جستجوی استاد راهنمای دانشگاهی

Ning Maمشاهده پروفایل

مدرس

• Speech and Hearing Technologies
• Machine Learning
• Healthcare
• +۴ مورد دیگر

Dr. Ning Ma is a Lecturer in Medical Computing at the Department of Computer Science , University of Sheffield, with a dual appointment as an Academic Directorate of Medical Imaging and Medical Physics at the Sheffield Teaching Hospitals NHS Foundation Trust. He holds a PhD in hearing-inspired automatic speech processing from the University of Sheffield and earned his first degree in Computer Science from South China University of Technology. Member of Pervasive Computing and Speech and Hearing research groups Research Theme Co-Director for Healthcare Data/AI at the Insigneo Institute His research focuses on speech and hearing technologies applied to healthcare , particularly in developing AI systems for acoustic monitoring of sleep-disordered breathing and respiratory diseases . He pioneers methods for interpreting sounds and low-cost sensor data to screen conditions like obstructive sleep apnea and tuberculosis through multimodal machine learning . His recent work explores music AI and its intersection with mental health. Dr. Ma has led grants totaling over £500,000 from UKRI, MRC, EPSRC, and Innovate UK, including projects like AI-Enabled Cough Sound Analysis for TB Screening and SOMNUS: Sleep Monitoring by Unobtrusive Sensors . He serves on the Technical Programme Committee for INTERSPEECH and reviews for journals and funders. He is a member of the British Sleep Society , British Thoracic Society , and IEEE . His publications (over 60 peer-reviewed papers) explore topics such as deep learning for sleep apnea screening , binaural sound localization , and noise-robust speech recognition . Notable collaborations include work with Guy Brown on home-based OSA detection and with Tu Zhen on hearing aid optimization using differentiable hearing loss models.

Midhun Parakkal Unniمشاهده پروفایل

مدرس

• Artificial Intelligence for Healthcare
• Physiological Modelling
• Dynamical Systems
• +۵ مورد دیگر

Dr. Midhun Parakkal Unni serves as an Academic Fellow in AI for Health at the Centre for Machine Intelligence within the School of Computer Science, University of Sheffield. His research integrates machine learning, dynamical systems theory, and signal processing to develop clinically deployable AI solutions for physiological understanding and prediction, with specific applications in neuromuscular control and wearable medical technologies. His academic foundation includes a PhD in Applied Mathematics from the University of Exeter, where he developed AI models for Parkinson's gait analysis and motor abnormality prediction. Professional experience spans research roles at the University of Manchester and TCS Innovation Labs, focusing on distribution shift mitigation in machine learning, human-in-the-loop methodologies, and dynamical systems modeling for cardiovascular pathologies. Dr. Unni's research program centers on creating uncertainty-aware and interpretable machine learning systems grounded in mechanistic physiological understanding. Key focus areas include: Healthcare AI Development : Building robust clinical AI systems that address real-world challenges like data distribution shifts between training and deployment environments Wearable Technology Integration : Analyzing physiological time-series data from sensor networks for continuous health monitoring Mathematical Modeling : Developing dynamical systems frameworks to mechanistically explain physiological processes Interpretable AI : Ensuring clinical meaningfulness through uncertainty quantification and human-AI collaboration He actively seeks collaborations with clinicians and researchers in AI for health, physiological modeling, and digital health tool development. While his profile indicates no current advisees or specific grants, he operates within the Centre for Machine Intelligence's collaborative ecosystem focused on translating AI innovations into practical healthcare solutions.

