Find Academic Supervisors

• Artificial Intelligence
• Robotics
• Educational Technology
• +3 more

Kai Guo is an academic affiliated with the School of Computer Science and Information Engineering at Hefei University of Technology, China. His research spans interdisciplinary areas including artificial intelligence, robotics, and educational technology. He has collaborated with institutions globally on projects involving AI applications in education, industrial process optimization, and computer vision. Key research interests include AI-driven educational tools, robotics systems, and data-driven industrial analysis. Notable contributions include developing prediction models for blast furnace operations, AI-enhanced language learning platforms, and visual perception techniques in virtual reality. His work often bridges theoretical computer science with practical applications in manufacturing, education, and healthcare. Publications from 2025 focus on advanced machine learning techniques such as graph-based retrieval systems, diffusion models for anomaly detection, and VR visualization methods. Collaborations with Samuel Kai-Wah Chu and David James Woo highlight his engagement in educational technology innovation. Guo has no listed scientific awards but maintains an active research agenda with over 185 publications. His work emphasizes cross-disciplinary approaches to solving complex technical and pedagogical challenges.

Jorma LaaksonenView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Jorma Laaksonen is a Senior Lecturer at the Department of Computer Science, Aalto University (School of Science). His research focuses on artificial intelligence, machine learning, computer vision, and multimedia systems, with notable contributions to cross-lingual evaluation, hyperspectral imaging, and audio-visual attention mechanisms. He holds a Doctoral degree in Engineering and Technology from Helsinki University of Technology (1997). Education: Doctoral degree, Engineering and Technology, Helsinki University of Technology (1997) Licentiate degree, Engineering and Technology, Helsinki University of Technology (1991) Master's degree, Engineering and Technology, Helsinki University of Technology (1990) His research interests span AI-driven visual computing, human-computer interaction, and multimedia systems. Key projects include USSEE (audio-visual scene understanding) and ARTISDIG (AI for forest spectral analysis). He has published over 280 papers and supervised multiple theses. Awards include the IEEE Best Paper Award (2017) and Microsoft's Best Practice Award (2016). Recent work includes cross-cultural language modeling, efficient text-to-video retrieval, and open-world video instance segmentation. He leads the Lecturer Laaksonen Jorma group and actively participates in editorial roles and doctoral thesis evaluations.

Kazimierz ChorośView profile

Professor

• digital image and video processing
• content-based video indexing and retrieval
• computer animations
• +4 more

Kazimierz Choroś is a Professor at the Department of Applied Informatics , Faculty of Information and Communication Technology , Wrocław University of Science and Technology. He held leadership roles as Deputy Director of the Institute of Informatics (2008-2014) and Deputy Dean of the Faculty of Computer Science and Management (2016-2020). Research interests: digital image/video processing, content-based video indexing, computer animations, multimedia systems, web systems analysis, and information systems design. Organizer and Chair of the International Conference on Multimedia & Network Information Systems (MISSI 2022) Chair of Special Session WebSys 2020 at ICCCI 2020 Other activities: Since 1993, member and former President (1993-2002) of the SAGE Association (Polish Graduates of French Grandes Ecoles). Since 1982, member of the Polish Numismatic Society, author of dozens of numismatic publications, and Editor-in-Chief of Wrocławskie Zapiski Numizmatyczne (2003-present). Contact: Email: kazimierz.choros@pwr.edu.pl Phone: +48-71.320.3799 Office: Building D2, Room 201/1 Address: Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland

Weiguo FanView profile

Professor

• Business Analytics
• Artificial Intelligence
• Natural Language Processing
• +15 more

