Find Academic Supervisors

• Computer Vision
• Medical Image Analysis
• Retinal Image Analysis
• +7 more

Emanuele (Manuel) Trucco is a Professor of Computing and holds the NRP Chair of Computational Vision in the School of Science and Engineering at the University of Dundee. He is also an Honorary Clinical Researcher at NHS Tayside and previously served as an Adjunct Professor at the Chinese Academy of Sciences (2018–2021). His research is centered on computational vision and medical image analysis, particularly in retinal imaging and its applications in systemic disease detection. PhD, Electronic Engineering, University of Genoa (1990) MSc, Electronic Engineering, University of Genoa (1984) Manuel Trucco's research focuses on computer vision and medical image analysis , with a strong emphasis on retinal image analysis for early detection of diseases such as diabetes, cardiovascular conditions, stroke, dementia, and neurodegenerative disorders. He co-directs the VAMPIRE (Vessel Assessment and Measurement Platform for Images of the Retina) initiative, a collaborative effort between the Universities of Dundee and Edinburgh. This platform enables automated, multi-modal analysis of retinal images and has been used in biomarker studies across the UK and internationally. His work integrates deep learning , artificial intelligence , and biomedical engineering to develop non-invasive, scalable diagnostic tools. Industrial collaborations include Canon Medical, OPTOS plc, NIDEK, and Epipole plc, while institutional partners include the Royal College of Ophthalmologists and the UK Biobank Eye and Vision Consortium. Recent publications highlight a strong trend in using AI and deep learning to extract clinical insights from retinal images, including predicting cardiovascular outcomes in diabetic patients, estimating biological age, and analyzing retinal vasculature changes under physiological stress. His work bridges computer science, ophthalmology, and public health, contributing to precision medicine and health equity. His scientific contributions have been recognized through fellowships: FRSA (Fellow of the Royal Society of Arts) FIAPR (Fellow of the International Association for Pattern Recognition) Trucco has led or co-led major research projects, including a £7M NIHR grant on precision medicine for diabetes (Dundee-Chennai), a £1.1M EPSRC grant on vascular dementia biomarkers (PI), the 3M-Euro ITN "REVAMMAD", and several PhD studentships sponsored by OPTOS, NIDEK, SINAPSE, and Toshiba. He has served on the organizing and program committees of major international conferences such as MICCAI and the European Conference on Computer Vision. He is a key member of the VAMPIRE research team and the UK Biobank Eye and Vision Consortium , contributing to large-scale data analysis efforts in vision and systemic disease. His work is at the forefront of AI-driven healthcare innovation, with real-world applications in early disease detection and personalized medicine.

• Information visualization
• Visual analytics
• Eye tracking
• +10 more

Tanja Blascheck is a PostDoc Researcher and Margarete von Wrangell Fellow at the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. Her work focuses on visual analytics , eye tracking , and microvisualizations for smartwatches and other wearable devices.

Marie CarlénView profile

Professor

• Neuroscience
• Cognitive Neuroscience
• Prefrontal Cortex
• +16 more

Marie Carlén is a Professor of Neuronal Networks at the Department of Neuroscience, Karolinska Institutet, where she leads the Neural Circuits of Cognition research group. Her work focuses on the prefrontal cortex (PFC), a brain region central to cognitive functions such as attention, decision-making, working memory, and goal-directed behavior. She employs cutting-edge techniques including optogenetics, large-scale electrophysiology, calcium imaging, and circuit tracing in rodent models to unravel the cellular and circuit mechanisms underlying cognition and their disruption in psychiatric disorders. Her academic journey began with a Ph.D. in medicine from Karolinska Institutet in 2005, followed by postdoctoral training at MIT’s Picower Institute under Professor Li-Huei Tsai. She returned to Karolinska Institutet in 2010 and was promoted to full Professor in 2022. She is also a Docent (2017) and has held prestigious fellowships including ERC Starting Grant and Wallenberg Scholar (2019, 2024). Marie Carlén's research spans systems and cellular neuroscience, with a strong emphasis on inhibitory interneurons (especially parvalbumin-expressing cells), neural oscillations, and PFC-striatum interactions. Her recent publications reveal a consistent focus on decoding prefrontal circuit dynamics, the role of specific neuron types in cognition, and comparative brain architecture. She collaborates extensively with her partner, Konstantinos Meletis, also a KI researcher. She has been recognized with numerous scientific honors: Member, Nobel Assembly at Karolinska Institutet (2025–) Member, The Royal Swedish Academy of Sciences (2024–) Wallenberg Scholar (2024, 2019) ERC Starting Grant (2013) Wallenberg Academy Fellow (2012) NARSAD Young Investigator Awards (2010, 2008) She actively mentors students and researchers, with open applications welcomed to her lab. Her work is supported by major grants, including from the Knut and Alice Wallenberg Foundation, enabling high-risk, high-reward research in brain function and disease. Her lab investigates the functional definition of the prefrontal cortex across species, develops novel tools for neural recording, and explores circuit imbalances in conditions like autism and schizophrenia. She is a strong advocate for ethical animal research and promotes gender equality in science.

