جستجوی استاد راهنمای دانشگاهی

Hafeez Ullah Aminمشاهده پروفایل

مدرس ارشد

• Neuroimaging
• Biomedical Signal Processing
• EEG Signal Processing with Machine Learning
• +۳ مورد دیگر

Hafeez Ullah Amin is a Senior Lecturer in the School of Computer Science at Edge Hill University, UK. Previously, he served as an Assistant Professor at the University of Nottingham Malaysia Campus (2020–2023) and held various research positions at Universiti Teknologi PETRONAS (UTP), including Postdoctoral Researcher and Research Scientist. He holds a PhD in Electrical and Electronic Engineering from UTP, specializing in EEG Signal Processing with Machine Learning. His expertise spans Neuroimaging, Biomedical Signal Processing, and Applied AI/ML in healthcare and education. Education: PhD (Electrical and Electronic Engineering), Universiti Teknologi PETRONAS, 2011–2015 MPhil (Computer Science with AI), Kohat University of Science and Technology, 2006–2009 BSc (Information Technology), Kohat University of Science and Technology, 2001–2005 Research Interests: Neuroimaging, EEG Signal Processing, AI in Mental Healthcare, Data Analytics, and Machine Learning applications in education and business. His work intersects neuroscience, biomedical engineering, and computational methods to address challenges in memory assessment, stress mitigation, and learning efficacy. Publications: Over 50 articles in high-impact journals and conferences, including work on EEG-based neurofeedback, machine learning for healthcare analytics, and 3D educational content efficacy. Recent trends focus on EEG functional connectivity, fraud detection via graph autoencoders, and energy forecasting. Awards: Senior Member, IEEE Fellow (FHEA), Advance HE, UK Advising & Grants: Supervises PhD students in AI/ML applications. Active in research grants involving EEG experiments, patent on LTM assessment via EEG, and collaborations in smart cities and energy systems. Teaching: Leads courses in Data Science, AI, Microprocessor Systems, and Knowledge Representation. Past roles include Module Leader for Programming Principles and Techniques. Labs/Tech Teams: Associated with Edge Hill's Data and Complex Systems Research Centre and the International Centre for Applied Research in Education.

UMAR KHANمشاهده پروفایل

مدرس ارشد

• Partial Discharge
• Condition Monitoring
• Future Smart Grids
• +۴ مورد دیگر

Dr. Umar Khan is a Senior Lecturer in Electrical and Electronic Engineering at Edge Hill University since September 2021. He previously held positions as Assistant Professor at Heriot-Watt University and Lecturer at Robert Gordon University. His research focuses on partial discharge, condition monitoring, sensors and instrumentation, hyperspectral imaging, spectroscopy, and robotics. He has collaborated extensively with industrial partners to address engineering challenges in sensor technology and condition monitoring. Khan is a Fellow of the Higher Education Academy and a member of IET and IEEE. Research Interests: Partial Discharge Detection and Localization Future Smart Grid Technologies Advanced Sensors and Instrumentation Robotics Applications in Engineering Hyperspectral Imaging and Spectroscopy Awards and Memberships: Higher Education Academy Fellow Member: IET and IEEE Advising and Collaboration: Dr. Khan supervises PhD candidates and serves as an external examiner for undergraduate and postgraduate programs. He has led collaborative projects with industry partners to develop long-term solutions for engineering challenges, including sensor-based fault detection and robotics integration. Labs and Teams: His work involves interdisciplinary teams focused on smart grid technologies, sensor networks, and condition monitoring systems, often involving partnerships with energy and manufacturing sectors.