Weiguo Fan is a Professor and the Henry B. Tippie Excellence Chair in Business Analytics at the Tippie College of Business, University of Iowa, where he also serves as Faculty Director of the Tippie Analytics Cooperative. He earned his Ph.D. in Information Systems from the University of Michigan and has built a distinguished career in business analytics, AI, and data science. Previously, he was a professor at Virginia Tech, where he co-founded the Center for Business Intelligence and Analytics. His educational background includes: BEng in Information and Control Engineering, Xi’an Jiaotong University MS in Computer Science, National University of Singapore PhD in Business Administration/Information Systems, University of Michigan, Ann Arbor Dr. Fan's research focuses on AI, natural language processing, machine learning, and social media analytics, with applications in business intelligence, healthcare, financial auditing, and digital innovation. He has pioneered the use of text analytics in financial risk assessment and product quality discovery, with his work featured in the New York Times and other media outlets. The 15 most recent publications reflect a strong trend in leveraging AI and data science for business decision-making, particularly in e-commerce, social media, healthcare, and knowledge management. His work frequently appears in top journals such as MIS Quarterly , Information Systems Research , and Production and Operations Management , demonstrating sustained impact across information systems, operations, and computer science domains. His scientific honors include: 7 ESI Highly Cited Papers Design Science Award, INFORMS (2017) Multiple Best Paper Awards (2017, 2020, 2022) Master of Business Analytics Instructor of the Year (2020, 2021) Nominee, INFORMS Workshop on Data Science Best Paper (2022) Dr. Fan has advised over 10 PhD students, many of whom now hold positions at top tech companies like Google, Amazon, and Microsoft, or in academia. His research has been funded by ARPA-H, NSF (five grants), PwC, KPMG, and MeritData. He has served as Associate Editor for several top journals, including MIS Quarterly , Information Systems Research , and Information & Management . He leads the Tippie Analytics Cooperative, a research and outreach initiative that bridges academic analytics with industry applications. His ongoing projects include healthcare analytics, adverse event detection, crowdfunding success prediction, and capital market research using text analytics.

Stuart MiddletonView profile

Associate Professor

• Natural Language Processing
• Information Extraction
• Human-in-the-loop NLP
• +17 more

Dr Stuart Middleton is an Associate Professor at the University of Southampton, affiliated with the School of Electronics and Computer Science within the Faculty of Engineering and Physical Sciences. He is a member of the Agents, Interaction and Complexity research group, the Centre for Machine Intelligence, and the Centre for Democratic Futures. His research focuses on Natural Language Processing (NLP), particularly information extraction and human-in-the-loop NLP, with applications in law enforcement, defence, mental health, and environmental science. His research interests include Natural Language Processing , Information Extraction , Human-in-the-loop NLP , Active Learning , Adversarial Training , Rationale-based Learning , and Argument Mining . He specializes in scenarios with limited or fragmented data, developing methods for few/zero-shot learning, graph-based models, and multimodal analysis. His work spans domains such as mental health, digital ethics (e.g., sharenting), defence, and climate science. The recent publications highlight a strong trend in applying NLP to societal challenges, including mental health monitoring through social media, ethical AI, multimodal argumentation in political debates, and climate data analysis. His work combines technical innovation in prompt engineering, summarization, and graph-based modeling with real-world impact. AI for defence: readiness, resilience and mental health (2024) Extraction and summarization of suicidal ideation evidence (2024) Sharenting and social media properties (2024) Implementing responsible innovation (2024) ConversationMoC: mood change detection (2024) Dr Middleton is actively involved in PhD supervision and academic leadership as Deputy Director of the UKRI MINDS Centre for Doctoral Training. He supervises multiple PhD students working on NLP and AI projects. He has secured research funding from ESRC and EPSRC for projects such as ProTecThem2.0 , SafeSpacesNLP , and GloSAT . He leads the Natural Language Processing modules COMP3225 (UG) and COMP6253 (PG) and mentors students in NLP research. He is a Fellow of The Alan Turing Institute and a member of the EPSRC peer review college, contributing to national research strategy and evaluation. His research is conducted within interdisciplinary teams, including collaborations with criminologists, social scientists, and environmental researchers. He is part of the Agents, Interaction and Complexity group and contributes to the Centre for Machine Intelligence and Centre for Democratic Futures , focusing on AI systems that interact meaningfully with humans and society.

Anurag KumarView profile

Researcher

• Audio Processing
• Speech Recognition
• Multimodal AI
• +3 more

Anurag Kumar is a Senior Staff Research Scientist at Google DeepMind, focusing on Audio, Speech, and Multimodal AI. His work emphasizes weakly supervised, self-supervised, and unsupervised learning methods. Before DeepMind, he spent six years at Meta and completed his PhD at Carnegie Mellon University (CMU) under Prof. Bhiksha Raj, where his thesis introduced weakly labeled learning of sounds. He holds an Electrical Engineering degree from IIT Kanpur (2013). Education: PhD in Computer Science, Carnegie Mellon University (2013–2018) Bachelor of Technology in Electrical Engineering, Indian Institute of Technology (IIT) Kanpur (2008–2013) Research Interests: Anurag’s research spans audio and speech processing, including speech enhancement, multimodal understanding, and generative AI. He explores techniques like neural radiance fields (NeRF) for audio-visual scene synthesis and diffusion models for music editing. His work bridges theoretical advancements with practical applications in real-world scenarios like room acoustics and egocentric object localization. Awards & Roles: MIT Technology Review 'Innovators Under 35' (2024) Associate Editor, IEEE Signal Processing Letters Technical Committee Member, IEEE AASP Organized workshops on Generative AI for Audio at NeurIPS 2024 and URGENT Challenge for Speech Enhancement Labs & Collaborations: Leads projects in multimodal AI at Google DeepMind, collaborating on tools like Torchaudio and frameworks for audiovisual learning. His work includes benchmark datasets like Real Acoustic Fields and advancements in neural field-based scene synthesis.

