Find Academic Supervisors

Sandra FernandoView profile

Associate Professor

• Assistive Technologies
• Big Data Analytics
• AI and Machine Learning
• +3 more

Dr. Sandra Fernando is an Associate Professor at the School of Computing and Digital Media at London Metropolitan University, leading the Assistive Technology Research Group. She holds a PhD in Computer Science and is a Senior Fellow of the Higher Education Academy. With over 20 years of academic and industry experience, she specializes in assistive technologies for disabled individuals, particularly blind and visually impaired users. She has pioneered computer-aided drawing tools for blind learners and contributed to national research initiatives through her leadership in the Empowering London Research Centre. Her academic leadership roles include managing international partnerships, leading the Data Science BSc program, and serving as the university's KTP Champion and ECR Lead. She has secured grants from the MACS charity and other organizations, producing research recognized as internationally excellent in the REF 2021 submission. Her work spans interdisciplinary collaborations with councils like Islington and Haringey, focusing on disability employment barriers and public health trends. Research Interests: Dr. Fernando's work focuses on assistive technologies, big data analytics, and AI-driven solutions for accessibility. She explores innovative interfaces for disabled users, including haptic feedback systems and smart glove-based sign language translation. Her recent projects analyze healthcare data (e.g., vitamin D prescriptions) and employ machine learning for age recognition and breast cancer detection. Awards & Grants: Her contributions include MACS-funded research, internal grants for NHS data intelligence, and a Springer publication on cloud-based prescription analysis. She serves as an Associate Editor for Assistive Technology and reviews for journals like Computational Intelligence . Teaching & Mentorship: She teaches Data and Web Development modules and mentors students through her role as a Vocational Subject Specialist. She actively supervises PhD candidates and champions inclusive education policies at the university level. Outreach: As part of the 'Giving back to the city' initiative, she collaborates with London boroughs to address disability-related employment gaps and healthcare disparities. Her research not only advances technical solutions but also informs urban policy decisions.

• Artificial Intelligence
• Pattern Recognition
• Biometrics
• +5 more

Venu Govindaraju is a SUNY Distinguished Professor and Vice President for Research and Economic Development at the University at Buffalo. He holds a joint appointment in the Department of Computer Science and Engineering within the School of Engineering and Applied Sciences. His research focuses on artificial intelligence, pattern recognition, and biometrics, with notable contributions to handwriting recognition, cybersecurity, and educational technology. Govindaraju leads the National AI Institute for Exceptional Education (funded by a $20M NSF grant), developing AI tools to screen children for dyslexia and dysgraphia. He also founded the Center for Unified Biometrics and Sensors (CUBS) and co-leads the Institute for Artificial Intelligence and Data Science (IAD) at UB. His work has revolutionized postal automation, digital libraries, and cybersecurity through CAPTCHA innovations. He has advised 47 doctoral students and holds key leadership roles in SUNY's AI initiatives and Empire AI, a $400M state consortium. Research Interests: AI for social good, handwriting analysis, biometric systems, educational AI applications, and cross-modal data fusion. His work bridges technical innovation with societal impact, addressing challenges in healthcare, education, and security. Awards: IEEE Technical Achievement Award (2010), ACM Fellow (2009), IEEE Fellow (2006), National Academy of Inventors (2015), SUNY's Person of the Year (2024). Labs/Initiatives: CUBS, IAD, National AI Institute for Exceptional Education, Empire AI. Grants: $20M NSF grant (2023), USPS-funded handwriting recognition projects, Lockheed Martin licensing agreements.

• Neuroimaging
• Brain-Computer Interfaces
• Cognitive Neuroscience
• +5 more

Aaron Newman is a Professor and Chair in the Department of Psychology and Neuroscience at Dalhousie University, with affiliations in Psychiatry, Surgery, and Pediatrics. He holds cross-departmental roles bridging cognitive neuroscience, neuroimaging, and translational research. Newman earned a BA (Honors) from the University of Winnipeg, followed by an MSc and PhD from the University of Oregon, and completed a Postdoctoral Fellowship at the University of Rochester. His research focuses on neuroimaging techniques (EEG, fMRI, fNIRS) to study relationships between brain activity and language processing. He directs the NeuroCognitive Imaging Lab (NCIL), advancing brain-computer interface technology and investigating applied contexts like second language learning, reading development, and assistive devices. Collaborations include Dr. Colin Conrad in the School of Information Management, exploring Neuro-Information Systems and trust in AI. Newman emphasizes interdisciplinary training, co-authoring textbooks Research Methods in Cognitive Neuroscience and Neural Data Science in Python . He also promotes creative problem-solving through the SURGE Innovation Sandbox, integrating entrepreneurship and design thinking into academic research. The NCIL lab supports trainees across undergraduate to postdoctoral levels, emphasizing both scientific rigor and professional skills for academic and industry careers.

