Peter K. Bol is the Charles H. Carswell Professor of East Asian Languages and Civilizations at Harvard University. His research focuses on China's cultural elites from the 7th to 17th centuries, geospatial analysis, and digital humanities projects including the China Historical Geographic Information Systems (CHGIS) and China Biographical Database (CBDB). His research interests include: Intellectual transitions in Tang and Sung China Neo-Confucianism and its historical context Geospatial analysis in historical research Biographical database development Digital approaches to Chinese history Bol has led significant university-wide initiatives including the establishment of Harvard's Center for Geographic Analysis in 2005 and has served as Vice Provost (2013-2018) overseeing HarvardX, the Harvard Initiative in Learning and Teaching, and online learning research.
Alberto Viglione is an Associate Professor at the Politecnico di Torino , Department of Environment, Land and Infrastructure Engineering (DIATI), and a member of the Interdepartmental Center SmartData@PoliTO. He has been a faculty member since 2019, following a decade as a Research Fellow at the Vienna University of Technology. University: Politecnico di Torino Department: DIATI – Department of Environment, Land and Infrastructure Engineering Rank: Associate Professor Email: alberto.viglione@polito.it His research focuses on flood hydrology, water resources, and hydro-meteorological extremes , integrating statistical analysis, climate change impacts, land use dynamics, and socio-hydrological modeling. He investigates the spatio-temporal dynamics of climatic, hydrological, and human processes in river basins and their implications for extreme event risks. His work emphasizes data integration, conceptual modeling, and risk assessment across scales. The recent publications highlight a strong trend in analyzing European flood dynamics , the impacts of climate change , and the development of socio-hydrological frameworks that incorporate human behavior and societal memory into flood risk modeling. His research spans from statistical hydrology in ungauged basins to large-scale assessments of climate-flood interactions. Scientific Awards and Honors: AMGA Award for best PhD thesis on water resources (2009) Editorial and Professional Service: Associate Editor, Water Resources Research (2014–present) Associate Editor, Hydrological Sciences Journal (2012–2018) Associate Editor, WIRES Water (2012–2020) Associate Editor, Journal of Hydrology and Hydromechanics (2019–present) Scientific Committee Member, European Geosciences Union (2019–2023) Secretary, International Commission on Water Resources Systems, IAHS (2015–present) Teaching and Advising: He teaches courses such as Bayesian Inference , Applied Hydrology , Fundamentals of Environmental Geosciences , and Hydro-meteorological Risk Assessment . He is a PhD supervisor and member of multiple PhD colleges in Civil and Environmental Engineering at Politecnico di Torino. He currently advises PhD students including Tsion Ayalew Kebede , Emanuele Mombrini , Luigi Cafiero , Luca Lombardo , and Matteo Pesce . His research is supported by grants from national (PRIN), EU, and commercial sources, including projects like Clim2FlEx , RETURN , and ATO4WATER . Research Labs and Teams: He is affiliated with the SmartData@PoliTO laboratory, focusing on big data and data science applications in hydrology and environmental systems.
