جست‌وجوی استاد و پژوهشگر

Andrew Riceمشاهده پروفایل

استاد

• Programming Languages
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Andrew Rice is a Professor of Computer Science at the University of Cambridge's Department of Computer Science and Technology, and holds the Hassabis Fellowship in Computer Science. He is also the Director of Studies in Computer Science at Queens' College. His research focuses on programming languages, software engineering, and machine learning applications in software development. He leads projects like Isaac Computer Science and ALTA (Automated Language Teaching and Assessment), advancing adaptive learning technologies. His work includes static analysis tools such as Error Prone at Google, energy efficiency studies in computing infrastructure, and contributions to the Computing for the Future of the Planet initiative. His teaching emphasizes practical skill development through flipped classrooms and video lectures, earning him the 2014 Pilkington Prize for teaching excellence. He has held visiting roles at Google and collaborated on energy consumption research for mobile devices and data centers. His research spans systems, networking, and natural language processing, with a strong focus on applying computational methods to real-world challenges. Key Projects: Isaac Physics/Computer Science, ALTA, Error Prone Static Analysis Research Themes: Programming Languages, Machine Learning, Energy Efficiency Awards: Pilkington Prize (2014)

Jossy Sayirمشاهده پروفایل

مدرس

• Information Theory
• Data Compression
• Error Correction Coding
• +۵ مورد دیگر

Jossy Sayir is an Affiliated Lecturer and Senior Research Associate in the Department of Engineering at the University of Cambridge . Holding a Dipl. El.-Ing. ETH and Dr. Techn.-Wiss. from ETH Zurich, Sayir’s work bridges Information Theory and Bioinformatics , focusing on DNA-based data storage and error correction systems. They serve as Director of Studies in Engineering at Newnham College and coordinate Engineering Admissions. Interdisciplinary collaboration with the European Bioinformatics Institute Research on DNA data storage efficiency and cost reduction Expertise in channel coding, source coding, and 5G algorithms Teaching spans mathematics and information engineering modules in Part I Engineering Tripos, with Part II contributions on information theory, error control coding, and cryptography. Sayir also oversees data compression labs and serves as Wine Committee Chair, reflecting diverse interests in food, coffee, wine, music , and jazz . Best Lecturer Award, 2017-18 Research Fellowships in coding theory Key research trends include DNA storage encoding , LDPC decoders , polar code optimization , and Sudoku-inspired constraint coding . Sayir’s work addresses both theoretical and practical challenges in high-density data storage and next-generation communication protocols .

Iñaki Esnaolaمشاهده پروفایل

مدرس ارشد

• Information Theory
• Machine Learning
• Cybersecurity
• +۶ مورد دیگر

Dr. Iñaki Esnaola is a Senior Lecturer at the Department of Automatic Control and Systems Engineering, University of Sheffield, and a Visiting Research Collaborator at Princeton University. He holds a MSc from the University of Navarra (2006) and a PhD from the University of Delaware (2011). His research focuses on information theory, machine learning, and cybersecurity, particularly in cyberphysical systems like smart grids. His work addresses data integrity, privacy, robust estimation, and optimal sensor placement. Research interests include: Information theory and data science, machine learning and high-dimensional statistics, cybersecurity (especially data injection attacks), privacy, robust estimation, and sensor placement optimization. Recent projects involve empirical risk minimization with regularization, stealth attacks on control systems, and compressive sensing for environmental monitoring. Key publications include studies on relative entropy in machine learning, sensor placement for sewer networks, and stealth attacks in smart grids. He leads a research group with ongoing projects in resilient cyberphysical systems and received a UKRI grant for advanced manufacturing. His work bridges theoretical foundations with real-world applications in energy systems and environmental monitoring.