• Computer Vision
• Machine Learning
• Human-Computer Interaction
• +3 more

Olga Russakovsky is an Associate Professor in the Computer Science Department at Princeton University. She serves as Associate Director of the Princeton AI Lab and Chair of the Board of Directors at AI4ALL, a nonprofit dedicated to diversity in AI leadership. Her research focuses on computer vision, machine learning, human-computer interaction, and fairness in AI. She specializes in developing AI systems that reason about the visual world, emphasizing fairness, accountability, and transparency. Her work integrates computer vision with ethical AI frameworks, and she is affiliated with Princeton’s Center for Statistics and Machine Learning and Center for Information Technology Policy. Her publications address biases in datasets, explainable AI, and generative models. Her recent research trends include: Bias detection in datasets (e.g., CelebA, ImageNet) Interactive and explainable AI systems Generative models like diffusion and vision-language integration Deepfake detection and AI forensics Conceptual learning and few-shot training Scientific awards: NSF CAREER Award for fairer computer vision systems Co-founder of AI4ALL and Stanford AI4ALL outreach programs She advises students through AI4ALL initiatives and leads the Visual AI Lab, which focuses on robust, inclusive AI development. Her work bridges technical innovation with societal impact, particularly in diversity-focused education.

Crystal BaeView profile

Assistant Professor

• Spatial Cognition
• Wayfinding and Navigation
• Geovisualization
• +2 more

Crystal Bae is an Assistant Professor of Geographic Information Science at the University of Chicago’s Center for Spatial Data Science since 2021. She earned her Ph.D. in Geography from the University of California, Santa Barbara, and completed a postdoctoral fellowship under Somayeh Dodge in the MOVE Lab, focusing on geographic visualization and spatial cognition. Research Interests: spatial cognition, wayfinding, geovisualization, travel behavior, and urban built environments. Teaching Roles: Courses include Spatial Cognition, Introduction to GIS, Cartographic Design, and Social Science Inquiry at UChicago; Urban Geography and Human Geography at UCSB. Scientific Awards: Certificate in College and University Teaching (CCUT), UCSB Excellence in Teaching Award, and recognition for innovative GIS lab design in ArcUser. Her work examines how individuals and groups cognitively process spatial environments through static and animated visualizations, collaborative navigation, and urban neighborhood boundaries. She has contributed to NSF-funded projects on movement ecology and spatial cognition, with publications in Movement Ecology, COSIT, and Built Environment. Crystal also co-hosts a jazz radio show, maintains a travel blog, and engages in bicycling adventures, reflecting her interdisciplinary approach to geography and personal connection to spatial experiences.

