جست‌وجوی استاد و پژوهشگر

Mike Burtonمشاهده پروفایل

استاد

• Cognitive Science
• Face Recognition
• Human Perception
• +۳ مورد دیگر

Mike Burton is a Professor of Psychology at the University of York, with a career spanning institutions across the UK, including the University of Aberdeen and University of Glasgow. His research focuses on face perception, emphasizing the mechanisms enabling identification across variable visual conditions and applications in computer-based face recognition and forensic settings. Education: BSc in Joint Honors Mathematics and Psychology (1980), University of Nottingham; PhD in Psychology (1983), University of Nottingham. Mike’s research explores how perceptual systems derive stable representations from variable face images, using experimental and computational modeling. Collaborations extend to security agencies and institutions like Kent University, Durham University, and the University of New South Wales. Recent work examines neural dynamics, multimodal cues, and virtual environments in face recognition. His publications span cognitive science, neuroscience, and forensic psychology, with a focus on unfamiliar face matching, neural correlates of familiarity, and computational modeling. Key grants include an ESRC Professorial Fellowship (2012–2016), an ERC Advanced Grant (2013–2018), and ongoing ESRC projects on high-fidelity avatars (2023–2026) and individual face recognition (2023–2026). Scientific Awards: Fellow of the British Academy (2017) Fellow of the Academy of Social Sciences (2019) Fellow of the Royal Society of Edinburgh (2006) Mike has held editorial roles in journals like Quarterly Journal of Experimental Psychology and Psychological Science , and served on REF2014 and REF2021 panels. He collaborates with researchers such as Rob Jenkins, Holger Wiese, and Markus Bindemann.

Karla K. Evansمشاهده پروفایل

دانشیار

• Visual Perception
• Attention
• Visual Cognition
• +۵ مورد دیگر

Karla K. Evans is a Senior Lecturer (Associate Professor) in the Department of Psychology at the University of York. Her academic career spans prestigious institutions including Princeton University, Harvard Medical School, and Brigham and Women's Hospital before joining York in 2013. She serves on the Psychology Department Athena Swan Committee and represents Psychology on the University's Athena Swan working group. Dr. Evans earned her BSc (Hons) in Experimental Psychology from the University of Trieste (1998), followed by an MA (2004) and PhD (2007) in Cognitive Psychology from Princeton University. She completed postdoctoral training at MIT (2007-2008) and Harvard Medical School/Brigham and Women's Hospital (2008-2013). Her research centers on how sensory systems integrate information to produce coherent perception, with expertise in attention, visual cognition, memory, multimodal perception, and medical image perception. She employs diverse methodologies including psychophysics, fMRI, EEG, and eye tracking. Her work bridges cognitive psychology with practical applications in medical imaging, particularly mammography analysis. Dr. Evans' publication record shows consistent output with 46 publications through 2025, demonstrating evolving focus from fundamental visual cognition to applied medical image perception. Her recent work explores scene complexity, visual memory schemas, and the 'gist' processing in mammograms that enables early cancer detection. She collaborates extensively with radiologists, computer scientists, and cognitive psychologists. She actively mentors students, accepting PhD candidates, and contributes to academic service as an ad hoc reviewer for vision, attention, and neuroscience journals, and as an NIH grant review panel member. Her research has been supported by grants including the Individual Ruth Kirschstein-NRSA Postdoctoral grant (2009-2012). Dr. Evans is affiliated with the Perception and Action (Vision group) and Cognition and Communication (Attention, Learning & Memory group) research teams at York, where she continues to advance understanding of visual perception mechanisms and their applications in medical diagnostics.

Nicolas Pugeaultمشاهده پروفایل

دانشیار

• Cognitive Vision
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Dr. Nicolas Pugeault is a Reader in Computing Science at the University of Glasgow's School of Computing. He holds a PhD from the University of Göttingen and has held roles including Postdoc at the University of Surrey and Assistant Professor at the University of Southern Denmark. His research focuses on cognitive vision, machine learning, and visual attention in complex control tasks. He has led projects like the EPSRC-funded DEVA grant and collaborated with institutions like the Alan Turing Institute. Education: Engineer degree from ESIEA Paris, MSc in Computational Intelligence from Plymouth, PhD from Göttingen. Research interests include human vision insights, visual attention modeling, and contextual scene understanding. Awards include the Alan Turing Institute Fellowship (2018–2020). Publications span anomaly detection in medical imaging, contextual object detection, and autonomous driving. His work integrates AI with robotics and environmental applications, such as weather forecasting and plankton analysis. He supervises a diverse cohort of PhD students across vision, robotics, and machine learning. Grants include leadership on EPSRC and InnovateUK projects. Teaching includes modules on Deep Learning and Data Science. His labs and teams contribute to advancements in cognitive vision systems and AI-driven solutions for real-world challenges.

