جست‌وجوی استاد و پژوهشگر

Michael McGuireمشاهده پروفایل

استادیار

• Computer Science
• Data Mining
• Environmental Informatics
• +۳ مورد دیگر

Michael McGuire is an Assistant Professor in the Department of Computer and Information Sciences at Towson University's College of Science and Mathematics. He holds a Ph.D. in Information Systems from the University of Maryland, Baltimore County (2010), with supporting degrees in Information Systems (MS, 2005) and Geography & Environmental Planning (BS, 1996). Education: Ph.D. & MS in Information Systems (UMBC), BS in Geography (Towson) Department: Computer and Information Sciences School: College of Science and Mathematics Email: mmcguire@towson.edu His research focuses on spatio-temporal data mining , sensor database systems , and environmental informatics applications. Current work examines deep learning approaches for climate prediction , including convolutional neural networks for tornado day forecasting and transformer models in hurricane intensity analysis. Earlier publications explore information visualization through eye-tracking studies and multi-criteria decision systems for route planning. Recent publications highlight machine learning applications in environmental contexts, with 2024 studies on knowledge graphs and 2023 work on federated learning . The 2022 tornado prediction papers demonstrate CNN capabilities in meteorological modeling, while 2021 eye-tracking research shows feature engineering approaches for visual processing tasks. Dr. McGuire's technical expertise spans distributed database systems , cloud computing architectures , and human-computer interaction through eye gaze analysis. His 2018-2020 publications compare SQL/NoSQL storage for climate data and develop safe route planning algorithms using Fuzzy TOPSIS and other decision frameworks.

Emanuele Della Valleمشاهده پروفایل

دانشیار

• Big Data
• Stream Processing
• Semantic Technologies
• +۳ مورد دیگر

Emanuele Della Valle is an Associate Professor at the Department of Electronics, Information and Bioengineering of Politecnico di Milano. He holds a PhD in Computer Science from Vrije Universiteit Amsterdam and a Master's in Computer Science and Engineering from Politecnico di Milano. His research focuses on Stream Reasoning, Big Data, Semantic Technologies, and their industrial applications. He has led initiatives to establish Stream Reasoning as a recognized field, including organizing over 15 workshops/tutorials and developing the C-SPARQL Engine (downloaded 9,000+ times). He is a member of the editorial board of the Journal of Web Semantics and has authored multiple books and over 70 publications. Education: PhD in Computer Science, Vrije Universiteit Amsterdam Master in Computer Science and Engineering, Politecnico di Milano Research Interests: Big Data and Stream Processing Semantic Web and Knowledge Representation Data Science and Real-Time Analytics Applications in IoT, Telecom, and Social Media Awards include the CAiSE’19 Best Paper Award, IBM Faculty Awards (2006, 2013), and multiple best paper recognitions. His work has been applied with industry partners like Telecom Italia, IBM, and Siemens. Teaching focuses on Big Data, Data Science, and AI, with courses such as 'Streaming Data Analytics' and 'Artificial Intelligence and Big Data Science'. He has coordinated EU projects (FP7 Service Finder, EIT Digital activities) and founded Quantia Consulting and motus ml.

• Big Data Management
• Scalable Data Science
• Spatial and Spatio-Temporal Data Processing
• +۵ مورد دیگر

Suprio Ray is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick, Canada, leading the Big Data Systems and Analytics Lab. He holds a PhD from the University of Toronto, an M.Sc. from the University of British Columbia, and a B.E. from NIT Trichy. Previously, he worked in industry roles at Oracle, Bell Labs, and Webtech Wireless, and completed a PhD internship at SAP. His research focuses on scalable data systems, spatial/spatio-temporal data management, and modern hardware utilization. Key projects include the Jackpine spatial database benchmark , DaskDB (a scalable data science system), and NUMA-aware query processing . He collaborates across disciplines with ECE and GGE departments, supported by grants from NBIF, NSERC, and industry partners. Research interests span Big Data systems, privacy/security in databases, blockchain analytics, and parallel/distributed computing. He has pioneered techniques like STILT multi-dimensional indexes , privacy-preserving spatial queries (Pystin) , and learned spatial indexes . Over 60 publications appear in top venues like SIGMOD, ICDE, and IEEE BigData. Education: PhD (Computer Science), University of Toronto (2015) M.Sc. (Computer Science), University of British Columbia (2003) B.E. (Computer Science & Engineering), NIT Trichy (2000) Teaching includes courses on big data systems (CS4545/6545), database foundations (CS6585), and data science (CS2545). He advises graduate students in scalable analytics and spatial systems, emphasizing industry-relevant software skills. Key awards include the 2024 ACM SIGSPATIAL best poster award , IBM Best Student Paper (2014) , and Harrison McCain Foundation award (2016) . His work has been recognized with 7 best paper awards and 3 patents. Current projects explore FPGA-accelerated joins , serverless data analytics , and privacy-preserving blockchain queries . Past contributions include the GEMM mobility model (2003) and foundational spatial benchmarks.

