جستجوی استاد راهنمای دانشگاهی

Bowei Chen is a Senior Lecturer (equivalent to Associate Professor) in Marketing Analytics and Data Science at the Adam Smith Business School, University of Glasgow. He holds a PhD in Computer Science from University College London (UCL). Prior roles include Lecturer (Assistant Professor) at the University of Lincoln and research visits at institutions like Copenhagen Business School, Trinity College Dublin, and Microsoft Research Cambridge. His research focuses on applications of probabilistic modeling and machine learning in business, particularly marketing and finance. Key areas include recommendation systems, business continuity management, data valuation frameworks, and automotive market analysis. He co-authored the textbook Business Analytics with Python: Essential Skills for Business Students (2025). Notable contributions include developing frameworks like GEO for automotive design and SRNI-CAR for Chinese automotive market analysis. His work bridges AI-driven solutions with practical business challenges, as seen in projects on traffic flow forecasting and energy service optimization via Innovate UK grants. Grants: Lead PI on £218k Innovate UK KTP project with Troo (2024-2026); Co-I in projects totaling over £4M across China and Europe. Teaching: Data Science for Marketing Analytics (MSc), Business Intelligence (MSc). Awards: INFORMS Meritorious Reviewer (2024), Marshall Institute Research Award (2023), and multiple best paper awards. Bowei is actively involved in editorial roles for journals like Electronic Commerce Research and Applications , and serves on program committees for NeurIPS, ICML, and AAAI.

• Data Science
• Machine Learning
• Quantitative Marketing
• +۳ مورد دیگر

Dr. Sakdirat Kaewunruen is a Professor and Reader in Railway and Civil Engineering at the University of Birmingham, UK. He leads the Birmingham Centre for Railway Research and Education (BCRRE) and convenes the BEng/MEng Railway Engineering and MSc Structural Engineering programs. His roles include Chief Editor of Frontiers in Transportation and Transit Systems and member of EU-funded research initiatives like RISEN and S-CODE. Education: PhD in Civil Engineering (Structures/Railway Infrastructure), University of Wollongong, Australia MBA (Executive), Australian Graduate School of Management BEng (Hons) in Civil Engineering, Suranaree University of Technology Research Interests: Focuses on resilient transport infrastructure, train-track interaction dynamics, smart materials, risk management, and sustainability. His work integrates advanced modeling, sensor technologies, and circular economy principles to enhance railway systems' reliability and environmental impact. Awards: NSW Young Tall Poppy Science Award (Australia) Japan Society for the Promotion of Sciences Long-term Fellowship World Top 100 Most Cited Scientist in Civil Engineering (Stanford University) Grants & Projects: Secured over £15M in research funding, including EU H2020 and UK DfT projects. Leads initiatives like RISEN (Euro1.2M) and collaborates with MIT, University of Illinois, and Southwest Jiaotong University. Labs & Teams: Coordinates interdisciplinary teams in railway resilience, smart infrastructure, and sustainable urban systems. Active in ISO/BSI standards for railway sleepers and recycling of rolling stocks.

• Railway Engineering
• Structural Dynamics
• Infrastructure Resilience
• +۵ مورد دیگر

Dr. Vien Ngo is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on bridging machine learning and artificial intelligence with robotics, emphasizing intelligent systems, reinforcement learning, and robotics applications such as motor skill learning and human-robot interaction. He supervises PhD students in areas like machine learning, robotics, and planning. His work addresses challenges in policy search, deep reinforcement learning, and motion planning under uncertainty. Key research themes include probabilistic modeling, Bayesian inference, and manipulation under uncertainty. He is open to supervising PhD candidates in these fields. His affiliations include the Faculty of Engineering and Physical Sciences.

