Sourya Roy is an Assistant Professor in the Department of Computer Science at the University of Iowa. Prior to his academic career, he worked as a Data Scientist at Foursquare. He earned his PhD in Computer Science from the University of California, Riverside in 2022, advised by Silas Richelson and Amey Bhangale. Education: PhD in Computer Science (University of California, Riverside, 2022) His research spans theoretical and applied domains, focusing on pseudorandomness, coding theory, and cryptography in theoretical computer science, while also contributing to computer vision and machine learning applications. Recent publications emphasize expander graph theory, coding algorithms, and spatio-temporal modeling. Dr. Roy is actively seeking PhD students to collaborate on theoretical computer science projects. His publications highlight expertise in pseudorandomness, expander graphs, and scalable unsupervised learning techniques. Key conferences include FOCS, ECCV, and IEEE Transactions on Information Theory.
Mark-David Hosale is an Associate Professor and Chair of Computational Arts at York University's School of the Arts, Media, Performance & Design (AMPD). He holds a BA in Music Composition from UC Santa Barbara. His work bridges computational art, performance, and architecture, focusing on worldmaking through interdisciplinary collaborations. Education: BA - Music Composition (UC Santa Barbara) Key Roles: Chair of Computational Arts, nD::StudioLab Director Research Labs: nD::StudioLab Research interests include the intersection of virtual and physical worlds, using digital fabrication and hardware/software integration to create immersive art. His theoretical practice centers on worldmaking —artworks that propose ontological alternatives through sensory experiences. Notable collaborations include the IceCube Neutrino Observatory and the PACIS project. His exhibitions span venues like SIGGRAPH, Dutch Electronic Art Festival, and Venice Biennale. Labs/Teams: nD::StudioLab, PACIS collaboration Grants: Multiple undisclosed research grants Labs include the nD::StudioLab, an experimental space for ArtScience research-creation.
Christian Graugaard is a Professor of Sexology at Aalborg University, affiliated with the Faculty of Medicine and the Department of Clinical Medicine. He is a key member of the Center for Sexology Research and leads Project SEXUS, aiming to study Danish sexual behavior comprehensively. His work emphasizes the intersection of biological, psychological, and cultural factors in human sexuality, challenging simplistic gender-based stereotypes. Research interests include gender differences in sexual behavior, societal norms influencing sexual health, and the cultural dimensions of human sexuality. He actively participates in public discourse, as seen in his DR-podcast interview on 'Ramt af kærlighed,' where he discussed the complex interplay between biology and culture in shaping sexual identities. Though no specific awards or grants are detailed here, his publications span advanced technical domains like spatiotemporal data analysis, federated learning, and trajectory modeling, suggesting interdisciplinary research collaborations. His work on systems like OneDB and SWASH highlights contributions to distributed computing and data science, which may underpin his methodologies in large-scale sexual behavior studies. He currently holds no listed students or formal advisees in the provided texts, and his involvement in labs/teams is limited to the Center for Sexology Research and Project SEXUS.
Mattia Stival is a Researcher at the Department of Economics , Ca' Foscari University of Venice , with a focus on social statistics (SSD: STAT-03/B). His work bridges Bayesian and computational statistics with applications in public health and sports science. Current statistician for the Planet4Health project Previously postdoctoral researcher for the Age-it project on multi-morbidity modeling PhD in Statistical Sciences (University of Padua, 2022) with thesis on Sports Performance Analysis with State Space Models Research Interests combine methodological innovation with real-world impact: Applied : Health inequalities, aging population dynamics, sport-for-health promotion, competitive sports analytics (talent identification, performance monitoring), diffusion models Methodological : Bayesian inference, computational statistics, spatio-temporal modeling, machine learning, Monte Carlo methods Publications demonstrate interdisciplinary trends across: Sports statistics (decathlon/heptathlon scoring, youth-to-elite transition) Bayesian spatio-temporal health modeling Missing data patterns in longitudinal athlete datasets Scientific Recognition : 2023 Honorable Mention for Best PhD Thesis in Applied Statistics by the Italian Statistical Society (SIS) Teaching includes statistics exercises for economics degrees and R coding in finance analytics. Office hours: Wednesday 10-12 by appointment.
