Find Academic Supervisors

Julie ThorpeView profile

Professor

• IT Security
• Authentication
• Biometrics
• +5 more

Dr. Julie Thorpe is a Professor in the Faculty of Business and Information Technology at Ontario Tech University. She holds a PhD in Computer Science from Carleton University (2008) and a Bachelor of Computer Science (First-Class Honours) from Dalhousie University. Her research focuses on IT security, particularly human factors in authentication systems, biometrics, and password security. Prior to joining Ontario Tech in 2010, she gained eight years of industry experience and taught part-time at Carleton University. She has served on program committees for conferences like ACM CCS and USENIX Security. Her work has been featured in prominent media outlets such as CBS News, Wired, and the New York Times. Education: PhD in Computer Science, Carleton University (2008) Bachelor of Computer Science (First-Class Honours), Dalhousie University Research Interests: Authentication mechanisms and graphical passwords Human factors influencing computer security practices Operating system and software security policies Biometric authentication systems Awards: Senate Medal in Computer Science (2008) Advising & Grants: No explicit student advisees listed No grants mentioned in provided texts Labs/Teams: Not explicitly affiliated with any research group listed in the provided materials.

Deepti GhadiyaramView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Safety of AI Systems
• +3 more

Deepti Ghadiyaram is an Assistant Professor in the Department of Computer Science at Boston University and concurrently serves as a Member of Technical Staff at Runway. She holds a Ph.D. from the University of Texas at Austin (2017), where her research focused on perceptual image and video quality assessment under Prof. Alan Bovik. Previously, she spent 5+ years at Meta AI Research advancing image/video understanding models and later joined Runway before joining BU. Her work emphasizes safety, interpretability, and robustness in computer vision systems. Education: Ph.D. in Computer Science (UT Austin, 2017); prior roles include Senior Researcher at Meta AI and technical contributions at Runway. She was named a 2024 Computing & Data Sciences (CDS) Faculty Fellow. Research Interests: Computer Vision fundamentals and applications Machine Learning model robustness Explainable AI (XAI) methodologies Fairness and bias mitigation in CV systems Large-scale dataset development for benchmarking Professional Service: Served as Program Chair for NeurIPS 2022 Dataset & Benchmarks Track and AIMLSystems 2023. Organized workshops on Responsible Computer Vision and Explainable AI at ECCV and CVPR. Area Chair for CVPR, WACV, and AAAI. Grants & Advising: Mentors a diverse cohort of PhD, MS, and undergraduate researchers. Active in advising through BU’s Computer Science program.

• Signal Processing
• Video Processing
• Bayesian Inference
• +5 more

Professor Anil Kokaram is a distinguished academic and researcher in Electronic Engineering at Trinity College Dublin (TCD), Ireland. He holds a PhD in Signal Processing from the University of Cambridge (1993). As a Fellow of Engineers Ireland and recipient of an Academy Award (Oscar) for his work in video processing, he is renowned for contributions to digital video restoration, multimedia forensics, and video compression. From 2011–2017, he led the Media Algorithms Team at YouTube/Google, advancing cloud-based video transcoding and enhancement technologies. His research bridges academia and industry, with innovations in Bayesian inference, motion estimation, and neural network applications for video processing. **Education**: PhD in Signal Processing, University of Cambridge (1993). **Key Roles**: Former Associate Editor of IEEE Transactions on Video Technology and Image Processing. Founded GreenParrotPictures (acquired by Google), producing video enhancement software. **Research Focus**: Video compression artifacts, perceptual quality metrics (e.g., ViSQOL), and adaptive streaming algorithms. **Notable Projects**: Developed frameworks for automated sports broadcasting, noise reduction in medical imaging, and synchronization of user-generated videos. **Awards**: 2007 Science & Engineering Academy Award (Oscar), 2007 Fellow of Engineers Ireland. **Labs/Teams**: Leads the Signal Media Algorithms group at TCD, collaborating with industry partners like Google on large-scale video analysis systems. His work emphasizes practical applications of signal processing in creative industries, including film post-production and virtual production.

Jiying ZhaoView profile

Professor

• Image Processing
• Video Processing
• Multimedia Communications
• +3 more

Jiying Zhao is a Professor at the School of Electrical Engineering and Computer Science (EECS) at the University of Ottawa. He holds a Ph.D. in Engineering from Keio University (Japan) and multiple engineering degrees from North China Electric Power University (China). His academic career includes roles as an instructor and associate professor at Thompson Rivers University (Canada) before joining the University of Ottawa in 2001, where he advanced to full professor in 2009. Dr. Zhao's research focuses on image/video processing , multimedia communications , and digital watermarking . Notable contributions include advancements in video super-resolution techniques, perceptual quality assessment of stereoscopic images, and robust watermarking algorithms resistant to geometric attacks and compression. His professional affiliations include IEEE, IEICE, and membership in Professional Engineers Ontario. While no specific awards or grants are listed, his extensive publication record demonstrates significant contributions to multimedia and signal processing fields. Advising and student mentorship details are not provided in the available text. Research interests are centered around 3D imaging , haptic communication , and content security , with applications in telepresence systems and video authentication. He maintains an active research website linked to the School of EECS.

