaches. The lack of student or lab affiliations suggests a primary focus on independent research contributions.

• Subsurface Physics
• Carbon Capture and Storage (CCS)
• Reservoir Engineering
• +۵ مورد دیگر

Professor Ann Muggeridge holds the Proconsul & Chair in Subsurface Physics at Imperial College London's Department of Earth Science & Engineering (Faculty of Engineering). Her research focuses on subsurface fluid flow dynamics, particularly CO2 storage security and reservoir physics. She has held prestigious roles including Chair of the Norwegian IOR Centre's Scientific Advisory Committee (2016-2021) and Technical Committee member for SPE Reservoir Simulation Conferences. Education: D.Phil. in Atmospheric Physics from University of Oxford (1983-1986), B.Sc. (Hons) in Physics from Imperial College London (1980-1983). Professional milestones include BP Technology Fellow (2006-2008) and keynote addresses at major geoscience conferences. Research interests span CO2 geological storage, reservoir heterogeneity effects, and enhanced oil recovery (EOR). Key contributions include dynamic risk mapping for CO2 sites and pore-scale observations of low salinity flooding. Her work integrates computational modeling with field-scale studies to address energy transition challenges. Awards: EAGE IOR Symposium Chair (2013-2019), SPE Technical Committee Member, BP Technology Fellow Labs & Teams: NORMS (Novel Reservoir Modelling & Simulation), Energy Futures Lab Affiliate Grants & Advising: Leads Imperial's Subsurface Physics research group; no listed advisees but mentors through collaborative projects Active in academia as Imperial's Faculty of Engineering Ambassador for Women and Departmental Athena SWAN coordinator, advancing gender equity in STEM.

Moulay Larbi Chalalمشاهده پروفایل

مدرس ارشد

• Urban Energy Planning
• Building Information Modeling (BIM)
• Sustainable Design
• +۳ مورد دیگر

Dr. Moulay Larbi Chalal is a Senior Lecturer in Architectural Technology at Nottingham Trent University’s School of Architecture Design and the Built Environment. He holds a PhD in Architecture from NTU (2018) and a Master’s with Distinction in Digital Architectural Design from Salford University. His academic responsibilities include curriculum design, studio instruction, and research supervision in architectural technology modules. Chalal’s research integrates computational design with sustainability, focusing on: Urban energy planning using GIS and BIM Generative algorithms for architectural optimization Eco-feedback systems for behavioral change Smart city infrastructure and autonomous vehicles His publications demonstrate a consistent focus on energy pattern analysis, sustainable urban development, and digital innovation in construction. Recent work explores AI applications in education and energy visualization frameworks. Awards & Recognition: Emerald Award for Best Master’s Dissertation (Salford University, 2013) NTU Vice Chancellor’s Scholarship for PhD research (2014) He advises doctoral candidates (e.g., F. Mekheimar’s work on digital archaeology) and collaborates with industry partners including Nottingham Energy Partnership and SETART Group. Chalal leads projects in NTU’s Creative and Virtual Technologies Research Lab, developing tools for sustainable urban planning.