• Intelligent Systems
• Machine Learning
• Robotics
• +۳ مورد دیگر

Nikos Gkekas serves as a Lecturer at Edinburgh Napier University since December 2021, following post-doctoral research positions at the École normale supérieure in Paris and the University of Nottingham. His academic qualifications include: Informatics training at Aristotle University of Thessaloniki, Greece Masters in Machine Learning and Data Mining from the University of Bristol Masters and PhD in Neuroinformatics from the University of Edinburgh Dr. Gkekas's research centers on cognitive neuroscience, investigating how humans seamlessly learn from environmental statistics through prior expectations, complexity limits, and interactions between temporal/structural learning mechanisms. Complementary work explores vision science and computational neuroscience, focusing on canonical neural computations (normalization, lateral inhibition, probabilistic formulations) that organize sensory input into perceptual representations across cortical levels. Current topics include adaptation across timescales, motion/speed perception, serial effects, and probabilistic perception models. No scientific awards were referenced in the source material. Information regarding student advising or research grants was not provided. No laboratories or research teams were specified in the text.

• Cognitive Neuroscience
• Vision Science
• Computational Neuroscience
• +۳ مورد دیگر

Dr. Simon Wells is a Lecturer at the School of Computing Engineering and the Built Environment, Edinburgh Napier University. His research bridges Artificial Intelligence, Argumentative Dialogue, and Human-Computer Interaction, focusing on formal models of reasoning and communication. He has developed computational systems enabling argumentation between humans and machines, applied across scientific, educational, and policy domains. University of Dundee (2011) University of Aberdeen (2012) Edinburgh Napier University (2014–present) His work integrates Behaviour Change and Interaction Design through gamification and persuasive technology, particularly in sustainable transport systems. Projects like SUPERHUB and West of Scotland Herring Hunt highlight his interdisciplinary approach, combining AI with environmental and cultural studies. Recent publications analyze the synergy between formal dialogue models and Large Language Models, offering insights into argument visualization, socio-technical trust, and segmentation-based behavior interventions. Key research areas include argumentation theory, evolutionary robotics, and digital ethnography. PhD Supervisor: Yagmur Yigit, Dana Khartabil Co-Supervisor: Thomas Farrenkopf Funded by the William Grant Foundation and Natural Environment Research Council, his projects span from citizen-science tools in Brazil to historical socio-economic studies in Scotland. Collaborations include institutions like the Open Microscopy Environment and SICSA.

• Artificial Intelligence
• Argumentative Dialogue
• Behaviour Change
• +۶ مورد دیگر

Omar Rivasplata is a Senior Lecturer in Machine Learning and Robotics, specializing in theoretical and applied machine learning. He is affiliated with the MCAIF: Centre for AI Fundamentals, focusing on advancing foundational aspects of artificial intelligence. His research spans reinforcement learning, Bayesian analysis, neural networks, and generalization bounds. Key research interests include the theoretical underpinnings of deep learning architectures, optimization strategies for large-scale models, and probabilistic methods in machine learning. Notable contributions include work on gradient clipping for wide/deep networks and semi-pessimistic reinforcement learning frameworks. Leading the MCAIF project (2021–2026), a multidisciplinary initiative exploring core AI principles. Active collaborations with institutions worldwide, emphasizing open-access publications (e.g., Transactions on Machine Learning Research). His articles explore topics like PAC-Bayesian theory, convergence of diffusion models, and meta-analyses of Bayesian methods. He advises a diverse cohort of postgraduate researchers in AI fundamentals.

• Machine Learning
• Deep Learning
• Reinforcement Learning
• +۶ مورد دیگر

Midhun Parakkal Unni is a Research Associate at the University of Manchester working in the area of Machine Learning and Mathematical Modelling. His research focuses on the synergetic applications of probabilistic machine learning and dynamical-systems theory, with particular emphasis on neuromuscular control and wearable medical devices. He is affiliated with the Christabel Pankhurst Institute and actively contributes to the Centre for AI Fundamentals (MCAIF) project. His research interests span across multiple interdisciplinary domains including biomedical engineering, neuroscience, and mathematical modeling. Dr. Unni's work primarily investigates Parkinsonian gait patterns, freezing of gait phenomena in Parkinson's disease, and the development of mathematical models for motor control analysis. His fingerprint analysis reveals strong expertise in Applied Torque, Functionals, Model Systems, Ordinary Differential Equations, Numerical Differentiation, and Numerical Solutions. The publication trends indicate a consistent research trajectory from 2017 to 2023, with increasing focus on Parkinson's disease applications. His work bridges theoretical mathematical concepts with practical biomedical applications, particularly in movement disorder analysis and prediction. The research demonstrates strong integration of machine learning techniques with biomechanical modeling approaches. As a Principal Investigator and Researcher on the MCAIF: Centre for AI Fundamentals project (2021-2026), Dr. Unni contributes to research in Reinforcement Learning, Neural Networks, Approximation Algorithms, Machine Learning, and Artificial Intelligence. His project work demonstrates significant impact with mentions in Wikipedia pages and substantial readership across academic platforms.