Nina Schwarz is Assistant Professor of human-environment interactions in cities at the Department of Urban and Regional Planning and Geo-Information Management, ITC—University of Twente. Holding a Diploma in Environmental Sciences (University of Lüneburg, 2003) and a PhD in Social and Economic Sciences (University of Kassel, 2007), she spent a decade as senior scientist at the Helmholtz Centre for Environmental Research – UFZ before joining ITC. Her interdisciplinary research integrates urban land-use science, ecosystem-service evaluation and advanced modelling techniques—especially agent-based models—to explore sustainable urban development under global change. Research interests revolve around three interconnected themes: (i) urban land-use change —understanding how residential, commercial and green-space dynamics co-evolve; (ii) urban ecosystem services —quantifying both supply and demand of services such as local climate regulation, recreation and biodiversity; and (iii) behavioural modelling —formalising human decision-making to simulate policy scenarios ranging from slum-upgrading to vineyard management. She actively links these themes to UN Sustainable Development Goals, notably SDG 11 (Sustainable Cities) and SDG 15 (Life on Land). Recent publications (2022-2025) reveal a methodological breadth spanning citizen-science impact assessment in Suriname, cooling effects of urban water bodies in Chinese mega-cities, European wine-growers’ adaptive behaviour, and methodological advances in upscaling and validating agent-based land-use models. Across these studies, Schwarz consistently combines empirical field data, geospatial analytics and participatory approaches to produce policy-relevant insights for cities in both the Global North and South. She has (co-)authored >80 peer-reviewed works, accumulating c. 5 900 citations and an h-index of 28 (Scopus). While specific honours are not itemised in the supplied text, her sustained citation impact and invited contributions to major conferences (e.g., iEMSs 2020, IAHR 2025) underscore scientific recognition. Schwarz frequently engages with societal stakeholders: her projects have generated open datasets on urban green-space monitoring in Paramaribo, e-learning modules for Latin-American universities, and interactive dashboards for sustainable water management. Within ITC she contributes to capacity-building programmes for emerging economies, supervises graduate researchers and maintains active collaborations across Europe, Latin America, Africa and Asia. No explicit lists of PhD students or personal grants are provided in the current corpus, but her leadership of externally funded projects (e.g., citizen-science evaluation, vineyard decision-making database) indicates ongoing acquisition of research funding.
Yulia Gel is a Professor in the Department of Statistics at Virginia Tech and serves as a Part-Time Program Director-Expert at the National Science Foundation (NSF). She holds a MSc (summa cum laude) and PhD in Mathematics from Saint Petersburg State University (Russia) and completed a postdoc in Statistics at the University of Washington. Her research focuses on uncertainty quantification in AI, statistical foundations of data science, spatio-temporal processes, and applications in climate science, healthcare, and blockchain analytics. She has received prestigious awards including the NSF Director’s Award (2023), ASA Distinguished Achievement Medal (2018), and TIES Abdel El-Shaarawi Award (2014). Gel has led grants on wildfire prediction, climate informatics, and blockchain data science. She serves on editorial boards of Statistica Sinica, Electronic Journal of Statistics, and Technometrics, and organizes workshops on AI for climate sustainability and fragile Earth systems. Her research group develops topological and geometric methods for graph neural networks, with applications to digital twins, environmental justice, and public health. Education: MSc (1997), PhD (2000) in Mathematics from Saint Petersburg State University; Postdoc in Statistics at University of Washington (2001–2003). Past roles include Professor at University of Texas at Dallas (2015–2024) and Associate Professor at University of Waterloo (2004–2014). Selected visiting positions include NASA Jet Propulsion Lab (2016–2017) and Isaac Newton Institute (2016–2017). She has pioneered statistical software packages like snowboot and funtimes for network inference and time-series analysis. Awards highlight her contributions to environmetrics and statistical methodologies. Current projects include NSF-funded research on AI-driven wildfire prediction and blockchain analytics for climate resilience. Her lab’s recent work emphasizes topological methods (e.g., zigzag persistence) for graph-based forecasting and adversarial robustness.
Dr. Chenhao Ma is an Assistant Professor at the School of Data Science , The Chinese University of Hong Kong, Shenzhen , where he works on large-scale data management and data mining. Previously, he was a Postdoctoral Fellow at the University of Hong Kong (2021–2022) and earned his PhD in Computer Science from the University of Hong Kong (2021) and B.Eng. from Shandong University (2017). Current research focuses on graph computing (dense subgraph discovery, motif analysis, graph learning), AI+DB (Text-to-SQL, vector search), and traffic data mining (trajectory analysis, outlier detection). He has published over 40 papers in top venues including SIGMOD, PVLDB, KDD and received the ACM SIGMOD Research Highlight Award 2021 and Best of SIGMOD 2020 (4/458). Scientific Awards : ACM SIGMOD Research Highlight Award 2021 Best of SIGMOD 2020 (4/458) Presidential Young Fellow at CUHK-Shenzhen (2023) Hong Kong and China Gas Scholarship (2019-2020) Reaching Out Award (2019) HKU Postgraduate Scholarship (2017-2021) ACM-ICPC Gold Medal (2015) National Scholarship (2014, 2015) Advising and Research Team : He leads a team including Postdoc Dr. Yuanyuan Zeng, PhD students Lujie Ban, Yuwei Xu, and MPhil students Yi Yang, Yuyang Liang. Former mentees like Yichen Xu (PhD at Berkeley) and Jiayang Pang (Master at UC San Diego) have achieved academic placements. Professional Service : He has served as PC member/reviewer for VLDB, KDD, ICDE, WWW, NeurIPS, TKDE , and guest editor for Applied Sciences and Frontiers in Big Data . He chairs sessions at ICDE and VLDB.
