Find Academic Supervisors

• Artificial Intelligence
• Explainable AI
• Medical Imaging Analysis
• +7 more

Daniel Sonntag is a Research Professor at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, Germany, with extensive contributions to artificial intelligence, particularly in medical applications and human-AI interaction. His work spans multiple domains including explainable AI, medical imaging analysis, eye tracking technology, and clinical decision support systems. His research interests focus on developing interpretable AI systems for healthcare applications, with particular emphasis on ophthalmology and radiology. He investigates how to make AI systems more transparent and trustworthy through concept bottleneck models, multi-agent systems, and interactive learning approaches. His work bridges the gap between theoretical AI advancements and practical clinical applications, ensuring that AI tools are both effective and understandable for medical professionals. Sonntag's publication record demonstrates a consistent trend toward more interactive and interpretable AI systems, with recent work focusing on multi-agent RAG systems for radiology report generation, clinical decision support tools for ophthalmology, and biodiversity monitoring through soundscape analysis. His research shows a strong interdisciplinary approach, combining computer vision, natural language processing, and human-computer interaction to solve complex problems in healthcare and environmental monitoring. His work has appeared in leading venues including IUI, ICMI, KI, and various IEEE and ACM conferences, reflecting his contributions to both the theoretical foundations and practical applications of artificial intelligence.

• Artificial Intelligence
• Machine Learning
• Computer Science
• +3 more

Ivan Radosavljevic is a researcher at Singidunum University's Department of Postgraduate Studies, actively contributing to the fields of Artificial Intelligence, Machine Learning, and Software Engineering. His work spans diverse applications including environmental monitoring, educational technology, medical diagnostics, and geospatial systems. Research interests focus on AI-driven solutions for air quality assessment, synthetic dataset generation for semantic segmentation, real estate price prediction from internet ads, and medical imaging analysis. He has developed software tools for remote rendering monitoring, automated grading, and eye-tracking-based code interaction studies. His publications demonstrate expertise in integrating fuzzy logic with GIS for environmental applications, creating innovative educational technologies, and applying machine learning to healthcare diagnostics. Ivan collaborates with colleagues across multiple domains while maintaining a strong focus on practical implementations of artificial intelligence.

Kenneth A. NormanView profile

Professor

• Neuroscience
• Computational Neuroscience
• Cognitive Psychology
• +3 more

Kenneth A. Norman is the Huo Professor in Computational and Theoretical Neuroscience and Professor of Psychology and Neuroscience at Princeton University. He investigates the neural and computational mechanisms underlying episodic memory, memory suppression, sleep-dependent memory consolidation, and the intersection of artificial intelligence with human cognition. University: Princeton University Department: Psychology and Neuroscience Email: knorman@Princeton.EDU His research spans Neuroscience , Computational Neuroscience , and Cognitive Psychology , focusing on: Episodic memory integration and differentiation REM sleep’s role in hippocampal memory plasticity Real-time fMRI neurofeedback for memory modulation Event segmentation during naturalistic learning Memory search dynamics and prediction errors Language models’ parallels to human memory systems Recent publications highlight trends in Neuroscience and Artificial Intelligence , including studies of LLMs’ episodic memory capabilities, neural codes during narrative processing, and sleep-based memory reactivation. His lab employs fMRI and computational modeling to bridge biological and artificial cognition. Norman contributes to open-source tools like BrainIAK for fMRI analysis and RT-Cloud for real-time neuroimaging. His work has implications for education, mental health (e.g., depression treatment via neurofeedback ), and understanding memory’s computational principles.

Qi FengView profile

Assistant Professor

• Deep Learning
• Computer Graphics
• Computer Vision
• +5 more

Qi Feng is an Assistant Professor at the Faculty of Science and Engineering, Waseda University, specializing in deep learning applications for computer graphics and vision, with a focus on virtual and augmented reality. He holds a Doctor of Engineering (2022), Master of Engineering (2019), and Bachelor of Engineering (2017) from Waseda University. Research Interests: His work spans 3D reconstruction, depth estimation, occlusion handling, and multimodal systems integrating eye tracking and speech recognition. Notable projects include SyncViolinist for audio-driven motion generation and the Depth360 dataset for omnidirectional imaging. Publications: Qi's research has been featured in top conferences like CHI, ISMR, ICCVW, and journals such as The Visual Computer. His articles emphasize practical solutions for immersive environments, language learning, and synchronized audio-visual editing. Contact: Email: fengqi@ruri.waseda.jp

Henning FemmerView profile

Professor

• Requirements Engineering
• IT Quality Management
• Software Engineering
• +6 more

