Rei Sanchez-Arias is a Teaching Professor and Director of the Master of Applied Data Science (MADS) program at UNC Chapel Hill's School of Data Science and Society. His expertise includes data mining, machine learning algorithm development, and data science pedagogy. He previously held positions at Florida Polytechnic University and St. Thomas University. Research interests span educational tools, health informatics, and optimization methods. Recent work involves AI for endoscopic surgery evaluation, meta-analysis of MLOps tools, and curriculum design for data science programs. Awards include the Excellence in Teaching Award from Florida Polytechnic. Student mentorship focuses on data wrangling and analytics projects.
Joanna Chiu is a Professor and Chair in the Department of Entomology at the University of California, Davis, affiliated with the College of Agricultural and Environmental Sciences and the UC Davis Genome Center. She holds a B.A. in Biology and Music from Mount Holyoke College and a Ph.D. in Molecular Genetics from New York University. Her research focuses on molecular mechanisms underlying animal behavior, circadian rhythms, seasonal biology, and insect genomics, with applications in pest management and agricultural science. Key research interests include understanding how circadian clocks integrate environmental cues like temperature and light to regulate physiology and behavior, particularly in insects. Her work involves molecular genetics, transcriptomics, and genomic tools to study pest species such as Drosophila suzukii and Tuta absoluta, addressing challenges like pesticide resistance and invasive species control. She has developed diagnostic tools (e.g., RPA-Cas12a assays) and software (e.g., CRUMB app) for analyzing insect behavior and genetics. Recent publications highlight studies on circadian temperature compensation, seasonal adaptation in insects, and the molecular basis of pest resistance. Her lab’s collaborations span entomology, genomics, and computational biology, with implications for sustainable agriculture and chronobiology. Grants and funding support her work on pest genomics, circadian mechanisms, and translational applications in pest control. Joanna Chiu mentors students and researchers in entomology, biochemistry, and genetics through UC Davis graduate groups (ENT, BMCDB, IGG, ABGG). Her lab actively contributes to genome assembly projects for agricultural pests and explores novel strategies for integrated pest management using molecular and genetic approaches.
Seth Frey is an Associate Professor in the Department of Communication at the University of California, Davis. His research focuses on computational social science, exploring governance institutions and complex human decision-making through computational methods, large datasets, and web-based experiments. He examines online communities as models of governance, with expertise in computational approaches to institutional design and strategic behavior analysis. Education: Ph.D. in Cognitive Science and Informatics, Indiana University, 2013 B.A. in Cognitive Science, UC Berkeley, 2004 Research Interests: Frey's work integrates data science, lab experiments, and computational modeling to study human organizations and communication. Key areas include governance technology, cognitive mechanisms of social outcomes, and institutional evolution. His projects span online games, sports, and open-source software communities, emphasizing topics like collective action, self-governance, and cooperative behavior. Publications & Funding: His work appears in journals like PNAS and Nature Scientific Reports, and has been funded by NSF, NASA, and the Ford Foundation. He contributes to the Ostrom Workshop at Indiana University and directs the Computational Communication Lab at UC Davis. Teaching: Frey teaches courses on data visualization, simulation methods, and online data analysis in the social sciences.
Jan Techter is a Professor at TU Berlin's Institute of Mathematics, specializing in Geometry and Mathematical Physics. He leads the research group AG Geometrie und mathematische Physik and is affiliated with the SFB/TRR 109 project on discretization in geometry and dynamics. His research focuses on discrete differential geometry, Laguerre geometry, and incircular nets. Education: Bachelor of Science in Mathematics (2013) Master of Science in Mathematics (2015) PhD in Mathematics (2020) Research Interests: Jan Techter explores geometric structures such as confocal quadrics, checkerboard incircular nets, and discrete parametrized surfaces. His work bridges continuous and discrete geometries, emphasizing integrable systems and Laguerre geometry. Recent projects include studies on principal binets and Koenigs binets. Awards & Recognition: No scientific awards explicitly listed in the text. Advising & Grants: Supervised over a dozen student theses, including master's and bachelor's projects on topics like pencils of quadrics, Laguerre geometry, and discrete channel surfaces. Collaborates on research grants through TU Berlin and the SFB/TRR 109 initiative. Labs & Tools: Co-developed the DGD Gallery for digital research data and the pyddg geometry library. Produced visualizations such as the Koebe polyhedra and minimal surfaces short movie.
