جست‌وجوی استاد و پژوهشگر

Christoforos Christoforouمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computational Neuroscience
• +۵ مورد دیگر

Dr. Christoforos Christoforou is an Associate Professor in the Division of Computer Science, Mathematics, and Sciences at St. John’s University’s Collins College of Professional Studies. He serves as Program Director for the Master’s in Computer Science program and leads the Neuro-Intelligence and Innovation Lab. He holds a PhD in Computer Science from the CUNY Graduate Center, complemented by advanced degrees from The City College of New York. Education: PhD, Computer Science, City University of New York - The Graduate Center MPhil, Computer Science, City University of New York - The Graduate Center MS, Computer Science, The City College of the City University of New York Research Interests: Dr. Christoforou’s work bridges computer science and neuroscience, focusing on machine learning and AI algorithms to decode neurophysiological signals (EEG) for neurotechnology applications. Key areas include brain-computer interfaces, neuro-cinematics, and understanding neurocognitive processes in reading disorders and spatial cognition. His research has pioneered computational frameworks for studying neural underpinnings of dyslexia and emotional responses to media. Publications Overview: His most recent work (2023–2021) explores neural congruency in phonological processing, EEG-based emotion recognition, and computational models for reading remediation. Earlier contributions include foundational studies on cortically coupled computer vision and predictive modeling of movie audience engagement. Lab & Innovation: The Neuro-Intelligence and Innovation Lab under his direction develops neurotechnology solutions through interdisciplinary collaboration, emphasizing real-world applications like assistive robotics and adaptive educational tools.

Zhaoxing Liمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Large Language Models
• Deep Reinforcement Learning
• +۴ مورد دیگر

Dr. Zhaoxing Li is a Research Fellow at the University of Southampton, specializing in Citizen-Centric Artificial Intelligence Systems (CCAIS). His work focuses on integrating Large Language Models (LLMs), Deep Reinforcement Learning, and Multi-Agent Systems to bridge technological innovation with societal impact. He emphasizes human-AI interaction, explainable AI, and ethical AI development. Zhaoxing collaborates on projects like FATES of Africa and contributes to educational technology through AI-driven learning systems. His research interests include Large Language Models Deep Reinforcement Learning Multi-Agent Systems Human-AI Interaction Explainable AI . Recent work highlights include advancing consensus-building algorithms, personalized learning recommendations via LLMs, and gesture recognition for multimodal interfaces. His publications span conferences and journals like Universal Access in the Information Society and Neurocomputing. No scientific awards are listed. Current projects emphasize citizen-centric AI architectures and ethical AI deployment.

Vladimir Todorovicمشاهده پروفایل

دانشیار

• Generative Art
• Artificial Intelligence
• Immersive Storytelling
• +۷ مورد دیگر

Vladimir Todorovic is an Associate Professor and Chair of Fine Arts at the School of Design, University of Western Australia (UWA), and a Visiting Professor at the University of Arts Belgrade. His work bridges art, technology, and environmental discourse through immersive storytelling, AI-driven generative art, and experimental film. He has exhibited globally at festivals like Annecy, IFFR, and Ars Electronica, winning over a dozen awards for his innovative projects. Research interests include generative art systems, artificial intelligence aesthetics, virtual reality narratives, and the ethical implications of technology in creative practices. He has organized major events such as ISEA2008 and the Environmental Visions conference (2014), emphasizing interdisciplinary collaboration between artists, scientists, and technologists. Current projects explore AI ethics, Indigenous storytelling through extended reality, and procedural modeling in digital media. Grants: 2023: 'Illustrating Nyangumarta' (AUD 57k) from WA's Department of Local Government 2022: 'Cabinet of Algodreams' (AUD 15k) Awards: 1st Prize Experimental, 19th Athens Animfest (2024) Philip K. Dick Science Fiction Festival Award (2024) Early-Career Research Award, UWA (2022) Labs/Teams: Leads the UWA Fine Arts Lab focusing on experimental digital media and collaborates with Indigenous communities on XR storytelling initiatives.