Yu ZhangView profile

Lecturer

• Mobile Computing
• Wireless Sensor Networks
• Embedded Systems
• +5 more

Yu Zhang is a Lecturer in Computing (IoT/Networking) at the School of Computing, Macquarie University, Australia. He holds a PhD in Computer Science from RMIT University, an M.Eng in Distributed Computing from the University of Melbourne, and a B.Eng in Software Engineering from Southwest University for Nationalities. His research focuses on mobile computing, wireless sensor networks, embedded systems, on-device machine learning, and IoT, with emphasis on security, edge computing, and privacy preservation. He is affiliated with the Future Communications Research Centre and Smart Green Cities Research Centre. Dr. Zhang’s work integrates theoretical advancements with practical applications in IoT and edge computing. His research outputs span secure device communication, energy-efficient networking protocols, and anomaly detection frameworks. Collaborations include projects like the CSIRO Data61 PhD Scholarship. He actively contributes to IEEE and ACM, addressing challenges in privacy-aware systems and federated learning.

Ramesh KarriView profile

Professor

• Hardware Cybersecurity
• Trustworthy Integrated Circuits
• Cyber-Physical Systems Security
• +6 more

Ramesh Karri is a Professor and Chair of the Electrical and Computer Engineering Department at New York University Tandon School of Engineering. He co-founded the NYU Center for Cyber Security (CCS) in 2009 and co-directed it from 2016-2024. He is also a Fellow of the IEEE and has led initiatives such as the Embedded Systems Challenge (ESC), a red-blue team cybersecurity competition. Education: Ph.D. in Computer Science and Engineering, University of California, San Diego (1993) B.E. in Electrical and Computer Engineering, Andhra University (1985) Research Focus: His work centers on hardware cybersecurity, including trustworthy integrated circuits, cyber-physical systems, nano-enabled security, and biochip security. He pioneered security-aware CAD tools and has developed metrics for hardware trojan detection through initiatives like the Trust-Hub. Awards & Leadership: Recipient of the Humboldt Fellowship and NSF CAREER Award Editor-in-Chief of ACM Journal of Emerging Computing Technologies Leadership roles at IEEE conferences (ICCD, HOST, DFTS) Grants & Labs: Directs the Center for Advanced Technology in Telecommunications (CATT) and NYU CCS. His labs focus on additive manufacturing security, digital microfluidic systems, and AI-driven hardware design.

Daniel MoreiraView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Media Forensics
• +3 more

Dr. Daniel Moreira is an Assistant Professor of Computer Science at Loyola University Chicago, affiliated with the College of Arts & Sciences. His research focuses on applying techniques from Computer Vision, Machine Learning, and Media Forensics to enhance scientific integrity and combat misinformation. He holds a PhD from the University of Campinas (Brazil), with postdoctoral experience at the University of Notre Dame's CVRL lab. Education: PhD in Computer Science (2016), University of Campinas MSc in Computer Science (2008), Federal University of Pernambuco BSc in Computer Science (2006), Federal University of Pará Research Interests: Media Forensics: Developing tools to detect synthetic content and paper mill fraud Biometrics: Post-mortem iris recognition and human-machine collaboration Scientific Integrity: Image provenance analysis for combating academic misconduct Computer Vision: Provenance tracking and manipulation localization Awards include the 2025 St. Ignatius Loyola Teaching Award and the 2017 Best Brazilian PhD Dissertation. He co-leads projects like Sci-Int (scientific integrity analysis) and SemaFor (semantic forensic analysis), with funding from NSF, DARPA, and HHS. Teaching includes courses on Machine Learning, Biometrics, and shell scripting at Loyola. He actively reviews for major journals/conferences like IEEE TIFS, CVPR, and ECCV, and serves as an Associate Editor for Pattern Recognition and IEEE Security & Privacy.

Joe MacInnesView profile

Senior Lecturer

• Machine Learning
• Attention Mechanisms
• Eye Tracking
• +4 more

Dr. Joe MacInnes is a Senior Lecturer in Computer Science at Swansea University's Faculty of Science and Engineering. His interdisciplinary research focuses on visual attention mechanisms through machine learning models that simulate human visual processing. He utilizes high-speed eye tracking and complex visual tasks (foraging, search, memorization) to understand attention allocation, with applications in both cognitive science and AI. His expertise spans machine learning, eye tracking, computational cognitive modeling, and AI. Dr. MacInnes has received multiple awards including the Best Paper Award at AppLens 2018. His research advances understanding of human attention to inform machine attention systems. Current work involves developing experimental paradigms using VR environments to study attention in dynamic scenarios. Dr. MacInnes collaborates with neuroscience labs and industry partners to translate research into practical AI implementations.

