Find Academic Supervisors

• Computer Vision
• Multimedia Systems
• Machine Learning Applications
• +7 more

Timothy K. Shih is an active academic researcher with over 30 years of scholarly contributions, evidenced by his extensive publication record from 1991 through 2025. With more than 380 publications spanning numerous prestigious venues including IEEE Access, Multimedia Tools and Applications, and Lecture Notes in Computer Science, he maintains a robust research profile with consistent annual output (20+ papers in peak years). His work demonstrates leadership through frequent senior/corresponding author positions and collaborations with numerous researchers across international institutions. Dr. Shih's research interests encompass a diverse range of computer science disciplines with particular emphasis on Computer Vision , Human-Computer Interaction , and AI Applications . His work bridges theoretical advancements with practical implementations in educational technology, accessibility solutions, and multimedia systems. Recent publications reveal a strategic focus on applying deep learning techniques to solve real-world problems in sign language recognition, gesture analysis, and wireless sensing applications. Analysis of his publication trends over the past five years shows increasing specialization in multimodal AI systems, with significant contributions to sign language technology (including Arabic Sign Language recognition), WiFi-based human activity recognition, and music technology applications. His research demonstrates strong interdisciplinary connections between computer vision, machine learning, and human-centered computing, with practical applications spanning educational technology, accessibility solutions, and smart environments. Through his mentorship, Dr. Shih has guided numerous junior researchers who have become frequent collaborators, including Chih-Yang Lin, Hsin-Hung Cho, and Tipajin Thaipisutikul. His research program appears well-funded through consistent publication output across multiple project areas, suggesting successful grant acquisition in computer vision, AI, and educational technology domains. Current work indicates active involvement in cutting-edge research on diffusion models for audio processing, enhanced sign language recognition systems, and novel approaches to WiFi-based human interaction analysis.

Frederico FonsecaView profile

Associate Professor

• Ontology
• Geographic Information Systems
• Information Systems
• +12 more

Frederico Fonseca is an Associate Professor in the College of Information Sciences and Technology at Pennsylvania State University. His research focuses on the conceptualization and implementation of information in computer systems, with a particular emphasis on ontologies related to the environment and geographic information systems (GIS). He is affiliated with interdisciplinary research themes in Health and the Environment and contributes to foundational work in information science. His research interests span ontology, geographic information systems, knowledge sharing, big data, information integration, hermeneutics, and the philosophical foundations of information. He explores how meaning is constructed, shared, and represented in digital environments, especially through computational ontologies and cyber-human systems. His work bridges technical, social, and philosophical dimensions of information science. The recent publications highlight a strong trend in the intersection of information theory, environmental modeling, and human cognition. His articles explore semantic structures in data, the epistemology of big data, and the socio-technical aspects of digital systems. Key domains include GIS, ontology engineering, digital divides, and ethical design, reflecting a deeply interdisciplinary approach grounded in both technical rigor and philosophical inquiry. Scientific Awards: No scientific awards mentioned in the provided text. Frederico Fonseca has been involved in significant research projects related to spatial data infrastructures, knowledge sharing, and semantic systems. While specific grants are not listed, his collaborative publications suggest involvement in federally or institutionally funded initiatives, particularly in geoinformatics and data science. He has advised or collaborated with multiple researchers and students, though no formal advisees are named in the text. He is associated with research groups focusing on cyber-human systems, geographic information science, and semantic technologies. His work contributes to broader teams exploring open data, information ethics, and environmental informatics, particularly in the context of global challenges like forest monitoring and digital equity.

David J. FleetView profile

Professor

• Computer Vision
• Machine Learning
• Image Processing
• +7 more

David J. Fleet is a Professor in the Department of Computer Science and the Department of Computer and Mathematical Sciences at the University of Toronto. He serves as a CIFAR AI Chair and Faculty Member at the Vector Institute, with additional affiliations at the UofT Robotics Institute. PhD in Computer Science (1990), University of Toronto Research focuses on computer vision, deep learning, visual neuroscience, and computational biology Key contributions in motion analysis, stereopsis, 3D reconstruction, and diffusion models Research Interests: His work explores mathematical foundations for visual motion analysis, human pose estimation, and 3D structure determination in electron cryo-microscopy. Recent projects involve deep learning and diffusion models for video and image synthesis. Scientific Recognition: Recipient of the Koenderink Prize, CIFAR AI Chair, and multiple best paper awards at NeurIPS, CVPR, and BMVC. Former Chair of UTSC's Computer and Mathematical Sciences Department (2012–2017). Students and Collaborations: Mentored numerous PhD and MSc students, including Ali Punjani (co-founder of Structura Biotech) and Sara Sabour (Google Brain). Collaborates with institutions like Google DeepMind (2020–present) and PARC (1999–2003).

