جست‌وجوی استاد و پژوهشگر

Jungong Hanمشاهده پروفایل

استاد

• Computer Vision
• Deep Learning
• Image Processing
• +۷ مورد دیگر

Professor Jungong Han is Chair Professor in Computer Vision at the Department of Computer Science, University of Sheffield, UK. He holds an Honorary Professorship at the University of Warwick and has previously served as Chair Professor and Director of Research at Aberystwyth University, Data Science Associate Professor at the University of Warwick, and Senior Lecturer at Lancaster University. University of Sheffield University of Warwick Aberystwyth University Lancaster University His research spans Computer Vision , Deep Learning , and Medical Imaging , with a focus on Salient Object Detection Image Dehazing Video Hashing Brain Tumor Segmentation Zero-Shot Learning . Prof. Han has authored or co-authored over 200 papers, including 90 in IEEE/ACM Transactions and 60+ in CORE A* conferences . His recent work emphasizes Multimodal Analysis Efficient Neural Architectures Weak Signal Detection Medical Image Segmentation Self-Paced Learning . Scientific recognition includes 2024: EASA Member 2022: IAPR and AAIA Fellow 2017: HEA Fellowship 2017: Emmy® Award for Video Identification 2015: Emmy® for Steganographic Technologies 2014: CIS Innovation Award . He mentors PhD/MSc students at University of Sheffield , Aberystwyth University , and University of Warwick , including Zhuang Shao, Yu Fu, and Xiaoqi Zhuang. Prof. Han also serves as Associate Editor for journals like IEEE Transactions on Image Processing and Neurocomputing , and as Tutorial Co-Chair for ACM Multimedia 2024.

• Data Science
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Luca Cagliero is an Associate Professor (L.240) at the Department of Control and Computer Science (DAUIN) at Politecnico di Torino . He serves as Coordinator of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory and Scientific Advisor for the Partnership Agreement with TIERRA. His research spans Data Science , Machine Learning , and Natural Language Processing , with a focus on Financial Data Mining , Generalized Pattern Mining , and Legal AI . Scientific Branch: IINF-05/A - Information Processing Systems (Area 0009 - Industrial and Information Engineering) ERC Sectors: PE6_7 (Artificial Intelligence), PE6_9 (Human Computer Interaction), PE6_11 (Machine Learning), PE6_10 (Web and Information Systems) His scientific awards include the GiovedìScienza Award (2013), Working Capital PNI (2011), and Optime. Recognition of Merit in the Study (2008). He is a Fellow of ACM (2010-) and Effective Member of IEEE (2010-). As an Associate Editor for journals like Expert Systems with Applications and Machine Learning with Applications , he contributes to academic publishing. His conference roles include Program Committee memberships at ACM SIGMOD 2023, IEEE ICDM 2021, and ACM CIKM 2018. Luca supervises PhD students in areas such as AI-driven Cybersecurity , Conversational AI , and Neural Explainers , including Aurora Gensale, Giuseppe Gallipoli, and Irene Benedetto. His commercial research contracts involve projects like AI for Trend Analysis (Intesa Sanpaolo), Predictive Maintenance , and Legal Document Processing . Key research trends include Retrieval Augmented Generation for visually-rich documents, Bias Mitigation in speech models, and Shapley Value Estimation for model explainability. His work bridges Natural Language Processing with Cybersecurity in automotive systems.

Niki Efthymiouمشاهده پروفایل

پژوهشگر

• Computer Vision
• Human-Robot Interaction
• Machine Learning
• +۴ مورد دیگر

Niki Efthymiou is a PostDoctoral Researcher at the National Technical University of Athens, affiliated with the School of Applied Mathematical and Physical Sciences. She works within the Computer Vision and Speech Processing (CVSP) Group, focusing on interdisciplinary research at the intersection of computer vision, human-robot interaction, and affective computing. Diploma in Applied Mathematics, NTUA (2010) MSc in Computational Mechanics, NTUA (2013) Her research interests center on multimodal perception systems for child-robot interaction, emphasizing audio-visual fusion, body posture analysis, and facial expression recognition. She explores joint affect recognition and sensor integration for robotics applications. Recent publications highlight her work on computational models for scene perception, action recognition, and object assembly guidance in human-robot contexts. Key areas include multimodal fusion techniques and their implementation in interactive robotics systems.

