Agnieszka Roginska is a Professor of Music Technology at the Steinhardt School, New York University, specializing in immersive audio, spatial sound, and auditory displays. Her work integrates acoustic science with virtual reality and medical applications, including postural stability studies for vestibular rehabilitation. She holds leadership roles as AES President-Elect and co-edited the authoritative book 'Immersive Sound'. Education: B.A. in Piano Performance and Computer Applications in Music (McGill University, 1996); M.M. in Music Technology (NYU, 1998); Ph.D. in Music Technology (Northwestern University, 2004). Research focuses on 3D audio technologies, auditory displays for virtual environments, and sensory integration studies. She leads NYU's Music and Audio Research Lab (MARL) and advises the Society for Women in TeCHnology (SWiTCH) at NYU. Awards include AES Fellowship and leadership in international audio engineering societies. Recent publications emphasize audio's role in postural control, distributed music performance frameworks (Holodeck), and VR sound design. Her work bridges technical innovation with artistic applications, including collaborative music systems in mixed reality.
Mark Bo Jensen is an Assistant Professor (tenure track) at the Department of Engineering Technology and Didactics, Technical University of Denmark (DTU), specializing in Energy Technology and Computer Science. His research is centered on Perception Engineering and Extended Reality technologies, particularly Virtual Reality (VR), with applications in human cognition, computer graphics, and scientific visualization. His research interests lie at the intersection of engineering and cognitive sciences, focusing on creating immersive and convincing extended reality experiences. Jensen applies his over 10 years of expertise in real-time computer graphics to advance VR systems for perception modeling, geometric data visualization, and material appearance simulation. His work contributes to fields such as medical diagnostics, 3D annotation, and photorealistic rendering. The recent publications highlight a strong trend in leveraging VR for scientific tasks, such as anatomical landmark annotation and visual field testing, as well as advancing core graphics techniques like meshlet optimization and diffusion-based stereo image generation. His research integrates computer vision, graphics algorithms, and human-centered design. While no scientific awards are currently listed, his active participation in research projects and consistent publication output indicate a growing academic profile. He has contributed to interdisciplinary collaborations involving medical, biological, and engineering domains. Jensen has been involved in advising and research projects, including serving as a PhD student in the 'Virtual Reality-Based Visualization of Geometric Data' project and currently as a project participant in 'AL-EYE: The Visual Aid'. These projects reflect his focus on applied VR solutions and data understanding. His work is conducted within the Energy Technology and Computer Science division at DTU, where he contributes to advancing perception-driven technologies and their practical implementation in scientific and medical contexts.
Natalia Andrienko is a Professor of Computer Science at City University London and Lead Scientist in the Knowledge Discovery department at Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme. Her work bridges visual analytics with mobility data science and machine learning, focusing on human-in-the-loop systems for pattern discovery and spatiotemporal data exploration. Professor, Computer Science, City University London (2013-present) Lead Scientist, Knowledge Discovery, Fraunhofer Institute (1997-present) Research interests center on visual analytics methodology for spatiotemporal data, human-centered machine learning, and mobility pattern analysis. She develops frameworks for interactive dashboards, trust visualization in ML, and semantic exploration of location-based data, with a focus on scalable and privacy-respecting techniques. Her recent publications investigate hybrid human-machine discovery of movement patterns, contextual visual analytics for multivariate events, and the integration of temporal periodization with spatial analysis. Articles emphasize applications in sports analytics, transportation systems, and collaborative visual analysis workflows. Key collaborations include work with Gennady Andrienko and Salvatore Rinzivillo. She has contributed to journals like Visual Informatics , IEEE Transactions on Visualization and Computer Graphics , and International Journal of Cartography , maintaining active research output across visual analytics, mobility science, and geospatial data modeling.
Abdulkadir Çelikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark, where he is part of the DKW (Data Science and Knowledge) research group. His research focuses on genome representation learning, graph representation learning, and machine learning applications in bioinformatics and network science. Research Interests: His work lies at the intersection of artificial intelligence and biological data analysis, with a strong emphasis on scalable methods for genome and metagenome representation using k-mer profiles, as well as modeling dynamic and complex networks. He develops novel machine learning models to capture the structure and evolution of graphs over time. Recent Research Trends: His recent publications, appearing in top-tier venues like NeurIPS, AAAI, and AISTATS, demonstrate a consistent focus on improving scalability and effectiveness in representation learning. Key themes include revisiting traditional k-mer methods for modern deep learning, modeling citation dynamics, and developing continuous-time node embedding techniques. His work bridges theoretical advances with practical applications in genomics and network analysis. Scientific Awards: Best Paper Award, TGL Workshop @ NeurIPS 2023 Top Reviewer, LoG 2024 Conference Advising and Grants: While current advisees are not listed, he is actively leading research projects as evidenced by his recent publications and project organization (e.g., Nordic ProbAI summer school). His work is supported through institutional affiliations and likely competitive research funding, given the high-impact venues of his publications. Labs and Teams: He is affiliated with the DKW group at Aalborg University. Previously, he was part of the Inria OPIS team and the Centre for Visual Computing during his Ph.D., and worked in the Section for Cognitive Systems at DTU Compute as a postdoctoral researcher.
Bernhard Jenny is an Associate Professor at Monash University's Faculty of Information Technology, specializing in immersive visualization and geospatial data. He holds a PhD in Cartography from ETH Zurich and has held previous roles at Oregon State University and RMIT University. His research focuses on virtual reality (VR), augmented reality (AR), and cartographic innovations for geospatial data representation. Education: Doctor of Sciences in Cartography, ETH Zurich (2010) Postgraduate Certificate in Computer Science, ETH Zurich (2005) Master of Science in Surveying, EPFL (2000) Research Interests: Combines cartography, computer graphics, and human-computer interaction to explore VR/AR applications for geospatial data. Current work includes immersive analytics, terrain visualization, adaptive map projections, and storytelling with geospatial data. Recent Articles: Focus on ambient occlusion for terrain shading, grammars for immersive visualization transitions, and AR/VR interfaces for spatial data. Awards: Henry Johns Award (2007, 2011, 2012) ETH Medal (2010) Best Paper Honorable Mentions (ACM CHI, DIS) Grants & Projects: Leads initiatives like the 'Immersive Analytics' extension and 'National Geographic Relief Shading' project. Collaborates on neural networks for cartographic relief shading and sustainable development goals (SDGs). Labs & Teams: Heads the Embodied Visualisation Lab at Monash, focusing on immersive analytics and geovisualization tools.
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.
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 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.
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 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 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.
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.
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 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.
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.