Jonah BergerView profile

Associate Professor

• Digital Marketing
• Influence
• Word of Mouth
• +5 more

Jonah Berger is an Associate Professor of Marketing at the Wharton School, University of Pennsylvania . A world-renowned expert on influence, word of mouth, natural language processing, and consumer behavior , he has published over 80 articles in top-tier journals and teaches one of the world’s most popular online courses. His research bridges marketing, psychology, sociology, and computer science to uncover why products, ideas, and behaviors go viral. Books: Contagious , Invisible Influence , The Catalyst , Magic Words (translated into 35+ languages) Industry Collaborations: Apple, Google, Nike, Amazon, GE, Moderna, Bill & Melinda Gates Foundation Research Focus: Berger’s work examines how language shapes consumer behavior , using natural language processing to extract insights from text data (e.g., lyrics, scripts, service calls). His recent projects explore confidence dynamics, communication mediums, and linguistic patterns in persuasion . Awards: Sheth Foundation Award (2025), Hunt/Maynard Award Finalist (2024), William F. O’Dell Award (2019), Berry-AMA Book Prize (2014) Media Impact: His work has been featured in The New York Times , Wall Street Journal , Harvard Business Review , and NPR, with over 100 media mentions spanning behavioral science, marketing, and social dynamics . Leadership: Co-founder of the Technology and Behavioral Science Initiative and organizer of interdisciplinary Behavioral Insights from Text conferences. He has keynoted major events like SXSW and Cannes Lions for Fortune 500 firms and startups alike.

Maria GallagherView profile

Lecturer

• Cognitive Neuroscience
• Multisensory Integration
• Virtual Reality
• +3 more

Dr Maria Gallagher serves as a Lecturer in Cognition and Neuroscience within the School of Psychology at the University of Kent. Her research focuses on vestibular-multisensory integration mechanisms for self-motion perception, with particular emphasis on virtual reality applications and cybersickness mitigation. Her academic background includes: PhD (2019) from Royal Holloway, University of London investigating visuo-vestibular conflicts in Virtual Reality and Cybersickness Three-year postdoctoral position at Cardiff University researching audio-visual motion integration during active self-motion Dr Gallagher's work centers on how vestibular system signals integrate with visual and auditory inputs to create coherent self-motion perception. She investigates how conflicts between these sensory modalities in virtual environments cause cybersickness and aftereffects, aiming to enhance VR technology usability through neurocognitive insights. Her research bridges experimental psychology, neuroscience, and human-computer interaction. As an educator, she teaches PSYC3000 - Introduction to Psychology Statistics and Practical. Dr Gallagher currently supervises PhD student Emily Perry and actively seeks new supervisees interested in vestibular system research and VR's cognitive impacts. She also serves as Finance Assistant for the Experimental Psychology Society, demonstrating professional engagement beyond her core academic duties.

• Crowdsourcing
• Human-Computer Interaction
• Digital Health
• +7 more

Simo Hosio is an Academy Research Fellow (2022-2027) and Professor of Computer Science and Engineering at University of Oulu's Center for Ubiquitous Computing, where he leads the Crowd Computing Research Group. He also maintains a visiting position at University of Tokyo, Japan. Having graduated as the first Finnish scholar under Microsoft Research Cambridge's Ph.D. scholarship program, he has published over 150 peer-reviewed scientific articles spanning two decades of research. Hosio's research spans three primary domains: crowdsourcing methodologies, human-computer interaction, and digital health applications. His work pioneers novel approaches to online labor markets, investigates the suitability of crowdsourcing for diverse applications, and explores HCI aspects of digital health solutions for chronic conditions. His research group, founded in 2020, has secured nearly two million USD in funding, demonstrating significant research impact and recognition. Analysis of Hosio's recent publications reveals a strong trend toward interdisciplinary research at the intersection of crowdsourcing, healthcare technology, and emerging AI systems. His work increasingly focuses on practical applications of crowd computing in health contexts, with growing attention to mental health, women's health, and workplace well-being solutions. The integration of AI and machine learning techniques with traditional HCI approaches represents another significant trajectory in his recent scholarship. Distinguished Paper Award (2024) Best Paper Honourable Mention Award (2022) PMCJ Best Research Paper (awarded in 2024) Best Paper Award (2022) Best Full Paper Award (2015) Honorable Mention Award (2014) Best Paper Presentation award (2010) As an educator, Hosio has taught Human-Computer Interaction (2019-2025) to over 260 students in 2024, Social Computing (2018-2021) to approximately 60 students annually, and Applied Computing (2015-2018) to around 50 students each year. His research group's nearly two million USD in secured funding demonstrates significant grant acquisition success, supporting innovative work at the intersection of crowd computing, health technology, and human-centered AI systems. The Crowd Computing Research Group, founded by Hosio in 2020, represents a significant research infrastructure focused on advancing methodologies for crowd-powered systems. The group's work spans from fundamental research on crowd labor markets to applied projects in healthcare, workplace well-being, and social computing, demonstrating a strong commitment to both theoretical advancement and practical impact.