• cooperative multi-robot systems
• decentralized coordination
• resilient autonomy
• +۶ مورد دیگر

Dr. Rui Paulo Pinto da Rocha is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Coimbra, with dual affiliation at the Institute of Systems and Robotics (ISR) since 2000. His career spans 15 years of academic leadership and 19 years of active research in cooperative robotics. Key affiliations include: Senior Member of IEEE (since 2021) Chair of the IEEE RAS Technical Committee on Multi-Robot Systems (since 2018) Coordinator of multiple EU-funded projects including REMOORA , EuroAGE+ , and SEMFIRE Research focuses on cooperative multi-robot systems with decentralized coordination, covering: Swarm robotics for aquatic resource monitoring Marsupial robot teams with communication constraints 3D volumetric mapping using entropy gradients Human-robot collaboration in urban fires Multi-sensor fusion for disaster response Edge computing for low-power inference Recent publications (2023-2025) demonstrate technical breadth across ROS cloud integration , semantic mapping , and human detection systems . Notable scientific contributions include: Development of Darwinian Swarm PSO algorithm Benchmarking framework for swarm exploration Communication-constrained coordination models Multi-robot patrolling optimization techniques As an educator, he supervises 2 PhD candidates and numerous MSc students, while teaching courses on autonomous systems, industrial automation, and software engineering. His laboratory leadership includes: Technical management of ISR research infrastructure (2014) Development of RACbot-RT real-time control system Advancements in multi-robot SLAM frameworks

• Adaptive and robust control
• Non-linear control systems
• Consensus and formation control
• +۳ مورد دیگر

Dr. Vu Phi Tran is a Researcher at the University of New South Wales (UNSW) Canberra, affiliated with the School of Engineering and Technology. He serves as a Research Assistant, Teaching Staff member, and Casual Professional staff within the Trusted Autonomy research group since 2020, contributing to high-impact publications in IEEE Transactions and Elsevier journals. His educational background includes a B.E. in Automation and Control Engineering from HCMC University of Technology and Education (2010), an M.S. in Mechatronics Engineering from Asian Institute of Technology (2015), and a Ph.D. in Aerospace Engineering from UNSW Canberra (2019). He previously served as a Lecturer at HCMC University from 2010-2012. Tran’s research spans adaptive and robust control , non-linear systems , UAV swarm coordination , and neural-fuzzy control architectures . His work focuses on real-world applications including gas source localization, resilient flight control for nano-drones, and formation control under disturbances, leveraging negative-imaginary systems theory and machine learning. Recent publications demonstrate a strong trend toward multi-robot environmental monitoring and adaptive learning-based controllers for uncertain environments. His scientific achievements include: Rockwell Automation Scholarship for Engineering Excellence THE HISAMATSU PRIZE for outstanding Mechatronics performance UNSW Tuition Fee Scholarship Dean’s Award for Outstanding PhD Theses T.F.C Lawrence Prize for Aeronautical Science Tran actively serves as a reviewer for IEEE Transactions on Robotics and Vehicular Technology, while leading projects funded by DST and AFOSR grants. His current work focuses on machine learning for obstacle avoidance in robot swarms and robust flight control systems. He operates from the Autonomous System Laboratory (Building 17, Room 131) at UNSW Canberra, collaborating with international researchers on UAV-UGV interaction systems.

Farid Breidiمشاهده پروفایل

استادیار

• Extended Reality Applications in Engineering Education
• Fluid Power Modeling and Simulation
• Technology Integration for Workforce Development
• +۳ مورد دیگر

Dr. Farid Breidi serves as an Assistant Professor in Mechanical Engineering Technology at Purdue University's Polytechnic Institute, specializing in Extended Reality applications, Fluid Power Modeling, and Engineering Education innovation. His interdisciplinary work bridges digital technologies with traditional engineering practices to enhance both educational experiences and industrial applications. Education: Ph.D. in Agricultural and Biological Engineering, Purdue University (2016) M.S. in Mechanical Engineering, University of Wisconsin-Madison (2012) B.E. in Mechanical Engineering, American University of Beirut (2010) Dr. Breidi's research focuses on Extended Reality applications in engineering education, technology integration for workforce development, and digital fluid power systems. His work develops immersive learning modules that transform how students understand complex engineering concepts, particularly in fluid power systems where virtual and augmented reality create safe, interactive learning environments that would be impractical in physical labs. His publication portfolio demonstrates consistent advancement in mixed reality educational tools and digital hydraulic systems, with recent work emphasizing safety training, personalized learning pathways, and electrification challenges in heavy equipment. This research trajectory shows increasing integration of sustainability considerations and industrial applicability. As Principal Investigator, Dr. Breidi has secured significant funding from the National Science Foundation (NSF) and National Fluid Power Association (NFPA) for projects advancing educational technology and fluid power innovation. His work includes developing transparent hydraulic demonstrators, miniature racing car design projects for freshmen, and digital monitoring systems for hydraulic technologies.