• Machine Learning
• Mathematical Modelling
• Probabilistic Machine Learning
• +۶ مورد دیگر

Sean Holden is a Professor in the Department of Computer Science and Technology at the University of Cambridge, affiliated with The Computer Laboratory. He holds positions at both the University and Trinity College. His research focuses on automated theorem proving, machine learning, and their intersections with formal methods and AI. He leads the development of the Connect++ theorem prover, which won the Best Newcomer award at CASC 2024. His work spans areas including connection calculus, graph neural networks, and reinforcement learning in games like Mahjong. Holden's academic contributions include over 50 publications in top venues such as IJCNN, ICPRAM, and Journal of Automated Reasoning. Notable works include foundational research on Bayesian methods in theorem proving and machine learning applications in bioinformatics and medical imaging. He serves as an Associate Editor for IEEE Transactions on Artificial Intelligence since 2023. His research group explores topics like protein graph embeddings, medical AI (e.g., breast cancer classification via MUGI-MRI), and the integration of machine learning with automated reasoning systems. Collaborations span institutions including Trinity College and international partners in computational biology and AI.

• Automated Theorem Proving
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Neil D. Lawrence is the DeepMind Professor of Machine Learning at the University of Cambridge and a Visiting Professor at the University of Sheffield. His work focuses on advancing machine learning techniques and their ethical applications in healthcare, computational biology, and global development. Research Interests: Machine Learning Ethics and Governance Data-driven approaches to personalized health Global data science initiatives in Africa (e.g., Data Science Africa workshops) Open Science and academic publishing reform Articles Trends: His publications emphasize critical discourse on AI ethics, data privacy, and the societal impacts of emerging technologies. Recent works include critiques of corporate data practices and advocacy for equitable data governance models. Grants & Labs: Leads initiatives like the Accelerate Science Programme, the AutoAI project, and the Digital Markets Unit at the Competition and Markets Authority (CMA). Active in the ELLIS Society and PMLR (Proceedings of Machine Learning Research). Labs/Teams: Former head of the ML@SITraN group (Sheffield) focused on probabilistic models in computational biology. Currently involved in ML@CL (Machine Learning at the Cambridge Computer Laboratory). Education: While specific academic qualifications aren’t listed, his roles imply expertise in machine learning, computer science, and interdisciplinary applications.

• Machine Learning
• Data Science
• AI Ethics
• +۵ مورد دیگر

Dr. Miquel Miravet Tenés is a Research Fellow at the University of Southampton's School of Mathematical Sciences. He is affiliated with the Southampton Theory Astrophysics and Gravity (STAG) Research Centre and the Gravity Group, focusing on neutron star mergers and gravitational wave physics. BSc in Physics , Universitat de València (2019) MSc in Advanced Physics , Universitat de València (2020) PhD in Physics , Universitat de València (2024) His research involves modeling magnetohydrodynamical turbulence in neutron star mergers using numerical relativity simulations . He investigates gravitational wave emission during the postmerger phase of binary neutron star systems and applies machine learning for rapid identification of gravitational wave sources. His work also explores sub-grid models for turbulence , fermion-axion star stability , and the effects of ultralight dark matter on compact objects. Recent publications highlight his contributions to magnetic field amplification mechanisms , gravitational wave memory effects on the CMB , and multi-messenger observation classification . Collaborations include Professor Ian Hawke and Professor Nils Andersson. He is actively involved in computational astrophysics and participates in interdisciplinary projects at the STAG Research Centre. No current teaching responsibilities or student supervision details are listed publicly.