• Electrical Engineering
• Signal Processing
• Instrumentation
• +۵ مورد دیگر

Pedro Miguel Pinto Ramos is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Lisbon (Técnico Lisboa), affiliated with the Higher Technical Institute. His research focuses on signal processing, instrumentation, power quality analysis, and non-destructive testing. He teaches courses such as 'Instrumentation and Measurements' and '2nd Cycle Integrative Project in Electronic Engineering.' His work spans embedded systems design, magnetic material characterization, and applications in particle accelerator magnets. Recent contributions include deep learning techniques for power quality monitoring, eddy current testing probes using magneto-resistive sensors, and impedance spectroscopy for sensor modeling. He collaborates on projects like smart composting monitoring and robotic tactile sensors. Ramos has published extensively on topics including harmonic estimation algorithms, genetic algorithms for circuit identification, and low-cost measurement systems. His technical contributions include DSP-based data acquisition systems and real-time processing for eddy currents testing. He also works on low-frequency impedance measurement techniques and uncertainty analysis in electrical measurements. His research lab focuses on advancing non-destructive testing (NDT) through eddy current methods, with applications in friction stir welding inspection and material characterization for high-field superconducting magnets. Collaborations include work with CERN on magnetic material properties for Large Hadron Collider (LHC) upgrades.

• Grid Computing
• Distributed Systems
• Cloud Computing
• +۷ مورد دیگر

João Nuno De Oliveira e Silva is an Assistant Professor at the Department of Informatics at Instituto Superior Técnico, University of Lisbon. He teaches Concurrent Programming, Systems Programming, and related courses during the 2024/2025 academic semester. His research focuses on distributed systems, grid computing, cloud computing, and cybersecurity, with notable contributions to secure dataflow programming for Android applications, blockchain-based agent collaboration, and distributed computing frameworks like NuBOINC. His work emphasizes parallel execution environments, resource efficiency in dataflow models, and secure middleware solutions for bags-of-tasks. Key research themes include: Grid and distributed computing architectures Secure distributed applications (e.g., SGX enclave migration, anti-repackaging systems) Cloud infrastructure optimization (A2Cloud, geo-replication QoS) Autonomous agent systems for internet routing domains Biometric authentication for legacy systems Automated biological monitoring systems Notable projects include Flowverine (privacy-sensitive Android apps), TEEnder (SGX enclave migration), and Mercury (automatic parallelization middleware). His work bridges theoretical computer science with practical implementations in distributed and cloud environments. He has collaborated on over 30 publications since 2017, focusing on both foundational research and applied systems development. His teaching and research emphasize hands-on experience with modern distributed computing challenges.

Xiaojuan Qiمشاهده پروفایل

استادیار

• 3D Vision
• Deep Learning
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Xiaojuan Qi is an Assistant Professor in the Department of Electrical and Electronic Engineering at The University of Hong Kong. Her research focuses on 3D vision, deep learning, and AI applications in medical imaging and science. She holds memberships in IEEE and CCF. Education: PhD, The Chinese University of Hong Kong (2018) BEng, Shanghai Jiao Tong University (2014) Postdoctoral Researcher, University of Oxford (prior to HKU) Research Interests: Dr. Qi develops scalable deep learning algorithms for medical and natural image analysis, 3D scene understanding, and neural network behavior in out-of-distribution scenarios. Her work emphasizes label efficiency, geometric reasoning, and cross-modal integration. Lab Contributions: As head of the Computer Vision and Machine Intelligence Lab (CVMI Lab), she explores open-world intelligence, 3D reconstruction, and AI applications in robotics, autonomous systems, and healthcare. Recent lab highlights include 10+ ICCV/CVPR/NeurIPS papers in 2023-2025. Awards: ImageNet Semantic Parsing Challenge (1st Place) Outstanding Reviewer Awards (ICCV 2017/2019) CVPR Doctoral Consortium Travel Award Advising & Grants: Supervises ~20 PhD students (listed in detail). Active in securing grants for embodied AI, medical imaging, and edge computing hardware. Lab Collaborations: Focuses on interdisciplinary projects with Intel, Oxford, and Toronto groups, emphasizing 3D vision, neuromorphic computing, and AI-driven medical solutions.