Michael NothnagelView profile

Professor

• Statistical Genetics
• Genetic Epidemiology
• Population Genetics
• +5 more

Michael Nothnagel is a Professor at the University of Cologne, where he leads the Department of Statistical Genetics and Bioinformatics within the Cologne Center for Genomics (CCG). His work spans statistical genetics, genetic epidemiology, and forensic genetics, focusing on methodological development and large-scale genomic data analysis. His research interests encompass theoretical and applied statistical genetics, with emphasis on human genetic diversity, disease etiology, and forensic applications. Key areas include Y-chromosomal phylogeography, genome-wide association studies for complex diseases, development of statistical methods for variant interpretation, and forensic marker optimization. His group leverages next-generation sequencing data and specialized forensic markers to address questions in population history, disease mechanisms, and identification systems. Recent publications reveal a strong focus on computational approaches to genetic analysis, including spatial frequency interpolation for haplogroup mapping, polygenic risk score applications for behavioral traits, and advanced methods for variant classification. His work demonstrates consistent integration of statistical theory with practical applications in medical and forensic genetics, often through international collaborations like the VISAGE Consortium. Nothnagel maintains active involvement in the Cologne Center for Genomics, contributing to seminars and collaborative projects including the upcoming 34th International Genetic Epidemiology Society meeting. His research group operates at the intersection of computational biology and medicine, with particular strengths in handling complex genomic datasets and developing novel analytical frameworks for genetic epidemiology.

• Biomedical Image Analysis
• Computational Neuroscience
• Biomedical Informatics
• +4 more

Tianming Liu serves as a Distinguished Research Professor in the School of Computing at the University of Georgia, with courtesy faculty appointments in the Department of Epidemiology and Biostatistics at the College of Public Health and the Institute of Bioinformatics. His academic career at UGA spans from Assistant Professor (2008-2013) to Associate Professor (2013-2015) to full Professor (2015-present), culminating in his recognition as a Distinguished Research Professor in 2017. He also serves as Graduate Program Faculty in the School of Computing. Education: Ph.D. in Computer Engineering, Shanghai Jiaotong University, China (2002) Master of Science in Computer Science, Northwestern Polytechnical University, China (1999) Bachelor of Arts in Computer Science, Northwestern Polytechnical University, China (1998) Dr. Liu's research focuses on the intersection of computer science and neuroscience, with particular expertise in biomedical image analysis, computational neuroscience, and biomedical informatics. His work centers on cortical architecture imaging and discovery, developing advanced computational methods for analyzing brain structure and function. His research spans multiple disciplines including neurosciences, cognitive sciences, biomedical engineering, and clinical sciences, with applications in understanding Alzheimer's disease progression, brain connectomics, and neural architecture. Analysis of Dr. Liu's recent publications reveals a strong trajectory in applying deep learning techniques to neuroimaging data. His work increasingly focuses on developing sophisticated neural network architectures specifically designed for brain connectome analysis, with particular attention to spatiotemporal dynamics and hierarchical organization of brain networks. Recent publications demonstrate his leadership in applying neural architecture search methods to optimize brain network analysis pipelines, with applications spanning from Alzheimer's disease research to fundamental neuroscience questions about cortical folding patterns. Scientific Recognition: Distinguished Research Professor at the University of Georgia (2017) Dr. Liu has secured substantial research funding through multiple competitive grants from NIH and NSF, demonstrating the significance and impact of his work. His most notable projects include the NIH R01 grant "Developing an Individualized Deep Connectome Framework for ADRD Analysis," the NIH R01 grant "Mapping Trajectories of Alzheimer's Progression via Personalized Brain Anchor-nodes," and the NSF CRCNS grant "Exploring the Mechanism of 3-Hinge Gyral Formation and its Role in Brain Networks." These projects highlight his leadership in applying computational methods to address critical challenges in neuroscience and medicine, particularly in the domain of Alzheimer's Disease and Related Dementias (ADRD). Dr. Liu collaborates extensively across disciplines, working with researchers at institutions including University of Virginia, Emory University, UNC Chapel Hill, and UT Arlington. His work has contributed to the development of BiomedGPT, an open-source visual-language foundation model for biomedical applications, demonstrating his commitment to creating accessible tools for the broader research community.

