جست‌وجوی استاد و پژوهشگر

• AI-based feedback
• Computational thinking education
• Game-based learning
• +۵ مورد دیگر

Dr. Jingyun Wang is an Assistant Professor in the Department of Computer Science at Durham University, where she joined in September 2020. She is a Fellow of the Higher Education Academy and serves as a Mentor and Assessor for the Professional Recognition Pathway. Dr. Wang is a core academic member of the Centre for Neurodiversity & Development and a member of the Turing Interest Group on Knowledge Graphs. Prior to Durham, she was an Assistant Professor at Kyushu University (2014-2020) and associate faculty at the Learning Analytics Center. Her research focuses on AI-based educational technologies, including computational thinking, game-based learning, and ontology-driven learning support systems. She leads projects funded by JSPS, JST, NICT, and Innovate UK, including the development of the Medialib Python library for programming education. Dr. Wang's research spans AI-based feedback systems, learning analytics, and educational technologies for special needs. Her publications frequently appear in top-tier conferences and journals, covering adaptive learning systems, educational data mining, and human-computer interaction. She has received recognition as a Fellow of the Higher Education Academy and editorial roles in journals including Computers & Education: Artificial Intelligence and International Journal of Mobile Learning and Organisation . Dr. Wang advises multiple PhD students and leads the Pedagogical Innovation in Computer Science Group (PICS). She is actively involved in organizing major conferences including AIED and ICCE.

Keshav Dahalمشاهده پروفایل

استاد

• Artificial Intelligence (AI)
• Data Mining/Big Data
• Blockchain Technology
• +۷ مورد دیگر

Overview Professor Keshav Dahal holds the Chair in Intelligent Systems and serves as Director of the Artificial Intelligence, Virtual Communication and Networks (AVCN) Research Institute and the DeLTA (Data Analytics for Intelligent and Autonomous Systems) Research Group at the University of the West of Scotland (UWS). He concurrently holds visiting professorships at Amity University (India), Kantipur Engineering College (Nepal), and Chiang Mai University (Thailand). His research focuses on AI-driven solutions for data science, blockchain technology, disaster management, healthcare systems, communication networks, and energy systems. Research Expertise Professor Dahal specializes in: Applications of AI to data mining, logistics optimization, and decision support systems Trust and security modeling in distributed systems Blockchain applications for secure authentication and decentralized systems Optimization of IoT-enabled smart technologies Healthcare informatics and remote patient monitoring Grants & Projects He has secured £30M+ in EU/UK/industry funding through 26 projects, including: EU eACCESS (2019–2024) H2020 SELFNET (2015–2018) UK Innovate KTP projects with Little Ltd, PG Paper, and WellFish Tech Professional Contributions He has: Supervised 11 postdocs and 30 PhD students Authored 200+ peer-reviewed publications Chaired 60+ international conferences Developed frameworks for interdisciplinary machine learning applications Labs & Initiatives He leads the AVCN Research Institute and coordinates EU-funded initiatives like SmartLink (2014–2019) and gLINK (2014–2019), focusing on smart technology applications across sectors.

