Find Academic Supervisors

Heesung WooView profile

Assistant Professor

• Robotics in Forestry
• Sensor Integration
• Precision Forestry
• +4 more

Dr. Heesung Woo is an Assistant Professor of Advanced Forestry at the College of Forestry, Oregon State University , specializing in robotics, sensor integration, and precision forestry. His work focuses on autonomous forestry machinery, AI-driven forest management, and sustainable practices. He advises two graduate students and collaborates internationally through research projects. Research Interests: Autonomous Forest Machinery Development Sensor Integration & ICT Solutions Precision Forestry via Remote Sensing/LiDAR/GIS Machine Learning for Forest Inventory Advanced Forestry Practices for Sustainability Publications emphasize innovative applications of technology in forestry, including LIDAR integration, harvester data analytics, and carbon offset project modeling. His work bridges engineering, environmental science, and policy. Dr. Woo leads the Advanced Forestry Lab at Oregon State, focusing on real-world deployment of cutting-edge technologies to address challenges in forest operations, sustainability, and resource optimization.

Saman RazaviView profile

Associate Professor

• Hydrology
• Water Resources Management
• Climate Change Impact Analysis
• +3 more

Dr. Saman Razavi is an Associate Professor at the University of Saskatchewan, holding dual appointments in the School of Environment and Sustainability (SENS) and the Department of Civil, Geological and Environmental Engineering in the College of Engineering. He is a member of the Global Institute for Water Security and leads the Razavi EnviroFutures Lab. His research focuses on hydrological modeling, water resources management, climate change impacts, and the integration of machine learning with environmental science. Dr. Razavi has earned a PhD in Civil and Environmental Engineering from the University of Waterloo. Education: PhD in Civil and Environmental Engineering, University of Waterloo MS in Civil and Environmental Engineering, Amirkabir University, Iran BS in Civil Engineering, Iran University of Science and Technology Research interests include hydrologic model development, optimization, uncertainty quantification, climate change analysis, and socio-hydrological modeling. He emphasizes interdisciplinary approaches to address water challenges through integrated frameworks that bridge natural science, engineering, and socio-economic factors. Award: Walter L. Huber Civil Engineering Research Prize from ASCE (2024) Advising & Grants: Dr. Razavi leads the Integrated Modelling Program for Canada under Global Water Futures, focusing on transboundary water systems. His work involves developing decision-support tools for flood/drought management and climate adaptation. Labs/Teams: The Razavi EnviroFutures Lab advances research on water-human systems, leveraging AI and big data to enhance resilience against water-related hazards. Current projects include flood-prone area mapping, drought prediction, and socio-hydrological modeling in transboundary basins.

Konstantinos PelechrinisView profile

Associate Professor

• Network Science
• Urban Informatics
• Sports Analytics
• +3 more

Konstantinos Pelechrinis is an Associate Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. He holds a Ph.D. in Computer Science from the University of California, Riverside. His research focuses on network science, urban informatics, and sports analytics. He has been recognized with the Army Research Office Young Investigator Award for his contributions. Education: Ph.D. in Computer Science, University of California, Riverside Research Interests: Urban mobility patterns and infrastructure analysis Sports performance quantification and strategy Data-driven decision-making in transportation systems Network science applications in social and urban systems His recent work explores topics such as implicit biases in sports refereeing, anomaly detection in NFT markets, and optimizing bike-sharing systems using predictive models. He also investigates urban infrastructure resilience through projects like the Epui platform for experimental urban informatics. Awards: Army Research Office Young Investigator Award He contributes to academic outreach through courses like TELCOM2125 (Network Science and Analysis) and collaborates on initiatives like the Healthy Ride Pittsburgh bike-sharing study. His lab focuses on bridging theoretical models with real-world urban and sports datasets.

Leila CharacterView profile

Assistant Professor

• Machine Learning
• Geospatial Analysis
• Marine Archaeology
• +6 more

Leila Character is an Assistant Professor at Texas A&M University, with expertise in machine learning and geospatial analysis. Her research bridges disciplines like archaeology, environmental science, and geospatial intelligence, often involving fieldwork and computational modeling. She has a multidisciplinary background as a professional geologist, environmental scientist, and AI researcher. Ph.D. and M.A. in Geography and the Environment from University of Texas at Austin B.S. in Geology with a minor in Anthropology/Archaeology from Sewanee: The University of the South Her active projects focus on underwater aircraft wreck detection for MIA service members, multimodal sensor fusion with autonomous underwater vehicles, seafloor characterization in West Africa, and ancient burial mound detection in Romania using vegetation indices. All projects emphasize combining computational rigor with field validation. Recent publications highlight deep learning applications in marine archaeology, Maya cave detection, and sensor fusion technologies. These articles span 2019–2025 and reflect cross-cutting themes in AI-driven geospatial analysis, underwater exploration, and archaeological discovery.