• Neutron stars and their mergers
• Numerical relativity
• Gravitational waves
• +۳ مورد دیگر

Jean-Marc Schwartz is a Senior Lecturer in the Division of Evolution, Infection and Genomics at the Faculty of Biology, Medicine and Health, University of Manchester. He has held this position since 2016, having previously served as Lecturer (2011-2016) and Physical/Life Sciences Interface Independent Research Fellow (2006-2011) at the University of Manchester. He also serves as an External Examiner at the University of Glasgow (2020-2027) and is affiliated with multiple research institutes including the Digital Futures Institute for Data Science and AI, Lydia Becker Institute, and Christabel Pankhurst Institute. His educational background includes: PhD in Computer Engineering from Laval University (Canada), 1999-2003 Research Associate at Max-Planck Institute for Biochemistry (Germany), 1995-1999 Engineering degree from École Centrale Paris (France), 1991-1994 Dr. Schwartz specializes in Systems Biology, with research focusing on constraint-based analysis of metabolic networks, integrative and dynamical modeling of biological systems, and systems pharmacology. His work bridges computational approaches with experimental biology to understand complex cellular processes through chemical and physical models. He develops innovative methodologies for data integration, network analysis, model construction and simulation to advance global understanding of biological functions beyond traditional reductionist approaches. His recent publications demonstrate strong trends in metabolic modeling, biomanufacturing optimization, and computational approaches to cellular dynamics. His work increasingly focuses on applying systems biology to pharmaceutical development and bioproduction challenges, particularly with Chinese Hamster Ovary (CHO) cells for therapeutic protein production, showing significant contributions to biomanufacturing processes and metabolic engineering. Dr. Schwartz is actively involved in research funding and collaboration: Co-Investigator on "Optimisation of CHO for Biotherapeutic Manufacture" (2021-2026) Member of the Digital Futures Institute for Data Science and AI Member of the Lydia Becker Institute Member of the Christabel Pankhurst Institute With 37 supervised students and 124 research outputs including 92 articles, 11 conference contributions, and 6 review articles, Dr. Schwartz maintains an active research program that bridges computational modeling with experimental biology. His work contributes to UN Sustainable Development Goals, particularly in health and well-being areas through systems pharmacology and drug metabolism research.

• Systems Biology
• Metabolic Networks
• Constraint-based Analysis
• +۵ مورد دیگر

Thomas Cass is a Professor of Mathematics at Imperial College London, affiliated with the Department of Mathematics within the Faculty of Natural Sciences. He directs the EPSRC Centre for Doctoral Training (CDT) in Mathematics of Random Systems, a collaboration with the University of Oxford, and leads DataSig II, an EPSRC Programme Grant focusing on streamed data analysis using rough path theory. His research bridges pure and applied mathematics, emphasizing stochastic analysis, rough path theory, and their applications in machine learning and finance. He holds a PhD from the University of Cambridge and previously worked at the University of Oxford, where he was a Fellow and Member of Christ Church’s Governing Body. His academic leadership includes editorial roles at journals like the Journal of the London Mathematical Society and the Bulletin of the London Mathematical Society. He has organized major conferences on stochastic analysis and computational data science at institutions like the Oberwolfach Research Institute and ICERM. His work on signature methods from rough path theory has advanced mathematical data science, with applications in radioastronomy and molecular biology. Recent EPSRC grants support foundational research in rough analysis and its integration with Large Language Models. Cass’s contributions span theoretical developments, open-source software tools, and interdisciplinary collaborations.

• Probability
• Stochastic Analysis
• Mathematical Data Science
• +۵ مورد دیگر

Davide Amato is an Assistant Professor in Spacecraft Engineering at the Department of Aeronautics, Faculty of Engineering at Imperial College London. He leads the Computational Astrodynamics (COAST) research group focused on developing advanced computational methods to enhance space situational awareness and satellite dynamics analysis. His work emphasizes error correction in orbital data, machine learning applications for space systems, and sustainable space operations. Education: PhD from Technical University of Madrid (2017), MSc and BSc from University of Naples Federico II (2013, 2009). Prior positions include Postdoctoral roles at University of Arizona and University of Colorado Boulder. Research interests include astrodynamics, space debris mitigation, orbit propagation, and computational methods. His group develops algorithms for satellite maneuver reconstruction, error-bounded orbit prediction, and scientific machine learning applications in space systems. Recent work addresses challenges in space catalog accuracy and collision avoidance through advanced mathematical techniques. He actively supports PhD applications for computational astrodynamics research via Imperial's President's PhD scholarships. His research spans applied mathematics, aerospace engineering, and interdisciplinary collaborations with the Space Lab and Artificial Intelligence Network at Imperial.