• Music
• Auditory perception
• Categorization
• +7 more

Pascal Gaillard is an Associate Professor at the University of Toulouse - Jean Jaurès, where he conducts research at the CLLE Lab (Cognition, Languages, Language, Ergonomics, UMR5263) in the "Language and Cognitive Processes" Team, while teaching in the Music Department at the National Higher Institute for Teaching and Education in Toulouse - Midi-Pyrénées (INSPE). His academic career spans over two decades, beginning as an Assistant Professor in Musicology at Université de Toulouse - Le Mirail from 1998 to 2000, before becoming an Associate Professor at the University of Toulouse since 2002. His educational background includes: PhD in Musicology and Auditory Perception (1996-2000) from Université de Toulouse - Le Mirail / Université de Paris - Jussieu Master (DEA) in Musicology - Ethnomusicology (1993-1994) from Université de Toulouse - Le Mirail Maîtrise in Musicology - Ethnomusicology (1989-1990) from Université de Toulouse - Le Mirail Licence in Musicology (1985-1989) from Université de Toulouse - Le Mirail Dr. Gaillard's research focuses on auditory perception and cognitive processes, particularly the categorization of sounds. His work spans multiple domains including musical timbre perception, speech perception in individuals with age-related hearing loss, environmental sound recognition, and auditory processing in deaf individuals with cochlear implants. He has pioneered studies on how humans organize their sound world through categorization, drawing on prototypical categorization theory developed by Rosch (1976). A key insight from his research is that auditory categorization is dynamic rather than fixed, varying according to listener needs, tasks, and action goals. His recent publications demonstrate a strong interdisciplinary approach, bridging music cognition, clinical audiology, and cognitive neuroscience. There's a clear trend toward applied research with clinical implications, particularly for deaf children with cochlear implants, as evidenced by multiple studies on humanoid robots for speech-language training. His work also shows increasing integration of computational approaches, including transfer learning for music preference prediction and ontology-based data management. Dr. Gaillard has secured numerous research grants from prestigious funding bodies including the French National Research Agency (ANR), Occitanie Regional Council, and ANSES. Current projects include "The hospital and its 'Beeps': evaluation of the hearing health of caregivers" (2025-2028), "SILENCE - Comparative acoustics: earth, planets and extreme environments" (2024-2026), and "Rehabilitation of age-related hearing loss: evolution of cognitive load in a 3D virtual environment - AgeHear" (2020-2024). He directs the "Cognition, Behavior and Use" facilities (CCU) which include specialized auditory booths for research. Since 2003, he has developed TCL-LabX, experimental software for conducting categorization studies. His research collaborations span international boundaries, working with labs in France, Canada, and the Netherlands, as well as industry partners in aeronautics and healthcare sectors.

Kahyun ChoiView profile

Assistant Professor

• Machine Learning in Libraries
• Music Information Retrieval
• Computational Analysis of Poetic Texts
• +10 more

Kahyun Choi is an Assistant Professor in the School of Information Sciences at the University of Illinois Urbana-Champaign (UIUC). Previously, she held the same position at Indiana University Bloomington (2019–2024). She earned her PhD in Library and Information Science from UIUC and worked as a software engineer at Naver, Korea's leading internet portal, prior to her academic career. Her research bridges technology and the humanities, focusing on ethical AI workflows for Libraries, Archives, and Museums (LAMs), music information retrieval, computational poetic text analysis, and public library-based AI education. Notable recognitions include the 2023 Indiana University Trustees Teaching Award and the 2022 IMLS Early Career Research Development Project Grant. Recent work highlights include analyzing poetry reading audio's acoustic patterns, developing text complexity models using word embeddings, and designing AI literacy programs for K-12 education. Her publications span conferences like ICASSP, iConference, and ASIS&T, reflecting interdisciplinary engagement with machine learning, natural language processing, and cultural informatics. Awards include teaching excellence (2023 IU Trustees Award), research grants (IMLS 2021/2022), and the Luddy Faculty Fellowship (2021). Her work emphasizes equitable AI implementation, ethical practices, and collaborative research between universities and public libraries.