jane l. eمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Computer Graphics
• Artificial Intelligence
• +۸ مورد دیگر

Jane L. E is a postdoctoral researcher at Stanford HAI in Computer Science, mentored by James Landay, and an Adjunct Assistant Professor in Computer Science at the National University of Singapore (NUS). She will join NUS School of Computing as an Assistant Professor in Fall 2025 and is actively recruiting PhD students. She is also affiliated with the Smart Systems Institute at NUS. Education: PhD in Computer Science, Stanford University (advised by James Landay and Pat Hanrahan) Postdoctoral Fellow, The Design Lab @ UCSD (mentored by Haijun Xia and Steven Dow) Her research lies at the intersection of human-computer interaction, computer graphics, and artificial intelligence. She focuses on designing computational tools that support novices in developing creative expertise, drawing from cognitive science and education theory. Her work emphasizes scaffolding artistic vision and enhancing creative learning through AI-augmented tools. The 15 most recent publications reflect a consistent trajectory in creative AI tools, design education, and interactive systems. Key themes include timing of feedback in design, AI-supported creativity, and tools for visual composition and storytelling. Her work integrates qualitative user studies with technical innovation, often resulting in deployable systems with real-world impact. Scientific Awards: Best Paper Award at CHI 2023 for 'DataParticles' UCSD CSE Undergraduate Research Award (awarded to advised student Mingyi Li) Jane L. E has advised multiple students including Mingyi Li, Grace Yen, Tone Xu, and Rima Cao. She has received recognition through speaking invitations at institutions such as University of Toronto, NUS, USC, Tsinghua, and UC Berkeley. While no formal grants are mentioned, her research has been conducted at prestigious labs including Stanford HAI, The Design Lab @ UCSD, and Stanford’s HCI group. She is building a strong research program focused on human-centered AI for creativity. Labs and Affiliations: Stanford HAI (current postdoc) The Design Lab @ UCSD (past postdoc) Smart Systems Institute @ NUS (affiliate)

Hamid Mukhtarمشاهده پروفایل

دانشیار

• Deep Learning
• Natural Language Processing
• Health Informatics
• +۷ مورد دیگر

Hamid Mukhtar is an Associate Professor in the School of Computer Science at the University of Birmingham, Dubai campus. His academic work spans research, teaching, and industry collaboration in computer science with a focus on intelligent systems and healthcare applications. His educational background includes: PhD in Computer Science, 2009 (National Institute for Telecommunications, France) MSc in Computer Science (French), 2006 MSc in Computer Science, 2005 BSc in Computer Science, 2003 Dr. Mukhtar's research interests center on deep learning, natural language processing, health informatics, and human behaviour modelling in pervasive environments . His work integrates machine learning with real-world applications in healthcare, wellness, and intelligent user interfaces. He has published extensively on topics including persuasive technology, IoT for health monitoring, and NLP for social and medical data. The recent trend in his publications shows a strong focus on applying AI to public health challenges—such as diabetes prediction, depression detection, and pandemic response—using data from electronic records, wearables, and social media. His interdisciplinary approach bridges computer science with healthcare and behavioral science. He actively supervises master's students and has engaged in industry collaborations and consultancy in data science, software usability, and mobile technologies. He also conducts professional workshops on NLP, Android development, and web technologies. Dr. Mukhtar has taught a wide range of computer science courses including operating systems, AI, NLP, databases, web engineering, and professional ethics.