Tim BuskerView profile

Researcher

• Flood Early-Warning Systems
• Impact-Based Forecasting
• Drought Risk Management
• +3 more

Dr. Tim Busker is a Postdoctoral Researcher at the Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, specializing in flood and drought early-warning systems and impact-based forecasting. He holds a PhD from VU Amsterdam (2024) and advanced degrees in Earth Surface and Water (Utrecht University) and Earth & Economics (VU Amsterdam). Education: Bachelor’s (cum laude) in Earth & Economics, Vrije Universiteit Amsterdam (2012–2015) Master’s in Earth Surface and Water, Utrecht University (2015–2017) His research focuses on translating forecasts into actionable warnings for regions like East Africa and Europe. Key interests include: Flood and drought forecasting methodologies Impact-based early action frameworks GIS/remote sensing applications in water management Recent work emphasizes machine learning for food security crises and transboundary flood risk assessments in NW Europe. Projects include JCAR-ATRACE and Resilience nEtwork of Smart Innovative cLImate-adapative rOoftops (RESILIENT). Grants/Projects: 6 active/funded projects, including EU-funded CoastMove ERC (2020–2026) Awards: EGU OSPP Award (2023), IWA-ASPIRE Poster Prize (2019)

Walid G. ArefView profile

Professor

• Databases
• Data Mining
• Spatial Databases
• +6 more

Walid G. Aref is a Professor of Computer Science at Purdue University since Fall 1999. His research focuses on database systems, spatial and spatio-temporal data management, query processing, and indexing. He has led projects funded by NSF, NIH, and industry partners. Aref is a Fellow of the IEEE and has received awards including the NSF CAREER Award (2001) and VLDB’s Ten-Year Best Paper Award (2016). He serves as Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems and has authored numerous influential papers. Education: BSc/MSc from Alexandria University (Egypt), PhD from University of Maryland (1993). Research Interests: Extending database functionality for emerging applications like spatial, graph, and sensor databases. Notable contributions include the Chameleon project, AQWA system for big spatial data, and privacy-preserving Casper framework. Selected Projects: NSF-funded work on multi-predicate spatial queries, in-memory graph relational systems, and adaptive spatio-textual processing. Systems like Tornado (spatio-textual streams) and LocationSpark (distributed spatial data) exemplify his contributions. Awards: Multiple best paper awards, IEEE Fellow, and leadership roles in ACM SIGSPATIAL.

Dieu Tien BuiView profile

Professor

• Geospatial Artificial Intelligence
• Machine Learning
• GIS and Geomatics
• +6 more

Dieu Tien Bui is a Full Professor in the Department of Business and IT at the University of South-Eastern Norway (USN) School of Business. His research focuses on Geospatial Artificial Intelligence Machine Learning GIS and Remote Sensing Natural Hazard Modeling Environmental Problems (landslides, floods, soil salinity, biomass) . He has contributed to over 15 recent publications in journals like Science of the Total Environment , Remote Sensing , and Geomorphology , emphasizing hybrid AI models for landslide and flood susceptibility. His work spans Vietnam, India, China, and Iran with applications in climate change adaptation and disaster management. Scientific Awards: Global Highly Cited Researcher PhD Supervision: He has supervised 8 PhD students at institutions including USN, NTNU, and Vietnamese universities.

Bing ZhouView profile

Assistant Professor

• Geospatial Data Science
• GeoAI
• Remote Sensing
• +6 more

Bing Zhou serves as an Assistant Teaching Professor in the Department of Geography and the John A. Dutton Institute for Teaching and Learning Excellence within the College of Earth & Mineral Sciences at The Pennsylvania State University, focusing on advancing geospatial education and research. His educational background includes: Ph.D. in Geography from Texas A&M University Dr. Zhou pioneers research in geospatial data science and GeoAI, developing advanced algorithms to extract insights from big geospatial data (crowd-sourced data, mobility data, social media) for climate resilience and disaster response. He founded the revolutionary field of Responsible GIScience, which integrates ethical principles, human values, and societal interests into geospatial research and education. His work specifically targets identifying location-based help information from social media to support disaster response in high-risk communities, aiming to build resilient, equitable, and healthy societies through responsible spatial thinking. He teaches core courses including GEOG 589: Emerging Trends in Remote Sensing and GEOG 480: Exploring Imagery and Elevation Data in GIS Applications. Information regarding scientific awards, student advising, grant funding, and laboratory teams is not documented in the provided materials.