• Computer Vision
• Image and Video Processing
• Machine Learning
• +۵ مورد دیگر

Alexander C. Loui is a Professor in the Department of Computer Engineering at Rochester Institute of Technology (RIT), leading the Multimodal Analysis and Perception (MAP) Lab. He holds a Ph.D., M.A.Sc., and B.A.Sc. in Electrical Engineering from the University of Toronto, Canada. His academic roles include Senior Area Editor of IEEE Transactions on Image Processing and Senior Editor of SPIE/IS&T Journal of Electronic Imaging. Loui specializes in computer vision, image/video processing, machine learning, and multimedia systems, with over 100 peer-reviewed publications and 95+ patents. Research interests span video summarization, medical image segmentation, and AI-driven multimedia applications. He has led technical teams at Kodak Alaris, Kodak Research Labs, and Bell Communications Research. Awards include IEEE Region 1 Technological Innovation Award and Fellowships from IEEE and SPIE. His work bridges academia and industry, emphasizing practical algorithm development and patent innovations. Teaching responsibilities include Digital Signal Processing and Multidisciplinary Senior Design courses. Key contributions: MAP Lab leadership, over 95 patents (e.g., video object segmentation, salient foreground detection), and editorial roles in top journals. His research trends focus on label-efficient learning, trustworthy AI, and predictive content curation systems.

Ashique KhudaBukhshمشاهده پروفایل

استادیار

• Natural Language Processing (NLP)
• AI for Social Impact
• Computational Social Science
• +۴ مورد دیگر

Ashique KhudaBukhsh is an Assistant Professor in the Department of Software Engineering at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). He directs the Social Insight Lab and is affiliated with the ESL Global Cybersecurity Institute. His research focuses on NLP and AI for Social Impact, addressing challenges in multilingual social media analysis, political polarization, and auditing AI systems for unintended harms. His work has been featured in top AI conferences and media outlets like The New York Times and BBC. Education: PhD in Artificial Intelligence from Carnegie Mellon University (2017). He joined RIT in 2021. Notable awards include the RIT GGCIS Emerging Scholar Award (2023) and selection for the AAAI 2024 New Faculty Highlight program. Research Interests: Machine Learning and NLP applications for social issues Political polarization analysis through AI Audit of generative AI for harmful content Multilingual and noisy social media text processing Teaching: Offers courses like 'Tracking Political Sentiments Using Machine Learning' (co-taught with political scientists) and advanced data science seminars. Current students include Sujan Dutta, Arka Dutta, and Soumyajit Datta. Awards and Media: Over 100 media mentions including coverage on BBC and Wired. His team exposed toxic content in Google's PaLM2 model, leading to model improvements. Labs and Collaborations: Leads the Social Insight Lab, collaborating on projects like analyzing gender bias in Bollywood and AI-based responses to global crises.

Kwanghee Wonمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Convolutional Neural Networks
• Deep Reinforcement Learning
• +۴ مورد دیگر

Kwanghee Won is an Associate Professor at the McComish Department of Electrical Engineering and Computer Science, South Dakota State University. His research focuses on computer vision, deep learning, and their applications in autonomous systems, precision agriculture, and structural health monitoring. He holds a Ph.D. from Kyungpook National University, Korea. Education: B.S., M.S., and Ph.D. in Computer Engineering from Kyungpook National University, Daegu, Korea Research Interests: Deep convolutional neural networks for anomaly detection and classification Autonomous vehicle systems and augmented reality applications Computer vision in smart agriculture and infrastructure assessment Model compression techniques for efficient neural networks Professional Contributions: Upsilon Pi Epsilon honor society faculty advisor (2021–present) ACM member and SIG Applied Computing participant Labs and Teams: Director of the Intelligent Vision Systems Laboratory , focusing on advanced vision-based solutions for real-world challenges.