Paul Fletcherمشاهده پروفایل

استاد

• Neuroscience
• Psychiatry
• Cognitive Psychology
• +۷ مورد دیگر

Professor Paul Fletcher is a Principal Investigator at the Institute of Metabolic Science, University of Cambridge, and holds the Bernard Wolfe Health Neuroscience Fund. He is affiliated with the Department of Psychiatry and collaborates with Professors Steve O’Rahilly, Sadaf Farooqi, Fiona Gribble, and Frank Reimann. His research bridges neuroscience and psychiatry to explore higher-level perceptual and learning processes shaping decision-making and behavior, particularly in relation to obesity and mental disorders. Academic Rank: Professor University: University of Cambridge School: Institute of Metabolic Science Department: Psychiatry Fletcher employs functional neuroimaging, behavioral studies, and pharmacological perturbations (e.g., dopamine agonists/antagonists) to investigate reward-related brain processes and their role in food choice, obesity, and psychiatric conditions like psychosis. His work emphasizes the integration of the brain with metabolic/endocrine signals and external environments. Key trends in his publications include predictive coding , delusions , neuroimaging , and virtual reality applications in mental health. Recent studies focus on anxiety , foraging behavior , schizophrenia , and health interventions . Scientific Awards Bernard Wolfe Health Neuroscience Fund Wellcome Trust Senior Clinical Fellow Fletcher’s research is funded by Wellcome and the Bernard Wolfe Fund, with collaborations spanning psychiatry, psychology, and clinical neuroscience. His work addresses mental health , obesity , and neurocognitive mechanisms of decision-making, supported by recent publications in top-tier journals.

• Digital Communications
• Signal Processing
• Networking
• +۳ مورد دیگر

Mahsa Derakhshani is a Senior Lecturer (equivalent to Associate Professor) in Digital Communications at Loughborough University's Wolfson School of Mechanical, Electrical and Manufacturing Engineering. She leads research in the Signal Processing and Networks Research Group (SPNRG) and serves as an Associate Editor for the IET Signal Processing Journal. Her academic journey includes a PhD from McGill University (2013), followed by postdoctoral roles at the University of Toronto and Imperial College London. Key awards include the Royal Academy of Engineering/The Leverhulme Trust Research Fellowship (2020-21) and NSERC Postdoctoral Fellowships (2015-2017). Research interests focus on digital communications, machine learning for signal processing, wireless networks, and reinforcement learning applications. Recent work addresses challenges in satellite communications, OTFS modulation, and opto-physiological monitoring. She has authored over 70 publications spanning topics like NOMA systems, MIMO optimization, and edge-assisted live streaming. Education: PhD (McGill, 2013), MSc (Sharif University, 2008), BSc (Sharif University, 2006) Affiliations: IEEE Senior Member, IET Member, Fellow of the Higher Education Academy Grants & Funding: Leverhulme Trust, Royal Academy of Engineering, NSERC Labs/Teams: Active in SPNRG and collaborates on projects involving 5G/6G networks, satellite systems, and biomedical signal processing. Current research emphasizes AI-driven solutions for communication networks and wearable health monitoring technologies.

Charles Gillanمشاهده پروفایل

مدرس ارشد

• High Performance Computing (HPC)
• Artificial Intelligence (AI) in Healthcare
• Quantum Computing
• +۴ مورد دیگر

Charles Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, affiliated with the High Performance and Distributed Computing department and the Institute of Electronics, Communications & Information Technology. His research bridges HPC systems, AI applications in healthcare, and computational physics. Key projects include managing ICU patient care via neural networks, exascale-ready mathematical packages, and edge computing architectures. Research interests focus on high-performance computing (HPC), quantum computing, real-time data analytics, and electron-molecule scattering simulations. Notable contributions include developing microserver architectures for edge analytics and advancing AI-driven clinical decision support systems. Gillan has collaborated on interdisciplinary projects like food authenticity testing using spectroscopy and improving ventilator management in intensive care units. Publications span AI in healthcare, HPC system design, and computational methods for physics problems. He has secured funding for initiatives such as the KTP partnership with Foods Connected Ltd and the HANDHELD olfactory detection project. Gillan's work emphasizes practical applications of advanced computing across healthcare, engineering, and cybersecurity domains.