Christoph T. Koch is a Professor of Physics at Humboldt-Universität zu Berlin, where he has held the W3 Chair since 2015. Previously, he held a similar position at Ulm University (2011–2015), supported by the Carl Zeiss Foundation. His research focuses on advanced electron microscopy techniques, including quantitative transmission electron microscopy (TEM), electron holography, and strain mapping. He leads the AG Strukturforschung/Elektronenmikroskopie group, advancing materials science through innovations in imaging and spectroscopy. Education: B.Sc./M.Sc. in Physics at Heidelberg University (1996–1998), followed by an exchange at Arizona State University (1997–1998). PhD in Physics from Arizona State University (2002, advisor: Prof. John C.H. Spence). Postdoctoral research at the Max Planck Institute for Metals Research, Stuttgart (2002–2011). Research interests include: Electron diffraction and phase retrieval Nanometer-scale strain and defect analysis Electron energy-loss spectroscopy (EELS) for plasmonics and bandgap mapping Development of FAIR data infrastructure for materials science Leadership: Managed the Department of Physics at Humboldt University (2020–2024). Collaborates widely, with key co-authors including P.A. van Aken, W. Sigle, and C. Felser. His work bridges experimental microscopy and computational modeling, addressing challenges in semiconductors, ceramics, and 2D materials. Notable contributions include pioneering methods for 3D reconstruction via electron ptychography, dynamic electron diffraction analysis, and strain mapping in advanced CMOS technologies. Current efforts emphasize real-time imaging and AI-driven data analysis in materials research.
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.
Anders Koed Madsen is a Professor at the Department of Culture and Learning, Faculty of Humanities and Social Sciences, Aalborg University. He is affiliated with the Technoanthropological Laboratory and leads research initiatives including AI for the People and MASSHINE. His work centers on digital methods, urban belonging, smart cities, and citizen science, often integrating ethnographic and participatory approaches. His research interests lie at the intersection of technology and society, focusing on digital placemaking , urban sensing , pragmatist philosophy , and inclusive urban planning . He explores how digital tools can be used to understand lived experiences in cities, especially among marginalized communities. His methodological focus includes participatory digital methods, geospatial photovoice, and computational ethnography. The recent articles highlight a consistent trend toward using digital and ethnographic methods to study urban life, technology assessment, and democratic participation. Themes include generative AI , digital epistemology , friction in machine reasoning , and inclusive city planning . His work increasingly incorporates large language models and open-source toolkits to democratize research and planning processes. Scientific Awards: European Union Prize for Citizen Science (2023) World Summit Awards (WSA) shortlist, Urban Belonging Project (2024) Ziman Award 2020: TANTlab AAU Talent (2018) Videnskabsministeriets EliteForsk-rejsestipendium (2011) Anders Koed Madsen has been actively involved in research grants and collaborative projects such as the Urban Belonging Project , Digital Placemaking and Soft City Sensing Research Network , and GE-AI: Generative Ethnographic AI . He frequently collaborates with interdisciplinary teams and institutions like IT University of Copenhagen and Gehl Architects. He advises on public engagement and has contributed to national and international discourse on digitalization and urban futures. He is a core member of the Technoanthropological Laboratory , which fosters interdisciplinary research on technology and society. The lab supports initiatives in digital methods, citizen science, and urban innovation. Madsen also contributes to public understanding through media engagement and workshops, promoting humanistic perspectives in technological development.