Bin ZHUمشاهده پروفایل

استادیار

• Human Centered Multimedia Computing
• Cross-modal Retrieval
• Multi-modal Large Language Models
• +۶ مورد دیگر

Bin ZHU is an Assistant Professor of Computer Science at Singapore Management University's School of Computing and Information Systems. Previously worked as a Postdoctoral Researcher at University of Bristol under EPSRC Visual AI Program Grant with Prof. Dima Damen. PhD in Computer Science (2021) - City University of Hong Kong MSc and BSc from Zhejiang University and Southeast University Research focuses on Human Centered Multimedia Computing with key areas: Cross-modal retrieval and Multi-modal Large Language Models Egocentric Video Understanding and Generative AI AI for Healthcare and Wellness Informatics Digital Transformation through Multimedia Systems Active in publishing at top venues (ICCV, CVPR, AAAI, ACM MM) with specialization in Visual Instruction Fine-Tuning and Adapter Modules. Recent work includes HD-EPIC dataset development and Dual-LoRA framework for efficient multimodal adaptation. Contact: binzhu@smu.edu.sg | bin.zhu@smu.edu.sg

Longzhi Yangمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۱۱ مورد دیگر

Longzhi Yang is a Professor at Northumbria University’s Department of Computer and Information Sciences, currently serving as Director of Education. He holds a PhD in Computing Science from Aberystwyth University (2011) and has extensive research experience in Artificial Intelligence, Robotics, Cybersecurity, and Manufacturing. His work emphasizes real-world applications under uncertain environments, supported by grants from EPSRC, Innovate UK, and industry partners. Research interests include Computational Intelligence, Machine Learning, Cybersecurity, Robotics, Planning/Scheduling, and Additive Manufacturing. Recent projects focus on energy storage systems, fuzzy logic control, and data-driven robotic systems. He has organized major conferences like the UK Workshop on Computational Intelligence and the IEEE ICMLA. Publications span topics such as reinforcement learning for EVs, fuzzy neural networks, and cybersecurity frameworks. Awards include the Best Paper Award at UKCI 2018 and 2016. He actively supervises PhD students and leads interdisciplinary initiatives like the COVER project for emergency response drones and 3D-printed furniture manufacturing. Education: PhD in Computing Science (Aberystwyth University, 2011) Funding: EPSRC, ESRC, Innovate UK, RAEng Grants: 4 active/funded projects including smart manufacturing and emergency response systems Labs/Teams: Involved in robotics, cybersecurity, and intelligent manufacturing research groups

• Emotion Recognition
• Facial Expression Analysis
• Machine Learning
• +۵ مورد دیگر

Kamlesh Mistry is an Assistant Professor in the Computer and Information Sciences Department at Northumbria University. His research focuses on Emotion Recognition, Facial Expression Analysis, Optimization Algorithms, and Machine Learning applications in Computer Vision and Humanoid Robotics. He holds a PhD from his work on intelligent facial expression recognition, supervised by Professor Li Zhang. Dr. Mistry has contributed to over 34 research outputs since 2007, with notable recent work in 2025 introducing adaptive nature-inspired optimization frameworks and multimodal emotion recognition systems. His research interests span facial expression recognition under varying conditions, optimization algorithm design (e.g., Firefly, Differential Evolution), and deep learning applications for semantic segmentation, medical imaging, and anomaly detection. Collaborations include studies on privacy-preserving location-sharing and short-term forecasting models for energy and weather systems. Recent publications highlight advancements in multimodal emotion recognition using transformers and variational autoencoders, semantic segmentation via GAN-synthesized images, and pose-invariant facial expression systems. His work emphasizes practical applications in healthcare, robotics, and cybersecurity. Advising and grants are not explicitly detailed in the provided text, but his doctoral thesis demonstrates expertise in evolutionary feature optimization and facial point detection. No scientific awards are mentioned.