Hartwig Henry Hochmair is a Professor of Geomatics at the School of Forest, Fisheries, and Geomatics Sciences , University of Florida, Fort Lauderdale Research and Education Center (FLREC). Since joining the Geomatics program in 2008, he has focused on spatio-temporal analysis, data quality assessment, and the application of Geographic Information Systems (GIS) to interdisciplinary challenges in transportation, natural resource management, and urban planning. His work bridges academic research with practical GIS tools, particularly through the use of freely available geospatial data sources like OpenStreetMap and USGS Topo Maps. Research Interests : Geomatics, GIS, spatio-temporal analysis, data quality evaluation, GeoAI, OpenStreetMap, invasive species monitoring, and urban canopy analysis. Teaching : Courses include SUR 3520 Measurement Science, SUR 4350/6536 Geodesy and Geodetic Positioning, SUR 5525 Least Squares Adjustment, SUR 5365 Digital Mapping, and SUR 6103 GIS Programming. Lab : Leads the Geomatics program at FLREC, focusing on geospatial data visualization, accessibility modeling, and network analysis. His recent publications emphasize the integration of AI in geospatial workflows, comparative studies of volunteered and authoritative street data, and pandemic-induced shifts in human mobility. While no formal awards are highlighted in the provided texts, his work frequently addresses environmental challenges, technological innovations in mapping, and open data accessibility.
Furkan Kıraç is an Assistant Professor in the Computer Science Department at Özyeğin University, specializing in Computer Vision and Machine Learning . He previously served as a Part-Time Instructor at the same university (2012-2013) and as a Research Assistant at Boğaziçi University (2009-2013). Education: PhD in Computer Engineering, Boğaziçi University (2013) MS in Systems and Control Engineering, Boğaziçi University (2002) BS in Mechanical Engineering, Boğaziçi University (2000) His research focuses on real-time hand pose estimation , deep learning , and computer vision applications in industrial automation. Recent publications highlight his work on pedestrian tracking, spatio-temporal mapping, and image processing pipelines for test oracle automation. Notable achievements include founding two computer vision companies ( Proksima and Fortibase ) and receiving awards at SIU conferences (2004, 2005, 2012). He has contributed to projects funded by TÜBİTAK and the Scientific and Technical Research Council of Turkey. Scientific Awards: 3rd place in best demo award (SIU 2012) Best application paper award (SIU 2012) 3rd degree in Turkish National Science Competition (1994, 1995) Gold/Silver/Bronze medals in National Computer Science Olympiads
Manuel Mucientes Molina is a Full Professor at the Research Center on Intelligent Technologies (CiTIUS) within the University of Santiago de Compostela . His research focuses on Artificial Intelligence , particularly in Computer Vision and Machine Learning , with applications in object detection, process mining, and healthcare diagnostics. Research Areas : Machine learning, Computer vision, Process mining, Deep learning, AI for healthcare. Projects : Protonterap-IA (2025), AZOR (2024), RAI4P (2021), eXplica-IA (2018), DronePlan (2014), SoftLearn (2012). Publications highlight advancements in few-shot object detection, small object tracking, and AI-driven conformance checking. Notable collaborations include work on X-ray vision systems and medical mask detection in operating rooms. Awards : Best Student Paper Nomination (2015), Runner-up best industry-oriented paper award (2008). Teaching includes courses on Statistical Learning, Deep Learning, and Automata Theory at the M.Sc. and B.Sc. levels.