Bang Tranمشاهده پروفایل

استادیار

• Machine Learning
• Internet of Things (IoT) Security
• Cybersecurity
• +۳ مورد دیگر

Bang Tran is an Assistant Professor of Computer Science at Assumption University’s D’Amour College of Liberal Arts and Sciences. Previously, he served as a Senior Lecturer at the University of Transport and Communications (UTC), Vietnam (2003–2018), receiving multiple UTC Excellent Teaching Awards (2013–2017). He earned his Ph.D. in Computer Science from the University of Massachusetts Boston (2023) under Prof. Xiaohui Liang, with prior degrees from Hanoi National University (BSc, 2001), Hochiminh National University (MSe, 2009), and additional research experience at Sonde Health Inc. His research focuses on machine learning applications for IoT privacy/security, voice assistant security, and cognitive health measurement. Notable achievements include the 2021 IEEE ICC Best Paper Award for work on mitigating voice assistant replay attacks, and the 2023 SOUPS poster award for VPASS, a voice privacy monitoring system. He teaches courses like Machine Learning, Data Structures, and Large Datasets. Recent grants include the 2025 Assumption Faculty Development Grant and a 2025 Summer Scholar Fellowship mentoring student Keegan Concannon on AI-driven network intrusion detection. He actively contributes to conferences like ICASSP, ICC, and SOUPS, and has supervised student projects in cybersecurity and AI. Education: Ph.D. UMass Boston (2023); MSe Hochiminh National University (2009); BSc Hanoi National University (2001) Awards: 3 UTC Teaching Awards, ICC2021 Best Paper, Erasmus Mundus Fellowship Labs/Teams: Assumption Cybersecurity Research Group, collaborations with Sonde Health Inc

Rinat Khusainovمشاهده پروفایل

استاد

• Applied Artificial Intelligence
• Health Informatics
• Machine Learning
• +۵ مورد دیگر

Rinat Khusainov is a Professor at the University of Portsmouth's Faculty of Technology, affiliated with the School of Computing and the Department of Computing. He holds roles as Associate Head (Research and Innovation), Innovation and Impact Development Manager, and PhD Supervisor. His research focuses on Applied Artificial Intelligence, particularly in health technologies, sensor systems, and addressing societal challenges through AI. He leads the University of Portsmouth's Cisco Networking Academy, contributing to undergraduate/postgraduate curriculum and professional development programs like Cisco CCNA. Khusainov has a PhD in Computer Science from University College Dublin (2004) and an MSc from the same institution (1998). His research interests span Machine Learning, Computer Vision, IoT, Cybersecurity, and healthcare analytics. He has collaborated with industry on projects involving assisted living technologies, smart sensors, and embedded systems. Key affiliations include the Centre for Healthcare Modelling and Informatics, Computational Intelligence Research Group, and Future of Law, Innovation and Technology Research Centre. Recent articles highlight innovations in AI-driven mental health solutions, edge-based violence detection systems, and steganographic methods. His work integrates interdisciplinary approaches, bridging AI with healthcare, cybersecurity, and renewable energy systems. He actively contributes to knowledge transfer initiatives, including EU-funded projects and industrial consultancy in sensor characterization and data management. Khusainov’s teaching portfolio includes computer networks, distributed systems, and machine learning. His academic leadership extends to managing research impact initiatives and fostering innovation in higher education through tech-enabled pedagogies. He remains engaged in advancing ethical AI applications and sustainable energy technologies through collaborative research.

• Computer Vision
• Deep Learning
• Object Detection
• +۴ مورد دیگر

Zheng Ge is an active researcher specializing in computer vision and deep learning, with a prolific publication record spanning from 2018 to 2025. They have authored or co-authored 81 publications in top-tier venues including CVPR, ICLR, ECCV, and NeurIPS, demonstrating consistent research productivity and impact in the field of artificial intelligence. Dr. Ge's primary research interests focus on computer vision with particular emphasis on object detection, 3D perception, and multimodal learning systems. Their work bridges theoretical advancements with practical applications, especially in video understanding, image generation, and large vision-language models. Recent research directions show a strong focus on developing more efficient and capable multimodal systems that can handle complex visual tasks with human-like understanding. The analysis of their recent publications reveals a clear trend toward building more sophisticated multimodal systems that integrate vision, language, and temporal understanding. Their work on video generation, perception modeling, and instruction tuning demonstrates a strategic focus on advancing the capabilities of multimodal large language models while addressing practical challenges in scalability and robustness. While specific awards are not documented in the available publication records, Zheng Ge's consistent presence in top-tier computer vision and machine learning conferences indicates significant recognition within the research community. Their work has contributed to advancing state-of-the-art techniques in multiple subfields of computer vision. Dr. Ge appears to be actively collaborating with a large research team, frequently working with researchers such as Xiangyu Zhang, Jianjian Sun, Chunrui Han, and Liang Zhao. These collaborations span multiple institutions and research groups, suggesting a well-integrated position within the computer vision research ecosystem. While specific grant information isn't available in the publication metadata, the scale and scope of their projects indicate substantial research funding support. Based on publication patterns and technical focus, Zheng Ge appears to be part of a large research organization or lab specializing in computer vision and multimodal AI systems. Their work on technical reports for video foundation models and large-scale multimodal systems suggests involvement in industry-leading AI research initiatives with strong engineering capabilities.

