Find Academic Supervisors

Elaine O. NsoesieView profile

Associate Professor

• Data Science in Public Health
• Machine Learning & AI for Global Health
• Epidemiology
• +5 more

Elaine O. Nsoesie is an Associate Professor at Boston University's School of Public Health, Department of Global Health, with affiliations in Biomedical Engineering and Computing & Data Sciences. Her work bridges computational methods with public health challenges, focusing on AI-driven solutions for global health equity. Key roles include leading the AIM-AHEAD consortium and advising on Sierra Leone's national data initiatives. Education: PhD in Computational Epidemiology, Virginia Tech MS from Virginia Tech Her research integrates non-traditional data sources (social media, satellite imagery) with machine learning to address issues like disease forecasting, health equity, and algorithmic bias. Notable projects include dengue outbreak prediction in Colombia and analyzing racial disparities in healthcare AI. Awards: 2024 Health Innovators to Watch 2024 Mozilla Rise25 Award NIH Director’s Award (2023) Junior Faculty Fellow (Hariri Institute, 2020–2023) Grants and initiatives include $2.4M NIH funding for AIM-AHEAD and leadership in Africa's AMedRA alliance. She co-directs Boston University's Evans Center projects and teaches courses on computational epidemiology. Labs/Teams: Active in Hariri Institute's data science group and Evans Center's interdisciplinary teams focused on global health technology solutions.

Alven YuView profile

Research Fellow

• Environmental Health
• Machine Learning
• Deep Learning
• +12 more

Alven Yu is a Research Fellow in Planetary Health at the School of Public Health and Preventive Medicine, Monash University. He completed his PhD in Epidemiology at Monash University between January 2021 and August 2024 and continues to contribute significantly to environmental health research. He also held an external position as Data Scientist at the University of Melbourne from February to August 2024. Education: PhD in Epidemiology, Monash University (2021–2024) His research focuses on environmental exposure measurement, advanced machine learning applications, and novel methodologies for health risk assessment. He has pioneered a deep ensemble machine learning framework for high-resolution global PM2.5 estimation (2000–2019) and assessed associated short-term mortality burdens. His work spans exposure assessment, biostatistics, environmental health, and climate-related health impacts, with a strong emphasis on big data and predictive modeling. His recent publications (2023–2025) demonstrate a consistent focus on global environmental health challenges, particularly air pollution from wildfires, urban heat, flood impacts, and green space deprivation. These studies leverage machine learning and large-scale datasets to assess health risks across multiple countries, often published in high-impact journals such as Lancet Planetary Health , Nature Sustainability , and Environment International . Scientific Awards and Recognition: Award for Outstanding Self-financed Students Abroad (2024) PhD Top-up funding from NHMRC CRE Monash Postgraduate Publications Award ISEE 2023 Travel Award ICUC 11 Presentation Prize ISEE-AWPC Membership Award 2024 Vice-Chancellor’s Award for Research Team of the Year Dean's Award for Excellence in Research - Team Award (2024) Alven Yu has secured approximately $90,000 in competitive external seed funding as Chief Investigator from NHMRC CRE, Centre for Safe Air, CAR, and MMCR. He has published 39 peer-reviewed articles, with 23 during his PhD (all in JCR Q1 journals), and 14 as first or corresponding author. His research has been covered by over 150 news outlets and mentioned in more than 3,350 social media posts. He is actively involved in two major research projects: Developing a Global Air Quality Alert and Forecast System and Global mortality burden attributable to short-term exposures to wildfire-related PM2.5 .

Bronson P. BullockView profile

Professor

• Forest Management
• Biometrics
• Silviculture
• +7 more

Dr. Bronson P. Bullock is a Professor at the University of Georgia's Warnell School of Forestry and Natural Resources and Director of the Plantation Management Research Cooperative (PMRC). He previously held positions at North Carolina State University. His research focuses on forest biometrics, silviculture, and quantitative timber management, with expertise in growth and yield modeling, spatial tree relationships, and Bayesian statistical methods. Education: PhD (2002), MS Biometrics (1998), MS Statistics (2000), and BS (1996) from Virginia Tech and Rutgers. His work bridges biometrics and practical forest management, emphasizing precision forestry and carbon dynamics. Research Interests: Volume equations, statistical sampling, silvicultural impacts, spatial matrix correlations, genetic homogeneity effects, diameter distributions, and carbon stock dynamics. He has published extensively in Forest Science , Forest Ecology and Management , and others. Labs/Centers: Harley Langdale Jr. Center for Forest Business and PMRC. Notable collaborations include studies on hurricane impacts and carbon sequestration in southern pine plantations.