Yu WangView profile

Professor

• Wireless Networks
• Smart Sensing
• Distributed Computing
• +6 more

Yu Wang is a Professor and Chair in the Department of Computer and Information Sciences at Temple University, leading the Wireless Networking and Sensing Lab (Wang Lab). His research focuses on wireless networks, smart sensing, distributed computing, and security/privacy, with notable contributions to federated learning, quantum networks, and edge computing. He has received prestigious awards including the IEEE Benjamin Franklin Key Award and Fellow of IEEE. Currently advising PhD students Jiyao Liu and Xuanzhang Liu, he has mentored 11 graduated PhD students now in academia and industry. Education: PhD from Illinois Institute of Technology (2004). Key roles include Editor for IEEE Transactions, organizing major conferences (e.g., IEEE IPCCC, IEEE MASS), and serving on NSF review panels. Funded by NSF, DoT, Microsoft, and others. His lab's research integrates emerging technologies to advance human-device interactions in smart environments. Recent research trends emphasize quantum-assisted computing, federated learning optimization, and wearable health monitoring. Over 300 publications span networking journals/conferences like IEEE/ACM Transactions and INFOCOM. Awards highlight contributions to vehicular networks and data placement strategies.

Ronald PoppeView profile

Associate Professor

• Social and Affective Computing
• Computer Vision
• Human-Computer Interaction
• +5 more

Dr. Ronald Poppe is an Associate Professor at Utrecht University's Faculty of Science within the Department of Social and Affective Computing. His research focuses on behavioral analysis, computer vision, human-computer interaction, and non-verbal behavior. Key themes include applied data science, dynamics of youth, game research, and human-centered AI. Recent work emphasizes automated analysis of parent-child interactions, gaze dynamics, and multimodal interaction modeling. He has received a 2023 fellowship for studying early life stress and parent-child interaction. His research also explores deception detection, egocentric vision, and gesture recognition, leveraging deep learning and transformer architectures. Poppe collaborates on projects like the 'Interactive Tag Playground' and contributes to datasets such as the Corpus of Social Touch (CoST). His work bridges computational methods with psychological and social science applications. Research Themes: Applied Data Science, Dynamics of Youth, Game Research, Human-Centered AI Expertise: Behavioral Analysis, Computer Vision, Pattern Recognition Publications span action understanding, multimodal interaction analysis, and social robotics. His work emphasizes translating computational insights into real-world applications for child development, human-robot interaction, and forensic analysis.

Loup MeuriceView profile

Researcher

• Database Systems
• Software Engineering
• Schema Evolution
• +5 more

Loup Meurice is a Computer Science researcher at the University of Namur specializing in database systems and human-computer interaction technologies. His work bridges theoretical computer science with practical applications in data management and accessibility. His academic foundation includes: Bachelor in Computer Science (2011, University of Namur) Master in Computer Science (2013, University of Namur) PhD in Computer Science (2017, University of Namur) Meurice's research focuses on evolutionary database systems and accessibility solutions. His HyDRa framework revolutionizes hybrid polystore management through dynamic schema evolution techniques, while his recent work applies transformer models to sign language recognition. These dual research streams demonstrate exceptional versatility across low-level database engineering and high-impact assistive technologies. His publication trends reveal a strategic shift from core database engineering (2022 schema evolution frameworks) toward socially impactful applications (2023 sign language translation systems), maintaining technical rigor while expanding societal relevance. Award recognition: Best Paper Award at QRS 2016 for reliability/security innovations As academic supervisor, Meurice co-promotes doctoral research on French language acquisition tools for deaf students, demonstrating commitment to educational technology. His collaborative network spans 10+ international researchers across database theory and AI accessibility domains. Current projects involve the HyDRa framework development and context-aware sign language translation systems, operating within the University of Namur's computer science research ecosystem through multidisciplinary teams combining database specialists and human-computer interaction experts.