• Control Theory
• Embedded Systems
• Distributed Systems
• +۶ مورد دیگر

Alessandro Vittorio Papadopoulos is a Professor of Electrical and Computer Engineering at Mälardalen University (MDU) and a QUALIFICA Fellow at the University of Málaga's Institute for Software Technology and Software Engineering. His research focuses on modeling and control of interconnected systems under uncertainty, integrating Control Theory with Embedded Systems , Distributed Systems , and Real-Time Systems . He has served as a Scientific Advisor for companies like ABB and Zero Point Technologies. Education: PhD from Politecnico di Milano (2013), supervised by Alberto Leva; Postdoctoral work at Politecnico di Milano and Lund University's Department of Automatic Control under Karl-Erik Årzén. Research Trends: His articles emphasize Swarm Robotics , Stochastic Scheduling , Privacy-Preserving Control , and Edge-Cloud Continuum . Key subfields include Distributed Control , Uncertainty Modeling , Real-Time Optimization , and Secure Communication . Awards: He received the Most Influential Paper Award at SEAMS 2025, QUALIFICA Fellowship , and Outstanding Paper Award at ECRTS 2021, alongside a Best Artefact Award in 2017. Advising: Supervised PhD theses by Anna Friebe and Daniel Bujosa Mateu. Labs & Collaborations: Affiliated with the Lund Center for Control of Complex Engineering Systems (LCCC) and MDU's research groups.

• Urban Analytics
• Machine Learning
• Machine Vision
• +۲۳ مورد دیگر

Dr. Baris Yuce is a Senior Lecturer and Program Lead for Engineering Management BEng/MEng at the Faculty of Environment, Science and Economy, University of Exeter. He holds a PhD in Engineering from Cardiff University and has previously worked as a Postdoctoral Researcher at Cardiff in sustainable engineering and machine vision. His academic background includes MSc and BSc degrees in Industrial Engineering from Sakarya University, Turkey. His research focuses on the integration of artificial intelligence, optimization, and data science into sustainable engineering systems. Key areas include smart cities, renewable energy management, sustainable supply chains, and Industry 4.0. He applies machine learning, multi-agent systems, fuzzy logic, and nature-inspired optimization algorithms to real-world engineering challenges in buildings, transportation, and manufacturing. Baris has contributed to a wide array of funded research projects, including EU FP7 initiatives like SPORTE2, KnoholEM, and MAS2TERING, as well as UKRPIF, KTP, STFC, and industry-funded efforts. These projects reflect a strong emphasis on digital twins, energy optimization, smart grids, and sustainable infrastructure. He is affiliated with the Environmental Intelligence @ Exeter research network, which aligns with his focus on data-driven sustainability solutions. His work bridges civil, mechanical, electrical, and manufacturing engineering domains with computer science and applied mathematics. Scientific Awards: No awards listed in the provided text. Baris Yuce has been involved in multiple research collaborations and grants, serving as Principal Investigator (PI) on an industrially funded project with Waterman Group and as Co-Investigator (Co-I) on several others including UKRPIF's CREWW ENZO, STFC Food Network+, KTP with Smart Manufacturing, RIVIC, SPORTE2, KnoholEM, EEO-Box, WANDA, and MAS2TERING. These roles highlight his leadership in interdisciplinary and applied research. While no formal advisees are listed, his role as Program Lead suggests academic leadership and mentorship responsibilities. He is part of the Environmental Intelligence @ Exeter initiative, a cross-disciplinary research network focused on leveraging data science and AI for environmental sustainability, indicating active participation in cutting-edge, team-based research.

Ozz Querinمشاهده پروفایل

استاد

• Topology Optimization
• Structural Optimisation
• Genetic Algorithms
• +۹ مورد دیگر

Professor Ozz Querin is a Professor of Design Optimisation at the School of Mechanical Engineering, University of Leeds, where he also serves as Pro Dean for Student Education. His research is centered on structural and design optimisation, with applications in aerospace and automotive systems. His research interests include: Topology and shape optimisation using evolutionary methods and genetic algorithms Finite Element Analysis (FEA), particularly Fixed Grid-FEA in 2D and 3D continua, plates, and shells Computer Based Learning, focusing on intelligent systems for course delivery Helicopter aerodynamics and dynamics, including flight simulator development using radio-controlled models He is affiliated with the Institute of Design, Robotics and Manufacturing and contributes to research on energy-efficient aerospace and automotive structures. His work integrates computational methods with engineering design to solve complex structural problems. Professional Recognitions: Fellow, Royal Aeronautical Society Senior Member, American Institute of Aeronautics and Astronautics (AIAA) Secretary and Member, ASMO-UK Member, ISSMO He holds a BE(Aero), ME(Res), and PhD from the University of Sydney. He actively supervises postgraduate research and welcomes PhD enquiries in his areas of expertise. While specific students and grants are not listed, his leadership role in student education and research groups underscores his academic engagement.