Karl Dietrich von Ellenriederمشاهده پروفایل

استاد

• Field Robotics
• Automatic Control
• Marine Vehicle Control
• +۴ مورد دیگر

Karl Dietrich von Ellenrieder is a Professor of Automation at the University of Bolzano's Faculty of Engineering. His career spans institutions like Florida Atlantic University (Ocean Engineering Professor, 2003-2016) and Monash University (Research Fellow, 1998-2002). A Senior Member of IEEE Control Systems and Oceanic Engineering Societies, he serves as Editor-in-Chief Elect of the IEEE Journal of Oceanic Engineering. Education: Bachelor of Science in Aeronautics & Astronautics, MIT (1990) Master of Science in Aeronautics & Astronautics, Stanford University (1992) Doctor of Philosophy in Aeronautics & Astronautics, Stanford University (1998) Dr. von Ellenrieder specializes in nonlinear control systems for marine and aerospace vehicles, focusing on actuator constraints, disturbance observers, and trajectory tracking. His work bridges theoretical control algorithms with practical implementations in autonomous underwater vehicles (AUVs), unmanned surface vessels (USVs), and quadrotor UAVs. His 15 most recent publications (2018-2024) demonstrate expertise in: Backstepping and sliding mode control Dynamic surface control techniques Shared human-robot control systems Control barrier function safety mechanisms Actuator saturation modeling Disturbance-robust control Scientific Honors: Senior Member, IEEE Control Systems Society Senior Member, IEEE Oceanic Engineering Society Editor-in-Chief Elect, IEEE Journal of Oceanic Engineering His research has secured funding from the US National Science Foundation (Award #1526016) and involves collaborations with institutions across the USA, Australia, and Europe.

• Computational Mathematics
• Numerical Analysis
• Interval Analysis
• +۶ مورد دیگر

Dimitris Sotiropoulos is an Assistant Professor at the Department of Electrical and Computer Engineering, University of Peloponnese, specializing in Engineering Mathematics . He holds a B.Sc. in Mathematics (1995) and a Ph.D. (2005) in Mathematical Programming and Numerical Analysis. Over 20 years of higher education teaching Author/co-author of 36+ scientific articles Supervised 20+ postgraduate theses Research Interests focus on computational mathematics, global optimization algorithms, and machine learning. Specific areas include interval analysis for automatic result verification, large-scale optimization, nonlinear equation solving, and artificial neural network training. His scientific work spans sustainability assessments for aerospace components, uncertainty quantification in composite materials, and stress modeling in human-computer interaction. Articles highlight applications of interval analysis to optimization problems and neural network training improvements via BFGS methods. Teaching roles include lectures at Ionian University's Department of Informatics and University of Peloponnese's Department of Computer Science and Technology. He also serves on Hellenic Open University's Supervisory Board since 2009, mentoring postgraduate students in computational mathematics.