Gaia CaligioreView profile

Lecturer

• Sign Language Studies
• Natural Language Processing
• Multimodal Communication
• +7 more

Gaia Caligiore is a contract lecturer at the Department of Humanities, University of Catania , specializing in Italian Sign Language (LIS) and its intersection with computational linguistics and medical communication. She holds a PhD in Interpretation Sciences from University of Catania (2024) and serves as a Junior Research Fellow at University of Modena and Reggio Emilia since 2024. Research Interests: Cognitive and socio-semiotic frameworks in LIS, multimodal dataset construction, medical communication, machine translation, treebank development, and bimodal bilingual education. Publications: Her recent work focuses on LIS annotation methodologies, medical domain LIS recognition, and educational approaches for deaf signers, with collaborations across Italy and Europe. Professional Affiliations: Member of Società di Linguistica Italiana, Associazione Italiana di Linguistica Computazionale, Linguistic Society of America, and Gruppo di Studio sulle Politiche Linguistiche. International Experience: Completed visiting research at Max Planck Institute for Intelligent Systems (Tübingen), Research trainee at University of Poitiers, and guest researcher at Max Planck Institute for Psycholinguistics (Nijmegen). Academic Contributions span LIS pedagogy, computational annotation, and healthcare communication improvements. Her work addresses both theoretical challenges (sociosemiotic frameworks, dependency parsing) and practical applications (medical LIS datasets, inclusive education).

• Neural Machine Translation
• Multilingual Neural Machine Translation
• Simultaneous Speech Translation
• +4 more

Dominik Macháček is a researcher at the Institute of Formal and Applied Linguistics (ÚFAL) at Charles University's Faculty of Mathematics and Physics in Prague. He successfully defended his PhD thesis titled 'Multi-Source Simultaneous Speech Translation' in June 2024 and is currently active in research while preparing for a post-doctoral fellowship at the University of Edinburgh starting in January 2026. His research focuses on Neural Machine Translation , particularly Simultaneous Speech Translation where translation happens in real-time with minimal latency (typically 2-4 seconds). His work spans Multilingual Neural Machine Translation, Quality Estimation for speech translation systems, and Human-to-Human Cross-Lingual Communication. His current 'Live Credible Translation' project aims to enhance simultaneous speech translation by adding quality estimation modules to improve credibility of real-time translations. Macháček has secured a US patent (12,056,457 B2) in August 2024 for a 'Computer-implemented method of real time speech translation and a computer system for carrying out the method'. His research has been presented at major conferences including ACL and IWSLT, particularly through his work on the SimulStreaming system which enables large foundation models like Whisper to operate in simultaneous mode. His publication record shows a consistent focus on real-time translation challenges, with recent work examining robustness to transcription errors, correlation between automatic metrics and human ratings, and continuous rating methodologies for human evaluation. His research trajectory demonstrates progression from theoretical foundations to practical applications with significant industry relevance. Macháček actively supervises Bachelor's and Master's theses and teaches the NPFL140 Large Language Models course. He is involved in multiple research projects including CEDMO 2.0, InCroMin, Sign LLM, NEUREM3, and ELITR. His future work will focus on advancing the state of the art in simultaneous speech translation through his 'Live Credible Translation' project at the University of Edinburgh.

Lily WangView profile

Lecturer

• Translation and Interpreting Studies
• Signed Language Interpreting
• Simultaneous Interpreting
• +7 more

Dr Lily Wang (English name for Jihong Wang) is a full-time Lecturer in the Master of Arts in Translation and Interpreting (MATI) program at The University of Queensland's School of Languages and Cultures. Certified by NAATI in Mandarin/English interpreting and Chinese translation, she holds a 2013 PhD from Macquarie University on working memory in signed language interpreting and previously worked there as a full-time researcher. Doctor of Philosophy (2013) - Macquarie University Her research focuses on cognitive processing in interpreting, covering simultaneous interpreting, telephone interpreting, and signed language interpreting. She investigates working memory dynamics, cognitive load management, expertise development, and technology impacts in translation. Her methodological toolkit includes experiments, corpus analysis, and qualitative interviews using NVivo and ELAN software. Recent publications address signed-to-spoken language interpreting strategies (2025), interpreter memory mechanisms (2025), and telephone interpreting challenges. She presented at Gallaudet University in 2019 on practice-research symbiosis. Current projects examine microanalysis of simultaneous interpreting data and remote interpreting protocols. Supervision highlights include: Current PhD: Chinese Business Law Translation (Principal Advisor) Completed PhD: Chinese Modal Verbs Acquisition (2022, Associate Advisor) Her empirical studies have produced assessment rubrics adopted by interpreter educators and informed telephone interpreting guidelines. Research methods span experiments, case studies, and corpus analysis using ELAN and SPSS, with publications in Interpreting , Target , and Perspectives .

