جستجوی استاد راهنمای دانشگاهی

Antonio Plaza is a Full Professor at the University of Extremadura, Spain, and Head of the Hyperspectral Computing Laboratory. With over 600 publications, he is a leading expert in hyperspectral data processing and parallel computing of remote sensing data. He serves as IEEE Fellow and has received numerous accolades, including the 2019 Excellent Teaching Award and multiple Highly Cited Researcher recognitions. Research Interests : His work bridges Hyperspectral Image Analysis , Medical Imaging , and High-Performance Computing . Recent projects focus on 3D anatomical modeling, AI-driven surgical tools, and deep learning applications for aortic dissection segmentation. Scientific Awards : 2019 Highly Cited Researcher (Geosciences) 2015 IEEE Fellow 2019 Excellent Teaching Award 2018 Highly Cited Researcher (Cross-Field) 2002 Best PhD Dissertation, University of Extremadura Editorial Leadership : Served as Editor-in-Chief of IEEE Transactions on Geoscience and Remote Sensing (2013–2017) and held multiple committee roles in IEEE GRSS. His articles reflect a shift from remote sensing to medical imaging, with a focus on Aortic Dissection Segmentation , Skull Reconstruction , and AI-driven Medical Tools .

• Hyperspectral Image Analysis
• Remote Sensing
• High-Performance Computing
• +۴ مورد دیگر

Yun Fu is a Distinguished Professor at Northeastern University, affiliated with the College of Engineering and Khoury College of Computer Science. He holds tenure in Electrical and Computer Engineering (ECE). His roles include Professor, Senior Vice President at Shiseido Americas, founder of Giaran (acquired by Shiseido), and co-founder of TVision Insights. He earned his Ph.D. from the University of Illinois at Urbana-Champaign. His research focuses on artificial intelligence, computer vision, machine learning, and data mining. Key achievements include over 500 publications, 50+ patents, and prestigious awards like IEEE Fellow, OSA Fellow, and AAIA Fellow. He leads the SMILE Lab, exploring AI applications in vision, robotics, and healthcare. Notable entrepreneurship includes AI-driven ventures in cosmetics and media analytics. Research interests emphasize AI-driven solutions for computer vision challenges, including anomaly detection, trajectory prediction, and multimodal learning. His work bridges academia and industry, with impactful contributions to both fields.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۱ مورد دیگر

Hajo A. Reijers is a Professor at the University of Utrecht, Netherlands, with a former affiliation at Vrije Universiteit Amsterdam. His research focuses on Business Process Management (BPM), Process Mining, and Robotic Process Automation (RPA), emphasizing practical applications in healthcare, organizational processes, and human-computer interaction. He contributes to developing tools like SWORD for detecting workarounds and DEUCE for auditing electronic health records. His work spans algorithm development for process discovery, predictive analytics, and optimization techniques. Key areas include analyzing event logs, modeling workplace behavior, and enhancing process transparency. Reijers collaborates extensively with industry partners, addressing challenges in process automation, employee acceptance of AI, and ethical monitoring. His contributions to conferences like BPM, CAiSE, and ICIS highlight interdisciplinary approaches, combining computer science with organizational studies. Notable projects include frameworks for task mining, reinforcement learning in care processes, and pattern recognition in government transparency assessments. Research initiatives often involve cross-disciplinary teams, exploring topics like workplace well-being through process mining, decision-making support systems, and overcoming barriers to BPM adoption. His work bridges theoretical advancements with real-world impact, influencing both academic discourse and practical business solutions.

• Business Process Management
• Process Mining
• Process Analytics
• +۴ مورد دیگر

Professor Andrew Bayliss is a Professor in Psychology at the University of East Anglia's School of Psychology, where he serves as Social Cognition Research Group Lead and UEA UOA4 Psychology REF coordinator. He joined UEA in 2011 after completing his PhD at Bangor University and holding postdoctoral fellowships with the ESRC, Leverhulme Trust, and University of Queensland. Undergraduate Degree: Bangor University PhD: Bangor University Postdoctoral Fellowships: ESRC, Leverhulme Trust, University of Queensland His research spans social cognition, attention-action interactions, and individual differences, with specific focus on face perception, eye gaze processing, and objects in social contexts. Utilizing methodologies including eye tracking, motion capture, EEG, and fMRI, his work examines how social cues guide attention and influence behavior. Current projects investigate gaze understanding development, interpersonal agency, and autism interventions using natural scenes. Analysis of his recent publications reveals a strong emphasis on social attention mechanisms, particularly gaze leading phenomena, interpersonal distance effects, and neural correlates of shared attention. His work increasingly integrates autism research with social cognition paradigms while maintaining core investigations into attentional orienting and agency perception. Bayliss actively contributes to the academic community through editorial roles at Psychological Review and Psychonomic Bulletin and Review, peer review activities, and consultancy work including the Social Neuroscience of Cinema Attendance project. He maintains international collaborations, notably with Paris Nanterre University. As Social Cognition Research Group Lead, he oversees a dynamic team investigating the neural and cognitive mechanisms underlying social interactions. His lab employs multimodal approaches to study real-time social processing, with recent work expanding into human-robot interaction and pandemic-related social adaptations.