Theresa A AntesView profile

Associate Professor

• Second Language Acquisition
• French Linguistics
• Vocabulary Acquisition
• +3 more

Theresa A Antes is an Associate Professor in French and Linguistics at the University of Florida, serving as Undergraduate Coordinator for French & Francophone Studies. She holds a PhD in French Linguistics from Cornell University (1993), with a focus on Second Language Acquisition. Her research emphasizes vocabulary acquisition, technology in language teaching, and pedagogical approaches to French syntax and grammar. Education: PhD in French Linguistics (Cornell University, 1993), MA in French Linguistics (Cornell, 1992), MA in French Language & Literature (University of Kentucky, 1989), and BA in French (Millersville University, 1986). Research interests include second language acquisition, French linguistics, and pedagogy, with recent work focusing on vocabulary instruction strategies and the role of technology. Notable contributions include studies on Parisian French lexical variation, verb acquisition, and binomial expressions in L2 classrooms. Teaching includes courses like French Grammar & Composition, Language Teaching Methodology, and Semiotics. She has directed 3 MA theses and served on numerous thesis committees. Active in professional service, she chairs CLAS Faculty Council and serves on University Senate, while reviewing for journals like French Review and TESL-EJ . Grants: Secured $10,718 from the Humanities Scholarship Enhancement Fund for research on vocabulary’s role in textual interpretation. Recent presentations include AAAL and ACTFL conferences on topics like cognate instruction and authentic materials in language learning. Professional affiliations include AATF, ACTFL, and TESOL. She has advised AP French Language and Culture curricula and led workshops on modern language pedagogy.

• Database Systems
• Virtual Reality Applications
• Digital Humanities
• +4 more

Yannis Ioannidis is a Professor at the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens. His research spans interdisciplinary areas including database systems, virtual reality applications, and cultural heritage technology. He leads projects integrating technology with education, art, and social sciences, such as VR environments for urban planning (ARSINOE VR) and AI-driven scientific discovery frameworks. His work emphasizes collaborative tools for digital storytelling and participatory cultural experiences. Ioannidis has contributed to advancements in database query optimization, text exploration through SQL extensions (DETEXA), and indexing techniques for data lakes. His recent focus includes exploring the intersection of tabletop role-playing games with historical education and ethical AI-human coevolution. He actively participates in initiatives like the EBRAINS infrastructure for brain research and the MagicARTS social VR project. Notable research trends in his publications include leveraging VR for immersive learning, designing hybrid museum kits, and applying AI to support UN Sustainable Development Goals. His work bridges technical innovation with societal impact, addressing challenges in education, art preservation, and ethical technology use.

• Artificial Intelligence
• Data Science
• Machine Learning
• +7 more

Wang Zhaoxia is an Associate Professor of Computer Science (Practice) at the School of Computing and Information Systems, Singapore Management University. Her expertise lies in Artificial Intelligence, Data Science, and Machine Learning with a focus on Natural Language Processing (NLP), Sentiment Analysis, and Knowledge Graphs. She holds a PhD from Nankai University (2004). Research Interests: Her work spans AI-driven solutions for sentiment analysis, maritime risk management, audio-to-music conversion, and social media analytics. Recent projects include leveraging Large Language Models (LLMs) for tasks like logical reasoning, translation optimization, and risk assessment. Publications: Recent work highlights include developing metrics for LLM performance (RAP), emotion analysis in images, and automated music transcription. Her research demonstrates cross-disciplinary applications of AI in fields like finance (ICO success prediction) and health (disease surveillance). Awards: No awards explicitly listed Advising: Supervises 3 PhD/Master students including HUANG Donghao and Nicole Anne Hui Ying TEO Contact: zxwang@smu.edu.sg | +65 68261347

• Cloud Computing
• Mobile Networks
• Security and Privacy in Networks
• +7 more

Dr. Melody Moh is a Professor and Interim Chair in the Department of Computer Science at San Jose State University, where she has been a faculty member since 1993. Her research focuses on cloud computing, mobile networks, security/privacy in networks, and machine learning applications. She coordinates Cyber Security Certificates and has published over 130 refereed papers in journals, conferences, and book chapters. Moh holds an MS and PhD in Computer Science from the University of California, Davis. Research Interests: Cloud and Network Security, 5G Networks, IoT Security, Blockchain Applications, Machine Learning for Cybersecurity, Smart Grid Authentication, and Fog Computing. Her work bridges theoretical advancements with practical implementations, such as developing efficient cache management for Cloud-RAN systems and adversarial defense mechanisms for deep learning models. Recent Publications: Recent work includes blockchain-based public key infrastructure, adversarial attacks on deep learning models, and autonomous driving systems using LiDAR data. Her research often involves student collaborators, with many publications featuring *SJSU students. Awards: Best Paper Runner-Up (2019 ACM Southeast Conference) for randomized load balancing research, and Honorable/Best Paper Award (2018 ACM Southeast) for cache management in 5G networks. Grants & Labs: Secured over $500K in NSF and industry grants. Leads research teams focusing on cybersecurity, edge computing, and AI-driven network optimization. Active in collaborative projects with industry partners and government agencies.