• Quantum Computing
• Fault-Tolerant Quantum Computing
• Topological Error Correcting Codes
• +۴ مورد دیگر

Earl Campbell is Professor of Quantum Computation in the School of Mathematical and Physical Sciences at the University of Sheffield. He holds an MSc in Physics and Philosophy from University of Bristol and a PhD in Quantum Computing from University of Oxford. His research develops fault-tolerant quantum computing protocols, error correction codes, and quantum software architectures to enable reliable quantum technologies. Key focus areas include topological error correction, magic state distillation, qudit computation, and bridging quantum theory with hardware implementation. Research Team: 1 Postdoctoral Researcher (Mark Howard) 1 PhD Student (Luke Heyfron) Major Grants: EPSRC Early Career Fellowship: 'Towards fault-tolerant quantum computing with minimal resources' (£675,867) Royal Commission of Great Exhibition of 1851 Fellowship

Anne Ferguson-Smithمشاهده پروفایل

استاد

• Genomic Imprinting
• Epigenetics
• Developmental Genetics
• +۳ مورد دیگر

Professor Anne Ferguson-Smith is a leading mammalian developmental geneticist and epigeneticist at the University of Cambridge, holding the Arthur Balfour Professorship of Genetics. As Pro-Vice-Chancellor for Research, she oversees the university's research strategy while maintaining her laboratory's focus on genomic imprinting and epigenetic inheritance . Her work bridges experimental and computational approaches through affiliations with the Cambridge Stem Cell Institute, Cambridge Neuroscience, and the Centre for Trophoblast Research. Research in her lab investigates epigenetic mechanisms in developmental processes , particularly through the lens of Dlk1-Dio3 imprinted domain studies. Current themes include: Stem cell epigenetic programming Environmental modulation of epigenetic states Role of repetitive elements in genomic regulation Her group integrates mouse and zebrafish models with high-throughput genomics and mathematical modeling . Key collaborations include: Wellcome Trust UKRI Medical Research Council BBSRC NIH Scientific honors include: Elected EMBO Member (2006) Academy of Medical Sciences (2012) Fellow of the Royal Society (2017) Commander of the Order of the British Empire (CBE) The lab maintains family-friendly research practices and actively participates in interdisciplinary collaborations across Cambridge and internationally.

Jaswinder Lotaمشاهده پروفایل

پژوهشگر

• Signal Processing
• Circuits and Systems
• Wireless Communication
• +۷ مورد دیگر

Dr. Jaswinder Lota is a Reader in Engineering at the University of East London , School of Architecture, Computing and Engineering, Department of Engineering & Construction. He is also a Visiting Academic at University College London’s Department of Electronic and Electrical Engineering, and a Chartered Engineer with extensive industry and academic experience. Education: BSc BEng MEng PGCert HE PhD Research Interests: Dr. Lota specializes in signal processing, circuits and systems, wireless communication, and their applications in radar systems (weather/military), low-power sustainable networks beyond 5G/6G (robotics, automation, healthcare), and electronic technologies for hydrogen propulsion. His work integrates AI-driven channel modeling and impulsive noise analysis. Scientific Awards: IEEE CAS Society Certificate of Appreciation (2019) Grants and Collaborations: He has secured significant funding, including a £2.5K International Research Collaboration Award (2016), £2.5K Research Internship Award (2015), £76K Impact Grant (2014), and a £7M MoD-funded project (1999-2004). Collaborators include UCL and NYU. Leadership: Dr. Lota leads the Smart Cities Research group at UEL and contributed to the REF 2021 submission. He has served as Associate Editor for IEEE TCAS I and Guest Editor for multiple IEEE journals.

Deborah Talmiمشاهده پروفایل

دانشیار

• Emotional Cognition
• Memory Mechanisms
• Neurobiology of Emotion
• +۷ مورد دیگر

Dr. Deborah Talmi is a University Associate Professor in the Department of Psychology and a Fellow of Lucy Cavendish College at the University of Cambridge. Her research focuses on the cognitive, computational, and neurobiological mechanisms underlying emotional processes, particularly their intersection with memory, perception, and attention. She leads the Emotional Cognition Lab, which employs behavioral experiments, EEG, fMRI, and computational modeling to explore how emotions influence memory formation and retrieval. Roles: Associate Professor (Department of Psychology), Fellow (Lucy Cavendish College), Principal Investigator (Emotional Cognition Lab) Affiliations: School of Biological Sciences, Cambridge Neuroscience Her research investigates questions such as why emotional experiences feel a certain way, how emotion shapes memory, and the neural basis of similarity perception. A recent study demonstrated that emotionally charged stimuli evoke more similar neural representations than neutral ones, even when controlling for thematic content. This work was published in the Journal of Neuroscience and is part of broader efforts to model emotional memory using frameworks like the Bayesian Brain hypothesis. Dr. Talmi collaborates with institutions like the Swiss Centre for Affective Sciences and employs interdisciplinary methods, including animal models of emotional memory with Prof. Amy Milton. She actively mentors PhD students and welcomes volunteers through the SONA platform. Her lab’s findings have implications for understanding chronic pain, decision-making, and mental health. Her work has been featured in The Conversation and TEDx, emphasizing public engagement. She is also committed to advancing education, as seen in her reflections on remote teaching during the pandemic.