Thirimachos BourlaiView profile

Professor

• Multispectral Imaging
• Biometrics
• Facial Recognition
• +7 more

Thirimachos Bourlai is a Professor in the School of Electrical and Computer Engineering at the University of Georgia. He concurrently holds adjunct roles at West Virginia University (Lane Department of Computer Science and Engineering and School of Medicine, Department of Ophthalmology) and the Savannah River National Labs. As founder and director of the Multi-Spectral Imagery Lab, his research focuses on biometric systems, multispectral imaging, and human authentication in challenging conditions. He has published extensively and serves on editorial boards and professional organizations like the IEEE Biometrics Council. His academic journey includes a Ph.D. from the University of Surrey’s CVSSP Group and postdoctoral work at Methodist Hospital/University of Houston (thermal imaging) and WVU. He has held various academic ranks, including Associate Professor at UGA (2020–2024) and WVU (2017–2020). His research spans facial recognition across spectral bands (UV, visible, IR), liveness detection, and forensic algorithms for law enforcement. Key contributions include datasets like MycorrhiSEE and systems like FaceSensor-ID for sensor origin identification. Awards include the Distinguished Faculty Fellow title and Student Career Success Influencer Award from UGA. His work bridges academia and industry, addressing real-world security challenges through AI and computer vision. Bourlai has presented globally and reviews for top journals like TPAMI and IEEE Transactions. Recent articles emphasize thermal imaging, GAN-based image synthesis, and cross-spectral authentication. His labs collaborate with national labs and agencies, addressing issues like occlusion-resistant face recognition and concealed object detection in surveillance systems.

Nesma HOUMANIView profile

Lecturer

• Biomedical Engineering
• Signal Processing
• Neuroscience
• +5 more

Nesma HOUMANI is a Lecturer at Telecom SudParis, part of the SAMOVAR research team. Her work focuses on biomedical signal processing, neuroimaging, and biometric authentication systems. She has contributed to Alzheimer’s disease detection via EEG analysis, gait quality measurement for neurological disorders, and neurofeedback applications for cognitive enhancement. Education: PhD in Biomedical Signal Processing (2011) from National Institute of Telecommunications (Institut National de Télécommunications) Research Interests: EEG-based diagnostics for neurological disorders Biometric systems (online signatures, gait analysis) Neurofeedback and neuromodulation Machine learning for medical signal analysis Recent Article Trends: Her recent work (2020-2025) emphasizes clinical applications of signal processing in Alzheimer’s detection, gait deviation measurement for hemiparesis patients, and neurofeedback for cognitive reserve enhancement in the elderly. Collaborations span interdisciplinary teams in neuroscience, engineering, and medicine. Patents: "Method for generating information about the production of a handwritten, hand-affixed or printed trace" (US 11989979B2, 2024) "Identity verification method using handwritten signatures on digital sensors" (EP3942442, 2022) Grants & Labs: Active in SAMOVAR’s projects on biomedical signal analysis and neurotechnology. Involved in multidisciplinary initiatives like the BV2 wellbeing project for chronic disease patients.

Dijana PetrovskaView profile

Lecturer

• Biometrics
• Speech Processing
• Computer Vision
• +3 more

Dijana Petrovska is a Lecturer at Telecom SudParis, part of the Université Paris-Saclay. Her work focuses on biometric systems, medical diagnostics, and privacy-preserving technologies. She has contributed to projects like the EMPATHIC Virtual Coach, aimed at improving elderly health through AI-driven virtual assistants. Her research interests include facial recognition, speech processing, and cryptographic key regeneration using biometrics. Notably, she has pioneered methodologies for early-stage Parkinson's disease detection via facial action units and voice analysis. She also explores secure authentication systems combining biometrics with cryptography, emphasizing privacy preservation. Publications span over two decades, with recent work emphasizing cross-disciplinary applications of AI in healthcare and security. While no specific academic awards are listed, her impactful contributions to the field of biometric and medical AI are evident through her prolific publication record and active project leadership. Her involvement in collaborative projects like SpeechXRays and EMPATHIC demonstrates a commitment to bridging technical innovation with real-world societal needs. Though no student advisees are listed, her work likely impacts both academic and industry research ecosystems.