Dr. Utku Norman is a Researcher at the Institute for Technology Assessment and Systems Analysis (ITAS), part of the Karlsruhe Institute of Technology (KIT) . His work focuses on Human-Robot Interaction and Artificial Intelligence applications in educational contexts, particularly through collaborative learning systems. Since 2023, he has contributed to projects like the Real-World Laboratory for Robotic Artificial Intelligence and JuBot , exploring the intersection of technology, cognition, and social impact. Ph.D. , Computer and Communication Sciences, EPFL, Switzerland (2023) M.Sc. , Computer Engineering, Bilkent University, Türkiye (2018) B.A. , Philosophy, Middle East Technical University, Türkiye (2018) B.Sc. , Electrical and Electronic Engineering, Middle East Technical University, Türkiye (2016) Norman's research integrates Technology Assessment and Systems Analysis to study how autonomous, self-learning AI systems interact with humans in practical settings. His work spans disciplines like Computational Thinking , Educational Robotics , and Machine Ethics , often combining empirical studies with algorithmic development. His publications highlight trends in Human-Robot Interaction , focusing on collaborative learning, robot perception in children, and ethical frameworks for autonomous systems. Notably, he contributed to establishing empirical methods for analyzing robot-mediated educational activities and developed tools like the ST-Steiner algorithm for genetic research in earlier career stages. Awards : Marie-Skłodowska-Curie PhD Fellowship (2018–2022) Norman has collaborated with institutions such as EPFL , Télécom Paris , and Bilkent University , participating in EU-funded projects like ANIMATAS . His current work involves analyzing social and psychological dimensions of robotics and developing empirical research methods in human-machine interaction. Contact: utku.norman@does-not-exist.kit.edu
Xin Tang is an Assistant Professor at the Michael Smith Laboratories and the Department of Computer Science in the Faculty of Science at the University of British Columbia. He leads the Tang Lab, which focuses on developing AI models to advance biological understanding at multiple scales and modalities. PhD in Engineering Sciences from Harvard University and the Broad Institute of MIT and Harvard Xin Tang's research spans computational cell biology, brain-computer interfaces, and in silico cellular digital twins. His work integrates explainable and interpretable AI with biological systems to address fundamental questions from molecular interactions to animal behaviors. Key areas include computational omics, multi-modality cell biology, spatio-temporal gene regulation, neuroengineering, and biological large language models. His lab develops autonomous AI approaches that serve as digital twins for biological systems, enabling in silico experiments that guide wet lab research. Analysis of Tang's recent publications reveals a strong focus on bridging AI and biology across multiple scales. His work spans from molecular and cellular levels (single-cell biology, multi-omics, spatial transcriptomics) to neural systems (brain-computer interfaces, neural activity tracking) and organ-level applications (cardiac interfaces). A consistent theme is the development of explainable and interpretable AI methods that provide mechanistic insights rather than just predictive power. His research has significant implications for understanding development, aging, and diseases like neurodegeneration. NSERC Discovery Grant (2025) Resource Allocation Competition of Digital Research Alliance of Canada (2025) Professor Tang actively supervises multiple graduate students, postdoctoral fellows, and undergraduate researchers across UBC's Computer Science, Bioinformatics, and Genome Science and Technology programs. His lab has received significant research funding including an NSERC Discovery Grant. He is committed to interdisciplinary collaboration and has established research partnerships with biologists, engineers, and clinicians to address complex biological questions related to neurodegenerative diseases, heart disease, and aging. The Tang Lab, located in the Michael Smith Laboratories at UBC, fosters a collaborative environment for researchers interested in AI for biology. The lab actively recruits dry-lab researchers with strong coding and machine learning backgrounds to work on projects spanning computational biology, neuro-inspired AI, explainable AI, biological LLMs, computational omics, and brain-computer interfaces. The lab has a remote work policy that allows flexible arrangements while maintaining strong collaborative ties.