Prof. Dr. Henning Femmer serves as Professor of Business Information Systems, especially Requirements Engineering and IT Quality Management at the University of Applied Sciences Südwestfalen within the Department of Technical Business Administration. He supervises the Software Systems Quality Lab and teaches courses including IT quality management, Information Management, Software internship, Software Engineering, and Requirements Engineering. His research focuses on: Requirements Engineering with AI/NLP applications for requirement capture and traceability IT quality management emphasizing usability DevOps practices and CI/CD implementation in educational settings Game-Based Learning and Serious Games in Higher Education UX Research using eye-tracking technology System Engineering through hands-on projects like the NASA Mars Rover recreation The Software Systems Quality Lab pursues four main objectives: replicating current software engineering methods, developing new tools, providing practical quality engineering experience, and collaborating with regional companies. The lab employs empirical research methods and features notable equipment including a self-built NASA Open Source Rover and a Tobii Pro Spark eye-tracking camera. His scientific recognition includes the NASA Group Achievement Award from the SARP project. Prof. Femmer actively supervises student theses in Software Engineering, Requirements Engineering, IT quality assurance, Text analysis, and Agile methods. He oversees practical projects like the NASA Mars Rover recreation where students gain hands-on experience with robotics, software development, and autonomous systems. The Software Systems Quality Lab team comprises Dipl.-Ing. Volker Weiss, Dehbia Kouadria (Research Associate focusing on Serious Games in Software Engineering), and student assistants Oliver Pachatka and Arina Vovna.

Damián BlasiView profile

Research Professor

• Linguistics
• Cognitive Science
• Cultural Evolution
• +7 more

Position: ICREA Research Professor at the Center for Brain & Cognition, School of Engineering, Pompeu Fabra University (since Nov 2023). Concurrent roles include Research Associate at Harvard University’s Culture, Cognition, and Coevolution Lab and External Researcher at Yale University’s Human Relations Area Files. Previously held positions include Harvard Data Science Initiative Fellow (2021–2023), Postdoctoral Researcher at the University of Zürich (2015–2019), and Maury Green Fellow at Harvard’s Radcliffe Institute (2019–2020). Education: Ph.D. in Computer Sciences (Summa Cum Laude) from the Max Planck Institute for Mathematics in the Sciences (Leipzig, Germany, 2018), M.Sc. and B.Sc. in Physics from Balseiro Institute (Argentina, 2004–2009). Research Interests: Evolution of languages, cultural evolution, computational linguistics, cognitive science, and their intersections with AI, genetics, and public health. Focuses on language diversity’s role in human cognition, behavior, and cultural adaptation, particularly in the context of human-machine interaction. Transdisciplinary methods include data science, evolutionary anthropology, and natural language processing. Key Achievements: Developed the D-PLACE database, co-authored foundational studies on language complexity and sound symbolism, and pioneered research on global equity in language technology. Recipient of multiple prestigious awards, including the Branco Weiss Fellowship (2020) and the Glushko Dissertation Prize (2020). Grants & Leadership: Led projects on cultural evolution, language technology equity, and the Bantu language expansion. Served as associate editor for Journal of Language Evolution and Open Mind , and reviewer for major journals like Nature and Proceedings of the National Academy of Sciences . Labs & Teams: Active in the Center for Brain & Cognition, Culture, Cognition, and Coevolution Lab (Harvard), and the Human Relations Area Files (Yale). Collaborates internationally on projects involving UNESCO, the Santa Fe Institute, and the Max Planck Society.

• Human Factors
• Cognitive Ergonomics
• Human-Automation Interaction
• +2 more

Brandon Pitts is the Elliott Sigal Rising Star Associate Professor at Purdue University's Edwardson School of Industrial Engineering and a Faculty Associate at the Center on Aging and the Life Course. He holds a PhD in Industrial and Operations Engineering from the University of Michigan (2016), an MSE from the same institution (2013), and a BS in Industrial Engineering from Louisiana State University (2010). His research focuses on cognitive ergonomics, human-automation interaction, multimodal information processing, adaptive interface design, and aging-technology integration. Recent investigations include driver-automation trust dynamics, aging populations' technology adaptation, and aviation human factors. Dr. Pitts' publications demonstrate a consistent focus on human performance in automated systems, with emerging themes in AI-assisted decision-making and cross-generational technology usability. Research spans automotive interfaces, aviation safety systems, and healthcare technology accessibility. Scientific Awards: Elliott Sigal Rising Star Professorship He maintains collaborations through the Center on Aging and leads research on adaptive interfaces for older adults in transportation contexts. Laboratory activities focus on human-automation testing and physiological monitoring during system interactions.