Ahmed M. A. Sayedمشاهده پروفایل

مدرس ارشد

• Distributed Systems
• Machine Learning Systems
• Federated Learning
• +۷ مورد دیگر

Dr. Ahmed M. A. Sayed is a Senior Lecturer (equivalent to Associate Professor) and Director of the MSc Big Data Science Programme at Queen Mary University of London's School of Electronic Engineering and Computer Science. He leads the SAYED Systems Group and focuses on distributed systems, federated learning, edge computing, and network optimization. His research bridges system design and machine learning, emphasizing scalability and efficiency. Education: PhD in Computer Science (HKUST, 2017), M.Sc. and B.Sc. (Assiut University, 2012 and 2007). Prior roles include Research Scientist at KAUST and Senior Researcher at Huawei's Future Network Lab. Research Interests: Systems for ML, federated learning, edge/Cloud computing, network congestion control, and IoT. He has secured £730K+ in grants, including a UKRI-EPSRC grant for the KUber project (2024–2027). Awards: 2024 Best Student Paper (IJCAI FL Workshop), Hong Kong PhD Fellowship (2013–2017), and numerous travel grants. Actively supervises PhD/MSc students and postdocs. Grants & Leadership: PI of UKRI-EPSRC KUber project, Co-I in HKRGC and KAUST grants. Organizes workshops at venues like MobiSys and serves on TPC for ICML, EuroSys, and NeurIPS. Labs: Leads SAYED Systems Group, affiliated with Networks Group and DT4SGD Lab at Queen Mary.

Anthony Gachaganمشاهده پروفایل

استاد

• Ultrasonics
• Non-Destructive Evaluation (NDE)
• Ultrasonic Transducer Design
• +۱۳ مورد دیگر

Professor Anthony Gachagan is a leading academic at the University of Strathclyde, serving as Head of Department and Research Director in the Department of Electronic and Electrical Engineering (EEE) within the Faculty of Engineering. He has been Director of the Centre for Ultrasonic Engineering (CUE) since 2010, leading a multidisciplinary team of around 55 researchers with over £5M in active funding. He also holds leadership roles in RCNDE as Academic Chair and Management Board member, and participates in BINDT committees. His research spans ultrasonic transducer design, non-destructive evaluation (NDE), robotics, high-power ultrasound, and industrial process control. His work is highly collaborative, involving national and international partnerships with industry and academia, and contributes to sectors such as energy, aerospace, nuclear, and healthcare. He is actively involved in major research initiatives aimed at net zero, structural integrity, and advanced manufacturing. Recent publications highlight his focus on robotic ultrasonic inspection, adaptive signal processing, phased array techniques, and in-process monitoring of additive manufacturing. These works demonstrate a strong trend toward automation, real-time defect detection, and integration of ultrasonic systems in industrial and safety-critical applications. Scientific Awards: The BINDT Annual Conference Award (2019) Prof Gachagan has secured significant grants from EPSRC, Innovate UK, and industry partners, including projects on future ultrasonic engineering, lightning impulse testing, and robotic inspection for offshore wind. He supervises numerous research students and leads large collaborative teams. He also contributes to research commercialization through IAA projects. He leads the Centre for Ultrasonic Engineering (CUE), a vibrant research unit integrating electronic, mechanical, and biomedical engineers, physicists, and material scientists. The centre focuses on next-generation ultrasonic technologies, robotics integration, and industrial deployment.