Huaping Liuمشاهده پروفایل

استاد

• Wireless Communication Systems
• Signal Processing
• MIMO Systems
• +۷ مورد دیگر

Huaping Liu is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University (College of Engineering). He holds a Ph.D. in Electrical Engineering from New Jersey Institute of Technology (1997) and previously worked at Lucent Technologies (1997–2001). His research focuses on wireless communication systems, including MIMO systems, ultrawideband technologies, spread spectrum systems, and channel coding. He has contributed to applications in healthcare monitoring and industrial automation. Education: Ph.D., Electrical Engineering, New Jersey Institute of Technology (1997) M.S., Electrical Engineering, Nanjing University of Posts and Telecommunications (1990) B.S., Electrical Engineering, Nanjing University of Posts and Telecommunications (1987) Research Interests: Wireless communication theories and systems Embodied intelligence and multi-agent robotics Tactile sensing and large-area robotic skin Autonomous navigation and vision-language models Awards and Recognition: 2017 IEEE UEMCON Best Paper Award 2015 OSU College of Engineering Research Collaboration Award 2011 DesignCon Best Paper Award 2010 IEEE VTS Best Paper Award 2007 & 2004 EECS IEEE Professor of the Year Advising and Grants: Dr. Liu emphasizes student-driven learning, fostering critical thinking through inquiry-based teaching. His work involves collaborative projects in robotics, cybersecurity, and sensor systems, supported by grants and industry partnerships. Labs/Teams: Active in developing embodied intelligence frameworks, robotic tactile sensing systems, and cybersecurity solutions for digital twins and vehicular networks. Collaborates on simulation platforms for human-robot collaboration (e.g., HumanTHOR).

Ciaran Eisingمشاهده پروفایل

دانشیار

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Ciaran Eising is an Associate Professor in the Department of Electronics & Computer Engineering at the University of Limerick. His research focuses on computer vision, machine learning, and their applications in intelligent transportation systems, particularly autonomous driving and driver assistance systems. He is a member of the Centre for Sustainable Digital (Re)Manufacturing, Data-Driven Computer Engineering Research Centre, and Lero – the SFI Research Centre for Software. Education: PhD from the National University of Ireland Galway (2010), BE (2003). External roles include Senior Expert at Valeo Vision Systems (2009–2020) and Adjunct Lecturer at NUI Galway (2016–present). His expertise spans automotive vision systems, embedded systems, and sensor fusion. Research Interests: Driver Assistance Systems, Autonomous Driving, Object Detection, Motion Segmentation, and Sensor Fusion. Over 94 publications since 2007, with recent work on BEV perception, multimodal fusion, and DDoS mitigation in healthcare IoT. Grants/Advising: Accepting PhD students; collaborations include industry partnerships and cross-disciplinary research. Labs/Teams: Active in Lero and other university research centers, contributing to UN SDGs via sustainable transportation innovations.

Zhang Junمشاهده پروفایل

استاد

• Biological Physics
• Nonlinear dynamics and complex systems
• Soft matter physics
• +۴ مورد دیگر

Zhang Jun is a Full Professor of Physics and Mathematics and Co-director of the Applied Math Lab at the Courant Institute, New York University (NYU), USA. He also serves as Co-director of the NYU-ECNU Joint Physics Research Institute in Shanghai, China, and holds an Affiliated Professorship at NYU Shanghai. His research focuses on experimental fluid physics, particularly fluid-structure interactions in biological and geophysical contexts, including bio-locomotion, flapping wings, and continental dynamics. Zhang has authored over 290 invited talks and peer-reviewed papers in journals like Nature and Physical Review Letters. He received the 2017 APS Fellow award for pioneering work in fluid-structure interactions. Beyond academia, he is a freelance illustrator with plans to publish a book of sketches. Education: PhD in Physics (1994), Niels Bohr Institute, University of Copenhagen PhD Candidate (1990-1991), Hebrew University of Jerusalem BSc in Physics (1985), Wuhan University Research Interests: Zhang’s work bridges physics, biology, and geophysics, exploring phenomena like flapping wing aerodynamics, animal locomotion, and Earth’s core-mantle interactions. His experiments often use novel fluid dynamics setups to model natural systems. Awards: APS Fellow (2017) Milton Van Dyke Award (2014) Antarctica Service Medal (2015) Labs & Teams: Co-directs the Courant Institute’s Applied Math Lab, specializing in fluid dynamics experiments. Collaborates with institutions globally, including NYU Shanghai and Aix-Marseille University.