• Social Cognition
• Attention Mechanisms
• Action Observation
• +۵ مورد دیگر

Riadul Islam serves as an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), maintaining his primary office in room 316 of the Information Technology and Engineering (ITE) Building. His academic appointment focuses on hardware design and verification within the institution's engineering framework. His educational qualifications include: Ph.D. in Computer Engineering from UCSC (2017) M.A.Sc. in Electrical and Computer Engineering from Concordia University, Montreal (2011) B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2007) Professor Islam's research centers on VLSI CAD tools and low-power digital/mixed-signal IC design , with significant contributions to current-mode clock networks, vehicular security systems, and error-robust circuit architectures. His work increasingly integrates machine learning for design automation while exploring neuromorphic computing applications and secure hardware implementations. This multidisciplinary approach bridges traditional IC design with modern AI-driven optimization techniques. Analysis of his 2023-2025 publications reveals three dominant research thrusts: (1) Machine learning applications in early-stage Design Rule Checking (DRC) prediction and clock network optimization, (2) Graph-based intrusion detection systems for automotive networks (particularly CAN bus security), and (3) Event-based vision systems and neuromorphic computing architectures. These areas demonstrate consistent innovation in merging hardware design with AI/ML methodologies for enhanced system reliability and efficiency. He directs the UMBC VLSI and SoC Research Group , which develops energy-efficient clocking networks, secure vehicular communication protocols, and compute-in-memory architectures. The lab maintains active collaboration with industry partners on hardware security and neuromorphic computing initiatives while supporting graduate student research in cutting-edge IC design methodologies.

• VLSI CAD
• Low-power IC Design
• Mixed-signal IC Design
• +۵ مورد دیگر

Kun An is a Professor at the Department of Traffic Information and Control Engineering within the College of Transportation Engineering at Tongji University. She has previously held academic positions at Monash University (Senior Lecturer, 2018–2019; Lecturer, 2016–2018) and conducted postdoctoral research at the University of Illinois at Urbana-Champaign (2015–2016) and The Hong Kong University of Science and Technology (2014–2015). Her academic journey includes a PhD in Civil Engineering from HKUST (2014) and a Bachelor's degree from Tongji University (2009). PhD: Civil Engineering, The Hong Kong University of Science and Technology (2014) Bachelor's: Transportation Engineering, Tongji University (2009) Dr. An specializes in intelligent transportation systems, focusing on optimizing complex traffic networks, characterizing traveler behavior in stochastic environments, and advancing electric vehicle sharing infrastructure. Her research spans urban transit planning, carsharing logistics, battery electric bus deployment, and real-time traffic signal optimization, with applications to mitigate rail disruptions and enhance multimodal connectivity. Her publications emphasize solving transport challenges through stochastic programming, game theory, and behavioral analysis. Key themes include autonomous vehicle integration, demand uncertainty modeling, and sustainable mobility solutions. Awards include the Hong Kong PhD Fellowship and multiple best paper recognitions. Hong Kong PhD Fellowship (2010–2014) TRBADB30 Best Paper Nomination (2014) Best Paper at 18th Hong Kong Transportation Annual Meeting (Second Author, 2013)

• Intelligent Transportation Systems
• Traffic Network Optimization
• Stochastic Demand Modeling
• +۳ مورد دیگر

Jin Lu is an Assistant Professor at the University of Georgia's School of Computing, part of the Franklin College of Arts & Sciences. He earned his Ph.D. (2019) and M.S. (2019) in Computer Science and Engineering from the University of Connecticut. Prior to his current role, he served as an Assistant Professor at the University of Michigan–Dearborn (2019–2023). Educational Background: Ph.D. in Computer Science and Engineering, University of Connecticut, 2019 M.S. in Computer Science and Engineering, University of Connecticut, 2019 Research Interests: Dr. Lu focuses on machine learning, optimization, bio-informatics, and smart mobility. His work spans federated learning, healthcare applications (e.g., depression and BMI monitoring), IoT systems, and computer vision. Recent projects explore AGI's potential in medical and educational contexts, leveraging models like CycleGAN and reinforcement learning. Grants & Funding: Develop digital brains to advance portable diagnosis of neurological conditions (Google, 2025) Lab/Teams: While specific lab affiliations are not explicitly stated, his research involves collaborations in interdisciplinary areas such as health informatics and smart mobility.