Baidaa Al-Banderمشاهده پروفایل

مدرس

• AI in Healthcare
• Computer Vision
• Explainable AI
• +۳ مورد دیگر

Baidaa Al-Bander is a Lecturer at Keele University's School of Computing and Mathematics. She holds an MSc in Computer Engineering from the University of Baghdad and a PhD in Electrical Engineering from the University of Liverpool (2018), followed by a Postdoctoral Research position at the University of Dundee. Her research focuses on AI-driven solutions for healthcare and real-world applications, including medical imaging analysis, explainable AI, and deep learning algorithms. She collaborates with experts in medicine, engineering, and computer science to address practical challenges, emphasizing interdisciplinary approaches. Al-Bander's work spans glaucoma diagnosis, melanoma detection, and autonomous medical systems, with publications in top-tier journals like PLoS One and Electronics . She actively reviews for leading conferences and journals, contributing to advancing AI ethics and applications. Her educational background and industry partnerships position her as a key figure in computational healthcare research. Education: MSc Computer Engineering, University of Baghdad (Iraq) PhD Electrical Engineering, University of Liverpool (2018) Research Interests: AI in Healthcare Computer Vision Explainable AI Deep Learning for Medical Imaging Data Science in Structural Engineering Key Contributions: Developed AI models for automated software testing using large language models. Pioneered emotion-aware mental health chatbots integrating BERT and GPT frameworks. Advanced glaucoma diagnosis techniques via deep learning-based retinal image analysis. Benchmarked deep learning algorithms for skin cancer and melanoma detection. Collaborations: Works with interdisciplinary teams in medicine, civil engineering, and cybersecurity to apply AI to real-world problems like seismic risk assessment and network intrusion detection.

• Machine Learning
• Neural Networks
• Computer Vision
• +۳ مورد دیگر

Michael Adjeisah is a Research Fellow at Bournemouth University's CfACTs Research Centre, focused on interdisciplinary research in machine learning and artificial intelligence. His work spans computer vision, natural language processing, health informatics, and data science, with applications in cultural heritage technology, low-resource language processing, and medical systems. Notable research areas include Adinkra symbol recognition using deep learning, graph neural networks for classification tasks, and sentiment analysis models leveraging attention mechanisms. He has contributed to advancements in neural machine translation for low-resource languages and blockchain-enabled privacy solutions for electronic health records. Adjeisah's publications reflect a strong emphasis on practical applications of AI, with recent work addressing challenges in spoken digit recognition for Amharic and respiration-based biometric systems. His research often integrates multi-sensor fusion and data augmentation techniques to enhance model performance in real-world scenarios.

Samer Sarsamمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Mining
• Machine Learning
• +۳ مورد دیگر

Dr. Samer Sarsam is an Assistant Professor in Coventry University's School of Strategy and Leadership. His research specializes in Business Analytics, Data Mining, Machine Learning, and AI-driven decision-making with applications in healthcare, finance, and education. He mentors undergraduate/postgraduate students and consults for tourism/healthcare industries. Research focuses on: Artificial Intelligence and predictive modeling Sentiment and opinion mining techniques Time-series analysis for behavioral prediction Text mining applications in healthcare Recipient of the 2021 Market Research Society Silver Medal for excellence in analytics research. Published extensively on machine learning applications, with recent work exploring generative AI in healthcare management (blood donation supply chains) and education. Consulting work helps organizations leverage data analytics for strategic decision-making across multiple sectors.

• Machine Learning
• Brain-Computer Interfaces (BCI)
• Neuroscience
• +۳ مورد دیگر

Xiaodong Qu is a Visiting Assistant Professor in the Department of Computer Science at Swarthmore College. He holds a Ph.D. in Computer Science from Brandeis University, advised by Professor Timothy J. Hickey. His research focuses on Machine Learning and Brain-Computer Interfaces (BCI), with a strong emphasis on EEG signal analysis and interdisciplinary applications in education and healthcare. Dr. Qu has led research projects involving 15 undergraduate students and 15 BCI headsets, fostering hands-on learning in EEG data analysis. His work has been recognized with a Best Paper Award at the 2020 International Conference on Brain Function Assessment in Learning. He actively participates in academic service, chairing sessions at HCI International 2022 and serving as Department Representative for Computer Science (2022-23). His teaching spans foundational courses like CPSC 21 (Introduction to Computer Science) and advanced topics such as Machine Learning and BCI. He emphasizes project-based learning, integrating real-world datasets and collaborative research opportunities. His lab at Swarthmore supports undergraduate research in AI, BCI, and EEG applications.

• Biometrics
• Computer Vision
• Pattern Recognition
• +۳ مورد دیگر

Maria De Marsico is an academic affiliated with Sapienza University of Rome, Italy, specializing in biometrics, computer vision, and pattern recognition. She actively contributes to conferences such as ICPRAM, serving as editor for multiple proceedings. Her research focuses on gait and facial recognition, wearable sensor applications, and multimodal interaction systems. She collaborates with institutions like SCITEPRESS and Springer, publishing extensively in journals like Pattern Recognition Letters and IEEE Transactions . Her work addresses challenges in biometric systems, emotion recognition, and accessibility through AI-driven solutions. Maria co-organizes workshops on Games-Human Interaction (GHItaly) and contributes to fields like e-learning accessibility for deaf students via SignWriting tools. Her projects include developing applications such as VQAsk for visually impaired users and exploring gait recognition via smartphone accelerometers. She also investigates robust machine learning models for facial expression analysis and dataset rebalancing to reduce bias in demographic classification. Her research spans from theoretical contributions, such as surveys on image integrity methods, to applied systems like the Biopen-Fusing authentication framework. She collaborates with global researchers on topics ranging from microplastics detection to UAV joystick interfaces, demonstrating interdisciplinary impact.

