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Gianvito PioView profile

Associate Professor

• Data Mining
• Bioinformatics
• Social Network Analysis
• +3 more

Gianvito Pio is an Associate Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. His research spans data mining, bioinformatics, social network analysis, multi-relational data mining, and big data analytics. He holds a PhD in Computer Science from the University of Bari (2015) and has taught courses such as Big Data Management and Analysis and Security in Blockchain Technology . As an expert in heterogeneous network analysis, he develops methods for anomaly detection in cryptocurrency trends, microbiome data interpretation, and legal judgment clustering. He leads the local research unit of the PRIN 2022 project COCOWEARS and contributes to journals like the Machine Learning Journal and Expert Systems with Applications as an associate editor. His recent work includes multi-view learning for risk identification in dynamic networks, spatially-aware models for energy forecasting, and biclustering algorithms for biological data. Collaborations with researchers such as Michelangelo Ceci and Antonio Pellicani highlight his interdisciplinary approach. Gianvito Pio also organizes academic events like the Discovery Science conference and contributes to open-source software tools including HOCCLUS2 and GENERE .

Mahyar MasoudiView profile

Assistant Professor

• Urban Ecology
• Geographic Information Science
• Environmental Justice
• +3 more

Mahyar Masoudi is Assistant Professor in the Department of Geography at Memorial University of Newfoundland, where he directs the Urban Ecology & Analytics Lab. He holds adjunct appointments at University of Waterloo's School of Planning and is affiliated with Nanyang Technological University's Resilient and Inclusive Cities Lab. His research examines how spatial patterns of urban elements influence environmental performance and human well-being. Education includes: PhD from National University of Singapore (2018), MSc from Universiti Teknologi Malaysia (2012), BSc from University of Guilan (2009), and postdoctoral work at Waterloo and NUS. Research integrates urban ecology , geographic information science , and environmental justice to study urban landscapes as social-ecological-technological systems. Key interests include urban heat mitigation, ecosystem services, and equitable access to environmental benefits using remote sensing, spatial statistics, and machine learning. Publications focus on urban thermal environments, ecosystem service modeling, and landscape pattern analysis. Recent work examines cooling efficiency of green spaces across Asian megacities and develops computational tools like the NCS2020 R package for ecosystem service assessment. No scientific awards documented. Leads the Urban Ecology & Analytics Lab developing geospatial methods to enhance urban sustainability and equity.

Soutir BandyopadhyayView profile

Professor

• Spatial and Environmental Statistics
• Time Series Analysis
• Bootstrap/Resampling Methods
• +4 more

Soutir Bandyopadhyay is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines. He holds a PhD in Statistics from Texas A&M University, an M.Stat from the Indian Statistical Institute, and a B.Sc in Statistics from St. Xavier’s College. His research focuses on spatial and environmental statistics, time series analysis, bootstrap methods, and large sample theory, with applications in energy systems, climate science, and machine learning. Recent work includes advancements in spatial extremes modeling using neural networks, risk analysis in oil and gas flowlines, and statistical frameworks for energy load forecasting. He has organized major conferences including the 2023 IISA Annual Conference and the 2024 workshop on uncertainty in civil/environmental systems. As Director of the Data Science Program at Mines since 2025, he leads interdisciplinary initiatives. Key contributions include development of the National Climate Database (NCDB), bias-correction methods for solar radiation data, and R packages like glmfitmiss for handling missing data in GLMs. His students have achieved notable recognitions, including Fulbright awards and ASA competition honors.

Carlos CardonhaView profile

Assistant Professor

• Discrete optimization
• Mixed-integer linear programming
• Combinatorial optimization
• +6 more

Carlos Cardonha is an Assistant Professor in the Department of Operations and Information Management at the University of Connecticut School of Business since 2019. He holds a Ph.D. in Mathematics from Technische Universität Berlin (2011) and degrees in Computer Science from the University of São Paulo (B.Sc. 2004, M.Sc. 2006). Previously, he worked as a Research Staff Member at IBM Research – Brazil (2012–2019). His research focuses on discrete optimization, approximation algorithms, and applications of machine learning and mathematical programming to operations research problems. He teaches courses such as OPIM 5641 (Business Decision Modeling) and OPIM 3511 (Business Data Analytics II), supported by DataCamp. Education: Ph.D. in Mathematics, Technische Universität Berlin, Germany (2011) M.Sc. in Computer Science, University of São Paulo, Brazil (2006) Bachelor in Computer Science, University of São Paulo, Brazil (2004) Research Interests: His work spans analytics, optimization, and theoretical computer science, with a focus on mixed-integer linear programming, combinatorial optimization, and algorithm design. He applies these techniques to real-world problems in scheduling, resource allocation, and machine learning model optimization. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: Not explicitly mentioned in the text.