Aude Oliva serves as MIT director of the MIT-IBM Watson AI Lab and director of strategic industry engagement at the MIT Schwarzman College of Computing. As a Senior Research Scientist at MIT CSAIL, she leads the Computational Perception and Cognition group, driving interdisciplinary research at the intersection of human intelligence and artificial systems. Her roles position her at the forefront of translating academic AI research into real-world applications through major industry partnerships. Dr. Oliva earned her MS and PhD in cognitive science from Institut National Polytechnique de Grenoble, France, establishing her foundation in human perception and computational modeling. Her research integrates computer vision, deep learning, and cognitive neuroscience to understand visual information processing in both biological and artificial systems. She develops computational models that mimic human visual recognition while creating AI systems capable of compositional reasoning and efficient video understanding. Current work emphasizes neuroscience-inspired architectures, resource-efficient deep learning, and multimodal representation learning, with applications spanning healthcare, robotics, and human-computer interaction. Her cross-disciplinary approach uniquely bridges theoretical neuroscience with practical AI development. Analysis of recent publications reveals a clear trajectory toward tighter integration of neuroscience and AI, particularly through brain imaging datasets like BOLD Moments. Her group consistently advances efficient deep learning techniques (Trans-LoRA, VA-RED²) while exploring fundamental questions in visual cognition through projects like the Algonauts Challenge. The work demonstrates increasing industry relevance with strong representation in NeurIPS and Nature Communications. Her major recognitions include: NSF Career Award in computational neuroscience Guggenheim fellowship in computer science Vannevar Bush Faculty Fellowship in cognitive neuroscience As director of the $240M MIT-IBM Watson AI Lab, Dr. Oliva oversees substantial research funding while advising graduate students through MIT's EECS department. Her lab benefits from unique industry-academic synergy, with students gaining access to IBM resources and real-world deployment challenges. The collaborative environment fosters innovation in efficient AI systems with tangible societal impact. The Computational Perception and Cognition group operates as a dynamic hub where computer scientists, neuroscientists, and cognitive scientists collaborate on fundamental questions of intelligence. Current projects focus on making AI systems more human-like in visual reasoning while ensuring computational efficiency for real-world deployment, leveraging the unique resources of the MIT-IBM partnership.
Christos Chalkias serves as Professor and Vice-Rector for Research, Development and Lifelong Learning at Harokopio University's Department of Geography. Based in Athens, Greece, his academic office is located in the Library Building (Office 4.5). His educational background includes a Degree in Geology (1991) and PhD in Physical Geography & Geoinformatics (1996), both completed at the National and Kapodistrian University of Athens. His research encompasses diverse geospatial domains with particular emphasis on: Geographic Information Systems & Science – Developing spatial analysis methodologies Applied Geography – Solving real-world environmental and societal challenges Spatial Analysis – Examining geographic patterns and processes Environmental Modeling – Simulating physical phenomena Health Geography – Analyzing spatial health disparities Digital Cartography – Innovating map visualization techniques Recent publications (2023-2025) demonstrate strong focus on geospatial applications in health epidemiology, environmental monitoring, and disaster management. His work frequently employs remote sensing, spatial statistics, and GIS technologies to address Mediterranean-region challenges including cardiovascular disease patterns, light pollution, soil degradation, and earthquake impacts. Distinct research trends include historical geospatial reconstruction, nocturnal earth observation, and community-engaged environmental sensing.