Hongbo Duمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Professor Hongbo Du is a faculty member at the University of Buckingham's School of Computing within the Faculty of Computing, Law and Psychology. He holds a BSc in Computer Automation from Beijing, an MSc in Computing from the University of Essex, and an MPhil in Computing. His research focuses on Data Mining, Machine Learning, Image Processing, and Time Series Analysis with applications in medical diagnostics. Notable collaborations include work with TenD Medical AI Technologies, DeepNet Security, and the Wellcome Trust Sanger Institute. He currently leads modules like Principles of Database Systems and Applied Techniques of Data Mining and Machine Learning, and directs GPP collaboration programmes. His research emphasizes practical problem-solving using machine learning, particularly in analyzing ultrasound and biomedical images for cancer diagnosis (e.g., breast and thyroid lesions). He has supervised multiple PhD and MRes projects in medical image analysis and co-authored numerous peer-reviewed publications. His work spans from technical innovations like Clustered-CAM visualization techniques to clinical applications such as Doppler feature analysis for thyroid cancer detection. Prof Du's contributions extend to curriculum development and educational methodologies, including data mining pedagogy. His projects often involve interdisciplinary approaches, combining medical expertise with computational techniques to address healthcare challenges.

• Human-Computer Interaction
• Crowdsourcing
• Machine Learning
• +۵ مورد دیگر

Alessandro Bozzon is a Researcher at Delft University of Technology, The Netherlands , specializing in Human-Computer Interaction , Crowdsourcing , and Artificial Intelligence . His work bridges technical systems with human-centric design, focusing on conversational interfaces, data integration, and ethical algorithmic frameworks. Key research areas include urban data analysis, AI explainability, and collaborative policy-making Collaborates extensively with multidisciplinary teams in computer vision, semantic web, and mental health diagnostics Recent publications analyze: Episodic framing in news media (2024) Greenspace accessibility for children (2023) Multi-stakeholder algorithmic assessments (2022) Conversational crowdsourcing systems (2019) His work emphasizes human-in-the-loop approaches across machine learning, urban planning, and media bias detection, with applications in smart homes, policy sandboxes, and public health diagnostics.

Adham Atyabiمشاهده پروفایل

استادیار

• Cognitive & Computational Neuroscience
• Autism Biomarker Discovery
• Electroencephalogram (EEG)
• +۷ مورد دیگر

Adham Atyabi is an Assistant Professor in the Department of Computer Science at the University of Colorado Colorado Springs (UCCS). He holds a B.Sc. from Azad University (Iran), an M.Sc. in Swarm Robotics from Multimedia University (Malaysia), and a Ph.D. in Cognitive and Computational Neuroscience from Flinders University (Australia). His postdoctoral research spanned multi-swarm robotics systems at the University of Salford (UK), autism biomarker research at Yale University and Seattle Children’s Research Institute (SCRI), and computational neuroscience in BCI and EEG analysis. Key research areas include cognitive neuroscience, autism biomarkers, EEG/fNIRS analysis, BCI systems, eye-tracking, machine learning, and swarm robotics. His work intersects neuroengineering, computational intelligence, and clinical applications, particularly in autism diagnostics and rehabilitation technologies. He has contributed to multi-site studies like the Autism Biomarkers Consortium for Clinical Trials (ABC-CT), focusing on data analytics and biomarker discovery. Education: B.Sc., Azad University of Mashhad, Iran M.Sc. (Swarm Robotics), Multimedia University, Malaysia Ph.D., Cognitive and Computational Neuroscience, Flinders University, Australia Awards: No specific awards listed. Grants & Labs: Involved in ABC-CT initiatives and DAAC (Data Acquisition and Analytics Core). Active in UCCS-ICORE (Interdisciplinarity & Complexity Oriented Researchers and Educators). Future Work: Expanding BCI applications, autism biomarker validation, and interdisciplinary collaborations in neurotechnology.

Marek Reformatمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Intelligence
• Fuzzy Systems
• +۷ مورد دیگر

Dr. Marek Reformat is a Professor in the Department of Electrical and Computer Engineering at the University of Alberta, Faculty of Engineering. His research focuses on developing human-like intelligent systems through the integration of computational intelligence (fuzzy systems, neural computing, evolutionary algorithms), uncertainty theories (probability, evidence theory), and semantic web technologies (RDF, ontologies). He also explores mathematical foundations like category theory and type theory for knowledge representation. His research interests include knowledge extraction via granular computing, semantic-based decision support systems, and applications in software quality analysis. He teaches courses such as ECE 220 (Programming for Electrical Engineering) and ECE 447 (Data Analytics and Machine Learning for Engineers). Dr. Reformat actively contributes to conferences like AAAI, NeurIPS, and IEEE WCCI. His work spans topics like fuzzy ontology integration, neural networks for medical imaging, and uncertainty modeling in decision-making systems. He currently advises eight graduate students and collaborates with industry partners on projects involving AI-driven solutions for complex systems.