Robert J. Brunner is a Professor at the University of Illinois with primary appointments in the Gies College of Business (Department of Accountancy) and the School of Information Sciences. He holds affiliate roles across multiple departments including Astronomy, Computer Science, and Statistics, as well as research centers like the Beckman Institute and NCSA. His research focuses on applying statistical/machine learning to solve complex problems in astronomy, finance, and large-scale data science. Education: Ph.D. in Astrophysics from Johns Hopkins University (advisor: Alex Szalay). Postdoctoral work at Caltech on the Digital Sky project. Research Interests: Machine learning applications, computational techniques, data management/visualization, and observational cosmology. His work bridges astrophysical data analysis with modern data science methodologies. Recent work includes developing spatio-temporal neural networks for forecasting, evaluating AI-driven financial analysis tools, and planning for the Vera C. Rubin Observatory. He collaborates internationally on large-scale surveys like the Dark Energy Survey and SDSS. Labs/Teams: Leads data science initiatives at the University of Illinois Research Park. Active in interdisciplinary teams at NCSA and Beckman Institute focusing on algorithm optimization and data-intensive research.
Dr. Zhenghao Chen is a Lecturer in Data Science at the University of Newcastle, affiliated with the School of Information and Physical Sciences. He earned his Ph.D. from the University of Sydney in 2022, following a B.Eng. H1 degree from the same institution in 2017. Prior to his current position, Dr. Chen served as a Postdoctoral Research Fellow at the University of Sydney (2022-2024), a Research Engineer at TikTok (2024), and as a Visiting Research Scientist at Microsoft Research and Disney Research (2022-2023). Dr. Chen's educational background includes: Doctor of Philosophy, University of Sydney (2022) Bachelor of Information Technology (B.Eng. H1), University of Sydney (2017) Dr. Chen's research spans multiple domains within artificial intelligence, with particular expertise in Computer Vision, Natural Language Processing, and Machine Learning. His work in Generative AI has garnered significant recognition, with applications in both academic and industrial settings. His research interests are reflected in his Fields of Research percentages: Deep Learning (30%), Computer Vision (30%), Natural Language Processing (20%), and Multimodal Analysis and Synthesis (20%). His publications in top-tier venues like CVPR, ICCV, ECCV, and journals like IEEE TPAMI demonstrate the breadth and impact of his work. Analysis of Dr. Chen's recent publications (2022-2025) reveals a consistent focus on neural compression techniques, 3D perception, and multimodal AI systems. His work spans medical imaging (CXR bone suppression), video compression, point cloud processing, and neural surface reconstruction. A notable trend is his exploration of efficient AI systems that work well under resource constraints, as evidenced by his involvement in the EMCLR workshop. His research often bridges theoretical advances with practical applications across multiple domains. Dr. Chen has received several prestigious awards: Microsoft Research Asia StarTrack Fellowship (2025) ACM SIGMM Award for Outstanding PhD Thesis in Multimedia Computing (2024) Australia Government Research Training Program (RTP) Fellowship (2019) Google Australia Prize for Excellence in Computer Science (2017) Dr. Chen is actively involved in the academic community, serving on the Program Committee for major AI conferences including CVPR, ICCV, ECCV, SIGGRAPH, AAAI, and others. He also organizes workshops in Multimedia and ICCV conferences, and serves as a reviewer for prestigious journals. His teaching responsibilities include courses on Intelligent Visual Signal Understanding, Video Intelligence and Compression, Database and Information Management, and Computing Fundamentals at both the University of Sydney and University of Newcastle.