Qian HeView profile

Assistant Professor

• Urban Planning
• Community Resilience
• Disaster Risk Management
• +6 more

Qian He, PhD, AICP is an Assistant Professor at Rowan University's Department of Geography, Planning, and Sustainability, and Director of the CARE Lab. Her research focuses on urban resilience, disaster risk management, and the intersection of public policy with community well-being in vulnerable populations. She holds a PhD in Urban Planning from the University of Texas at Arlington and has conducted postdoctoral research at the University of Maryland's A. James Clark School of Engineering. Her work integrates quantitative methods (econometrics, spatial analysis) with qualitative approaches like community engagement. Research interests include crime modeling, climate change impacts (sea-level rise, flooding), pandemic mobility, and urban informatics. She has secured grants from EPA, NASA, and NJ State Office of Planning Advocacy. Collaborations with nonprofits focus on equitable urban development and participatory policymaking. Recent publications analyze crime-mobility relationships, transit accessibility barriers, and disaster-induced housing instability. CARE Lab emphasizes applied research addressing community resilience challenges through interdisciplinary approaches.

Shenghua GaoView profile

Associate Professor

• Spatio-temporal Data Analysis
• Machine Learning
• Database Systems
• +4 more

Professor Shenghua Gao is an Associate Professor at the School of Computing and Data Science of the University of Hong Kong (HKU), concurrently serving as Assistant Director for Shanghai Initiatives. He holds a PhD from Nanyang Technological University. His research focuses on integrating machine learning, spatio-temporal data analysis, and database systems to address challenges in mobility prediction, traffic management, and geospatial representation learning. He has contributed significantly to trajectory modeling, indexing frameworks for multi-dimensional data, and the application of large language models (LLMs) in spatio-temporal contexts. Key research interests include: Spatio-Temporal Data Science: Developing frameworks for efficient processing and analysis of point cloud, trajectory, and traffic data. Machine Learning for Databases: Innovating indexing algorithms (e.g., BMTree, MAST) and query optimization techniques leveraging ML. Trajectory and Mobility Prediction: Creating personalized models for next-location prediction and transfer learning across regions. Geographic AI (GeoAI): Enhancing road network representation and urban function inference using physics-guided and foundation models. Recent work highlights include the ST-LLM+ framework for traffic prediction, the MAST system for point cloud analytics, and the exploration of City Foundation Models for urban challenges. His publications span top venues in databases (SIGMOD, VLDB) and AI/data science (ICML, NeurIPS). While no awards are explicitly mentioned, his prolific output and leadership roles indicate significant academic contributions. He is actively involved in teaching and supervising research in the School’s undergraduate and postgraduate programs, including MSc(AI) and MPhil/PhD tracks.

Michael S. StricklandView profile

Professor

• Microbial Ecology
• Soil Ecology
• Ecosystem Ecology
• +3 more

Michael S. Strickland is a Professor of Soil Microbial Ecology at the University of Idaho's Department of Soil and Water Systems, within the College of Agricultural & Life Sciences. His research focuses on soil, microbial, and ecosystem ecology, particularly addressing how soil communities and processes respond to environmental and anthropogenic changes. He holds a Ph.D. from the University of Georgia (2009) and a B.A. from William Jewell College (2005). Key research areas include antibiotic impacts on soil ecosystems, decomposition dynamics, and cover crop effects on soil health. His lab employs field and lab experiments, stable isotope techniques, and microbial community analysis. Recent studies highlight antibiotic legacies on microbial communities, nitrogen deposition effects on carbon dynamics, and the role of volatile organic compounds in litter decomposition. Scientific Awards: 2019 Outstanding Presentation Award (Soil Ecology Society) Grants & Funding: NSF CAREER Award (2019), Deep Soil Ecotron facility development (2021) Strickland advocates for soil health in agricultural systems and collaborates on projects like the 'Necrobiome' framework. His lab's work bridges microbial community structure and ecosystem function, emphasizing the urgency of protecting imperiled soil systems.