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

Elmar Rueckert is a Professor at the Cyber-Physical-Systems Institute of Montanuniversität Leoben in Austria. He previously held research roles at Graz University of Technology and Technical University of Darmstadt, focusing on robotics and machine learning. PhD in Computer Science (2014), Graz University of Technology Senior Researcher and Research Group Leader, TU Darmstadt (4 years) Assistant position (3 years) at TU Darmstadt Research interests span robotics , deep reinforcement learning , SLAM algorithms , and cyber-physical systems . His work addresses challenges in autonomous navigation, tactile response prediction, and open-source robotics platforms. Recent publications highlight applications of reinforcement learning in mobile robot navigation, SLAM failure analysis in indoor environments, and ROS-based mobile robot development. Key themes include autonomous systems , machine learning robustness , and real-time decision-making .

• Wireless Multimedia & Networking
• Cognitive Radio
• 5G/4G Networks
• +۷ مورد دیگر

Professor Christos Politis is a Chair in Digital Technologies at the School of Computer Science and Mathematics , Kingston University London, where he serves as Director of the Digital Information Research Centre (DIRC). His research focuses span Wireless Multimedia & Networking , Cognitive Radio , 5G/4G Networks , and Emergency Communication Systems , with significant contributions to Energy Efficiency , Security Protocols , and IoT Applications . He has led 23 academics 15 postdoctoral researchers Over 40 PhD students at DIRC. With over 200 publications and two patents , his work is funded by EU and UK programs (FP5/6/7, H2020, EPSRC, Innovate UK). He consults for governmental and commercial organizations in EU, UK, Canada, Greece, Qatar, China, and Malaysia , and holds a PhD and MSc in Electrical and Electronic Engineering from the University of Surrey, along with a BEng from the University of Athens. His academic journey includes roles at Ofcom, the University of Surrey, and placements in Greece. Recent publications highlight advancements in AI-driven ECG analysis , UAV-assisted post-disaster networks , V2X security , and smart energy systems . He is a senior member of the IEEE and a UK chartered engineer.

Vishal M. Patelمشاهده پروفایل

استادیار

• Signal and image processing
• Computer vision
• Machine learning
• +۲ مورد دیگر

Vishal M. Patel is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, with research focusing on signal processing, computer vision, and biomedical applications. His work bridges machine learning with medical imaging and biometrics. Ph.D. in Electrical Engineering, University of Maryland, College Park (2010) Dr. Patel's research centers on medical image analysis, surgical video processing, and bias mitigation in generative models. He develops diffusion-based frameworks for ophthalmic applications like cataract surgery analysis and nystagmus video generation, while advancing face recognition systems and image restoration techniques under unconstrained conditions. His recent work integrates vision-language models for zero-shot classification in satellite imagery and maritime surveillance. His 2025 publications reveal a strong emphasis on medical AI, particularly in surgical video understanding and neurological disease phenotyping using eye movement data. Key trends include physics-aware video generation guided by large language models, identity-preserving face restoration, and robust bias mitigation in diffusion-based synthesis. The work consistently addresses real-world challenges in medical imaging, remote sensing, and biometric security. Major recognitions include: 2016 ONR Young Investigator Award 2016 Jimmy Lin Award for Invention A. Walter Tyson Assistant Professorship Award Best Paper Awards at IEEE AVSS 2017 and IEEE BTAS 2015 Two Best Student Paper Awards at IAPR ICPR 2018 Best Poster Awards at BTAS 2015 and 2016 As an Associate Editor for IEEE Signal Processing Magazine and IEEE Biometrics Compendium, Dr. Patel shapes scholarly discourse in his field. His ONR Young Investigator Award supports high-impact research in active learning for surgical videos, while editorial roles and technical committee memberships demonstrate leadership. Though student names aren't specified, his lab trains graduate researchers in medical computer vision. Dr. Patel leads a research laboratory focused on computer vision for biomedical applications, developing frameworks like GenVOG for nystagmus video generation and StepAL for surgical step detection. The lab pioneers diffusion models for medical image synthesis, surgical skill assessment, and robust biometric systems, with strong ties to ophthalmology and neurology applications.