• Lexicology
• English for Specific Purposes
• Language of Color
• +3 more

Isabel Espinosa Zaragoza is an Assistant Professor in the Department of English Philology at the University of Alicante, Faculty of Philosophy and Letters I. She holds a PhD in Linguistic Studies (cum laude, 2022) and is renowned for her work in lexicology and English for Specific Purposes, particularly the language of color. PhD in Linguistic Studies (cum laude, University of Alicante, 2022) Master’s in English and Spanish for Specific Purposes (University of Alicante, 2016, extraordinary award) B.A. in English Philology (University of Alicante, 2014, extraordinary award) Her research focuses on lexical creativity, color terminology, and natural language processing applications for accessibility. She contributes to projects like CLEARTEXT (CENID) and leads in text simplification for cognitive disabilities. Published in journals: Procesamiento del lenguaje natural , Feminismo/s , Color Culture and Science Journal Book chapters with Cambridge Scholars Publishing and Universidad de Alicante She participated in 15 conferences (PICS 2022, BICLCE 2024) and contributed to 8 public research projects (2019-2025), including EU-funded initiatives. Teaching spans 10 years across 12 courses in English for International Relations, Cultural Management, and Computational Linguistics.

Travis B. ThompsonView profile

Assistant Professor

• Mathematical Modeling
• Scientific Computing
• Machine Learning in Medicine
• +5 more

Travis B. Thompson, Ph.D. is an Assistant Professor in the Department of Mathematics and Statistics at Texas Tech University, leading the TM4 (Texas Tech Translational and Theoretical Mathematical Modeling and Machine Learning in Medicine) research group. His academic journey includes postdoctoral work at Rice University, Simula Research Laboratory, and the University of Oxford, focusing on mathematics applied to neurodegenerative diseases. Education: Ph.D. in Mathematics from Texas A&M University (2013) Dr. Thompson develops theoretical mathematical models and applies scientific computing and machine learning to study neurological pathologies, particularly Alzheimer’s disease. His work explores complex biological processes on networks, translational healthcare applications, and nutritional security implications. Current research trends integrate neuroimaging data with finite element simulations to model tau progression , amyloid beta dynamics , and glymphatic clearance in age-related diseases. Scientific awards and honors were not explicitly mentioned in the provided materials. Dr. Thompson’s interdisciplinary approach connects computational neuroscience with biomedical engineering , utilizing techniques like diffusion tensor imaging and level set methods to analyze pathological protein spread and brain tissue mechanics . The TM4 research group focuses on network neurodegeneration , personalized medicine , and machine learning diagnostics . Their work spans from microfluidic cancer detection to computational modeling of brain clearance mechanisms , addressing challenges in both neurodegenerative diseases and biomedical engineering through rigorous mathematical frameworks.

• Hybrid Intelligence
• Human-centered Generative AI
• Federated Learning
• +1 more

Florian Leiser is a Professor at the Chair of Information Infrastructures (led by Prof. Dr. Ali Sunyaev) at Technical University of Munich's Heilbronn campus. His research focuses on human-AI collaboration, privacy-preserving algorithms, and explainability in machine learning systems. Current research areas include Hybrid Intelligence, Human-centered Generative AI (LLMs), Federated Learning, and Health Information Systems Recent publications demonstrate expertise in Explainable AI for medical imaging LLM hallucination detection Federated learning architectures Human-in-the-loop systems Healthcare data applications He contributes to teaching through Human-Centered Artifact Design courses Collaborative teaching roles in machine learning Supervising student projects