Miguel Mahecha is Professor of Environmental Data Science and Remote Sensing at the University of Leipzig, where he serves as Institute Head of the Institute for Earth System Science and Remote Sensing. He is also affiliated with the Remote Sensing Centre for Earth System Research, a collaboration between Leipzig University and the Helmholtz Centre for Environmental Research (UFZ). Mahecha is a member of the German Centre for Integrative Biodiversity Research (iDiv) and serves as Principal Investigator in the Centre for Scalable Data Analytics and Artificial Intelligence. Additionally, he is a Fellow of the European Laboratory for Learning and Intelligent Systems and co-spokesperson for the National Research Data Infrastructure for Earth System Sciences (NFDI4Earth). Full Professor for Modelling Approaches in Remote Sensing, University of Leipzig (since 03/2020) Research Group Leader: Empirical Inference in the Earth System, Max Planck Institute for Biogeochemistry, Jena (12/2012 - 03/2020) PostDoc, Max Planck Institute for Biogeochemistry, Jena (10/2009 - 11/2012) PhD in Environmental Sciences, ETH Zürich (06/2006 - 09/2009) Diploma in Geoecology, Bayreuth University (10/2000 - 04/2006) Mahecha's research focuses on understanding ecosystem responses to climate extremes and human-environment relationships during these events. He investigates macro-ecological dynamics and ecosystem functioning using data-driven methods and high-dimensional Earth observations. A key contribution is his co-development of the Earth System Data Cube concept, which integrates empirical methods with theoretical understanding to analyze complex Earth system interactions. His work spans biogeography, ecosystem functioning, and advanced data science methodologies for environmental monitoring. His recent publications demonstrate a strong emphasis on analyzing compound climate extremes, particularly heatwaves and droughts, and their impacts on ecosystems. Mahecha has pioneered methods using Earth System Data Cubes to integrate diverse environmental datasets, enabling novel insights into biosphere-atmosphere interactions. His research increasingly incorporates artificial intelligence and machine learning approaches to understand spatiotemporal patterns in ecological systems, with applications in real-time forest monitoring and biodiversity assessment. Fellow of the European Laboratory for Learning and Intelligent Systems Co-spokesperson for NFDI4Earth (National Research Data Infrastructure for Earth System Sciences) Mahecha leads multiple significant research projects including Digital Forest (real-time forest monitoring), NFDI4BioDiversity, and XAIDA (extreme events: AI for Detection and Attribution). His work receives funding from diverse sources including EU, DFG, and Stiftungen Inland. He collaborates extensively with the German Centre for Integrative Biodiversity Research (iDiv) and the Centre for Scalable Data Analytics and Artificial Intelligence. His research group, Earth System Data Science (ESDS), focuses on developing methods to extract valuable information from long-term environmental observations to understand coupled Earth system dynamics. At the Remote Sensing Centre for Earth System Research, Mahecha's ESDS group investigates how ecosystem functions respond to climate extremes, societal vulnerability to environmental hazards, and nonlinear interactions in coupled Earth systems. The group leverages citizen science data, remote sensing observations, and advanced computational methods to address pressing environmental questions.
Willem Jonker is a Full Professor at the Digital Society Institute, specializing in Semantics, Cybersecurity & Services. His research focuses on encryption schemes, access control, and privacy-preserving technologies. He has contributed to over 120 publications, with recent work addressing CVE-to-CWE mapping, anomaly detection in network traffic, and functional encryption systems. His expertise aligns with UN Sustainable Development Goals related to secure digital systems and privacy. Jonker has supervised 10 students and actively participates in academic conferences, presenting on topics like secure data management and cryptographic protocols. Research interests include cryptographic protocols, secure data management, and cybersecurity solutions. Notable projects involve developing methods for detecting covert channels, enhancing data privacy in healthcare, and improving secure search over encrypted data. He has also contributed to standards in digital rights management and forensic image recognition.