Pekka Toivanenمشاهده پروفایل

استاد

• Artificial Intelligence
• Healthcare Systems
• IoT and Smart Grids
• +۳ مورد دیگر

Pekka Toivanen is a Professor at the School of Computing, Faculty of Science, Forestry and Technology at the University of Eastern Finland. His research focuses on artificial intelligence, healthcare systems, IoT, cybersecurity, and machine learning. He has contributed extensively to fields like medical image analysis, smart grids, and robotics, with over 100 peer-reviewed publications. His work bridges theoretical advancements and practical applications, such as improving healthcare diagnostics via AI, enhancing IoT security, and optimizing energy management in microgrids. Research Interests: Dr. Toivanen’s primary areas include AI-driven healthcare solutions, cybersecurity for IoT devices, deep learning applications in medical imaging, and energy-efficient smart grid systems. He has pioneered methods for tuberculosis risk prediction and developed novel encryption algorithms for Bluetooth security. Publications: His recent work emphasizes AI in healthcare (e.g., tuberculosis prediction models) and energy systems (e.g., reinforcement learning for microgrid management). He also explores vulnerabilities in wireless technologies like Bluetooth and ZigBee, proposing robust countermeasures. Grants/Teams: Leads projects on AI distribution platforms for healthcare and secure IoT architectures. Collaborates with interdisciplinary teams in medical and engineering domains.

Faran SABETIمشاهده پروفایل

دانشیار

• Clinical Research
• Rehabilitation
• Ophthalmology
• +۷ مورد دیگر

Faran SABETI is an Associate Professor in Optometry and Vision Science at the University of Canberra. He holds adjunct positions as Associate Professor in Neuroscience at the Australian National University (since 2020) and is an active researcher in clinical vision science. His work focuses on objective perimetry, retinal function assessment, and translational rehabilitation strategies for vision disorders. SABETI leads and collaborates on projects addressing diabetic retinopathy, age-related macular degeneration, and mild traumatic brain injury-related vision deficits. Affiliations: University of Canberra (Optometry & Vision Science), Australian National University (Neuroscience) Educations: PhD in Dichoptic Multifocal Pupillographic Perimetry (Australian National University, 2011) Bachelor of Optometry (University of New South Wales, 2004) Graduate Certificate in Ocular Therapeutics (University of New South Wales, 2011) Fellow of Higher Education Academy (ANU, 2020) Research Interests: His work spans objective perimetry techniques for early disease detection (glaucoma, diabetes-related retinopathy), retinal function biomarkers in AMD and diabetic maculopathy, and translational interventions like levodopa eye drops for vision restoration. He also investigates vision loss impact on quality-of-life domains like art appreciation and social interaction. Key Projects (2023-2027): VA-RIS: Vision Assessment pathway in rehabilitation settings ($4M project) Stochastic resonance for visual aids in retinal disease ($3.5M) Enhancing dementia care in aged facilities through allied health integration ($6M) Awards: 2020 Fellow of Higher Education Academy (ANU). Grants & Collaborations: Active in interdisciplinary teams with clinicians, engineers, and neuroscientists. Leads studies on multifocal pupillography, diabetic eye disease biomarkers, and levodopa therapy safety. Labs & Teams: Co-director of the Vision Science Research Group at UC, collaborating with institutions like UNSW, ANU, and industry partners in medical device development.

Christian Grimmeمشاهده پروفایل

دانشیار

• Multiobjective Evolutionary Computation
• Social Media Analysis
• Disinformation Detection
• +۴ مورد دیگر

Prof. Dr.-Ing. Christian Grimme is an Associate Professor and Extraordinary Professor in the Department of Information Systems at the University of Münster. He leads the Computational Social Science and Systems Analysis research group. His roles include acting professorships, research group leadership, and academic co-direction of the ERCIS Competence Center for Social Media Analytics. He holds a Dr.-Ing. in Computer Science and has extensive postdoctoral and habilitation experience. Education Timeline: 2015–2018: Habilitation and venia legendi in Information Systems 2006–2012: PhD in Computer Science (Dr.-Ing.) 1999–2006: Diploma in Computer Science Research Interests focus on Multiobjective Evolutionary Computation, Social Media Analysis, Disinformation Detection, and AI Ethics. His work bridges algorithmic innovation (e.g., optimization algorithms) with societal challenges (e.g., automated propaganda detection). Recent projects include analyzing Large Language Models' role in disinformation mitigation and real-time social media content analysis using human attention mechanisms. Awards include the Best Teaching Award (2024), PPSN XIV Best Paper Award (2016), and multiple travel grants from ACM and DAAD. He actively participates in conferences like GECCO and EMO, contributing to both theoretical and applied research. Advising and grants highlight his role in guiding over 20 theses, spanning Master's and Bachelor's projects in IS/WI. Notable grants include DAAD-funded collaborations and internal university funding for projects like MODERAT! (moderation tools) and ERCIS SMA Competence Center. Labs/Teams: Leads the Computational Social Science & Systems Analysis group, collaborating with global partners via ERCIS. Engages in initiatives like CLAIRE and the Integrity & Security Initiative to address AI ethics and information security challenges.