• Semantics
• Pragmatics
• Syntax
• +2 more

Kyle Rawlins is a Professor in the Department of Cognitive Science at Johns Hopkins University (JHU), where he heads the Semantics Lab located in Krieger Hall 105. His research focuses on semantics, pragmatics, syntax, and their intersections, often collaborating with labs like the Van Durme Lab in Computer Science. The Semantics Lab facilitates weekly meetings for discussing research, structured as seminars or traditional lab sessions, and co-organizes projects such as the Decomp Project and MegaAttitude Project . Current lab members include PhD students Karl Mulligan and Natalia Talmina. Notable collaborators: Ilaria Frana (University of Enna), Barbara Landau (JHU), Ben Van Durme (JHU CS), and Aaron Steven White (Rochester). His research spans topics like: Questions Under Discussion (QUD) frameworks Evidentiality and bias in language Multimodal semantic parsing Decompositional semantics Pragmatic particles in Italian Recent publications analyze polar questions, neural network representations, and discourse particles. The lab maintains a mailing list for event announcements, emphasizing collaborative research in semantics and computational linguistics.

Yuhan JiangView profile

Assistant Professor

• Artificial Intelligence in Architecture Engineering Construction and Operations
• Robotics and Remote Sensing
• Computer Vision and Machine Learning
• +7 more

Dr. Yuhan Jiang is an Assistant Professor in the Department of Built Environment at North Carolina A&T State University's College of Science and Technology. He serves as the Founding Director of the HUD Center of Excellence for Innovation in Affordable Housing and Sustainable Communities (CIAHSC). Dr. Jiang leads a multidisciplinary research team focused on integrating robotics, artificial intelligence, and Building Information Modeling in construction operations and infrastructure management. Ph.D. in Civil Engineering from Marquette University M.M. in Construction Management from Guangzhou University Additional Construction Management degree from Guangzhou University Dr. Jiang's research primarily focuses on artificial intelligence applications in architecture, engineering, construction, and operations (AECO). His work integrates robotics and remote sensing for data collection, computer vision and machine learning for data processing, and BIM, GIS, and AR/VR for data visualization. His research enables more efficient construction operations, building inspection, and infrastructure management. Additionally, he has extensive experience in community redevelopment planning and complex systems simulation, including investigating urban village formation mechanisms. Analysis of Dr. Jiang's recent publications reveals a strong focus on applying drone technology, computer vision, and deep learning to construction and infrastructure challenges. His work spans multiple domains including façade modeling, pavement evaluation, sidewalk inspection, earthwork calculation, and 3D reconstruction. A consistent theme across his research is the development of automated systems that improve efficiency, accuracy, and safety in construction and infrastructure management through AI and robotics. N.C. A&T and CoST Junior Faculty Teaching Excellence Award 2024-25 N.C. A&T and CoST Rookie Researcher of the Year Award 2024 ASCE Journal of Architectural Engineering Best Paper Award 2022 ASCE CI & CRC Joint Conference Best Paper Award 2024 AAAS HBCU Making and Innovation Showcase 1st Place 2024 CoST SciTech Week Innovation Challenge awards (2023-2025) N.C. A&T Provost's Faculty Fellow (2023 & 2024) Dr. Jiang has successfully secured over $4.5 million in research funding as PI or Co-PI, including a $2.5 million HUD Center of Excellence grant. He has mentored students who won 1st and 3rd place in the 2023 & 2024 Sci-Tech Week Innovation Challenge competitions and 1st place at the 2024 AAAS HBCU Making and Innovation Showcase. His funded projects span AI-driven BIM education tools, smart farming with robotics, drone-based façade modeling, and digital twin applications for infrastructure management. As Founding Director of the HUD Center of Excellence for Innovation in Affordable Housing and Sustainable Communities (CIAHSC), Dr. Jiang leads a multidisciplinary team focused on innovative approaches to affordable housing and sustainable community development. His lab work integrates drone technology, computer vision, and AI to create practical solutions for real-world construction and infrastructure challenges.