Yu LanView profile

Research Fellow

• Spatial Epidemiology
• Health Geography
• Infectious Diseases
• +3 more

Yu Lan is a Research Fellow at the Yale School of Public Health , specializing in spatial epidemiology and health geography . Her work integrates genomic data (e.g., WGS) with geographic information systems (GIS) to analyze transmission patterns of infectious diseases like COVID-19 and tuberculosis . Education: PhD in Geography, University of North Carolina at Charlotte MA in Geography, University of North Carolina at Charlotte Research Interests focus on space-time disease modeling , infectious disease transmission , and data-driven public health tools . She develops web-based systems for real-time disease surveillance and environmental risk assessment, including tools for private well contamination and urban neighborhood dynamics . Scientific Awards include the SISMID Scholarship (2024) , Student Honors Paper Competition Finalist (2023) , and David Woodward Digital Map Award (2021) . Collaborations include work with the Ted Cohen Lab and researchers like Joshua Warren and Eric Delmelle . Her publications emphasize genomic-spatial integration and cluster detection algorithms for diseases such as tuberculosis and SARS-CoV-2.

• Cryptography
• Blockchain Technology
• Cybersecurity
• +4 more

Sri AravindaKrishnan Thyagarajan is a Lecturer at The University of Sydney's School of Computer Science, specializing in cryptography. He focuses on enhancing security and fairness in distributed systems, blockchains, and multi-party computations with applications in post-quantum cryptography. His work spans top-tier conferences like ACM CCS and IEEE SP. Education: B.Tech, Computer Science & Engineering, National Institute of Technology Trichy, India (2011–2015) M.Sc., Computer Science, Saarland University, Germany (2015–2016) Ph.D., Applied Cryptography, Friedrich Alexander Universität Erlangen-Nürnberg, Germany (2016–2021) Research Interests: Cryptography (classical and post-quantum), blockchain security, cryptocurrencies, fairness in multi-party computations, privacy-preserving protocols, and decentralized systems. Recent Articles: Focus on cryptographic protocols for blockchains, threshold cryptography, verifiable randomness, and secure atomic swaps. Recent work includes advancements in post-quantum signatures, distributed randomness generation, and privacy-preserving payment systems. Awards: Nominated for GI Dissertationspreis 2021 Advising & Grants: Advising Ankit Kapoor. Active in program committees for CCS, IEEE SP, and FC. Currently Program Chair for Crypto Valley Conference 2025. Labs/Teams: Part of the Sydney Blockchain Centre and cybersecurity cluster at the University of Sydney. Collaborates globally with institutions like NTT Research, Carnegie Mellon University, and Bocconi University.

Arpan Man SainjuView profile

Assistant Professor

• Spatial Big Data Analytics
• Spatial and Spatiotemporal Data Mining
• Geographic Information Systems (GIS)
• +3 more

Dr. Arpan Man Sainju is an Assistant Professor and Internship Coordinator in the Department of Computer Science at Middle Tennessee State University (MTSU). He holds a PhD (2021) and MS (2020) from the University of Alabama, and a B.E. (2011) from Tribhuvan University. His research focuses on spatial big data analytics, spatiotemporal data mining, and GIS applications in environmental modeling, disaster management, and geospatial science. He develops innovative algorithms for Earth imagery segmentation, flood inundation mapping, and physics-aware machine learning models. Education: PhD in Computer Science, University of Alabama (2021) MS in Computer Science, University of Alabama (2020) B.E. in Computer Science, Tribhuvan University (2011) Key research interests include deep learning for geospatial tasks, semi-supervised learning with limited labels, and parallel computing for big spatial data. His work bridges computer science and environmental science, addressing challenges in hydrology, urban safety, and disaster response. He has published extensively in top journals like ACM TIST, IEEE TKDE, and Environmental Modelling & Software, focusing on applications like flood modeling, road safety analysis, and 3D shape analysis. Dr. Sainju collaborates on interdisciplinary projects involving physics-guided models, hidden Markov structures, and GPU-accelerated algorithms. His research has been applied to real-world scenarios such as hurricane flood analysis and malware detection through Windows log analysis.