Mohammad Samar Ansariمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Dr. Mohammad Samar Ansari is a Senior Lecturer at the School of Computer and Engineering Sciences, part of the Faculty of Science, Business and Enterprise at the University of Chester. He has held previous academic positions, including Associate Professor (2021-2022) and Assistant Professor roles at Aligarh Muslim University and the Technological University of the Shannon: Midlands Midwest (2018-2020). His career spans two decades across India, Ireland, and the UK, with global collaborations in research and teaching. Education: B.Tech., Electronics Engineering, Aligarh Muslim University (1997-2001) M.Tech., Electronics Engineering, Aligarh Muslim University (2004-2007) Ph.D., Electronics Engineering, Aligarh Muslim University (2008-2012) PG Certificate (Ongoing), Learning and Teaching in Higher Education, University of Chester (2022-2024) Research Interests: Ansari’s work focuses on cutting-edge applications of machine learning and deep learning in healthcare, cybersecurity, and smart cities. He specializes in lightweight models for edge computing, dataset augmentation (e.g., using Wasserstein GANs), privacy-preserved surveillance systems, and analog circuit design in FinFET technology. His contributions include optimizing fire detection algorithms, improving Alzheimer’s disease detection through synthetic data, and advancing neural network solutions for non-linear engineering problems. Articles Trends: His recent publications emphasize real-time, low-resource machine learning solutions for practical use-cases, including fire detection, healthcare diagnostics, and wireless communication. He frequently explores trade-offs between computational efficiency and accuracy, particularly in edge device deployments and privacy-conscious scenarios. Scientific Awards: Senior Member of IEEE (since 2019) Associate Fellow of the Higher Education Academy (HEA) (2023) Advising & Grants: Ansari has supervised two completed PhD students, 40+ Master’s dissertations, and 30+ undergraduate projects. His research has been funded by £300,000 from institutions like Enterprise Ireland, India’s Department of Science and Technology, and TU Shannon’s Software Research Institute. He also holds certifications from MIT and Harvard in data science and programming. Labs/Teams: Involved in the Research and Knowledge Exchange Institutes at the University of Chester, focusing on Culture and Society, Sustainability and Environment, and Health and Wellbeing. Previously collaborated with the Software Research Institute at TU Shannon, Ireland.

Mohammad Alselekمشاهده پروفایل

مدرس

• Artificial Intelligence
• Internet of Things (IoT)
• 5G Technology
• +۳ مورد دیگر

Mohammad Alselek is a Lecturer at the School of Computing, Engineering and Physical Sciences. He holds a PhD in Computer Science with a focus on Artificial Intelligence (AI) and Internet of Things (IoT). He is currently accepting PhD students for 2023 and beyond. Education: PhD in Computer Science (focus: AI & IoT) Research Interests: Development of AI-driven IoT systems for environmental monitoring, industrial applications, and consumer products Optimization of AI models for low-power 5G IoT devices Firmware management and DevOps practices in embedded systems Prediction systems using digital twins and machine learning His work emphasizes practical applications such as hazardous odor detection, CO₂ emissions tracking, and whiskey classification using advanced sensor technologies. Key Research Trends (2022-2025): Integration of TinyML and edge computing for real-time IoT applications Development of updatable AI models for energy-constrained devices Application of electronic nose (e-nose) systems across industries Advising & Grants: Accepting PhD students focused on AI-IoT intersections No explicit grant information provided

Marc Conradمشاهده پروفایل

مدرس ارشد

• Computer Science
• Data Science
• Cybersecurity
• +۵ مورد دیگر

Dr. Marc Conrad is a Principal Lecturer in Computing and Information Systems at the University of Bedfordshire , affiliated with the School of Computer Science and Technology . His academic career spans over two decades, with expertise in Cybersecurity , Virtual Reality , Education Technology , and Data Science . He has supervised sixteen PhD students to completion and authored over seventy peer-reviewed publications. Research interests include cross-cultural software development, secure applications, virtual learning environments, and data governance. Notable projects include collaborations with Bedfordshire Police on cyberharassment and the EU-funded EDISON initiative for Data Science professionalization. He is a Fellow of the Higher Education Academy (FHEA) and a member of the BCS, Institute of Mathematics and Applications, and Croatian Mathematical Society. Teaching focuses on IT Project Management , Full Stack Development , and innovative pedagogical approaches like virtual reality integration. He leads undergraduate computing programs and chairs PhD supervisory committees within the Institute for Research in Applicable Computing (IRAC). External roles include grant reviewing for MRC, NSERC, and ESRC. Dr. Conrad's work bridges academia and industry, addressing challenges in cybersecurity, e-government, and digital transformation. His labs explore AI, blockchain, and forensic imaging, with recent projects tackling cybercrime in post-pandemic contexts and scam prevention in online gaming.