Yannick Benezeth is a Professor of Computer Science at Université de Bourgogne Franche-Comté in Dijon, France, where he teaches courses on databases, optimization, and image/video processing at the IUT de Dijon. He conducts research at the ImViA research laboratory (EA7535), focusing on video health monitoring and video analytics applications. His academic journey includes serving as an Associate Professor from 2011-2024 and earning his Habilitation à Diriger des Recherches (HDR) in 2019. Dr. Benezeth's research interests center on video-based health monitoring systems, particularly remote photoplethysmography (rPPG) for non-contact vital sign measurement. His work spans computer vision , video analytics , and physiological signal processing , with applications in stress detection, abnormal event recognition, and health monitoring. He has developed several publicly available datasets including UBFC-Phys, UBFC-RPPG, and IMVIA-NIR that have become valuable resources for researchers in affective computing and remote physiological monitoring. His publication record demonstrates consistent contributions to top computer vision venues including CVPR, ICPR, and IEEE Transactions. Recent work shows a clear trend toward multimodal approaches combining video analysis with physiological signal processing, particularly in psychophysiological stress studies. The UBFC-Phys dataset published in 2021 represents a significant contribution to affective computing research with over 50 participants and comprehensive physiological measurements. As a research supervisor with HDR qualification, Dr. Benezeth leads projects in the ImViA laboratory focusing on video analytics for healthcare applications. His team has developed innovative methods for background subtraction, abnormal event detection, and skin tissue segmentation that have been adopted by other researchers through his publicly shared code and datasets. Current work appears focused on improving the robustness of video-based physiological measurement under realistic conditions.
Andreas Weber is an Associate Professor at the University of Twente's Digital Society Institute , specializing in the Knowledge, Transformation & Society (KiTeS) research group . His work examines the long-term historical and global relationship between Science, Technology, and Society , with particular focus on colonial histories of natural history, chemistry, and sustainability , as well as computational technologies for contextualizing digitized archives . He leads the HAICu project (2023–2029) on digital cultural heritage and coordinates STS PhD training for the Netherlands Graduate Research School (2019–2024). MA & PhD in History (2005 & 2012), Leiden University Assistant Professor (2017–2023), University of Twente Andreas' research integrates digital humanities with colonial science history , emphasizing global histories of minerals , digital humanism , and AI's societal context . His 15 most recent publications (2016–2025) explore topics like colonial bias in natural history collections , FAIR data implementation , and semantic annotation of handwritten archives , spanning disciplines from history of science to computer science and museum studies . He has received 7 scientific awards , including multiple Best Teacher Awards (2020–2023) and the IEEE eScience Best Poster Award (2018). Andreas supervises PhD and postdoctoral projects while engaging in media commentary on colonial heritage issues and co-organizing international conferences like Hydrogen Pasts and Futures (2024).
Hanan Samet is a Distinguished University Professor at the University of Maryland's Computer Science Department, affiliated with the Institute for Advanced Computer Studies (UMIACS) and the Center for Automation Research. He holds a Ph.D. from Stanford University (1975) and specializes in spatial databases, data structures, and geographic information systems. His research bridges computer science and geospatial analytics, with applications in image databases, computer vision, and spatio-temporal data management. Education: Ph.D., Computer Science, Stanford University, 1975 Research Interests: Focuses on spatial data structures, GIS, spatio-textual systems like NewsStand and CoronaViz, trajectory analysis, and metric indexing. His work emphasizes scalable algorithms for spatial networks and multimedia databases. Notable Projects: CoronaViz : Tracks disease spread via spatio-temporal data visualization NewsStand : Maps news articles geospatially SAND: Spatial browser for digital government Awards: ACM Paris Kanellakis Award (2014), IEEE McDowell Award (2015), UCGIS Research Award, and Fellowships in ACM/IEEE/AAAS. Recognized for advancing spatial database theory and practice. Grants/Advising: Leads NSF-funded projects on spatio-textual extraction and similarity search. Advises graduate students (e.g., Nicole Schneider, Montana Hoover) and undergraduate researchers. Labs/Teams: Active in UMIACS and the Center for Automation Research, collaborating on projects like VASCO (spatial visualization tools) and MARCO (image database systems).