• Accessibility
• Deaf Studies
• Human-Computer Interaction
• +4 more

Abraham Glasser serves as an Assistant Professor in the Department of Accessible Human-Centered Computing within Gallaudet University's School of Science, Technology, Accessibility, Mathematics, and Public Health. His teaching portfolio includes graduate courses such as AHC-604 Applied Information Accessibility, AHC-691 Thesis Accessibility Project Planning, and specialized topics in accessibility standards and data visualization, reflecting his commitment to advancing accessible technology education. Professor Glasser's research centers on dismantling digital barriers for deaf and hard of hearing communities through innovative human-computer interaction solutions. His work pioneers sign language interfaces for personal assistants, investigates captioning system optimization, and explores AI-driven accessibility enhancements. Methodologically, he combines empirical user studies with technical development to create genuinely inclusive technologies that address real-world communication challenges faced by deaf users in digital environments. Analysis of his recent publications reveals a strong trajectory toward integrating artificial intelligence with accessibility needs, particularly through large language models for caption customization and robust sign language recognition systems. His research consistently prioritizes deaf user perspectives in technology design, with increasing focus on ethical AI implementation and workplace accessibility solutions as evidenced by his 2025 publications on employment barriers and AI action planning. Professor Glasser actively mentors graduate students, currently advising five master's candidates in accessibility-focused research. His professional service includes leadership roles as Chair for the ASSETS 2023 Student Research Competition and Associate Chair for ACM CHI 2024, demonstrating significant contributions to the accessibility research community. He has secured internal funding through Gallaudet's Faculty Development Committee grant to advance his accessibility initiatives.

Khai N. TRUONGView profile

Professor

• Human-Computer Interaction (HCI)
• Ubiquitous Computing
• Health Technologies
• +3 more

Khai N. Truong is a Professor in the Department of Computer Science at the University of Toronto. He holds a Bachelor's degree in Computer Engineering from the School of Electrical and Computer Engineering and a Ph.D. in Computer Science from the College of Computing at the Georgia Institute of Technology. Research Interests : His work focuses on Human-Computer Interaction (HCI) and Ubiquitous Computing, with a strong emphasis on health and assistive technologies . He investigates tools for developing novel ubiquitous systems and techniques to enhance interactions with off-desktop devices. Recent Articles : His publications from 2020–2025 address topics like clinical chatbot design, music accessibility for d/Deaf communities, menstrual health data visualization, and COPD patient support systems. These contributions reflect a blend of technical innovation and user-centered design principles. Professional Service : He has served as Associate Chair for CHI 2024 Papers Program, IMWUT Distinguished Papers Award Co-chair (2021–), and ASSETS Papers Program Committee member (2020, 2025). Teaching : Recent courses include CSC318 The Design of Interactive Computational Media and CSC309 Programming on the Web . Advisees : Current students include Ph.D. candidates Mohammad Kianpisheh, Brenna Li (co-advised), Blue Lin (co-advised), and Sue Yoo, along with multiple undergraduate researchers.