• Machine Learning
• Explainable Artificial Intelligence (XAI)
• Rule Extraction from Neural Networks
• +4 more

Guido Bologna is an Assistant Professor at the Haute école du paysage, d'ingénierie et d'architecture de Genève (HEPIA), within the School of Technique et IT and the Department of Informatique et systèmes de communication. His research focuses on explainable artificial intelligence (XAI), rule extraction from neural networks, and their applications in healthcare and computer vision. His work emphasizes transparent decision-making in AI systems, particularly for medical diagnostics (e.g., melanoma detection from images) and radiotherapy side-effect prediction. He co-leads the HES-XPLAIN platform, an open-source initiative to democratize XAI development. Key projects include PRE-ACT (predicting radiotherapy side effects) and Fidex/FidexGlo algorithms for explaining ensemble models and SVMs. Notable contributions include rule extraction from deep neural networks using techniques like DIMLP ensembles and DCT-based feature maps. His research bridges theoretical advances in AI with practical applications, such as sensory substitution devices for visually impaired individuals and gait analysis for elderly mobility aids. Bologna collaborates internationally on interdisciplinary projects, spanning healthcare informatics, robotics, and neuroscience. His work addresses challenges in algorithmic transparency, fairness, and privacy in AI systems, with a focus on real-world deployable solutions.

• Artificial Intelligence
• Computer Vision
• 3D Modeling
• +3 more

Liangyan Gui is a Research Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Department of Computer Science within the College of Engineering. His research focuses on cutting-edge topics in Artificial Intelligence, particularly in computer vision, 3D modeling, human motion prediction, and robotics. He teaches courses such as CS 446 (Machine Learning) and CS 598 GUI (Efficient & Predictive Vision). Gui's research interests span 3D scene understanding, human-object interaction, and generative models. He explores AI-driven solutions for tasks like motion editing, physics-based simulation, and multimodal reasoning. His work bridges computer vision and robotics, with applications in 3D reconstruction, animation, and AI for dynamic environments. Recent publications highlight advancements in 3D photo editing, human-object interaction generation, and self-supervised learning. His research often integrates textual inputs with visual data to enhance AI systems' capabilities in reasoning and generation. Gui has been involved in initiatives such as the AICE Center grants (as part of a team), though specific grant details are not explicitly attributed to him in the provided text. His contributions extend to foundational AI models and their practical implementations in real-world scenarios.

Liu YangView profile

Professor

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +3 more

Prof. Liu Yang is a full Professor and University Leadership Forum Chair at the School of Computer Science and Engineering, Nanyang Technological University (NTU). He holds the President’s Chair in Computer Science and Engineering and serves as Executive Director of the Cyber Research Programme Office and Cyber Security Research Centre @ NTU. His research focuses on Cybersecurity, Software Engineering, and Artificial Intelligence, with contributions in formal verification, program analysis, and deep learning systems. He leads projects like the Securify initiative and develops tools such as the Process Analysis Toolkit (PAT) for system verification. Education: Ph.D. (2010) and Bachelor of Computing Science (2005) from National University of Singapore. Research Interests: Cybersecurity (malware analysis, vulnerability detection), Software Engineering (formal verification, program analysis), AI (security, robustness, explainability), and Automotive Security. His work bridges formal methods, program analysis, and machine learning to address challenges in secure systems and autonomous systems. Notable Achievements: Over 6000 citations (2020), H-index 38, awards include Nanyang Assistant Professor (2013), NRF Investigatorship (2020), and ACM Distinguished Speaker. His research spans journals like IEEE Transactions on Software Engineering and conferences such as NeurIPS, CVPR, and ICSE. Grants and Leadership: Principal Investigator of grants totaling over 5M SGD, including projects on vulnerability detection, formal verification, and cybersecurity. Organizes international conferences like ASE, ICSE, and FM, and serves on editorial boards such as IEEE Access. Labs/Teams: Leads the Cyber Security Research Centre @ NTU and collaborates on projects like the HP-NTU Digital Manufacturing Corp Lab and the National Satellite of Excellence in Singapore.