• Computer Graphics
• Machine Learning
• Data Analysis
• +۷ مورد دیگر

Prof. Dr. Volker Ahlers is a Professor in the Department of Computer Science at Hochschule Hannover – University of Applied Sciences and Arts . He serves as the International Coordinator for the department and is a member of the Senate Commission for Research . His academic career spans over two decades, with prior appointments at Goethe University Hospital, WiSenT GmbH, and the University of Potsdam. Education : PhD in Physics (University of Potsdam, 2001), M.Sc. in Physics (Georg August University Göttingen, 1998) Research Interests : Focus on Computer Graphics , Machine Learning , Data Analysis , Information Visualization , and Urban Logistics . His work bridges network security and smart city applications. Grants : Led projects funded by BMBF (EKiKo-KI, GLACIER, USEfUL), BMDV (5GAPS), and DAAD collaborations. Professional Roles : Conference chair (IDAACS 2023, ASIM 2018), reviewer for journals like PLOS ONE and Bioinformatics , and member of accreditation agencies.

Mohammed Abouheafمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Control Systems
• Machine Learning
• +۶ مورد دیگر

Dr. Mohammed Abouheaf is an Associate Professor in the School of Engineering at Bowling Green State University (BGSU) and a founding architect of BGSU's Robotics Engineering program. He directs the Autonomous and Intelligent Systems Laboratory (AISL), focusing on adaptive control, reinforcement learning, and robotics. Previously, he held research positions at the University of Ottawa, Polytechnique Montréal, and King Fahd University. His research integrates Systems & Control , Machine Learning , and Applied Mathematics to advance autonomous systems. Key areas include: Reinforcement learning for robotic/unmanned vehicle control Game-theoretic approaches for cyber-physical systems Non-convex optimization in power grids and smart grids Mean-field swarm coordination Real-time adaptive control for nonlinear systems His publications emphasize data-driven control, optimization, and applications in energy/robotics, with recurring themes in multi-agent systems and adaptive learning. He received a Best Paper Award (2016) for work on graphical games and reinforcement learning. As Co-PI, he secured $1.8M+ in grants: NSF RET Site: Advanced Manufacturing Training ($620K; 2022–2025) NSF: Autonomous Learning for STEM Education ($298K; 2022–2025) Ohio RAPIDS: Additive Manufacturing ($150K) & Advanced Manufacturing ($764K)

Md Nazmul Hudaمشاهده پروفایل

مدرس ارشد

• Robotics
• Control systems
• Artificial Intelligence
• +۸ مورد دیگر

Dr Md Nazmul Huda is a Senior Lecturer in Electronic & Electrical Engineering and Associate Dean (Student Experience) at Brunel University London, affiliated with the College of Engineering, Design and Physical Sciences. His research and academic activities span robotics, control systems, artificial intelligence, and renewable energy. BSc (Hons) in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET), 2008 MSc by Research in Computing Science from Staffordshire University, 2011 PhD in Robotics and Control from Bournemouth University, 2016 Postgraduate Certificate in Academic Practice in Higher Education from Coventry University, 2018 Dr Huda's research focuses on Robotics, Control Systems, Artificial Intelligence, and Renewable Energy. Key areas include capsule robots for endoscopy, sensor fusion for autonomous vehicles, underactuated systems, and energy harvesting technologies. His work bridges theoretical and applied domains in robotics and sustainable energy solutions. His recent publications demonstrate strong emphasis on robotic perception, autonomous navigation, and renewable energy systems. Articles address pedestrian intent prediction, sensor fusion for vehicle safety, bio-inspired robotic design, and energy optimization in hybrid systems. Dr Huda supervises PhD students in robotics and AI, including Sarfraz Ahmed and Ihechiluru Fortune Chinatu Anya. He has led research projects funded by EPSRC and Innovate UK, focusing on robotics, control, and energy management systems. He is an active member of IEEE, IET, IEEE Robotics and Automation Society, and the EPSRC Peer Review College. Additionally, he serves as an associate editor for the Sensors journal and reviews for flagship robotics conferences and journals.