• Computer Vision
• Image Processing
• Pattern Recognition
• +5 more

Prof Qiang Wu is a Professor and Deputy Head of School (Research) at the School of Electrical and Data Engineering, University of Technology Sydney. He is a core member of the Global Big Data Technologies Centre. His research focuses on computer vision, image processing, pattern recognition, and machine learning, with applications in video surveillance, biometrics, and human-computer interaction. He holds a B.Eng. and M.Eng. from Harbin Institute of Technology and a Ph.D. from UTS. Prof Wu has led industry collaborations with organizations like Meat & Livestock Australia, Canon, and Westpac Bank. He has authored over 300 publications, including works on pedestrian detection, virtual clothing fitting, and gait recognition patented in the U.S. He serves as an Associate Editor for IEEE Transactions on Multimedia and is a Senior IEEE Member. His research has been funded by grants from bodies like the Australian Egg Corporation and the Australian Research Council. He advises on PhD and Master’s students in computer vision and related areas.

• Bioinformatics
• Health Informatics
• Affective Computing
• +5 more

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University, specializing in bioinformatics, health informatics, affective computing, and GPU computing. His research bridges machine learning with medical applications, including EEG-based biometrics, emotion recognition, and medical imaging analysis. He holds a PhD in Computer Science from the National and Kapodistrian University of Athens (Greece) and previously held roles at the University of the West of Scotland and the University of Athens. Education: BSc (Hons.) in Informatics and Telecommunications (National and Kapodistrian University of Athens), MSc in Computer Science (Athens University of Economics and Business), PhD in Computer Science (National and Kapodistrian University of Athens). Research focuses on applying AI to healthcare, including biometric identification via EEG signals, remote physiological monitoring, and automated tutoring systems. Recent work involves deep learning for medical image analysis (e.g., chest X-rays, masonry façade cracks) and AI ethics governance (e.g., contributions to UK parliamentary inquiries on AI regulation). Publications span IEEE journals and conferences, emphasizing interdisciplinary applications of machine learning in health, education, and security. Active supervision of postgraduate students in AI-driven health technologies and computer vision.

Stefan FrankView profile

Associate Professor

• Computational Psycholinguistics
• Language Comprehension
• Bilingualism
• +2 more

Stefan Frank is Associate Professor at Radboud University's Centre for Language Studies and Educational Developer for Radboud AI. Education: PhD, Radboud University MSc, VU University Amsterdam Research develops computational models of language processing, focusing on bilingual cognition, neural network simulations of comprehension, and multilingual neurocognition. Leads projects examining how language statistics reveal mechanisms of multilingual minds. Publications integrate cognitive modeling with experimental data from eye-tracking, EEG, and behavioral measures to explain human language capabilities. Teaches courses in Computational Psycholinguistics and Data Science. Supervises PhD candidates modeling bilingual sentence production and syntactic processing.

Tracy LoveView profile

Professor

• Cognitive Neuroscience
• Aphasia
• Language Disorders
• +4 more

Tracy Love is a Professor in the School of Speech, Language, and Hearing Sciences at San Diego State University (SDSU) and the inaugural Dean of the College of Graduate Studies. She holds a PhD in Cognitive Neuroscience from UC San Diego and has extensive experience in neurocognitive research, particularly in aphasia and language disorders. Her roles include directing the Joint SDSU/UCSD Doctoral Program in Language and Communicative Disorders and leading the Center for Clinical and Cognitive Neuroscience. Education: B.A., Psychology, Brandeis University (1991) M.A., Psychology, UC San Diego (1994) Ph.D., Cognitive Neuroscience, UC San Diego (1998) Research Focus: Neurocognitive mechanisms of language processing, aphasia rehabilitation, bilingual language processing, and neuroimaging techniques. Her awards include the 2019 Distinguished Faculty Award and recognition for Outstanding Faculty contributions. Tracy advocates for diversity in graduate education and leads initiatives to enhance research capacity and international collaboration. Her work spans over $4 million in grants, including NIH-funded studies on language recovery and bilingual aphasia. Labs/Teams: Director of the Language and Neuroscience Group and member of the Center for Clinical and Cognitive Neuroscience at SDSU.

Weiyi MaView profile

Associate Professor

• Developmental Science
• Cognitive Neuroscience
• Language Acquisition
• +3 more

Dr. Weiyi Ma is an Associate Professor at the University of Arkansas's School of Human Environmental Sciences, part of the Dale Bumpers College of Agricultural, Food and Life Sciences. His research bridges developmental science, education, and neurobiology, focusing on communicative and cognitive development across the lifespan. Key interests include language acquisition, emotional perception of speech/music, music's impact on learning, and technology's role in education. Education: PhD in Education and MA in Linguistics from the University of Delaware. Research spans child development, neurocognitive processes, and cross-cultural studies. Recent work explores how music tempo influences attention in children and the effects of video gaming on cognitive functions like visual working memory and emotional perception. Publications emphasize topics such as classifier use in language learning, verb semantics, and neural plasticity in gamers. No awards listed, but active in editorial reviews and teaching.