• Statistics
• Spatial Statistics
• Spatio-Temporal Modeling
• +7 more

Donato Posa is a Full Professor in Statistics at the Department of Economic Sciences, University of Salento. He holds the academic position of Professor Ordinario (Full Professor) in the field of Statistics (SECS-S/01) and is actively engaged in research and academic leadership. His work bridges economics, environmental science, and advanced statistical modeling. University: University of Salento Department: Department of Economic Sciences Academic Rank: Professor Email: donato.posa@unisalento.it Office: Centro Ecotekne Pal. C, S.P. 6, Lecce - Monteroni, LECCE (LE) Phone: +39 0832 29 8737 Donato Posa earned his degree in Physics, cum laude, from the University of Bari with a thesis conducted at CERN, Geneva, and completed a specialization in Physics. His international academic experience includes extended research stays at Stanford University (USA), the University of Arizona, and the University of North Carolina. His research focuses on spatial and spatio-temporal statistics, with major contributions in geostatistics, multivariate geostatistics, stochastic simulation, and time series analysis. He has developed theoretical and applied models for environmental monitoring, pollution assessment, and socioeconomic data analysis. His work has been applied in risk mapping, environmental policy, and urban planning. The 15 most recent articles reflect a consistent trend in spatio-temporal modeling and geostatistical innovation. They span theoretical developments in covariance modeling and variogram analysis to practical applications in environmental monitoring, pollution dispersion, and ecosystem well-being. The research demonstrates a strong interdisciplinary focus, integrating statistical theory with environmental, economic, and computational sciences. His scientific recognition includes multiple CNR and NATO research grants, CERN awards, and an International Diploma of Honour from the American Biographical Institute for contributions to geostatistics. He has served on editorial boards, chaired international conferences (e.g., GeoEnv 2012), and participated in national scientific qualification committees. Posa has supervised numerous research projects funded by CNR, MIUR, Fondazione Caripuglia, and EU programs. He has acted as a scientific referee for leading journals such as Stochastic Environmental Research and Risk Assessment and Computational Statistics and Data Analysis . He has also led educational initiatives in statistical methods and GIS for environmental and cultural heritage applications. He has been actively involved in academic governance, serving as President of the Research Observatory at the University of Salento (2013–2017) and as a member of national evaluation committees. He has chaired international conferences and contributed to major scientific associations including the Bernoulli Society, the International Association for Statistical Computing, and the American Statistical Association.

• Target Tracking
• Navigation
• SLAM
• +4 more

Edmund Førland Brekke is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU). He leads the Autosit and Autosight projects, and serves as a work package leader in the SFI Autoship center. His research focuses on target tracking, navigation, and SLAM, with applications in collision avoidance for unmanned vessels. He co-founded the company Zeabuz for marine surface autonomy solutions. PhD in Technical Cybernetics (NTNU, 2010) MSc in Industrial Mathematics (NTNU, 2005) Research Interests: Edmund specializes in theoretical foundations of multitarget tracking, integrating target tracking with navigation/SLAM, and applying these to autonomous maritime collision avoidance. His work addresses challenges in heavy-tailed clutter, sensor fusion, and situational awareness for autonomous ferries and river barges. Publications Trends: His recent work (2018–2022) emphasizes collision avoidance algorithms, sensor fusion, and SLAM for autonomous vessels. Key areas include maritime radar tracking, trajectory prediction using AIS data, and integration of visual/Lidar sensors for navigation. Guidance: He has supervised numerous PhD candidates on topics like digital twins, multi-sensor tracking, and risk assessment for autonomous ships, including graduates from 2014–2024. Projects: Active in Autoferry (autonomous ferries), Autobarge (river barges), and the ORCAS initiative. Collaborates with the SFI Autoship center.