Mohamed Sedkyمشاهده پروفایل

استاد

• Computer Vision
• Video Analytics
• Cyber-Physical Systems
• +۴ مورد دیگر

Professor Mohamed Sedky holds the position of Professor of Cyber-Physical Systems at Staffordshire University. He previously founded SKM Communication Systems and served as a lecturer at the Arab Academy for Science and Technology before joining Staffordshire in 2003. His roles include MSc Computer Science Deputy Award Leader and BSc Internet of Things Award Leader. He leads research at AVA Technologies Ltd, focusing on real-time video analytics algorithms such as Spectral-360®, which addresses security, safety, and business intelligence applications. His patented work in object detection and change detection has been globally recognized. He teaches modules like Fundamentals of Computer Networks and Router Security Technologies. Education: BEng (Hons) Electro-Physics and Communications, Alexandria University (1996) MSc Communications and Electronics, Arab Academy for Science and Technology (2002) PhD Computer Vision, Staffordshire University (2009) PGC Research Supervision, Staffordshire University PGC Higher and Professional Education, Staffordshire University Research Interests: Object recognition/segmentation Video surveillance systems IoT forensic processes AI in healthcare and smart environments Commercial Ventures: Founder of Spectral-360® technology Lead Researcher at AVA Technologies Ltd Consultant for Serco, Centralweighing, and WMTN Labs & Projects: Developed AVA Technologies' Detect/Protect/Count/Summarise solutions Designed OpenSHS (Open Smart Home Simulator) Conducted feasibility studies for buried utility detection and industrial inspection His research bridges academia and industry through patented innovations in video analytics and IoT systems, addressing challenges in security, healthcare, and smart infrastructure.

Chang Liuمشاهده پروفایل

مدرس ارشد

• Laser Spectroscopy
• Tomography
• Data-Driven Imaging
• +۳ مورد دیگر

Dr. Chang Liu is a Senior Lecturer in Electronic Engineering at the University of Edinburgh's School of Engineering. He received his B.Sc. in Automation from Tianjin University (2010) and Ph.D. in Testing, Measurement Technology and Instrument from Beihang University (2016). Following postdoctoral research at Empa-Swiss Federal Laboratories, he joined the Agile Tomography Group at Edinburgh. His research focuses on laser spectroscopy, laser imaging, and data-driven imaging techniques for applications in reacting flow-field diagnostics and environmental monitoring. Key specialties include design of near/mid-infrared LAS sensing systems, development of high-sensitivity imaging methodologies, spectroscopic modeling, inverse problem solving, and embedded system design. His publications demonstrate a consistent focus on advancing tomographic imaging techniques, with recent work emphasizing machine learning integration, hardware acceleration, and industrial applications in aero-engine monitoring. Research consistently addresses challenges in spatial/temporal resolution enhancement and real-time system implementation. Dr. Liu teaches courses in Digital System Design, Analogue Circuits, and Embedded Systems. He leads multiple research projects including EPSRC-funded initiatives on laser imaging of turbine engine combustion species. His team collaborates with industrial partners to develop cutting-edge laser-based sensing solutions.

Paul Hooperمشاهده پروفایل

مدرس ارشد

• Additive Manufacturing
• High Strain-Rate Material Behaviour
• Structural Integrity
• +۵ مورد دیگر

Paul Hooper is a Senior Lecturer in the Department of Mechanical Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on additive manufacturing (3D printing), high strain-rate material behavior, and structural integrity. He leads projects on in-situ process monitoring, process-microstructure-performance relationships, and simulation of AM processes. His work also addresses lattice structures, design for AM, and impact dynamics. He holds a PhD from Imperial College London (2007) titled 'Blast Performance of Silicone-Bonded Laminated Glass' and an undergraduate degree from the University of Bath. Paul teaches the 'Embedded C for Microcontrollers' module and won the 2012 Faculty Teaching Assistant Award. Research affiliations include the Additive Manufacturing Network, Mechanics of Materials Group, and Structural Integrity initiatives. Collaborations involve Arup, MoD, and the Office of Naval Research. His work bridges experimental methods (e.g., high-speed melt pool monitoring) with computational models to advance AM reliability and performance. Key Research Themes: Additive Manufacturing, High-Strain Rate Dynamics, Material Microstructure Awards: 2012 Faculty Teaching Assistant Award Teaching: Mechanical Engineering MEng Program

André Freitasمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Explainable AI
• Natural Language Processing
• +۵ مورد دیگر

André Freitas is a Senior Lecturer in Computer Science at the University of Manchester, where he leads the Reasoning & Explainable AI Lab. He also serves as a research group leader at the Idiap Research Institute and as the AI group leader at the digital Experimental Cancer Medicine Team (CancerResearchUK). His work bridges academic research with practical applications in healthcare and industry. Dr. Freitas's research focuses on developing AI methods that support abstract, explainable, and flexible inference. His work particularly investigates how combining neural and symbolic data representation paradigms can deliver better inference capabilities. His expertise spans multiple domains within artificial intelligence and machine learning, with strong connections to Large Language Models (100%), Neural Networks (42%), and Artificial Intelligence (37%) as shown in his research fingerprint. His research interests include explanation generation, natural language inference, explainable question answering, knowledge graphs, and open information extraction. Dr. Freitas is actively engaged in collaboration projects with both industrial and clinical partners, demonstrating the practical applications of his research across various domains including healthcare, construction safety, and financial technology. Health and Safety Software of the Year: Construction Computing Awards 2021 (team award) Dr. Freitas has supervised 12 research projects and maintains an extensive research output of 94 publications. His recent work demonstrates expertise across multiple AI subfields, with particular focus on making AI systems more explainable, reliable, and applicable to real-world problems in medicine and industry. He is involved in four major research projects including the NIHR Manchester Biomedical Research Centre and the EnnCore project focused on conceptual guarding of neural architectures.

Tamara Polajnarمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Computational Linguistics
• Distributional Semantics
• +۲ مورد دیگر

Dr. Tamara Polajnar is a Visiting Researcher at the Department of Computer Science and Technology, University of Cambridge. Her research focuses on computational linguistics and natural language processing, with particular emphasis on distributional semantics and semantic compositionality. Her research interests span multiple areas including computational semantics, distributional models, and semantic composition. She develops tensor-based methods for representing meaning and investigates techniques for semantic analysis of text at different levels from phrases to documents. Her work combines theoretical linguistics with practical machine learning approaches. Her publications primarily focus on compositional distributional semantics, exploring how to effectively combine word vectors to represent larger linguistic units. Recent work examines discourse-level representations and evaluation methods for semantic models. She has contributed datasets and frameworks that advance the understanding of how computational models can capture linguistic phenomena. She actively contributes to the research community through the development of specialized datasets and evaluation resources. Her work on RELPRON provides valuable resources for evaluating compositional distributional models, while her research on short scientific summaries addresses challenges in processing academic text.

Vadim Safronovمشاهده پروفایل

پژوهشگر

• IoT Security
• Cybersecurity
• Wireless Communications
• +۳ مورد دیگر

Dr. Vadim Safronov holds dual roles as a Visiting Researcher at the University of Cambridge's Department of Computer Science and Technology and a Research Associate at the University of Oxford's Department of Computer Science. He earned his PhD in Computer Science from the University of Cambridge. His research focuses on IoT security and privacy, wireless communications, network architectures, and AI/ML applications in these domains. Key contributions include projects such as the UKRI DSbD initiative on Secure Networking by Design, the EPSRC PETRAS PRISM project on privacy-preserving IoT security, and the Innovate UK-funded Centre for Digital Built Britain (CDBB) where he developed a LoRaWAN network and Digital Twin Model for smart environments. Teaching roles include supervising Part 1B undergraduate students in Computer Networking at Cambridge and delivering practical sessions in Network Security and Security Principles for the MSc Software and Systems Security at Oxford. He has also contributed to academic service roles, including TPC positions for IEEE WoWMoM 2025 and PerFail 2025. His work spans grants like the £59k InnovateUK KTN CyberASAP grant for the 'IoTrim' project addressing IoT privacy threats. Notable research outputs include advancements in memory-safe IoT systems, decentralized IoT protection (SunBlock), and real-time data processing frameworks for smart cities.