James Teoمشاهده پروفایل

استاد

• Stroke rehabilitation
• Clinical informatics
• Natural Language Processing
• +۷ مورد دیگر

James Teo is a Professor of Neurology at King's College London's Faculty of Life Sciences & Medicine, Department of Basic and Clinical Neuroscience. He serves as Joint Director for Data Science and Artificial Intelligence at King's College Hospital and Guy's & St Thomas' Hospital NHS Foundation Trust, where he leads the development and deployment of the CogStack platform as Clinical Lead and Founder. Additionally, he is the KCH Investigator for the London Medical Imaging and AI Centre for Value Based Healthcare. Dr. Teo's research interests focus on the intersection of neurology and data science, with particular expertise in stroke rehabilitation, general neurology, brain injury, and clinical informatics. His work heavily emphasizes the application of artificial intelligence, especially natural language processing and machine vision, to healthcare data. He has pioneered approaches using smartphones for Parkinson's disease assessment and wearable cardiac patches for stroke prevention through the EPACS trial. His publication record demonstrates a clear trajectory toward integrating AI into clinical practice, with recent work focusing on hospital-wide NLP systems for processing millions of patient records, discharge summary generation, and deep generative modeling of patient timelines. His research spans both technical AI development and practical clinical implementation. AI Awards from NHSX for natural language processing work Reviewed by the World Economic Forum for healthcare AI contributions As Clinical Director of Data and AI at both King's College Hospital (since 2014) and Guy's & St Thomas' NHS Foundation Trust (since 2020), Dr. Teo has been instrumental in establishing data science infrastructure across major London healthcare institutions. His leadership extends to the Confidentiality Advisory Group of the Health Research Authority since 2023, where he contributes to national healthcare data governance. Dr. Teo's work significantly contributes to UN Sustainable Development Goals related to good health and well-being through his innovations in healthcare data science and AI applications for improved patient outcomes.

Faisal Qureshiمشاهده پروفایل

دانشیار

• Computer Science
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Prof. Faisal Qureshi is an Associate Professor of Computer Science at the University of Ontario Institute of Technology (OntarioTechU), where he directs the Visual Computing Lab. He is also a Guest Professor in Deep Learning/Machine Learning at the same institution and a Visiting Professor affiliated with Computer Engineering and Electronics. His research spans computer vision, embedded IoT systems, and video summarization. Ph.D. and M.Sc. in Computer Science from the University of Toronto M.Sc. in Electronics from Quaid-e-Azam University, Pakistan B.Sc. in Mathematics and Physics from Punjab University, Pakistan Research Focus: Faisal Qureshi's work emphasizes computer vision, camera networks, and data-intensive IoT applications. His projects include Embedded Deep Learning Systems (EDGY) , integrating machine learning with real-time embedded hardware. Scientific Awards: CRV 2017 Best Vision Paper award ACM/IEEE Distributed Smart Cameras 2007 Outstanding Paper award Professional Affiliations: Senior Member of IEEE, member of ACM, and CIPPRS. He has chaired workshops at CVPR (2011-13) and co-chaired CRV (2015/16).

Sonia Haiducمشاهده پروفایل

دانشیار

• Software Engineering
• Program Comprehension
• Source Code Search
• +۴ مورد دیگر