Sangmi Lee PallickaraView profile

Professor

• Big Data
• Data Science
• Data Engineering
• +13 more

Sangmi Lee Pallickara is a Professor of Computer Science and the Clare Booth Luce Professor at Colorado State University. She is affiliated with the Department of Computer Science in the College of Natural Sciences. Her research is supported by major agencies including the National Science Foundation, Department of Homeland Security, ARPA-E, and NIFA, with ongoing projects in AI Institutes, Cyberinfrastructure, and CyberPhysical Systems. Research Interests: Her work focuses on Big Data systems for scientific applications, including scalable storage, retrieval, metadata management, predictive analytics, and interactive visualization. She applies these to domains such as agriculture, atmospheric science, environmental monitoring, and epidemiology. Her research integrates data science, distributed systems, and deep learning to enable scalable knowledge extraction from high-velocity, voluminous datasets. Publication Trends: Her recent publications emphasize scalable solutions for geospatial and spatiotemporal data, including efficient storage (e.g., ATLAS), visualization (e.g., Glance, Iris), and deep learning (e.g., Argus, CloudNet). There is a strong focus on real-world applications such as wildfire prediction, satellite data imputation, and precision agriculture, leveraging generative models, embeddings, and ensemble methods. Scientific Awards: NSF CAREER Award IEEE TCSC Award for Excellence in Scalable Computing Best Paper Award at IEEE/ACM UCC 2019 Best Paper Award at IEEE CLUSTER 2019 Best Paper Award at IEEE/ACM UCC 2014 Finalist for Best Paper Award at IEEE BDCloud 2018 Advising and Grants: She advises numerous Ph.D. and Master’s students, many of whom have gone on to careers in industry and academia. Her research is funded by the NSF (AI Institutes, CPS), DHS, ARPA-E, NIFA, and the Environmental Defense Fund. She also leads the SWiFT outreach program for K–12 STEM education. Labs and Teams: She leads a vibrant research group focused on Big Data systems, with students working on distributed storage, deep learning for satellite imagery, spatiotemporal analytics, and interactive visualization. The team collaborates with domain scientists in agriculture, climate, and public health.

Jay AlderView profile

Academic

• Climate Modeling
• Hydrology
• Climate Change
• +7 more

Jay Alder is a Research Associate at the College of Earth, Ocean, and Atmospheric Sciences (CEOAS) at Oregon State University, holding a courtesy appointment. He is also a Research Physical Scientist with the U.S. Geological Survey (USGS) in Corvallis, Oregon, where he has been employed since 2012. His work bridges academic and federal research, focusing on climate and hydrological modeling. Education: PhD in Geography, Oregon State University (2011) MS in Geography, Oregon State University (2009) BS in Environmental Science, University of California, Riverside (2004) BS in Computer Science, University of California, Riverside (2004) Jay Alder specializes in climate modeling across temporal scales—past, present, and future—utilizing General Circulation Models (GCMs) and Regional Climate Models (RCMs). His research emphasizes dynamical downscaling , hydroclimate analysis , and the visualization and dissemination of climate data . He is particularly interested in making climate projections usable for interdisciplinary applications, including ecosystem and water resource management. His expertise also includes data communication, mathematical modeling, and software development for scientific applications. The available articles highlight a focus on hydrological impacts of climate change in western U.S. watersheds and paleoclimatic studies of North Pacific warming. These works reflect a consistent theme of applying climate models to real-world environmental planning and adaptation challenges, particularly for Indigenous communities and ecosystem resilience. Scientific Contributions: Lead contributor to streamflow projection studies for the Tongue River, supporting Northern Cheyenne water management. Team member on the POP3 project, using geological records of past warming to inform future climate predictions. Jay has been actively contributing to climate science since at least 2009. His grants and collaborative projects are primarily conducted through the USGS and involve partnerships with climate adaptation centers and water science programs. Though no formal advisees are listed, his role supports interdisciplinary science translation. He leads efforts in climate data usability, interactive mapping, and model visualization, contributing to broader scientific accessibility. Labs and Teams: He is affiliated with the Geology, Minerals, Energy, and Geophysics Science Center at USGS and collaborates with the Climate Adaptation Science Centers and Water Resources Mission Area. At Oregon State, he works through CEOAS, particularly in climate modeling and data dissemination initiatives.