Paul Baysمشاهده پروفایل

استاد

• Visual Working Memory
• Computational Neuroscience
• Cognitive Psychology
• +۵ مورد دیگر

Professor Paul Bays is a leading cognitive neuroscientist at the University of Cambridge, holding a professorship in the Department of Psychology within the School of Biological Sciences. He directs the Computational Cognition Group (Bays Lab), where his research focuses on understanding the neural and computational mechanisms underlying visual working memory and perception. His laboratory employs a diverse range of methodologies including visual psychophysics, extended reality (XR) technology, eye and body movement recordings, mathematical models, and artificial neural networks. Bays Lab collaborates extensively with researchers using brain imaging, recording and stimulation techniques, as well as neuropsychologists studying cognitive aging, mental illness, and neurological disorders. Professor Bays' research has revolutionized our understanding of visual working memory, demonstrating that memory capacity operates as a flexible resource rather than a fixed number of "slots." His work has shown how this resource is dynamically allocated across visual features and how it influences perception, decision-making, and action. A major focus examines how working memory bridges discrete transitions in visual input during eye movements, allowing for continuous visual experience despite frequent shifts in gaze. His laboratory has made significant contributions to understanding sensory prediction and motor learning, particularly regarding tactile attenuation (the phenomenon where self-generated touch feels weaker than externally generated touch). Recent work has applied efficient coding principles to explain various perceptual phenomena including weight illusions. Research Theme: Adaptive Brain Computations Research Theme: Lifelong Brain Development and Brain Ageing Research Theme: Brains and Machines Professor Bays actively supervises PhD students and postdoctoral researchers in the Bays Lab, fostering the next generation of cognitive neuroscientists. His laboratory has developed valuable research tools including the Analogue Report Toolbox for analyzing visual working memory data.

Alastair Kayمشاهده پروفایل

مدرس

• Quantum Computation
• Quantum Information Theory
• Quantum Cryptography
• +۱۳ مورد دیگر

Dr. Alastair Kay is a Lecturer in the Department of Mathematics at Royal Holloway, University of London. His research focuses on theoretical quantum computation, quantum information theory, and quantum cryptography, particularly addressing challenges in quantum state transfer, error correction, and networked quantum systems. He holds a PhD from the University of Cambridge under Prof. Artur Ekert and a physics degree from Keble College, University of Oxford. His research spans topics such as Quantum state transfer protocols using engineered Hamiltonians Quantum error correction mechanisms for experimental systems Entanglement properties in graph states and spin networks Applications of quantum computing in cryptography and information theory The articles listed reflect his work in quantum information science, computational physics, and theoretical cryptography. Key trends include advancements in fault-tolerant quantum communication, optimization of spin chain dynamics, and foundational studies in quantum correlations and nonlocality. Alastair actively develops software tools like quantikz for quantum circuit diagrams and ConTeXi for LaTeX equation integration in Microsoft Office. He also emphasizes open science principles and reproducibility in quantum research through personal commentary and collaboration with his fiancée, a Panton Fellow in open research practices.

Zheng Yuanمشاهده پروفایل

مدرس ارشد

• Educational Technology
• Language Acquisition
• Multilingual NLP
• +۳ مورد دیگر

Dr. Zheng Yuan is a Senior Lecturer (Associate Professor) in Natural Language Processing at the School of Computer Science, University of Sheffield. He holds affiliated positions at the University of Cambridge and King's College London, and is a Fellow of Trinity College, Cambridge. His research focuses on NLP applications in education, healthcare, and multilingual systems. Education: PhD in Natural Language Processing, University of Cambridge MPhil in Advanced Computer Science, University of Cambridge BSc(Eng) from Queen Mary University of London Research: Dr. Yuan's work spans educational NLP, multilingual systems under low-resource conditions, and explainable machine learning. His group develops technologies for grammatical error correction, automated assessment, and cross-lingual applications, with significant contributions to computer-assisted language learning and computational creativity. Publications: Recent works (2023-2025) demonstrate strong focus on educational NLP, multilingual systems, and LLM evaluation. Key themes include grammatical error correction for code-switched languages, creativity assessment frameworks, and robust evaluation methods for large language models across diverse linguistic contexts. Awards: Winning systems at SemEval-2021 and CoNLL-2014 Fellowship at Trinity College Cambridge Fellow of Higher Education Academy Grants & Advising: Principal Investigator for Royal Society grant 'Large Language Models as Agents for Intelligent Language Tutoring' (2025-2027). Actively supervises PhD students in NLP and machine learning, welcoming new research collaborations. Affiliations: Member of Alan Turing Institute (Data-Centric Engineering), King's Institute for AI, and ACL committees. Organizes major NLP workshops including ACL/NAACL BEA workshops and AIED tutorials.