Aykut C. SATICIمشاهده پروفایل

دانشیار

• Robotics
• Nonlinear Control
• Machine Learning
• +۹ مورد دیگر

Aykut C. SATICI is an Associate Professor in the Mechanical and Biomedical Engineering department at Boise State University's College of Engineering. He directs the Robot Control Laboratory (RCL), focusing on advanced robotics research with applications in aerospace, medical rehabilitation, and autonomous systems. His academic background spans Electrical Engineering, Mathematics, and Mechatronics Engineering from University of Texas at Dallas and Sabanci University. Ph.D. in Electrical Engineering (2014), University of Texas at Dallas M.Sc. in Mathematics (2013), University of Texas at Dallas M.Sc. in Mechatronics Engineering (2010), Sabanci University B.Sc. in Mechatronics Engineering (2008), Sabanci University His research focuses on robotics and nonlinear control , integrating machine learning for complex system challenges. Key areas include motion planning , convex optimization , optimal control , and reinforcement learning , with applications in geometric mechanics , multi-agent control , and environmental contact robotics . His work combines theoretical advancements with practical implementations for aerial vehicles and rehabilitation systems. Recent publications demonstrate trends in data-driven control for underactuated systems, passivity-based control frameworks, and Bayesian inference integration. His research group has published extensively in journals like Automatica , IEEE Transactions , and International Journal of Control , with notable contributions to quadrotor UAV control , ballbot dynamics , and robotic contact scenarios . NIH COBRE Grant : Cellular mechanotransduction research Army SBIR Grant : Robust robotic manipulation NSF MRI Grant : Real-time simulation infrastructure Navy SBIR Grants : Drone technology applications DoE/USDA Grants : Sensor deployment and power systems As an advisor, he mentors multiple PhD and MS students in robotics and biomedical engineering. His lab coordinates with external partners like Pitch Aeronautics and PRISMA Lab at University of Naples. The Robot Control Laboratory serves as a hub for interdisciplinary research addressing aerospace, healthcare, and energy grid challenges.

Moritz Weißbrichمشاهده پروفایل

پژوهشگر

• Processor Architecture
• Low-power Electronics
• Approximate Computing
• +۳ مورد دیگر

M.Sc. Moritz Weißbrich is a Researcher at the Chair for Chip Design for Embedded Computing , part of the Institute of Theoretical Computer Science at Technische Universität Braunschweig. Holding an M.Sc. in Electrical Engineering and Information Technology from Leibniz Universität Hannover, his academic focus spans high-performance/low-power processor architectures , approximating arithmetic circuits , and stochastic computation techniques for fault-tolerant systems. His research portfolio includes 20+ peer-reviewed publications since 2017, with recent work on Nano-scale controllers for FPGA systems Biomedical sensor design in 22nm FDSOI Energy-aware VLIW processor optimization Stochastic timing analysis frameworks He has been developing ultra-low-power embedded solutions since 2021, following prior research assistant roles at Leibniz Universität Hannover's Institute of Microelectronic Systems (2017-2021). Key technical contributions appear in venues like Springer LNCS , IEEE RFIC Symposium , and Journal of Systems Architecture , focusing on processor customization , energy harvesting systems , and radiation-hardened circuit design . His work addresses challenges in autonomous computing, harsh environment electronics, and precision agriculture applications.

Divita Mathurمشاهده پروفایل

استادیار

• Biochemistry
• Medicinal Chemistry
• Structural DNA Nanotechnology
• +۵ مورد دیگر