Sheng WangView profile

Assistant Professor

• Generative AI
• Biomedical Imaging
• Chromatin Structure Prediction
• +5 more

Sheng Wang is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington Seattle. His work bridges Artificial Intelligence and Biomedical Applications through foundation models, generative AI, and large-scale biomedical data analysis. He holds a B.S. and Ph.D. in Computer Science from Peking University and University of Illinois at Urbana-Champaign, followed by postdoctoral research at Stanford School of Medicine. Research Interests: Developing generative AI for biomedical imaging (e.g., 40,000x40,000 pixel digital pathology via GigaPath ) Chromatin structure prediction using neural radiance fields ( CryoNeRF ) Genomics-based drug discovery through multi-modal data augmentation ( Pisces ) Multi-omics analysis with foundation models Collaborations: His work is deployed at institutions like Mayo Clinic , Chan Zuckerberg Biohub , and Providence Genomics . Current projects are supported by R01 and R21 grants for biomedical imaging and chromatin modeling. Teaching: Spring: AI for Drug Discovery Fall: AI for Medicine

Doina PrecupView profile

Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +3 more

Doina Precup is a Canada-CIFAR AI Chair and Professor at the School of Computer Science, McGill University, and holds a Research Director role at DeepMind. She co-founded the AI4Good Lab and Eastern European Machine Learning Summer School, focusing on improving diversity and access in AI. Her research emphasizes reinforcement learning, temporal abstraction, and applications in medicine and automated control. Her work includes foundational contributions like the 'options framework' for temporal abstraction in reinforcement learning (Classic Paper Award, 2019) and the option-critic architecture (AAAI Outstanding Student Paper). She is a Fellow of the Royal Society of Canada (2022), CIFAR Senior Fellow (2017), and recipient of the Creative Destruction Lab's Ideas Award (2017). She has served as Associate Dean of Research in McGill's Faculty of Science (2015–2017), Associate Scientific Director of Healthy Brains for Healthy Lives, and leader of UNIQUE's Learning Research Axis. Her leadership roles include chairing the NSERC Discovery Grant committee and serving on CSCan-InfoCan's executive committee.

Shengyao ZhuangView profile

Lecturer

• Information Retrieval
• Neural Networks
• Machine Learning
• +3 more

Dr. Shengyao Zhuang is an Adjunct Lecturer at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on advancing information retrieval systems, particularly leveraging neural networks, large language models (LLMs), and adversarial machine learning to enhance robustness and efficiency. He specializes in dense retrievers, query processing with typos, and federated search frameworks. His recent work explores vulnerabilities in retrieval systems, cross-modal applications, and optimization of retrieval models through techniques like Matryoshka training and reinforcement learning. Dr. Zhuang is affiliated with the IELAB research group, evident in collaborative TREC track submissions. His publications span conferences such as SIGIR and ACM venues, addressing challenges in zero-shot search, pseudo relevance feedback, and multimodal document processing. Notable contributions include developing the Tevatron toolkit and investigating the impact of adversarial attacks (e.g., pixel poisoning) on retrieval systems. He has also explored environmental considerations of IR models, emphasizing sustainable computing practices.

• Cybersecurity
• Machine Learning
• Internet-of-Things (IoT)
• +5 more

Sejun Song, Ph.D., is a Professor at the School of Computer & Cyber Sciences (SCCS) at Augusta University, Georgia. He has held prior roles including Assistant Professor at Texas A&M University and founding director of the Cisco Test Engineering Center (Cisco-TEC). His research focuses on Cybersecurity, Machine Learning, IoT, and Mobile/Wireless Communications for applications in smart cities, eHealth, transportation, and agriculture. Song holds a Ph.D. in Computer Science and Engineering from the University of Minnesota (2001) and has industry experience at Cisco Systems and Honeywell Research Lab. His work spans funded projects by NSF, DoD, NASA, NIH, and others. Notable contributions include AI-driven cybersecurity solutions, crowd dynamics modeling, and edge computing frameworks for surveillance. Awards include the NASA Summer Fellowship and multiple best paper/video awards at conferences like CCNC and Mobisys. His interdisciplinary projects include smart infrastructure development and technology education for underserved populations. Research Highlights: Deepfake detection using corneal reflections (READFake, MobiDeep) Resilient network systems for IoT and space communications Energy-efficient semantic communication protocols Funding Agencies: NSF, DoD, ONR, AFOSR, NASA, NIH, CDC Awards reflect multiple domains: teaching excellence (Center for Teaching Excellence Scholar), research innovation (Air Force Visiting Fellowship), and applied results (best video/paper awards). Key labs/teams involve Cisco-TEC and collaborations on edge computing, federated networking, and smart city infrastructure. Current research emphasizes trustworthy AI systems, green communication, and pandemic-era crowd safety analytics.

• French Language Practice
• Romance Studies
• Computer Vision
• +3 more

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.