• Astrodynamics
• Space Situational Awareness
• Orbit Propagation
• +۵ مورد دیگر

Zahra Sharif Khodaei is a Professor in Aerospace Structural Durability and Health Monitoring at Imperial College London's Department of Aeronautics within the Faculty of Engineering. She earned her PhD in numerical modelling of functionally graded materials from Czech Technical University in 2008. Her professional experience includes a research associate role at Imperial College from 2009 before transitioning to a lectureship in 2014. Professor Khodaei holds prestigious fellowships from the Royal Aeronautical Society and Women in Engineering Society. Her research focuses on advanced structural health monitoring (SHM) techniques for aerospace composites, including fatigue analysis, damage detection via guided waves, and integration of physics-informed machine learning. Key areas include development of wireless sensor solutions, probabilistic frameworks for impact localization, and optimization of sensor networks for large composite structures. She also explores metamaterials, fiber optic sensing systems, and reliability analysis under environmental variations. Expertise: SHM, composite materials, fatigue analysis, neural networks, guided wave techniques Affiliations: Aerospace Materials and Structures, Space Engineering, Structural Integrity and Health Monitoring, The Composites Centre Recent work emphasizes baseline-free damage detection methods, probabilistic impact localization, and multi-objective sensor path optimization. Her publications span structural simulations, material characterization under extreme conditions, and hybrid sensor systems for aerospace applications. Scientific Awards: Fellow of Royal Aeronautical Society, Fellow of Women in Engineering Society Current research includes development of digital clone platforms for SHM system testing under uncertainty, vibration-based offshore wind turbine monitoring, and 3D-printed flexible strain sensors. She also investigates automated crack growth quantification using neural networks and temperature compensation frameworks for guided wave systems in large composites.

• Structural Health Monitoring (SHM)
• Composite Materials
• Fatigue Analysis
• +۹ مورد دیگر

Prof. Roberto Trotta is a Professor of Theoretical Physics at SISSA (Trieste, Italy) and Visiting Professor of Astrostatistics at Imperial College London. He leads the Theoretical and Scientific Data Science group at SISSA and directs its Interdisciplinary Lab. His research focuses on cosmology, dark matter/energy, and applying machine learning/AI to astrophysical data. He holds a PhD from the University of Geneva and an MSc from ETH Zurich. He has been a faculty member at Imperial College London (2008–2023) and a Visiting Professor at Gresham College (2019–2022). Education: PhD in Theoretical Physics, University of Geneva, Switzerland (2004) MSc (Hons) in Physics, ETH Zurich, Switzerland (2011) Research: Analyzes cosmological observations to study dark matter/energy, early universe physics, and particle-physics connections. Develops Bayesian methods, machine learning, and AI for data analysis. Leads projects like STAR NRE and StratLearn-z for improved astrophysical modeling. Collaborates on experiments like LISA, DARWIN, and EDGES. Public Engagement: Award-winning science communicator; authored The Edge of the Sky (2014) and STARBORN (2023). Public lectures at Gresham College and international festivals. Recognized with the Annie Maunder Medal (2020) and Foreign Policy's Global Thinkers (2014). Awards: Annie Maunder Medal (Royal Astronomical Society, 2020) Chair Georges Lemaître (2018) Foreign Policy 100 Global Thinkers (2014) Michelson Prize (2008) Leadership: Directed Imperial’s Centre for Languages, Culture, and Communication (2015–2020). Founded Data Fusion Consultants (2012–2020) for statistical consultancy. Collaborates with museums and artists on science communication. Labs/Teams: Leads the SISSA Data Science group and collaborates with the Theoretical and Scientific Data Science team at SISSA. Active in interdisciplinary projects like the AstroML group and DARWIN observatory collaborations.

• Cosmology
• Astrophysics
• Dark Matter and Dark Energy
• +۴ مورد دیگر