Divita Mathur is the Frank Hovorka Assistant Professor of Chemistry at Case Western Reserve University's College of Arts and Sciences. Her research focuses on leveraging DNA nanotechnology to address biochemical challenges in gene delivery, optoelectronic materials, and molecular cognition. Education: PhD, Bioinformatics & Computational Biology, Iowa State University (2016) Postdoctoral Research, US Naval Research Lab & George Mason University BEng, Biotechnology, Delhi College of Engineering (2010) Mathur's work integrates DNA scaffolds with organic/inorganic molecules to enhance photophysical properties and develop therapeutic platforms. She leads the Mathur Nano Lab, which explores four key areas: optoelectronic tailoring, gene origami, cytosolic stability, and molecular cognition through VR-enabled education tools. Research trends indicate a focus on DNA-templated optical systems, quantum dot integration, and stability analysis in biological environments. Collaborations with Crespo and Parker groups highlight interdisciplinary approaches to dye-DNA constructs, while partnerships with Iowa State University colleagues address STEM education in DNA nanotechnology.

• Artificial Intelligence
• Control Systems
• Data Analytics
• +۳ مورد دیگر

Professor Jiamei Deng serves as Professor of Artificial Intelligence, Control, and Energy at Leeds Beckett University's School of Built Environment, Engineering and Computing since 2015. Her research is funded by EPSRC, Innovate UK, Royal Academy of Engineering, and British Council, focusing on data analytics, AI, and control systems for biomedical engineering, construction, civil engineering, automotive engineering, and nuclear power plants. She holds two EU patents and has authored over 100 papers including IEEE Transactions publications, plus a monograph and three book chapters. Her core research spans Artificial Intelligence , Control Systems , Data Analytics , Energy Systems , Nuclear Power Plant Safety , and Construction Engineering . She develops practical applications for safety-critical systems, particularly through generative AI for construction cost management and fault-tolerant control in nuclear reactors. Recent work integrates mixed-reality data capture and BIM platforms to enhance construction productivity and circularity. Analysis of her 2022-2025 publications reveals dominant trends in generative AI for construction (BIM-GAIcost, QS-GAI) and advanced control techniques for nuclear safety. Her research bridges theoretical control engineering with industrial applications, featuring multi-institutional collaborations across the UK and Turkey. Professor Deng has secured significant funding through projects including: BIM-enabled Generative AI Platform for Construction Cost Planning (Innovate UK, 2024-2025) Generative AI for Interim Valuations in Quantity Surveying (Innovate UK, 2024-2025) AI-Driven BIM for Circular Construction (British Council, 2023-2026) Fault tolerant control for nuclear power plants (EPSRC, 2018-2023) She actively supervises PhD students in data analytics and visualization. Her work aligns with UN Sustainable Development Goals through research groups including Smart and pervasive technologies for environmental sustainability , Green Networking and Cloud Computing (GENial) , and Intelligent Computing initiatives at Leeds Beckett University.

Vladimír Kubelkaمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Sensor Fusion
• Localization
• +۳ مورد دیگر

Vladimír Kubelka is a robotics researcher specializing in sensor fusion , localization , and state estimation for mobile ground robots operating in harsh environments . He is currently a postdoc at the Robot Navigation and Perception Lab (RNP) at Örebro University , focusing on winter conditions and radar-based navigation . Previously, he was a postdoc fellow at Laval University's NORLAB , where he worked on DARPA Subterranean Challenge systems. Education: Ph.D. in Artificial Intelligence and Biocybernetics (2018), Czech Technical University in Prague (CTU) Ing. in Cybernetics and Robotics (Air and Space Systems) (2013), CTU Bc. in Electrical Engineering (Cybernetics and Measurement) (2011), CTU His research focuses on data fusion and localization algorithms for robots in adverse conditions including darkness, smoke, unstable terrain , and deep snow . He has contributed to radio propagation modeling , point cloud registration , and robust sensor integration . Recent publications highlight advancements in GNSS satellite visibility prediction , extreme motion lidar registration , and winter navigation systems . Collaborations include the CTU-CRAS-NORLAB team in the DARPA challenge. Scientific Awards: Best Robotic Vision Paper Award (2020) He has participated in EU-funded projects NIFTi and TRADR , focusing on real-world search and rescue deployments . His work addresses challenges like sensor outages and semi-structured environments .