Heiko Schüttمشاهده پروفایل

دانشیار

• Computational Cognitive Science
• Visual Perception
• Computational Neuroscience
• +۷ مورد دیگر

Heiko Schütt is Associate Professor for Computational Cognitive Science and Modeling at the Université du Luxembourg. His research focuses on developing mechanistic models of visual perception and cognition using deep neural networks, Bayesian inference, and efficient coding principles. He is also the developer of widely used toolboxes such as rsatoolbox for representational similarity analysis and Psignifit 4 for psychometric function fitting. His work lies at the intersection of cognitive science, computational neuroscience, and machine learning, with a strong emphasis on creating and evaluating models of human perception and decision-making. He investigates early visual processing, eye movement dynamics, and model evaluation methodologies, contributing both theoretical frameworks and practical tools to the scientific community. The recent publications reflect a strong trend toward developing rigorous statistical methods for comparing neural and cognitive models, particularly in the context of representational geometries and perceptual decision-making. His work increasingly bridges human cognition and artificial neural networks, exploring parallels in generalization and representation. Much of his research is image-computable and grounded in empirical psychophysics. He has previously held postdoctoral positions with Weiji Ma at New York University and Niko Kriegeskorte at the Zuckerman Institute, Columbia University. His PhD was jointly conducted with Felix Wichmann at Tübingen and Ralf Engbert at Potsdam, focusing on early visual processing and eye movements. No scientific awards are mentioned in the provided text. While no formal advisees are listed, his active research program and development of major scientific toolboxes suggest a role in mentoring students and collaborators. There is no mention of specific grants, but his work likely involves funded research given the scale and impact of his projects. He maintains active code repositories on GitHub for rsatoolbox, Psignifit 4, early vision models, and eye movement modeling, indicating leadership in open science and computational tool development. These resources support a broad community in cognitive and computational neuroscience.

• Computer Engineering
• Internet of Things (IoT)
• Human-Computer Interaction
• +۷ مورد دیگر

Diјana Stoјić is a Lecturer at the Department of Computer and Software Engineering , Faculty of Technical Sciences Čačak , affiliated with the University of Kragujevac . She holds a Master's degree in Computer Engineering and is currently pursuing her PhD in Electrical and Computer Engineering at the same faculty. Her academic role involves teaching subjects such as Programming Languages, Human-Computer Interaction, and Visual Programming. Born: March 7, 1988, in Foča, Bosnia and Herzegovina Education: Primary (Vuk Karadžić School, Čačak), Technical School in Čačak (Computer Engineering), BSc and MSc from the Technical Faculty in Čačak Her research interests span Computer Engineering , Internet of Things (IoT) , Machine Learning , and Human-Computer Interaction . Recent publications highlight her work in artificial neural networks , augmented reality applications , and image classification for agricultural and medical purposes. She has contributed to projects like the TR32043 project on low-power systems and the DeSKoll project on German-Serbian language resources. Notable scientific awards include: Award for Best First-Year Student in Electrical Engineering (Technical Faculty in Čačak) Scholarship from the Ministry of Education, Science, and Technological Development (2013-2017) Her grant involvement includes participation in the TR32043 project (2011-2019) and the DeSKoll project (2024-2025). She has also collaborated on lab experiments and remote engineering education initiatives during the pandemic.

• Robotics
• Artificial Intelligence
• Cognitive Architectures
• +۵ مورد دیگر

Miguel Ángel González Santamarta is an Assistant Professor at the University of León, affiliated with the College of Industrial, Computer Science, and Aerospace Engineering, Department of Mechanical, Computer Science, and Aerospace Engineering. His research focuses on Robotics, Artificial Intelligence, and Cognitive Architectures, particularly in Autonomous Systems, Human-Robot Interaction, and Explainable AI. Doctor of Philosophy (2024) from the University of León, with a thesis on hybrid cognitive architectures for autonomous robot behavior generation. His recent work emphasizes integrating Quantized Large Language Models (LLMs) into robotics for Edge AI applications, enhancing explainability and accountability in autonomous systems. Key projects include the VAULT mobile mapping system for ROS 2, the ROXIE explanation engine, and frameworks for Forensic Threat Detection using LLMs. He contributes to educational innovation, such as applying Github Classroom for teaching enterprise-level development. His articles span topics like ROS 2-based architectures , vision-language models , and cybersecurity in autonomous systems .