Alina BialkowskiView profile

Senior Lecturer

• Medical Imaging
• Biomedical Engineering
• Computer Vision
• +6 more

Dr Alina Bialkowski is a Senior Lecturer at the School of Electrical Engineering and Computer Science , part of the Faculty of Engineering, Architecture and Information Technology at The University of Queensland. Her research focuses on interpretable machine learning and computer vision to enhance AI transparency and solve real-world challenges. Prior to joining UQ in late 2017, she held postdoctoral positions at University College London (2015–2017), where she studied human perception in driving, and Disney Research Pittsburgh (2014), analyzing team sports using spatiotemporal data. Dr Bialkowski earned her PhD and Bachelor of Engineering (Electrical Engineering) from Queensland University of Technology, Australia. Her doctoral research centered on group behavior analysis from visual and spatiotemporal data, with applications in sports analytics and intelligent surveillance systems. Her research interests span medical imaging (especially electromagnetic imaging of strokes), human attention modeling in driving, intelligent transport systems , surveillance systems , and sports analytics . She emphasizes explainable AI to bridge the gap between technical systems and human understanding, employing methods like feature visualization and attribution. Her work also explores sensors for non-invasive imaging and machine learning frameworks to ensure ethical AI. Dr Bialkowski has received significant recognition, including the Best Paper Prize at the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) . Her research has led to 6 international patents with collaborators such as Disney Research, Toyota Motor Europe, and The University of Queensland, focusing on electromagnetic imaging and AI-driven solutions. She actively contributes to interdisciplinary projects like The Lanyard Project (traffic sensor fusion) and co-authored a SmartSat CRC-funded research report on machine learning for satellites. Dr Bialkowski is available for supervision and advocates for AI systems that integrate human-centric principles into their design and evaluation.

Patrick JamesView profile

Professor

• Energy Systems
• Renewable Energy
• Built Environment Energy Use
• +4 more

Professor Patrick James holds the position of Professor of Energy and Buildings within the Engineering and Physical Sciences faculty at the University of Southampton. His research focuses on energy use across scales from individual dwellings to urban regions, with active projects in the UK, China, sub-Saharan Africa, and the Middle East. He leads major initiatives like the EPSRC-funded LATENT study exploring heat-as-a-service systems and the DESIREABLE project addressing rural milk chilling in Rwanda. As Deputy Editor of the IET Renewable Power Generation Journal, he emphasizes energy policy analysis while assessing funding proposals for national agencies. His teaching portfolio includes module leadership for MSc Energy and Sustainability programs (CENV2034 LiveableCities, SESG6041 Introduction to Energy Technologies) and contributions to six additional modules. Current research projects span renewable energy adoption in Saudi Arabia, resilient energy networks for developing nations, and small-area socioeconomic indicators via big data. Awards include the 2023 Best Paper Award at MC2023. Key Roles: EPSRC PI, Journal Deputy Editor, PhD Supervisor Grants: EPSRC, EU H2020, Saudi Ministry of Education Labs/Teams: Energy and Climate Change Group, Collaboration with Bahaj Group on thermal retrofit solutions His work integrates technical innovation with societal impact, addressing energy equity through projects like FORTIS UNUM and rural electrification in Africa while advancing decarbonization strategies for heating systems.

Jun ZhangView profile

Lecturer

• Socio-technical transitions of ICT-enabled societies
• Digital inequality and digital rights
• Smart city governance
• +3 more

Dr Jun Zhang is a Lecturer in Information Systems at the School of Information, Journalism and Communication, University of Sheffield. He holds an MSc and PhD from the University of Sheffield. Prior to academia, he worked in China's mapping industry on Smart City projects. His research focuses on socio-technical dynamics of IS innovations, digital rights, and urban governance. He currently serves as Exams Officer for BSc Data Science and Deputy Programme Coordinator for MSc Information Systems. Education: MSc (Sheffield), PhD (Sheffield) Research interests include critical analysis of smart city technologies, power dynamics in urban governance, digital inequality, and robotic urbanism. He contributes to the Information Systems Research Group and Urban Automation & Robotics initiatives. Recent work explores citizen participation in AI-driven urban systems and robotic urbanism's societal impacts. Selected publications span smart city entrepreneurship, cooperative value ecosystems, and critical analyses of authoritarian smart governance. He actively participates in interdisciplinary research collaborations and policy dialogues around digital urban innovation.