• Satellite Remote Sensing
• Climate Science
• Sustainable Development
• +3 more

Dr. Astrid Werkmeister is a Knowledge Exchange Associate at the University of Strathclyde's Department of Mechanical and Aerospace Engineering, Faculty of Engineering. She specializes in satellite remote sensing applications for climate science and sustainable development, with a focus on extreme weather events and climate-induced human migration. Education : PhD in Applied Marine Physics (2024), University of Miami MSc in Atmospheric Science (2011), Leibniz University Hannover Research Interests combine satellite remote sensing, climate change mitigation, and sustainable development. She develops predictive models for climate displacement and applies satellite data to achieve UN Sustainable Development Goals, particularly in climate action and disaster risk reduction. Projects include leading the EPSRC-funded 'Decoding Climate Displacement' initiative (2024-2026) and collaborating on the ESA-supported OSCAR project for offshore wind farm monitoring. Her work integrates SAR imagery, Python programming, and interdisciplinary partnerships with industry and policymakers. Public Engagement highlights: Speaker at EEE Accelerate Challenge - Space Challenge (2025) Participant in ClimateXChange workshops (2025) Member of Scottish Space Academics Forum (2025)

Gianluca AloiView profile

Associate Professor

• Wireless Networks
• Cellular Networks
• Sensor Networks
• +7 more

Gianluca Aloi serves as an Associate Professor in Telecommunications (IINF-03/A) at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) of the University of Calabria, Italy. He holds the position of scientific director for the Telecommunications and Information Theory for Advanced Networking Laboratory (TITAN Lab.). Dr. Aloi earned his PhD in Systems Engineering and Computer Science from the University of Calabria in 2003 and became a University Researcher in Telecommunications (ING-INF/03) in 2004 before advancing to his current academic rank. His research expertise spans wireless networks, cellular networks, sensor networks, Internet of Things systems and their interoperability, management of resources and services in the Cloud/Edge and IoT (CEI) Continuum, and management and orchestration of network resources using Artificial Intelligence. His work particularly focuses on UAV-assisted IoT systems for industrial applications, geological hazard monitoring, and maritime environments. Analysis of Dr. Aloi's recent publications (2023-2025) reveals a strong emphasis on applying reinforcement learning and deep learning techniques to solve complex networking challenges. His research shows a clear trajectory toward developing intelligent network architectures that optimize data collection, improve system resilience, and enhance resource management across the edge-to-cloud continuum. Key application areas include smart factories, disaster monitoring, and urban vehicular systems. Dr. Aloi teaches courses including Fundamentals of Telecommunications Networks and Telecommunications Networks for both Electronic Engineering and Computer Engineering programs. He maintains regular reception hours every Tuesday from 9am to 11am or by appointment via email. As scientific director of TITAN Lab, Dr. Aloi leads research initiatives focused on advanced networking technologies, with particular emphasis on developing solutions for next-generation communication systems that integrate artificial intelligence with traditional networking paradigms to address real-world challenges in telecommunications and IoT applications.