• Machine Learning
• Optimization
• Bio-informatics
• +۶ مورد دیگر

Dongheui Lee is an Assistant Professor at the Institute of Automatic Control Engineering (LSR) within the Faculty of Electrical Engineering and Information Technology at Technische Universität München (TUM). She leads the Dynamic Human Robot Interaction for Automation System Lab. Her research focuses on human motion understanding, physical human-robot interaction, and machine learning in robotics. Education: B.S. and M.S. in Mechanical Engineering from Kyunghee University (2001-2003), PhD in Mechano-Informatics from the University of Tokyo (2007). Prior roles include research scientist at KIST Korea (2001-2004) and project assistant professor at the University of Tokyo (2007-2009). Research Interests: Human-robot collaboration, probabilistic robotics, motion recognition, and incremental lifelong learning mechanisms. She has contributed to advancements in motion primitives, compliant physical interaction, and real-time object tracking. Selected Awards: Finalist for KUKA Service Robotics Best Paper Award (2009), Hirose Scholarship (2006-2007), and multiple grants from KRF, KOSEF, and international robotics competitions. Key Publications: Focus on prioritized inverse kinematics, motion imitation, and adaptive control systems. Her work bridges robotics theory and practical applications in humanoid robots and human-robot interaction.

• Human motion understanding
• Physical human-robot interaction
• Machine learning in robotics
• +۵ مورد دیگر

Aggelos K. Katsaggelos is a Professor in the Department of Electrical Engineering and Computer Science at Northwestern University's McCormick School of Engineering. His research focuses on biomedical imaging, machine learning, and computer vision applications in healthcare. He has collaborated extensively with interdisciplinary teams, including clinicians and engineers, to develop advanced algorithms for medical diagnosis and image analysis. Key research interests include medical image processing, deep learning for diagnostics, and computational methods in cardiology. His work spans applications such as MRI and ultrasound analysis, automated pathology detection, and multimodal sensing for health monitoring. Recent articles highlight contributions to myocardial scar quantification, lung ultrasound scoring, and AI-driven cough detection. His methodologies often combine domain-specific physics with modern machine learning techniques to solve real-world clinical challenges. Notable collaborations include projects with institutions like the University of Chicago and international teams in astrophysics and cognitive science. His work emphasizes translating algorithmic advancements into practical clinical tools.

• Biomedical Engineering
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Senthil Arumugam Muthukumaraswamy is an Associate Professor at the School of Engineering & Physical Sciences, Heriot-Watt University. His research focuses on robotics, machine learning, IoT applications, and automation, with contributions to the UN Sustainable Development Goals in areas like healthcare and agriculture. His work spans smart navigation systems for healthcare robots, surveillance systems for livestock health, facial recognition algorithms, and automated agricultural solutions like aquaponics and vertical farming. He explores path-planning algorithms for firefighting and military robotics, emphasizing AI-driven problem-solving in hazardous environments. Research trends include integrating bio-inspired algorithms, neural networks, and IoT for real-world applications such as elderly health monitoring, smart home automation, and energy-efficient systems. Collaborations span multiple countries, reflecting global interest in his interdisciplinary approaches. No scientific awards are listed, but his extensive publication record highlights active grant-funded research in robotics, automation, and renewable energy. He leads projects in robotics design, control systems, and sustainable farming technologies, contributing to both academic and industry partnerships. Labs and teams include collaborations on robotics, AI, and IoT systems, though specific lab names are not detailed in the provided texts.

• Robotics
• Machine Learning
• IoT Applications
• +۴ مورد دیگر

George Shaker is an Adjunct Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada, and Lab Director of the Wireless Sensors and Devices Laboratory at the Schlegel-UW Research Institute for Aging. He is also Chief Scientist at Spark Technology Labs. His research focuses on wireless sensor technologies for healthcare, autonomous systems, and IoT. He earned his bachelor's from Cairo University and master's/PhD from the University of Waterloo. Education: Bachelor’s degree, Cairo University, Egypt Master’s degree, University of Waterloo, Canada PhD, University of Waterloo, Canada Research Interests: Dr. Shaker’s work spans advanced wireless sensor systems for healthcare monitoring, UAVs, and automotive applications. His lab developed the MIRADA initiative for aging populations and pioneered radar-based non-invasive glucose monitoring. Key areas include mm-wave radar, antenna design, bioelectromagnetics, and machine learning integration. He has co-authored over 200 publications and holds 35+ patents, collaborating with companies like Google, Apple, and Toyota. Recent Article Trends: His 2025 work emphasizes AI-driven radar systems for activity recognition, bio-sensing metasurfaces, and UAV classification using digital twins. Projects include 4D radar imaging, low-cost milk quality monitoring, and smart furniture for cardiac health. Awards: IEEE AP-S Best Paper Award IEEE MTT-S Graduate Fellowship arXiv Top Downloaded Medical Article URSI Young Scientist Award Multiple student awards (see full list above) Advising & Grants: He advises graduate students in ECE and has led projects funded by NSERC and industry partners. His students have won Velocity Fund, NASA Tech Briefs, and Canadian Space Agency awards. Collaborates with over 40 companies including Amazon, Microsoft, and Medella Health. Labs & Initiatives: Leads the Wireless Sensors & Devices Lab and co-founded MIRADA, a smart apartment for aging healthcare. Active in Spark Labs for wireless innovation.