• Sustainable Building Practices
• Facility Management Strategy
• Workplace Design and Ergonomics
• +3 more

Eunhwa Yang is an Associate Professor and Curriculum Committee Chair at the School of Building Construction, Georgia Institute of Technology. She holds a Ph.D. in Human Behavior and Design from Cornell University and an MS in Building Construction from Georgia Tech. Her research focuses on the interplay between built environments and human outcomes, employing Bronfenbrenner’s ecological systems theory to study workplaces, campuses, and homes. Key areas include sustainable building practices, facility management optimization, and aging-in-place strategies for individuals with mild cognitive impairment. As an advocate for innovative teaching methods, Dr. Yang has developed facility management courses using active learning and service learning approaches. She has led workshops on pedagogical strategies at GT’s Center for Teaching and Learning and Cornell’s Center for Teaching Excellence. Professional affiliations include the International Facility Management Association (IFMA) and CoreNet Global, where she has received young researcher awards and fellowships. Her recent work emphasizes data-driven solutions for energy performance contracting, hybrid workspaces, and inclusive housing designs. The Workplace Ecology Lab under her direction explores environmental factors influencing sleep health, workplace safety, and aging-in-place technologies. Over 15 years of grant-funded research has produced impactful studies on green leasing, building lifecycle analysis, and multi-stakeholder decision-making in urban transformation projects. Awards: IFMA Fellowships, IAPS Workshop Grant Grants: CoreNet Global Academic Challenges, IAPS sponsored projects Labs: Workplace Ecology Lab at Georgia Tech School of Building Construction Dr. Yang’s advisory work supports facility managers, corporate real estate directors, and designers in optimizing space utilization through evidence-based strategies. Her research bridges environmental science, architectural design, and human behavior studies to create healthier, more equitable built environments.

• Artificial Intelligence
• Internet of Things (IoT)
• Wireless Communications
• +4 more

Professor Wei Xiang holds the Cisco Chair of AI and IoT at La Trobe University, leading the Cisco-La Trobe Centre for AI and IoT and the Australian Centre for AI in Medical Innovation. He previously established Australia's first IoT Engineering degree program at James Cook University, earning recognition in the Pearcy Foundation's Hall of Fame. His expertise spans AI, IoT, wireless communications, and medical AI innovation. As an IEEE Associate Editor for multiple journals, he has published over 450 peer-reviewed papers and books. Key roles include: Director & Chief Scientist: Australian Centre for AI in Medical Innovation Founding Director: Cisco-La Trobe AIoT Centre Adjunct Professor: James Cook University Vice Chair: IEEE Northern Australia Section (2016-2020) Research focuses on AI-driven IoT systems, smart agriculture, and medical applications. Awards include La Trobe Research Excellence Award (2021), Pearcey Entrepreneurship Award (2017), and multiple fellowships. Current grants involve AIoT in smart farming, medical innovation, and satellite IoT. He supervises research students in AIoT and advises on collaborative projects. His labs pioneer technologies like radar-based health monitoring and UAV-enabled environmental sensing. Recent publications highlight advancements in wireless communication systems, deep learning models for remote sensing, and hybrid networks.

• Political Institutions
• Political Economy
• Media and Politics
• +3 more

Kevin DeLuca is an Assistant Professor of Political Science at Yale University and a Resident Fellow at the Institution for Social and Policy Studies (ISPS). He is also a Faculty Affiliate at the Center for the Study of American Politics (CSAP). His research focuses on political institutions, the political economy of media, and election laws, particularly examining the role of local newspapers in shaping political outcomes. DeLuca holds a Ph.D. in Political Economy and Government from Harvard University (2023), an M.A. in Economics from the University of Texas at Austin (2015), and a B.A./B.S. in Economics and Psychology from the same university (2014). He teaches courses such as 'Data Analytics for Redistricting and Voting Rights' and 'Regulating Elections: Voting Rights and Election Law.' His work intersects with computational methods in redistricting analysis and the influence of media on electoral processes. Previously, he collaborated with institutions like the MIT Election Data and Science Lab (MEDSL) and the Stanford-MIT Healthy Elections Project. His research emphasizes methodologies like Bayesian geocoding for redistricting validation and explores partisan dynamics in gerrymandering solutions. He has contributed to groundbreaking studies on candidate quality measurement via media endorsements and the impact of top-tier elections on down-ballot races.