Ihtiyor BobojonovView profile

Researcher

• Agricultural Economics
• Climate Change
• Risk Management
• +4 more

Dr. Ihtiyor Bobojonov is a Researcher at the Leibniz Institute of Agricultural Development in Transition and Emerging Economies (IAMO) , where he has worked since 2012. His research focuses on climate change adaptation , agricultural insurance , and supply chain dynamics in transition economies, particularly Central Asia. He completed his PhD at the University of Bonn , analyzing crop and water allocation under uncertainty in Uzbekistan, and received the ZEF Doctoral Thesis Prize (2007–2009). His work addresses agricultural market potentials in CIS countries, emphasizing supply chain transformation 's impact on producer welfare and insurance market development for climate resilience. He leads IAMO’s Central Asia International Research Group and coordinates the German-Uzbek Chair on Central Asian Agricultural Economics at Tashkent International Agricultural University. His methodological expertise spans bioeconomic modeling , machine learning , and experimental economics , with applications in weather index insurance and remote sensing for yield estimation. Key scientific awards include ZEF’s best thesis prize. His 15 most recent articles (2025–2020) explore topics across agricultural insurance markets , climate risk mitigation , wheat yield modeling , and peer influence on adaptation strategies , with empirical studies in Uzbekistan, Kyrgyzstan, and Mongolia. Research projects like KlimALEZ and DETECCT highlight his commitment to building climate-resilient agrifood systems through innovative financial instruments and digital technologies .

Olivier VerscheureView profile

Adjunct Professor

• Stream and Big Data Mining
• Geospatial Analysis
• Large-scale Data Management
• +4 more

Olivier Verscheure serves as the Executive Director of the Swiss Data Science Center (SDSC), a national R&D center organizationally hosted by both École Polytechnique Fédérale de Lausanne (EPFL) and ETH Zurich. He also holds multiple Adjunct Professor appointments at EPFL, specifically within the School of Computer and Communication Sciences (SIN and SSC) and the School of Engineering (SEL). His educational background includes: Ph.D. in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), June 1999 Verscheure's research focuses on the intersection of data science and real-world applications. His work centers on stream and big data mining, geospatial analysis, and large-scale data management. These technical capabilities are applied across diverse domains including personalized health and medicine, Intelligent Transportation Systems, telecommunications, smart building technologies, Smart Grid infrastructure, healthcare analytics, and waste water management systems. His approach emphasizes creating practical data science solutions that address complex challenges in these sectors while considering the constraints of real-world deployment. An analysis of his recent publication record reveals a strong focus on real-time data processing and analytics, particularly for transportation and urban systems. His work frequently addresses challenges in handling massive time series data, developing efficient architectures for low-latency analytics, and creating practical applications for smart city infrastructure. There's a clear progression from theoretical data science contributions to production-ready systems that can process billions of data points daily, demonstrating his ability to bridge research and practical implementation. His notable achievements include: Two IBM Outstanding Technical Achievement Awards Best Paper Award for his research Student Best Paper Award Verscheure has substantial experience in research leadership and mentoring. During his tenure at IBM, he managed the Exploratory Stream Analytics research group and led a technical and management team of approximately 40 people at the IBM Research lab in Ireland. He has served on PhD committees at major universities and published nearly 100 research papers that have garnered over 2,400 citations. His work has resulted in more than 40 US and international patents, demonstrating both academic and practical impact. As Executive Director of the Swiss Data Science Center, Verscheure oversees a distributed multi-disciplinary team working across domains including personalized health, transportation, earth and environmental science, social science and digital humanities, and economics. The center aims to federate data providers, data and computer scientists, and subject-matter experts around a cutting-edge analytics platform while addressing security and privacy issues. Under his leadership, the SDSC develops embedded data science support, offers end-to-end data science services, and fosters a community to share tools and knowledge in data science.

• Transport Planning
• Urban Analytics
• Traffic Modeling
• +5 more

Sekhar Somenahalli is a Senior Lecturer at University of South Australia 's UniSA STEM school, affiliated with the School of Natural and Built Environments . His work spans transport planning, urban analytics, and sustainable mobility systems, with a focus on walkability, traffic modeling, and public transport optimization. Research Themes : Walkability Assessment : Developed novel metrics for measuring land-use mix and pedestrian infrastructure quality Traffic Modeling : Pioneered multi-tier frameworks like the Tactical Adelaide Model Public Transport Innovation : Investigated bus-based transit corridors and interchange efficiency Shared Mobility : Identified regulatory and infrastructural barriers to sharing economy services His recent publications analyze census-based mobility prediction, spatiotemporal traffic patterns, and socio-demographic walkability variations. Despite extensive grant funding (e.g., CRC for Low Carbon Living), no specific awards are documented in the provided materials. He supervises research students and applies GIS extensively in urban infrastructure analysis.