• Artificial Intelligence
• Machine Learning
• Generative AI
• +5 more

Jesse Spencer-Smith is a Professor of the Practice of Computer Science at Vanderbilt University's School of Engineering. His research focuses on applying artificial intelligence and machine learning to interdisciplinary challenges, including bioinformatics, astrophysics, software engineering, and archaeology. He is particularly known for pioneering work in generative AI frameworks and prompt engineering methodologies. Recent research trends include developing AI tools for protein structure analysis (e.g., SuperMetal and SuperWater), gravitational wave signal processing, and optimizing large language models for coding and archaeological artifact analysis. His work bridges computational methods with domain-specific applications, emphasizing practical problem-solving across multiple scientific disciplines. Dr. Spencer-Smith has contributed to foundational studies in prompt engineering, publishing multiple catalogs of prompt patterns to systematize interactions with AI systems like ChatGPT. His interdisciplinary approach reflects a commitment to advancing AI's role in both theoretical and applied contexts. No scientific awards or grants were explicitly mentioned in the provided texts. He currently advises no formally listed students, though his research collaborations likely involve graduate and undergraduate participants. No specific lab affiliations or teams were detailed in the available information.

Janice ChenView profile

Assistant Professor

• Cognitive Neuroscience
• Memory Science
• Temporal Dynamics
• +4 more

Janice Chen is an Assistant Professor in the Department of Psychological & Brain Sciences at Johns Hopkins University, affiliated with the Krieger School of Arts & Sciences. Her research focuses on understanding how real-world memories are constructed and retrieved, using advanced neuroimaging and behavioral methods. Her work emphasizes the interplay between sensory input and mnemonic systems across multiple timescales, particularly during naturalistic experiences like movie viewing and narrative recall. Chen's education includes a PhD in Psychological & Brain Sciences. She leads a lab investigating topics such as neural coding of social interactions, temporal dynamics of memory, and cross-modal processing in blind individuals. Her lab employs fMRI, EEG, and behavioral experiments to map brain activity patterns during perception and recall. Current projects explore how causal structures in narratives shape memory organization, and the role of gaze behavior in linking visual perception to memory retrieval. Key contributions include developing datasets like 'Sherlock' and 'Film Festival,' widely used in cognitive neuroscience research. Her team collaborates on analyzing shared neural responses across individuals during narrative comprehension and transmission. She advises a diverse group of postdoctoral researchers and graduate students in cognitive and computational neuroscience. Chen's lab is not currently recruiting but maintains active research collaborations. Findings emphasize the brain's shared representational structures during memory recall, suggesting systematic neural reorganization from perception to memory storage.

Marina GavrilovaView profile

Professor

• Machine Learning
• Biometric Security
• Image Processing
• +4 more

Marina Gavrilova is a Professor and Associate Head (Research and Strategic Planning) in the Department of Computer Science at the University of Calgary. Her research focuses on biometric security, machine learning, pattern recognition, and interdisciplinary computational sciences. She is a co-founder of the Biometric Technologies Laboratory and the SPARCS laboratory, and serves as Founding Editor-in-Chief of Springer's Transactions on Computational Sciences. Her work spans ethical AI frameworks, multimodal biometric systems, and healthcare applications. Dr. Gavrilova holds Senior ACM and IEEE membership statuses and has received prestigious awards including the Canada Foundation for Innovation Grant and the University of Calgary’s U Make a Difference Award. Her editorial roles include positions with IEEE Transactions on Computational Social Sciences, IEEE Access, and multiple biometrics journals. Her research explores emotion-aware de-identification systems, trustworthy AI, and bias mitigation in healthcare machine learning. Key contributions include advancements in generative adversarial networks, fusion strategies for multimodal data, and computational methods for medical imaging. She advocates for ethical AI practices and interdisciplinary collaboration to address societal challenges in security and privacy. Lab affiliations include the Biometric Technologies Lab and SPARCS Lab, which focus on computational security and interdisciplinary research. Ongoing work emphasizes social behavioral biometrics, autonomous systems, and AI-driven healthcare solutions.