• Human-Computer Interaction
• Health Informatics
• Behavioral Cybersecurity
• +۴ مورد دیگر

Marc T. P. Adam is an Associate Professor in Computing and Information Technology at the School of Information and Physical Sciences, University of Newcastle . With a PhD in Information Systems from Karlsruhe Institute of Technology, he specializes in Human-Computer Interaction with applications in Health Informatics and Behavioral Cybersecurity . Education : PhD (Dr. rer. pol.) from Karlsruhe Institute of Technology; Diploma in Computer Science from University of Applied Sciences Würzburg-Schweinfurt Academic Affiliation : University of Newcastle (2014-present); Karlsruhe Institute of Technology (2010-2014) His research explores the intersection of technology and human behavior, focusing on NeuroIS applications for understanding emotional responses in digital environments. Key projects include the HealthyEatingQuiz.com.au and NMNT.com.au platforms for nutrition behavior change. He employs interdisciplinary methodologies combining design science research with physiological measurements like eye tracking and heart rate variability. Recent work demonstrates the impact of nature imagery in user interfaces on consumer trust and aesthetics, while his cybersecurity research investigates how time pressure affects organizational security behaviors. His publications span topics from fake news detection to mHealth system design , with a particular interest in digital nudging for health behavior change. As an educator, he emphasizes applied IT approaches through business analytics , human-centred design , and experimental research methods . His work features collaborations with international researchers across Australia, Germany, and China.

Jason Riggleمشاهده پروفایل

دانشیار

• Computational Linguistics
• Phonology
• Learnability
• +۴ مورد دیگر

Jason Riggle is an Associate Professor in the Department of Linguistics at the University of Chicago, serving as Director of the Chicago Language Modeling Lab and Resident Dean of The Max Palevsky Residential Commons. He holds a PhD from UCLA (2004) and has taught at UChicago since 2004. His research focuses on computational linguistics, phonology, and learnability, particularly exploring how models of grammar and learning predict linguistic typology. He investigates articulatory frequencies across languages, dialects, and contexts, with special attention to phonetic and phonological patterns. Recent courses include Introduction to Phonetics and Phonology , The Language of Deception and Humor , and Computational Models in Phonology . His work spans theoretical frameworks like Optimality Theory and computational methods for analyzing sign languages (e.g., ASL fingerspelling), with publications on topics such as vowel harmony, constraint-based grammars, and linguistic universals. Riggle’s research emphasizes interdisciplinary approaches, combining formal models with empirical data to address questions in phonological typology and learnability. His academic contributions include co-editing The Handbook of Phonological Theory (2nd ed., 2011) and developing tools like Erculator for constraint-based phonology. No scientific awards are listed in the provided materials, though his extensive publication record reflects sustained academic impact. Riggle advises students in phonetics, computational linguistics, and related fields, though specific advisee names are not detailed here.

Aphrodite Galataمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Human-Robot Interaction
• +۳ مورد دیگر

Aphrodite Galata is a Lecturer in Advanced Interfaces at the Human Computer Systems department. She holds a Doctor of Philosophy from the University of Leeds (2001) in Learning Variable Length Markov Models of Behaviour. Her research focuses on Computer Vision and Machine Learning, with special emphasis on sign language processing, human-robot interaction, and embodied learning systems. She has contributed to advancements in artificial agents learning sign language through imitation, phonological property recognition, and dataset development for American Sign Language analysis. Her research interests also span robotics, 3D pose estimation, and real-time tracking systems. Key topics in her work include gesture analysis, multimodal communication, and neural network architectures for feature representation. She has published widely in top-tier conferences including IEEE ICASSP and ACL, with notable contributions to datasets like WLASL-LEX. Notable projects include developing algorithms for hand tracking from monocular RGB data and exploring anisotropic cost functions for facial landmark detection. She has collaborated with researchers like Angelo Cangelosi on embodied sign language systems. Her work bridges computer vision techniques with cognitive modeling to enhance human-agent communication systems. Her research is aligned with the Digital Futures research beacon, emphasizing innovation in human-computer systems. While no specific awards are noted, her work has garnered significant readership on platforms like Mendeley, reflecting its academic impact. She has supervised 8 research works, contributing to the training of future scholars in advanced interface technologies.