Chris WeaverView profile

Associate Professor

• Information visualization
• Visual analytics
• Human-computer interaction
• +3 more

Chris Weaver is an Associate Professor in the School of Computer Science at the University of Oklahoma. He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison, an M.S. in Computer Science from the same institution, and a B.S. in Chemistry and Mathematics from Michigan State University. His research focuses on information visualization, visual analytics, and human-computer interaction, with applications in digital humanities and geospatial analysis. He is the principal developer of Improvise , a toolkit for building interactive visualizations of multidimensional data. Roles: Associate Professor, Associate Director of the Center for Spatial Analysis Key Projects: NSF CAREER Award-funded gesture-based visualization research, Digital Latin Library collaboration Research Interests: Interactive visualization architectures Visual query languages Applications in digital humanities and geovisual analytics Awards: NSF CAREER Award (2014) Kavli Frontiers of Science Fellow (2015) IEEE Visualization Service Award (2013) Advising & Grants: Advised 10+ Master’s and Ph.D. students Secured $2M+ in grants including NSF and Mellon Foundation funding

• Signal Processing
• Speech Processing
• Audio Processing
• +3 more

Dr. Turgay KOÇ is a Lecturer at the Department of Electrical and Electronics Engineering, affiliated with the Faculty of Engineering and Natural Sciences at Middle East Technical University. He holds a doctoral degree in Electrical-Electronic Engineering from the same institution (2012) and a bachelor's degree from Nigde University (2001). His research focuses on Signal Processing, Speech Processing, and applications of AI in healthcare, including lung disease detection and vocal fold analysis. He has contributed to advancements in CNN-based denoising, medical image segmentation with U-Net architectures, and forensic audio analysis. His work spans over 15 years, with notable publications on nonlinear source-filter models and glottal inverse filtering techniques. Dr. KOÇ's research interests also encompass audio, image, and video processing techniques, particularly in biomedical contexts. His recent work (2020–2025) emphasizes AI-driven solutions for post-COVID-19 lung disease detection and spoofing attack identification. He has explored hybrid modeling approaches for signal denoising and developed algorithms for vocal fold segmentation in high-speed video imagery. His contributions bridge theoretical signal processing with practical medical and cybersecurity applications. No scientific awards or grants are explicitly mentioned in the provided texts. He has advised no listed students, and lab affiliations remain unspecified.

Alexandra Branzan AlbuView profile

Professor

• Computer Vision
• Pattern Recognition
• Machine Learning
• +6 more

Dr. Alexandra Branzan Albu is a Professor in the Department of Electrical and Computer Engineering at the University of Victoria. She holds additional roles as Program Director for the Electrical Engineering program and Chair of the Academic Women Caucus. Her research focuses on computer vision applications in environmental monitoring, medical imaging, and document analysis. She earned her BSc, MSc, and PhD from the University of Bucharest and is a Professional Engineer (PEng). Education: BSc in Electrical Engineering MSc in Electrical Engineering PhD in Electrical Engineering Research Interests: Computer vision for underwater environmental monitoring (collaborations with ASL Environmental Sciences, Ocean Networks Canada) Medical imaging analysis and bioprosthetic heart valve durability assessment Document image retrieval and segmentation (e.g., mixed-media documents, business forms) Deep learning architectures for low-light image enhancement and anomaly detection Teaching Approach: Emphasizes problem-based learning and experiential education through hands-on projects. Courses include Computer Vision (ECE471/536) and Medical Image Processing (ECE435/BME403). Published pedagogical research explores societal impacts of technology and critical thinking development in STEM education. Labs & Collaborations: Active partnerships with industry (Cascadia Seaweed, VIVITRO Labs, SAP Canada) Leadership in underwater image analysis projects using Ocean Networks Canada's observatory data Development of Imageria outreach initiatives to increase women's participation in engineering

Fredrik WahlbergView profile

Assistant Professor

• Computational Linguistics
• Historical Document Analysis
• Handwriting Recognition
• +3 more

Fredrik Wahlberg serves as Assistant Professor in Computational Linguistics at Uppsala University's Department of Linguistics and Philology, where he develops computational methods for historical manuscript analysis. His work bridges computer science and humanities through machine learning applications in paleography and digital archives. His research encompasses: Computational Linguistics for historical texts Handwriting recognition and word spotting in medieval manuscripts Deep learning for scribal attribution and dating Neural networks for archival image restoration AI-driven metadata enrichment in cultural heritage Analysis of his 2011-2022 publications reveals consistent innovation in combining image processing and language modeling for large-scale manuscript analysis. Key trends include advancing convolutional neural networks for dating accuracy, developing quill-curvature features for writer identification, and extending into critical AI applications for gender-inclusive historical collections. His methodologies significantly enhance accessibility of pre-modern documents through computational approaches.