Simon Kentمشاهده پروفایل

دانشیار

• Digital Education
• Machine Learning
• Distributed Computing
• +۴ مورد دیگر

Dr Simon Kent serves as an Honorary Reader in the Department of Computer Science within Brunel University London's College of Engineering, Design and Physical Sciences, while currently working at the University of Nottingham. Previously, he held the position of Director of Learning and Teaching in Brunel's Computer Science Department, leveraging his dual background in academia and financial services software architecture to bridge industry-academia gaps. His educational qualifications include a PhD in Artificial Intelligence focused on robot path planning and a Postgraduate Certificate in Learning and Teaching Higher Education. Kent is recognized as a Fellow of the Higher Education Academy and maintains membership in the British Computer Society. Research interests span digital education analytics, machine learning, distributed/cloud computing, and software engineering pedagogy. Kent pioneers industry-aligned educational innovations, notably implementing the UK's first large-scale BYOD digital examination system at Brunel and analyzing assessment data to enhance student outcomes. His work consistently applies computational techniques like genetic programming to cross-disciplinary challenges in healthcare, defense, and finance. Publication trends reveal an evolution from foundational AI work in the 1990s (oral cancer diagnosis, missile systems) through distributed computing applications in the 2000s (video rendering, diabetes monitoring) toward contemporary digital education research. Key thematic threads include real-world problem solving, industry collaboration, and translating technical innovation into educational impact. Scientific recognition includes: Fellow of the Higher Education Academy Member of the British Computer Society As an educator, Kent directs the Level 2 Group Project (CS2001) simulating commercial software development environments and mentors across undergraduate/master's programs. He secured significant research funding including a Sucden UK collaboration for distributed order matching systems and Diabetes UK proposals for diabetic teenager monitoring applications, emphasizing practical industry partnerships. Affiliated with Brunel's CIKM and CSSB research groups, Kent continues advancing digital assessment methodologies while exploring IoT applications for home energy optimization through appliance collaboration networks.

László Blázovicsمشاهده پروفایل

مدرس ارشد

• Robotics
• Distributed Systems
• Mobile Networks
• +۲ مورد دیگر

László Blázovics is a Senior Lecturer at the Department of Automation and Applied Informatics, Budapest University of Technology and Economics. He leads the Applied Mobile Research Group (AMORG) focusing on mobile computing and distributed systems. His office is located at Q.B222, Magyar tudósok krt. 2., Budapest 1117, Hungary, with contact available via phone (+36 1 463-1648) and email. His primary research explores: Swarm robotics including drone coordination, uniform dispersal algorithms, and obstacle avoidance Distributed systems featuring sensor networks, multi-robot frameworks, and decentralized algorithms Mobile technologies spanning 5G networks, VR teleoperation, and cognitive communication AI applications such as human activity recognition and sensor fusion techniques Publication analysis reveals consistent focus on autonomous systems since 2011, with recent expansion into 5G-enabled applications and AI-driven solutions. Earlier work established foundations in swarm robotics and distributed algorithms, while recent publications demonstrate applied research in VR streaming, drone networks, and neural networks. He maintains active involvement in the Applied Mobile Research Group (AMORG), developing simulation frameworks for multi-robot systems and investigating cognitive drone applications.