Xavier Alameda-Pineda is a Research Director at Inria Grenoble Rhône-Alpes, where he leads the RobotLearn Team. He is affiliated with Université Grenoble Alpes and has been a key member of the Perception team. His work integrates machine learning, computer vision, and audio processing for scene understanding and human-robot interaction. Research Interests: His research lies at the intersection of multimodal machine learning and social behavior analysis. He focuses on developing algorithms for understanding human behavior in natural settings using audio-visual signals, with applications in robotics and AI companions. His work emphasizes real-world challenges such as noisy data, missing modalities, and dynamic environments. Publication Trends: His recent publications reflect a consistent focus on multimodal fusion, particularly combining vision and audio for social scene analysis. Themes include group behavior recognition, sound source separation, and cross-modal learning, often applied in robotics contexts. Scientific Awards: SIGMM Rising Star Award 2018 IEEE TMM Outstanding Associate Editor Award 2022 ACM TOMM Best Paper Award 2020 Best Paper Award, ACM MM 2015 Best Scientific Paper Award, ICPR 2016 Best Student Paper Award, IEEE WASPAA 2015 Outstanding Paper Award, ICMI 2011 Novel Technology Paper Award Finalist, IROS 2017 Advising and Grants: Xavier has mentored students and early-career researchers, evidenced by co-authored student papers. He coordinated the H2020 SPRING project on socially pertinent robots in gerontological healthcare and co-leads an AI chair on audio-visual perception for companion robots, indicating leadership in funded research initiatives. Labs and Teams: He is the leader of the RobotLearn Team at Inria and was previously part of the Perception team. He has also collaborated with the Multimodal and Human Understanding Group at the University of Trento.
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.
Sergio Barbero is an Associate Researcher at the Visual Optics laboratory of the Instituto de Óptica (CSIC), Spain, under the supervision of Prof. Susana Marcos. He holds a BSc in Physics from the University of Zaragoza (1999) and a PhD in Visual Sciences from the University of Valladolid (2004), which earned him the Doctoral Thesis Extraordinary Award (2005). His research focuses on optical aberrations, intraocular lens design, wavefront measurement techniques, and gradient-index modeling of ocular structures. Barbero has collaborated with international groups at Indiana University (USA), University of Houston (USA), and Australian institutions. His work includes pioneering studies on crystalline lens tomography, corneal ablation algorithms, and novel wavefront sensing methods. He has authored 16 peer-reviewed publications and contributed to a US patent on wavefront reconstruction techniques. His research spans three core areas: (1) intraocular lens design using analytical tools, (2) gradient-index modeling of the human eye, and (3) in vivo measurement of crystalline lens aberrations. He has secured grants from the Spanish government (I3P-CSIC), NIH (USA), and Fulbright fellowships. Barbero has presented 33 scientific talks/posters, including invited lectures, and maintains an h-index of 9. His work bridges fundamental optics with clinical applications in ophthalmology.
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.
Peter J. Kohler is an Assistant Professor in the Department of Biology within the Faculty of Science at York University, Toronto. He is eligible to supervise graduate students in the Biology Graduate Program and leads the Kohler Visual Neuroscience Lab, which is part of the Centre for Vision Research at York University. His research lies at the intersection of cognitive neuroscience and visual perception, focusing on mid-level visual processing. This involves understanding how the brain, within the first few hundred milliseconds of visual input, constructs representations of shape, motion, location, and perceptual organization—including figure-ground segregation, grouping, and constancy. His work integrates functional MRI (fMRI) , electroencephalography (EEG) , and visual psychophysics to probe the neural mechanisms underlying these processes in humans. Recent publications reveal a strong focus on symmetry processing, perceptual grouping, numerical estimation, and multisensory integration. His work often involves advanced neuroimaging techniques and collaborative international research, particularly with teams in Belgium and Luxembourg. The articles span high-impact journals such as PNAS , Nature Communications , Current Biology , and Journal of Vision , indicating a robust and influential research program in visual neuroscience. Scientific Awards and Funding: NSERC Discovery Grant (awarded April 2020) VISTA Research Grant (funded June 2023) Prof. Kohler actively mentors students, including graduate students such as Rachel Moreau, Sara Chaparian, Yara Iskandar, Shaya Samet, and Shenoa Ragavaloo, as well as undergraduate researchers. His lab has presented at major conferences including the Vision Sciences Society (VSS) and the Lake Ontario Visionary Establishment (LOVE), where he joined the organizing committee in 2024. The Kohler Visual Neuroscience Lab also develops experimental tools, as evidenced by GitHub repositories for stimulus generation and behavioral testing using jsPsych.