• Wireless Sensor Technologies
• Biomedical Engineering
• Antennas and Propagation
• +۵ مورد دیگر

Professor Will Browne is the Chair in Manufacturing Robotics at QUT, collaborating with the ARM Hub and CSIRO to advance robotics in manufacturing, healthcare, and industry. He holds a Doctorate in Engineering from the University of Wales and has over 30 years of expertise in AI, robotics, and Learning Classifier Systems (LCS). His research focuses on transparent AI systems like LCS for Explainable AI (XAI), human-robot collaboration, and advanced manufacturing solutions. He co-leads the $5M SfTI Robotics Spearhead project and is internationally recognized for LCS contributions, including co-authoring the first LCS textbook with Ryan Urbanowicz. Publications emphasize emotion recognition, embodied AI, and scalable machine learning. His work bridges academia and industry through the ARM Hub, fostering innovation in robotics and design-led manufacturing. Key roles include co-track chair at GECCO (2011–2025) and editorial board memberships. Research spans evolutionary computation, robotics control, and energy systems optimization.

• Artificial Intelligence
• Robotics
• Manufacturing Systems
• +۵ مورد دیگر

Ceyhun Burak Akgül is a Part-Time Lecturer specializing in Computer Vision, Machine Learning, and Statistical Data Analysis. His research focuses on interdisciplinary applications of visual data processing, including medical imaging and 3D object recognition. He maintains a personal website at cba-research.com and can be contacted at cb.akgul@gmail.com . His work spans topics such as image captioning, visual dictionaries, and symbolic feature detection. Key contributions include developing algorithms for action recognition using depth cameras and frameworks for leaf and object recognition. His research also integrates medical applications, such as analyzing Alzheimer’s patient movements and automated diagnosis using imaging data. Akgül’s publications frequently address challenges in 3D shape descriptors, feature selection, and interdisciplinary methodologies. His recent work includes exploring visual dictionaries and improving image processing techniques through model-driven approaches. His academic contributions are evident in journals like the Journal of Visual Communication and Image Representation, and he has participated in competitions like SHREC. Despite his extensive publication record, no formal awards or grants are explicitly mentioned in the provided data.

• Computer vision
• Machine learning
• Statistical data analysis
• +۳ مورد دیگر

Andrea Saracino is an Associate Professor specializing in cybersecurity, privacy-preserving technologies, and machine learning applications. His research focuses on enhancing security in IoT systems, smart homes, and mobile devices, with a particular emphasis on Android malware detection and usage control frameworks. He has received the IEEE TCCPS Early-Career Award 2023 for his contributions. Key projects include the SIFIS-Home initiative for privacy in globalized smart homes and the ACE framework for access control. His work addresses challenges in balancing privacy, utility, and explainability in machine learning models, particularly in image and tabular data analysis. He actively explores cybersecurity in emerging domains like software-defined vehicles and industrial control systems.

• Cybersecurity
• Privacy-Preserving Technologies
• Machine Learning Applications
• +۳ مورد دیگر

Ravinder Dahiya is a Professor in the Department of Electrical and Computer Engineering at Northeastern University's College of Engineering. He is also an Affiliated Researcher at the Dublin Innovation Institute. His research focuses on flexible printed electronics, soft robotics, electronic skin, and sustainable technologies, emphasizing biodegradable materials and energy-efficient systems. Education: PhD in Microelectronics from the Italian Institute of Technology (2009). Awards include Fellowships from IEEE, The Royal Society of Edinburgh, and The Institution of Engineers in Scotland, alongside the Microelectronic Engineering Young Investigator Award (2016). Research interests span tactile sensing, haptics, and wearable systems, with a lab (BEST Group) developing multidisciplinary solutions for societal challenges like electronic waste reduction. Key projects include self-powered energy harvesters and biodegradable triboelectric nanogenerators. Recent grants include a $230,000 NSF EAGER award for robotic e-skin integration. Media highlights include features in TechXplore and Chemical & Engineering News, along with speaking roles at global summits like the AI for Good Global Summit (2023). Labs/Teams: Bendable Electronics and Sustainable Technologies (BEST) Group, focusing on printed electronics, material science, and robotics.

• Flexible Printed Electronics
• Soft Robotics
• Electronic Skin
• +۶ مورد دیگر