Daniele Apiletti is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Control and Computer Engineering (DAUIN). He serves as a member of the Interdepartmental Center SmartData@PoliTO and acts as Academic Advisor for the Master's degree program in Data Science and Engineering. Research Groups: DBDM - Database and Data Mining Group (DAUIN) ERC Sectors: Algorithms, Artificial Intelligence, Machine Learning, Web and Information Systems Research Interests span Big Data Analytics, Data Science, Machine Learning, Computer Vision, and Quantum Computing. His work focuses on integrating data-driven and theory-guided approaches for heterogeneous data querying, cloud continuum machine learning, and spatio-temporal models for crisis management. Recent Publications highlight trends in medical image segmentation, predictive industrial modeling, and fault-tolerant data systems. Key subfields include AI in healthcare, scalable manufacturing analytics, and vision-language models for game tutorials. Teaching roles include course ownership of Big Data: Architectures and Data Analytics and Internships across multiple academic years. He has collaborated on courses in Data Science, Database Technologies, and Data Management. PhD Students Supervised: Etibar Vazirov (Cloud Continuum Machine Learning) Gabriele Scaffidi Militone (Cloud Storage Microservices) Daniele Rege Cambrin (Spatio-Temporal Ecology Models) Simone Monaco (Theory-Guided Data Science) Research Projects include commercial contracts on: - Natural language querying of corporate research archives - National tourism ecosystem platforms - AI for thermotechnical system design - Machine Learning in clinical trials and supply chains
Anna Monreale is an Associate Professor in the Department of Computer Science at the University of Pisa and a key member of the Knowledge Discovery and Data Mining Laboratory (KDD-Lab), a joint research group with the Information Science and Technology Institute of the National Research Council (ISTI-CNR) in Pisa. Her academic career is rooted in the University of Pisa, where she completed her Bachelor's, Master's, and Ph.D. in Computer Science. Her research focuses on privacy-preserving data analytics, with core interests in big data analytics, social network analysis, spatio-temporal mining, and explainable AI. She is particularly known for her work on privacy-by-design in data mining and evaluating privacy risks in analytical processes. Her research bridges technical innovation with ethical and legal considerations in data science. Her recent publications reveal a strong trend toward explainable AI, privacy in federated learning, and risk assessment in mobility and health data. She actively contributes to developing methods for explaining black-box models, assessing privacy exposure, and balancing privacy, utility, and fairness in AI systems. Privacy by Design Ambassador (2014) ISTI-CNR Young++ Researcher Award (2014) Monreale has advised and co-chaired several international workshops, including PriSMO, PinSoDa, and MoKMaSD, and serves on editorial boards such as Transactions on Data Privacy. She teaches advanced data mining, big data ethics, and database systems across multiple graduate and undergraduate programs. She is involved in major EU projects like SoBigData, XAI, TAILOR, and HumMingBird, reflecting her leadership in data science and AI ethics. She is affiliated with the KDD-Lab, a prominent research group focused on knowledge discovery, social mining, and big data analytics, contributing to both theoretical advances and real-world applications in privacy-aware data science.
Praveen Tripathi is a Research Assistant Professor in the Department of Computer Science at Stony Brook University. His research focuses on Machine Learning, Data Mining, Spatio-Temporal Data Analysis, and Time Series Data Analysis. He has contributed to trajectory analysis frameworks, recommendation systems with temporal influence, and optimization algorithms. While his biography section is not detailed here, his work emphasizes practical applications of spatio-temporal data and multi-objective optimization. Awards are listed in the menu but specific details are not provided in the text. His publications span cybersecurity, trajectory analysis, and financial market dynamics, reflecting a strong interdisciplinary approach. No advising or grant information is explicitly mentioned in the provided content.