Sonia Haiduc is an Associate Professor in the Department of Computer Science at Florida State University (FSU). Her research focuses on software engineering, particularly software maintenance, evolution, program comprehension, and source code search. She holds a Ph.D. (2013) and M.Sc. (2009) from Wayne State University, and a B.Sc. (2006) from Babeș-Bolyai University in Romania. **Education:** Ph.D. in Computer Science, Wayne State University, 2013 M.Sc. in Computer Science, Wayne State University, 2009 B.Sc. in Computer Science, Babeș-Bolyai University, 2006 **Research Interests:** Software Engineering Methodologies Source Code Analysis Empirical Software Engineering Video Tutorials for Software Learning Developer Communications Human Factors in Software Development Her service contributions include organizing key conferences like IEEE International Conference on Program Comprehension (ICPC) and chairing roles in workshops such as Mining Unstructured Data (MUD). She has been recognized with awards including the Google Anita Borg Scholarship and multiple ACM SIGSOFT travel awards. **Grants & Labs:** Director of the SERENE Group at FSU National Science Foundation Award CCF-1526929 (2015–2018): Focuses on text retrieval in software engineering Teaching spans courses like Software Engineering II and Graduate Software Engineering, with extensive TA experience in foundational computer science courses.

Aymeric Histaceمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Computer Aided Diagnosis
• +۴ مورد دیگر

Aymeric Histace is a Full Professor at the École Nationale Supérieure d'Électronique et de ses Applications (ENSEA) within CY Cergy Paris University. He leads the CELL research group at the ETIS laboratory, specializing in AI-driven medical diagnostics and embedded systems. His work focuses on computer vision, machine learning, and their applications in healthcare, particularly in capsule endoscopy and disease-carrying insect surveillance. Key roles: Teacher-researcher, CAD-CAP Scientific Council member Research areas: Medical AI, embedded systems, computer-aided diagnosis, and health technologies Notable contributions: Developed algorithms for automated analysis of gastrointestinal imaging and AI systems for vector surveillance His research bridges clinical needs and technological innovation, with over 50 publications since 2018. Recent work includes benchmarking polyp detection systems, improving bowel preparation assessments, and creating datasets for insect species classification using wing interferential patterns (WIPs). Publications: Over 40 peer-reviewed articles in journals like Medical Image Analysis and IEEE Transactions Tools developed: GTCreator annotation tool, AAEGAN image synthesis framework Grants & collaborations: Active in EU-funded projects on medical AI and embedded systems Labs/Teams: ETIS laboratory (Computer Science/Signal Processing), CELL research group (AI in Healthcare)

Pasquale Lisenaمشاهده پروفایل

پژوهشگر ارشد

• Semantic Web
• Knowledge Engineering
• Recommendation Systems
• +۳ مورد دیگر

Pasquale Lisena is a Postdoctoral Researcher at EURECOM's Data Science department, working in the Data2Knowledge group. His research focuses on Semantic Web technologies, Knowledge Engineering, and Recommender Systems, particularly in the context of Digital Humanities and cultural heritage. Education: PhD in Data Science (2019), EURECOM + Sorbonne Université, France MSc in Cinema and Media Engineering (2015), Politecnico di Torino, Italy Research Interests: Semantic Web & Knowledge Graphs Recommender Systems Digital Humanities Event Relation Extraction Projects: kFLOW (ANR JCJC project coordinator, 2021) Odeuropa ANTRACT DOREMUS Awards: Best Resource Paper Award (2022)

Igor Menezesمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Computer Vision
• Data Science
• +۶ مورد دیگر

Dr. Igor Menezes is a Senior Lecturer (Associate Professor) in Organizational Behavior and People Analytics at the Hull University Business School, part of the University of Hull. He holds dual professional qualifications as a psychometrician and Chartered Psychologist (CPsychol), along with the Graduate Statistician (GradStat) status from the Royal Statistical Society. His academic journey includes postdoctoral research at the University of Cambridge (2013–2014) and a research associate role at the Judge Business School (2016). Education: BSc MSc PhD/DPhil Research Interests: Dr. Menezes specializes in applying advanced quantitative methods to organizational psychology, AI, and computer vision. His work bridges academic theory with practical applications, focusing on: Psychometric model development Causal inference and data science People analytics for business optimization AI-driven solutions for human-centric challenges Awards & Fellowships: Research Productivity Fellowship (CNPq, Brazil, 2014–2016) British Psychological Society Associate Fellowship (AFBPsS) Advising & Grants: With 17+ years of research leadership, he has managed multiple grants and supervised numerous students. Projects include collaborations with industry partners in the UK and Latin America, focusing on workplace behavior assessment and organizational performance improvement. Key Contributions: He has developed validated instruments like the Multidimensional Turnover Intentions Scale and the Cognitive Distortions Questionnaire for Adolescents, emphasizing practical impact through evidence-based methodologies.