Renato PajarolaView profile

Professor

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +5 more

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.

Fernando JaramilloView profile

Assistant Professor

• Flood Detection and Mapping
• Remote Sensing
• Deep Learning
• +3 more

Fernando Jaramillo is an Assistant Professor at Stockholm University and a Co-Principal Investigator (Co-PI) in the DeepFlood project. He is affiliated with the Digital Futures Faculty, a cross-disciplinary research center focused on advancing digital technologies to address societal challenges. Previously, he served as the main supervisor for the Deep Wetlands project, which explored wetland ecosystems using advanced analytical methods. His research interests revolve around flood detection and environmental monitoring, leveraging PolSAR satellite data, metaheuristic algorithms, and deep learning techniques. These methods aim to enhance large-scale flood mapping accuracy and support sustainable environmental management. As part of Digital Futures, Jaramillo collaborates with institutions like KTH Royal Institute of Technology and RISE Research Institutes of Sweden. The center’s work spans innovation, knowledge generation, and training future leaders in high-impact industrial fields. Contact information for Digital Futures is available at digitalfutures.kth.se .

Alan MurrayView profile

Professor

• Geographic Information Science
• Spatial Optimization
• Emergency Response Planning
• +5 more

Alan Murray is a **Professor and Vice Chair for Academic Personnel** in the Department of Geography at the University of California, Santa Barbara (UCSB), where he also holds the Yardi Chair in Geography. He previously held professorial roles at Ohio State University, Arizona State University, and Drexel University before returning to UCSB in 2015. Murray earned his BS in Mathematics, Master’s in Statistics and Applied Probability, and PhD in Geography, all from UCSB. His research focuses on **geographic information science (GIScience)**, **spatial optimization**, **emergency response**, **health informatics**, **urban development**, and **infrastructure systems**. He has pioneered methodologies for integrating multi-objective spatial optimization with GIS to address urban planning challenges, such as smart mobility and wildfire risk mitigation. His work emphasizes practical applications in disaster response, public health, and sustainable land use planning. Murray has advised numerous graduate students and contributed to cross-disciplinary initiatives in GIScience education and technology integration. His research has been disseminated through high-impact publications in journals like *Geographical Analysis* and *International Journal of Geographical Information Science*. He has also directed centers such as the Center for Urban and Regional Analysis. Key areas of current research include: GIScience education reform Spatial optimization for emergency response systems Wildfire risk assessment and community resilience Equitable urban planning frameworks His work bridges theoretical advancements with real-world applications, addressing pressing issues like climate adaptation, infrastructure resilience, and smart city design.

Shovanur HaqueView profile

Lecturer

• Bayesian Statistical Modelling
• Spatial Data Analysis
• Machine Learning
• +3 more

Dr. Shovanur Haque is an Associate Lecturer in the School of Mathematical Sciences and a Postdoctoral Research Fellow in the School of Public Health & Social Work at Queensland University of Technology (QUT). She holds a PhD in Statistics (2018) from QUT, focusing on developing the MaCSim method for evaluating record linkage accuracy. Her work spans academia and government, including roles at the Australian Bureau of Statistics and Queensland Department of Environment and Science. Currently, she leads a Centre for Data Science (CDS) project investigating spatial data aggregation effects on medical decision-making. Her research interests include Bayesian modeling, spatial data analysis, machine learning, and record linkage. Key research projects involve assessing Modifiable Areal Unit Problem (MAUP) impacts on epidemiological inference, modeling infectious disease transmission dynamics, and developing climate-driven early warning systems for public health. She contributes to interdisciplinary collaborations, combining statistical methods with health, environmental, and social sciences. Education: PhD in Statistics (QUT, 2018) Research Focus: Spatial epidemiology, Bayesian methods, record linkage, and MAUP analysis Affiliations: Centre for Data Science, Centre for Immunology and Infection Control Her recent publications (2021–2025) emphasize Bayesian structural time series modeling, spatial aggregation effects in health data, and cross-border disease surveillance networks. She actively engages in methodological advancements for public health analytics and data-driven policy decisions.

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.