• Education
• Technology-Enhanced Learning
• Pedagogy
• +7 more

Sophie Meace-Williams is a Senior Lecturer in the Faculty of Life Sciences and Education at Cardiff Metropolitan University, where she contributes to the Pedagogy Research and Innovation Group. Her work focuses on integrating technology into educational practices, particularly in secondary and post-compulsory settings. PhD in Conceptualising Secondary School Teachers' Pedagogic Use of Technology: A Welsh Case Study (Cardiff Metropolitan University, 2024) PGCE in Post-Compulsory Education and Training (Cardiff University, 2016) BSc (Hons) in Computer Studies (2015) Her research interests lie at the intersection of education and technology, emphasizing teacher development, student engagement, cybersecurity awareness, and informal learning through digital platforms. She investigates how technology can foster authentic, community-based, and independent learning environments. The recent trend in her publications demonstrates a strong focus on digital technology integration in education, especially in teacher confidence, cross-national cybersecurity education, and the use of social media for student engagement. Her work combines empirical case studies with comparative and collaborative educational models. While no formal scientific awards are listed, her research has garnered attention in academic and public spheres, with presentations at international conferences and readership on platforms like Mendeley. Sophie actively contributes to academic discourse through oral presentations and collaborative projects. She has co-presented on empowering students through authentic learning and enhancing policy through graduate collaboration. Her research is supported by active engagement in scholarly activities and knowledge exchange. She is part of the Pedagogy Research and Innovation Group, which drives forward-thinking educational practices through research on technology-enhanced learning, policy development, and pedagogical innovation.

Shuo NiuView profile

Assistant Professor

• Human-Computer Interaction
• Social Computing
• Video-Sharing Platforms
• +4 more

Dr. Shuo Niu is an Assistant Professor in Computer Science at Clark University, specializing in human-computer interaction and social computing. His research focuses on analyzing collaborative and community activities on video-sharing platforms like YouTube and TikTok, with particular interest in socio-technical infrastructure, mental health implications, and AI algorithm impacts. Ph.D. in Computer Science, Virginia Tech (2019) B.S. and M.E. in Computer Science/Digital Media, Shandong University (2012-2013) His research employs machine learning, natural language processing, and crowdsourcing to analyze vast video datasets, investigating creator-fan dynamics, misinformation patterns, and platform design ethics. Recent work includes studies on substance abuse misinformation, disability disclosure, and generative AI in content creation. Dr. Niu has published at top-tier conferences including ACM CHI, CSCW, and GROUP. Current projects examine racism/racial movements, mental health support mechanisms, and socio-technical aspects of video-sharing platforms. He received multiple grants including the NEBHE North Star Fellowship and Clark University Academic Innovation Fund. Faculty Development Award, Clark University (2025) Professor David A. Stevens Fund (2025) LEEP Student Fellows Grant (2022) Academic Innovation Fund (2022) NEBHE North Star Fellowship (2022) He teaches courses in web development, mobile software development, data science, and computing fundamentals, emphasizing hands-on learning and real-world applications. Dr. Niu previously worked at Virginia Tech's Center for Human-Computer Interaction and has advised on multiple research projects.

• Creative Industry
• Digital Economics
• Misinformation
• +14 more

Shinichi Yamaguchi is an Associate Professor at the International University of Japan . His research focuses on creative industries , digital economics , and misinformation in Japan, with a strong emphasis on technology acceptance , consumer behavior , and policy analysis . He has collaborated extensively with researchers from Keio University , Kanda University of International Studies , and The University of Kitakyusyu . Research Trends : Yamaguchi's work spans digital goods , online misinformation , and transportation economics . His empirical studies use conjoint analysis , instrumental variables , and big data to explore topics like mobile gaming wellbeing , vaccine hesitancy , and Northern Sea Route risks . Collaborations : He frequently works with Hidetaka Oshima (International University of Japan), Tomoaki Watanabe (Keio University), and Tatsuo Tanaka (Keio University) on cross-disciplinary projects.

Maria PapaioannouView profile

Researcher

• Cybersecurity
• Machine Learning
• Internet of Things
• +3 more

Maria Papaioannou is a Postdoctoral Researcher at the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark, specializing in cybersecurity engineering with contributions to UN Sustainable Development Goals through secure technology development. Her research focuses on Machine Learning applications for Internet of Things security, including intrusion detection systems, honeypot optimization, and user-centric authentication. She investigates technical implementations of tiny machine learning for resource-constrained devices while addressing human factors in security adoption, particularly for emerging technologies like passkeys. Recent publications reveal strong thematic convergence across machine learning-driven security solutions for IoT ecosystems, emphasizing both algorithmic innovation and usability considerations. Her review articles systematically analyze research gaps in intrusion detection scalability, passkey adoption barriers, and adaptive deception technologies, highlighting interdisciplinary connections between cybersecurity, human factors, and embedded systems engineering.