Mohamed Ilyes LakhalView profile

Research Fellow

• Computer Vision
• Deep Learning
• Image Synthesis
• +4 more

Mohamed Ilyes Lakhal is a Research Fellow in Computer Vision and Deep Learning at the University of Surrey, working with Professor Richard Bowden in the Cognitive Vision Lab at the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. His primary focus is on advancing the state-of-the-art in image and video synthesis, with particular emphasis on sign language production and human avatar synthesis. Dr. Lakhal earned his PhD in Computer Science from Queen Mary University of London, where he was affiliated with the Centre for Intelligent Sensing (CIS). His doctoral research laid the foundation for his current work in novel-view synthesis and human action modeling. Dr. Lakhal's research spans several cutting-edge areas in computer vision and deep learning. His primary interests include photorealistic human avatar synthesis, sign language production and translation, multi-view video synthesis, and pose-guided image generation. His work on GaussianGAN represents a significant advancement in real-time photorealistic human avatar rendering, while his research on diversity-aware sign language production addresses critical challenges in generating diverse signing outputs. He has also made important contributions to novel-view human action synthesis through innovative approaches to texture mapping and 3D mesh processing. Dr. Lakhal's publication record demonstrates a clear evolution from foundational techniques in multi-view video synthesis toward more specialized applications in sign language technology and photorealistic human modeling. The increasing focus on sign language production and translation in his 2024-2025 publications indicates a strategic research direction with significant societal impact potential, particularly in making communication technology more accessible. Dr. Lakhal actively participates in the research community, contributing to initiatives like the SLRTP2025 Sign Language Production Challenge which established standardized evaluation metrics for the field. His lab appears to be engaged in recruiting PhD students interested in image and video generation, indicating an active research group focused on advancing visual synthesis techniques. Based at the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey, Dr. Lakhal is part of one of the UK's leading research centers in computer vision and multimedia processing. His work intersects with several ongoing projects in human-centered AI, accessible technology, and visual content creation, with practical applications in virtual reality, communication technology, and human-computer interaction.

Özge Mercanoğlu SincanView profile

Research Fellow

• Computer Vision
• Deep Learning
• Sign Language Recognition
• +3 more

Dr. Özge Mercanoğlu Sincan is a Research Fellow at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP), where she collaborates with Prof. Richard Bowden on sign language recognition and translation. Her expertise lies at the intersection of computer vision and deep learning, with a focus on advancing technologies for sign language processing. Education: BSc in Computer Engineering, Ankara University (2012) MSc in Computer Engineering, Ankara University (2015) PhD in Computer Engineering, Ankara University (2021) Research Interests: Her work includes developing algorithms for sign language production, translation, and segmentation using deep learning frameworks. She has contributed to large-scale datasets like AUTSL and pioneered methods for contextual-aware neural translation systems, significantly improving BLEU-4 scores. Her research emphasizes scalable models for diverse domains and robust evaluation metrics. Publications: Her recent work spans challenges in sign language production (CVPR 2025), continuous sign segmentation (FG 2025), and gloss alignment techniques. These studies highlight advancements in transformer-based architectures, contextual modeling, and dataset standardization. Labs/Teams: She is part of the CVSSP, a leading center for multimodal signal processing, fostering interdisciplinary collaborations in vision and speech technologies.

• Computational Linguistics
• Natural Language Processing
• Machine Translation
• +3 more

Dr. Cristina España i Bonet is a Researcher at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken, Germany, affiliated with the Language Technology and Multilingualism group. Her work spans computational linguistics, sign language technology, and multilingual natural language processing. Research Focus : Vision-language models for sign language data curation, cross-lingual disambiguation of idiomatic expressions, and continual learning in multilingual sign language translation. Collaborations : Active contributions to conferences like RANLP, NAACL, and workshops on multiword expressions.

Shebuti RayanaView profile

Assistant Professor

• Misinformation Mining
• Hate Speech and Stigma Detection
• Event and Anomaly Detection
• +3 more

Shebuti Rayana is an Assistant Professor of Computer Science in the Department of Mathematics, Computer & Information Science at the State University of New York at Old Westbury (SUNY OW). She holds a PhD from Stony Brook University, specializing in Computer Science, and completed her Master's and Bachelor's degrees in Computer Science and Engineering at Stony Brook University and Bangladesh University of Engineering and Technology (BUET), respectively. Education: PhD in Computer Science, Stony Brook University (advised by Leman Akoglu) MS in Computer Science, Stony Brook University B.Sc. in Computer Science and Engineering, BUET (advised by Saidur Rahman) Her research focuses on misinformation detection, hate speech analysis, anomaly mining, and graph-based data mining . She has led projects funded by NSF, DARPA, and industry partnerships, including a recent NSF CISE-MSI grant to enhance big data research infrastructure at SUNY OW. She collaborates with the Third Space Research Group at the University of Toronto on social media stigma detection. Key contributions include developing ensemble methods for anomaly detection, such as SELECT and CARE, and creating datasets like the 'COVID-Stigma' collection of anti-Asian tweets. She has held roles as a Research Assistant Professor at Stony Brook University and a Research Intern at IBM Watson Research Center. Awards: Member of the 2020-21 SUNY PRODiG Cohort (Promoting Research Initiatives in the Disciplines). Teaching: Courses include Computer Programming, Data Structures & Algorithms, and Data Mining. She emphasizes integrating research into undergraduate education through seminars and workshops.