Dong ChenView profile

Assistant Professor

• Deep Learning
• Robotics
• Control Systems
• +5 more

Dr. Dong Chen is an Assistant Professor of Agricultural and Biological Engineering at Mississippi State University (MSU), effective October 2024. Previously, he served as a Postdoc Researcher at the University of Virginia (UVA), affiliated with the Environmental Institute and Link Lab, and earned his Ph.D. in Electrical and Computer Engineering from Michigan State University in 2023. His research focuses on the intersection of control theory, multi-agent systems, and deep learning, with applications to autonomous vehicles, robotics, and smart agriculture. Key projects include soft robotics for fruit harvesting, multi-robot collaboration, and controlled environment agriculture (CEA). Education: Ph.D. in Electrical and Computer Engineering (Michigan State University, 2023); M.S. in Electrical and Computer Engineering (Michigan State University, 2020); B.S. in Mechanical Engineering (University of Electronic Science and Technology of China, 2017). Research interests include reinforcement learning, computer vision, plant phenotyping, and label-efficient learning in agriculture. He has secured grants from the Mississippi Soybean Promotion Board and USDA NIFA, and his work has been recognized with awards such as the ASABE Presentation Excellence Award (2023) and ITSC Paper Award (2023). He advises multiple PhD students, including Linyuan Wang, Bishal Adhikari, Ouyang, and Moeen. His lab collaborates widely, focusing on sustainable agricultural technologies and intelligent transportation systems. Professional memberships include ASABE, IEEE, and the IEEE Robotics and Automation Society.

Kaitlyn ZavaletaView profile

Senior Lecturer

• Bilingualism
• Multilingualism
• Second Language Acquisition (SLA)
• +5 more

Dr. Kaitlyn Zavaleta is a Senior Lecturer in Psychology at De Montfort University, affiliated with the Faculty of Health and Life Sciences and the School of Applied Social Sciences. Her research focuses on bilingualism, second language acquisition (SLA), and cognitive processes underlying language production, comprehension, and control. PhD in Cognition and Neural Systems in Psychology, University of Arizona MA in Cognition and Neural Systems in Psychology, University of Arizona BA in Linguistics, University of Illinois at Urbana-Champaign Dr. Zavaleta’s work investigates how bilingual and multilingual individuals manage language switching, speech errors, and individual differences in language learning. Her methodologies include eye-tracking, experimental tasks, and digital tools like podcasts for public engagement. She co-hosts The Language Scientists podcast to disseminate research and promote language learning accessibility. Her funded projects include the British Academy Talent Development Award (2021-2022) for research on multilingual education and internal grants at DMU to study working memory in language learning, digital barriers, and language control mechanisms. She currently accepts PhD supervision applications. British Psychological Society (2022–present), CPsychol American Psychological Society (2020–2022) Psychonomic Society (2019–2022) Society for the Teaching of Psychology (2014–2016, 2020) American Association for Applied Linguistics (2013–2016, 2020) Dr. Zavaleta leads the Language Lab at DMU, integrating digital tools like mouse-tracking and virtual reality to explore language learning dynamics. Her recent projects emphasize heritage language education and community engagement.

Ana I. SchwartzView profile

Professor

• Bilingual Language Processing
• Cognitive Psychology
• Lexical Access
• +2 more

Dr. Ana I. Schwartz is a Professor of Psychology and Undergraduate Program Director at the University of Texas at El Paso's College of Liberal Arts. Her research program investigates cognitive mechanisms underlying bilingual and multilingual language processing, with emphasis on lexical activation during reading comprehension. Using methodologies including eye-tracking and behavioral experiments, she examines how cross-language interactions influence word identification and discourse integration. Dr. Schwartz directs the PROBAR Lab where she explores cognate effects on anaphoric resolution, bilingual novel word learning, and cognitive control mechanisms in language switching. Her work has significantly contributed to theoretical models of the bilingual lexicon and sentence comprehension processes. Current investigations focus on how language co-activation impacts higher-order comprehension and memory formation in multilingual speakers. Analysis of Dr. Schwartz's extensive publication record reveals sustained focus on cross-language lexical interactions across different linguistic contexts. Her research demonstrates how bilingual cognitive architecture adapts to linguistic constraints during real-time processing, with implications for educational approaches to second language acquisition.