Thierry WarinView profile

Professor

• Data Science for Global Transformations
• Platform Economy
• Industrial Organization
• +7 more

Thierry Warin is a Full Professor of Data Science for International Business at HEC Montréal, directing the Department of International Business. He holds the Professorship in Data Science for International Business and is a Principal Investigator at CIRANO, leading the World Economy theme. His roles include affiliations with Harvard Business School’s Microeconomics of Competitiveness program and the International Trade and Finance Association presidency (2020-2022). Education: PhD from ESSEC Business School (France, 2000). Professional development includes the Harvard Business Analytics Program (2018-2020) and GIS training at Harvard. Research Interests: Data science applications in global economic transformations, including network theory, natural language processing, and computational methods. Focus areas: algorithmic collusion, platform economies, and metadata-driven analyses. He develops open-source tools like the statcanR package and advocates for reproducible research. Articles Trends: Recent work explores AI regulation, algorithmic competition, climate transition plans, and central bank speech analysis. Methodologies span structural topic modeling, social media analytics, and entropy-based frameworks. Awards: Honored with the Highly Commended Paper Award (2017-2018) and Emerald Literati Award (2018) for reverse innovation research. Recognized for contributions to computational social science and regulatory frameworks. Advising & Grants: Supervised 16 master’s projects since 2019, focusing on data science applications in global business challenges. Active in interdisciplinary initiatives like the St. Lawrence–Great Lakes corridor data hub. Labs & Philanthropy: Founded quantum simulations and leads Ed’Haîti , an NGO addressing education in Haiti. Collaborates on Science des données au féminin en Afrique , empowering 200 African women with data science skills.

Timothy FraserView profile

Researcher

• Climate Change Adaptation
• Disaster Resilience
• Energy Policy
• +5 more

Dr. Timothy Fraser is a Computational Social Scientist serving as Ezra Systems Research Associate in Cornell University's Systems Engineering Program and Coordinator for the Center for Transportation, Environment, & Community Health (CTECH). Holding a PhD in Political Science from Northeastern University (2022), he focuses on climate change adaptation, disaster resilience, and energy policy using big data analytics, GIS, and AI. His work bridges computational methods with societal challenges, emphasizing community engagement and policy impact. Education: PhD in Political Science, Northeastern University (2022) MA in Political Science, Northeastern University BA in International and Global Studies, Middlebury College Research Interests: Fraser’s research integrates computational social science with environmental policy, exploring how social networks and governance structures influence cities' climate adaptation strategies. Key areas include renewable energy adoption, disaster evacuation dynamics, and pandemic response mechanisms. He employs mixed methods such as network analysis, statistical modeling, and fieldwork. Grants & Awards: Fulbright Fellowship (2016), Kyushu University Japan Foundation Doctoral Fellowship (2020) USDOT Multimillion Grant Administrator (2022-2023) Teaching & Mentorship: Fraser teaches statistical methods and research design at Cornell, advising over 30+ students in projects published in leading journals. He coordinates capstone teams and mentors researchers in data science and policy analysis. Labs & Projects: Leads the Climate Action in Transportation dashboard initiative (Gao Labs), developing tools for emissions visualization. Collaborates on UNDP social capital mapping projects in Mexico and Paraguay, applying spatial analysis techniques for vulnerable communities.

Harrison SmithView profile

Lecturer

• Political Economy of Data Analytics
• Consumer Surveillance
• Smart Cities
• +6 more

Dr Harrison Smith is a Lecturer in Digital Media & Society at the University of Sheffield's School of Sociological Studies, Politics and International Relations. He holds a PhD from the University of Toronto’s Faculty of Information, alongside MA and BA degrees in Sociology from Queen’s University, Canada. His research focuses on the political economy of data analytics, particularly in smart cities and consumer surveillance contexts. Education: PhD in Information Studies, University of Toronto (Canada) MA in Sociology, Queen’s University (Canada) BA (Hons) in Sociology, Queen’s University (Canada) Smith’s research examines how data infrastructures shape socio-economic inequality through processes like surveillance, classification, and market segmentation. Key areas include location-based marketing, 5G data infrastructures, and industry consolidation in data analytics. His work critiques how digital technologies reconfigure urban spaces and labor markets, particularly in 'smart city' contexts. Recent publications explore the metaverse's industrial implications, blockchain’s role in dispute resolution, and surveillance practices at music festivals. His teaching includes leading the module SCS2016: The Sociology of the Media. Smith’s expertise bridges media theory, urban informatics, and critical data studies, emphasizing ethical and political dimensions of digital technologies in everyday life.

• Data Science
• Computational Social Science
• Misinformation
• +3 more

Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.