Erik Otárola-Castillo is an Associate Professor in the Department of Anthropology at Purdue University, where he joined the faculty in 2015 and was promoted to Associate Professor. His interdisciplinary work bridges archaeology, human evolutionary biology, and biostatistics, focusing on prehistoric and modern hunter-gatherer populations and their responses to environmental change. His educational background includes: Ph.D. in Anthropological Science, Stony Brook University M.A. in Anthropology, Iowa State University B.A. in Anthropology, Stony Brook University Dr. Otárola-Castillo specializes in the evolution, ecology, and diversity of behavior in hunter-gatherer societies, with particular emphasis on climate change impacts and food availability effects on early North American native diets. As a computational anthropologist, he develops quantitative tools including 3D morphometrics software, statistical models for zooarchaeologists, and spatio-temporal analysis frameworks. His research integrates Geographic Information Systems (GIS) and Bayesian statistics to address core archaeological questions of space, time, and form, with applications ranging from bone surface modification analysis to human-megafauna interactions. Recent publications reveal a strong methodological trend toward Bayesian inference for archaeological hypothesis testing and computational morphometric analysis of artifacts and skeletal remains. Key thematic areas include lithic technology standardization, subsistence intensification mechanisms among Great Plains hunters, and health impacts of urbanization on indigenous Peruvian populations, demonstrating consistent application of quantitative rigor across diverse anthropological contexts. He is recognized as an international authority, evidenced by an invitation to contribute a review on Bayesian approaches to the Annual Review of Anthropology (2018). His work appears in high-impact journals including The Proceedings of the National Academy of Sciences (PNAS) and PLoS One. Dr. Otárola-Castillo directs the Laboratory for Computational Anthropology and Anthroinformatics (LCA), which develops open-source tools like geomorph and zooaRch for anthropological research. The lab's work enables advanced morphometric analysis and zooarchaeological quantification, supporting collaborative projects worldwide through computational innovation in data collection, analysis, and visualization.
Hoda Eldardiry is an Associate Professor in the Department of Computer Science at Virginia Tech, where she directs the Machine Learning Laboratory. Her research focuses on artificial intelligence and machine learning, particularly in building human-machine collaborative AI systems that can learn context-aware and explainable models from multisource and interconnected data. Prior to joining Virginia Tech, she led research at Palo Alto Research Center (Xerox PARC) in the machine learning research group. Dr. Eldardiry received her educational qualifications from: BE in Computer and Systems Engineering from Alexandria University, Egypt MS and PhD in Computer Science from Purdue University Her research interests span multiple domains of AI and machine learning. She specializes in robust machine learning for information extraction, forecasting, and control. Her work integrates graph neural networks, time-series analysis, and relation extraction to develop explainable and context-aware AI systems. She also investigates the intersection of AI with ethics, policy, and governance, exploring how to build responsible AI systems that align with human values and societal needs. Dr. Eldardiry's recent publications demonstrate a strong focus on advancing graph-based time-series modeling, zero-shot learning techniques, and optimal control systems. Her work bridges theoretical advancements with practical applications in healthcare, transportation, and e-commerce. She has made significant contributions to knowledge graph construction, explainable AI, and federated learning frameworks that operate efficiently in resource-constrained environments. Her scientific achievements have been recognized with several prestigious awards: Purdue University College of Science Early Career Scientist Award for the Department of Computer Science (2021) Honorable Mention Best Paper Award for Exploring Approaches to Artificial Intelligence Governance: From Ethics to Policy (IEEE Ethics 2023) Most Cited Paper Award for COVID-19 Pandemic Impacts on Traffic System Delay, Fuel Consumption and Emissions (2023) Purdue CS Women's History Month Celebration Recognition (2022) VT CS Women's History Month Celebration Recognition (2023) Early Career Distinguished Scientist Award from Purdue University College of Science (2021) Purdue University College of Science Distinguished Alumni (2021) Dr. Eldardiry has successfully secured substantial research funding, with total grant funding of $27,424,460 ($13,808,328 share) from diverse sources including VT, IARPA, DOE, NSF, DARPA, NIH-iTHRIV, CCI, EBAY, SIEMENS, ADOBE, P&G and XEROX. Her current projects include NSF-funded research on Advancing Health Equity using Interactive Condition Assessment and Monitoring and Exploring How AI Engineers Perceive and Develop Translational Ethical Competency, as well as industry collaborations with EBAY on Heterogeneous Hypergraph Modeling for Zero-Shot Product Aspect Identification. As director of the Machine Learning Laboratory at Virginia Tech, Dr. Eldardiry leads a research team that bridges theoretical AI advancements with real-world applications. Her lab collaborates extensively with industry partners and government agencies to develop practical AI solutions while maintaining a strong commitment to ethical considerations and societal impact.