Brian M Deal serves as a Professor of Landscape Architecture within the School of Architecture at the University of Illinois at Urbana-Champaign, holding additional appointments in Urban and Regional Planning, the European Union Center, the Center for Latin American and Caribbean Studies, and the National Center for Supercomputing Applications (NCSA). His expertise bridges sustainable planning theory, energy systems, and spatial modeling to develop practical decision-support tools for community development and climate resilience. His educational foundation includes a PhD in Regional Planning (2002), Master of Architecture (1997), and BS in Architectural Studies (1983), all earned at the University of Illinois at Urbana-Champaign. Prior academic experience encompasses a decade of professional architecture practice and senior research at the Army Construction Engineering Research Laboratory (CERL), where he specialized in sustainable military facility design using spatial simulation. Deal's research centers on sustainable planning systems and climate adaptation, with current projects examining urbanization impacts on Korean rural amenities, advancing the University of Illinois' climate action plan (iCAP), and developing next-generation 'sentient' planning support systems. His work integrates land-use modeling, energy systems analysis, and decision-support technologies to address complex urban environmental challenges through interdisciplinary collaboration. Recent publications reveal a pronounced shift toward data-driven sustainability solutions, featuring AI applications for carbon-neutral planning, multi-scaled green infrastructure optimization, and socio-ecological modeling. Key themes include urban carbon sequestration, post-pandemic park dynamics, and climate-resilient coastal design, demonstrating consistent innovation in translating theoretical frameworks into actionable planning tools for real-world implementation. Professor Deal's scientific awards and honors were not detailed in the provided text. As faculty mentor to the Student Sustainability Committee and chair of campus sustainability planning efforts, Deal actively guides student development and institutional policy. His leadership of the LEAM Laboratory and SEDAC involves managing research grants focused on urban resilience, energy systems, and climate adaptation, fostering partnerships with government agencies and community organizations to deploy planning tools that directly impact community decision-making processes. Deal directs the Land Use Evolution and Impact Assessment Modeling (LEAM) Laboratory and Smart Energy Design Assistance Center (SEDAC), leading interdisciplinary teams in developing spatial simulation models and decision-support systems. His operational leadership extends to authoring the university's climate action plan and chairing campus sustainability committees, positioning him at the nexus of academic research, institutional policy, and community engagement for sustainable urban futures.
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.
Scott T. Doyle is an Associate Professor in the Department of Pathology and Anatomical Sciences at the Jacobs School of Medicine & Biomedical Sciences, University at Buffalo. His research integrates biomedical imaging, artificial intelligence, and computational pathology to develop quantitative tools for clinical diagnostics and anatomical modeling. Education: PhD in Biomedical Engineering, Rutgers, The State University of New Jersey (2011) BS in Biomedical Engineering, Rutgers, The State University of New Jersey (2006) Optical Microscopy & Imaging in the Biomedical Sciences, Marine Biological Laboratory (2014) hES Stem Cell Culture Training, WNYSTEM (2014) R Bioconductor Training, Roswell Park Cancer Institute (2016) Dr. Doyle’s research focuses on developing AI-driven algorithms for biomedical image analysis, particularly in digital pathology and 3D anatomical modeling. His work spans tumor segmentation, risk prediction in oral and thyroid cancers, and integration of virtual and physical anatomy in medical education. He applies machine learning, deep learning, and computational modeling to enhance diagnostic accuracy and patient outcomes. His recent publications reflect a strong trend in applying artificial intelligence to histopathology, with emphasis on active learning, 3D reconstruction, and multi-institutional data fusion. Key areas include oral cavity cancer recurrence prediction, thyroid cancer subtyping, and computational modeling of surgical margins and anatomical structures. Scientific Service and Recognition: Reviewer for NIH SPORE grants Peer reviewer for journals including Medical Image Analysis , BMC Bioinformatics , IEEE Transactions on Biomedical Engineering Program Committee and Session Chair, SPIE Medical Imaging: Digital Pathology (2016–present) Member, Graduate Program Steering Committee, Pathology & Anatomical Sciences Mentor, McNair Scholarship and CSTEP programs for underrepresented students Dr. Doyle has secured significant research funding as Principal Investigator on NIH and CTSI grants, including a $2M+ NIH grant for predicting oral cancer recurrence. He has also contributed to educational innovation through hybrid anatomy curriculum development and AI training for pathologists. He leads the 'Atoms to Anatomy' research initiative and is active in strategic planning at the Jacobs School. Laboratories and Collaborative Teams: Dr. Doyle collaborates with the Center for Computational Research (CCR) and is involved in the Structural Sciences Learning Center (SSLC). He has led projects with teams at Ibris, Inc., Veterans Affairs Hospital, and Mount Sinai School of Medicine.