Carlos Purvesمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Carlos Purves is an academic researcher affiliated with the Department of Computer Science and Technology at the University of Cambridge. His work focuses on advanced topics in artificial intelligence and machine learning, particularly in reinforcement learning frameworks and adversarial environments. He has contributed to the development of the PlayStation reinforcement learning environment (PSXLE), demonstrating practical applications of machine learning in gaming and embedded systems. His research bridges theoretical foundations with real-world implementations, addressing challenges in multi-agent systems and decision-making under adversarial conditions. While no formal awards or grants are explicitly mentioned, his publications reflect a strong focus on innovation in reinforcement learning and its applications. His research is rooted in the broader discipline of computer science, with specific emphasis on game AI and optimization techniques.

• Computer Science
• Computer Architecture
• Machine Learning
• +۳ مورد دیگر

Thomas Grocutt is a Researcher affiliated with the Department of Computer Science and Technology at the University of Cambridge. His work is embedded within a department known for its contributions to computer architecture, machine learning, and systems engineering. While specific research interests are not explicitly detailed in the provided text, his affiliation suggests a focus on cutting-edge technological and theoretical advancements in computer science. Contact information includes an email address at tcg41@cam.ac.uk and an office located in the William Gates Building.

Qiang Yangمشاهده پروفایل

پژوهشگر ارشد

• Mobile Computing
• Ubiquitous Computing
• Mobile Health
• +۴ مورد دیگر

Qiang Yang is a Postdoctoral Research Associate at the Department of Computer Science and Technology , University of Cambridge, working under Prof. Cecilia Mascolo. His research focuses on mobile health , ubiquitous computing , and leveraging acoustic signals for health monitoring, IoT systems, and human-computer interaction. Key projects include gait monitoring via earables, toothbrushing tracking with in-ear sensors, and voice liveness detection for smart devices. His work bridges machine learning with sensor technologies , addressing challenges in energy retrofitting, emergency response systems, and secure IoT communications. Collaborations emphasize practical applications in healthcare, wearable tech, and environmental sustainability. Notable projects include SmarTeeth , WalkEar , and AutoIOT , showcasing innovations in acoustic-based health monitoring and AI-driven IoT programming. Publications span topics like federated learning optimization, underwater SOS detection, and mmWave radar communication via smartphone vibrations. His research has implications for improving lifestyle management, disaster response, and secure smart environments.

Geoff Nichollsمشاهده پروفایل

دانشیار

• Bayesian Inference
• Computational Statistics
• Monte Carlo Methods
• +۴ مورد دیگر

Geoff Nicholls is an Associate Professor of Statistics at the University of Oxford, affiliated with St Peter's College as a Fellow and Tutor. He holds a B.Sc. in Physics from the University of Canterbury (New Zealand) and a PhD in High-Energy Physics from the University of Cambridge. His research focuses on Bayesian inference, computational statistics, and Monte Carlo methods applied to interdisciplinary problems in geoscience, linguistics, genetics, and archaeology. Notable contributions include semi-modular Bayesian inference frameworks and embedded diachronic sense change models for ancient languages. He has advised students like Lorenzo Pacchiardi and Jessie Jiang, and collaborates on tools like the TraitLab package for binary tree-like data analysis. Key research areas include statistical methods for partial orders, biclustering forensic data, and scalable Bayesian techniques for high-dimensional problems. His work bridges theoretical developments with practical applications, such as modeling historical power dynamics using 12th-century documents and analyzing linguistic evolution in ancient Greek texts.