Altti Ilari Maarala is a Researcher specializing in computational genomics and bioinformatics, with active participation in multiple Academy of Finland-funded cancer research initiatives. His work bridges computer science and genomics through scalable computational methods. Research Focus Altti develops distributed computing solutions for genomic data challenges, including: Pan-genome indexing and compressed data structures for sequence alignment Spark-based frameworks for genome assembly and genotype imputation High-throughput sequencing analytics in population genomics Visualization tools for tumor evolution dynamics Active Projects Key collaborative efforts: DYNAMITE (2025-2028): Targeting transcription factor dynamics in ovarian cancer therapy MULTISTANC (2025-2027): Multi-modal data integration to overcome chemotherapy resistance iCAN Digital Precision Cancer Medicine (2022-2026): Flagship program for data-driven oncology Publication Trends Altti's recent work emphasizes scalable cloud-based genomics, with publications focusing on distributed algorithms for genome assembly (Spark), compressed pan-genome indexing, population-scale data analytics, and cancer evolution visualization. His research consistently integrates high-performance computing with biological data challenges.
Gillian Lawson is an Associate Professor at the School of Landscape Architecture at Lincoln University, New Zealand, where she has been serving since 2018. She also holds the position of Co-Director of the China-New Zealand Joint International Lab for Climate Change Response with Beijing Forestry University since 2024. Previously, she was Head of School of Landscape Architecture at Lincoln University from 2018-2022 and held various academic positions at Queensland University of Technology in Australia from 2003-2017, including Head of Discipline of Landscape Architecture from 2014-2017. Dr. Lawson earned her PhD from Queensland University of Technology (2001-2007), Master of Agricultural Science (Research) from University of Sydney (1985-1991), Bachelor of Agricultural Science (Honours) from University of New England (1980-1983), Graduate Certificate in Education from QUT (1998-2000), and Graduate Diploma of Landscape Architecture (Distinction) from QUT (1995-1996). Her research interests focus on the intersection of landscape architecture with climate change adaptation, particularly examining water and food systems as catalysts for improving urban resilience. She investigates landscape pedagogy, landscape visualization techniques, and landscape sociology across Australia, New Zealand, and other Asia-Pacific countries. Her work explores social practices in public and private open spaces, green infrastructure, and waterfront communities in landscape planning and design. Dr. Lawson's scholarly contributions demonstrate a strong focus on sustainable urban development, flood mitigation through green infrastructure, mental health benefits of landscape design, and climate change adaptation strategies. Her recent work shows increasing emphasis on international collaborations, particularly with China, and on translating research into practical applications for community resilience. Elected to Board of Trustees for the Landscape Research Group (UK) (2021-present) Associate Editor for Landscape Research (2022-present) Chief Editor for Landscape Review (2023-present) Guest Editor for Special Issue in Land on Innovative Solutions for Mitigating Coastal Flooding (2023) Co-Director of China-New Zealand Joint International Lab for Climate Change Response (2024-present) Senior Fellow, UK Professional Standards Framework (2017-present) Registered Landscape Architect (New Zealand Institute of Landscape Architects, 2019-present) Dr. Lawson has supervised numerous PhD students to completion across diverse topics including carbon sequestration in urban wetlands, flood mitigation strategies, participatory landscape architecture, and campus landscape design for health promotion. She currently supervises four PhD candidates working on projects related to nature deficit disorder, water citizenship, high-density communities with blue-green infrastructure, and playground design for UV protection. Her research has been supported by various scholarships including LU Doctoral Scholarships, CSC Scholarships, and AusAid Scholarships. She is actively involved with the Pūharakekenui Styx Living Laboratory Trust as an MOU Partner Advisor and collaborates with the Landscape Research Group (UK) and the New Zealand Institute of Landscape Architects. Her work aligns with multiple UN Sustainable Development Goals including Zero Hunger, Good Health and Well-Being, Quality Education, Clean Water and Sanitation, Sustainable Cities and Communities, Responsible Consumption and Production, Climate Action, and Life on Land.
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