• Big Data Analysis
• Bayesian Statistics
• Genomics Data Analysis
• +۳ مورد دیگر

Madhuchhanda Bhattacharjee is a Professor of Statistics and Data Science with expertise in statistical methodology for large-scale data analysis. Her research focuses on Bayesian integrated modeling of omics data, spatio-temporal processes, and stochastic processes. She holds advanced degrees including a Ph.D. in Statistics from the University of Pune (2000), an M.Phil. in Statistics from the University of Pune (1995), and a Master of Statistics from the Indian Statistical Institute (1993). She is a Fellow of the Royal Statistical Society and an Elected Member of the International Statistical Institute, among other prestigious memberships. Her work addresses critical areas such as microbiome analysis, spatial epidemiology of diseases like cancer, and modeling climate variability using random matrix theory. She has contributed to the development of novel statistical methods for assessing dependence structures in spatio-temporal data and has published extensively on Bayesian approaches to integrate multi-omics datasets. Key research themes include: Inference on large-scale data using random matrices and parallel processing Bayesian modeling of heterogeneous biomedical data Spatio-temporal modeling of public health crises like the 2020 pandemic Her recent work explores statistical methods for predicting preterm birth via maternal blood multi-omics integration and spatial association measures for time series. She co-leads the Statistical Advisory Unit (SAU), providing advanced statistical methodologies for interdisciplinary research projects. Major awards include Fellow of the Royal Statistical Society (2005) and Elected Membership in the International Statistical Institute (2005). She has advised on several grants focusing on omics data integration and disease marker identification. Her contributions span academic leadership roles, including memberships in global statistical societies like the International Indian Statistical Association and the Caucus for Women in Statistics. She has pioneered statistical methodologies applied to environmental and healthcare challenges, emphasizing reproducibility in data-intensive research.

Kenan Zhangمشاهده پروفایل

استادیار

• Urban Transportation Systems
• Operations Research
• Transportation Engineering
• +۶ مورد دیگر

Dr. Kenan Zhang is a Tenure Track Assistant Professor at EPFL's School of Architecture, Civil and Environmental Engineering, leading the Laboratory for Human-Oriented Mobility Eco-system (HOMES). She holds dual roles in teaching Civil Engineering and contributes to the EDCE and SGC programs. Her research focuses on mathematical modeling, optimization, and operations management of urban transportation systems, with emphasis on emerging mobility services and technologies. Education: BSc in Civil Engineering, Tsinghua University MSc in Architecture-Engineering-Construction Management (AECM), Carnegie Mellon University PhD in Civil Engineering (Transportation System Analysis & Planning) and second MSc in Statistics, Northwestern University Postdoctoral Researcher at ETH Zurich Research Interests: Urban transportation systems, optimization algorithms, sustainable mobility, traffic flow theory, and emerging technologies like autonomous vehicles and MaaS platforms. Her work integrates data-driven approaches with theoretical models to address real-world challenges in urban mobility. Awards & Recognition: CEE Rising Star Award COTA Best Dissertation Award TRB Committee Membership (ACP 50) Editorial Advisory Board, Transportation Research Part C Teaching & Advising: Supervises PhD students Liu Xuhang & Ma Xinyu. Teaches courses on urban transport systems, transportation network modeling, and seminars in civil/environmental engineering. Active in curriculum development for interdisciplinary mobility studies. Labs & Collaborations: Directs HOMES Lab exploring human-centric mobility ecosystems. Engages in international collaborations and policy analysis for sustainable transport solutions.

• Anthropology
• Political Science
• Development Studies
• +۹ مورد دیگر

Elliott Edward Prasse-Freeman serves as Assistant Professor in the Department of Sociology and Anthropology at the National University of Singapore. His academic career centers on Southeast Asian politics with particular emphasis on Myanmar, drawing from extensive ethnographic research on Burmese subaltern political thought, activism, and contentious politics within semi-authoritarian settings. Prasse-Freeman earned his PhD from Yale University's Department of Anthropology, establishing the methodological and theoretical foundation for his fieldwork in Myanmar. His educational background has equipped him with sophisticated anthropological frameworks for analyzing political phenomena in complex authoritarian contexts. His research spans anthropology, political science, development studies, historical studies, gender studies, human geography, and policy administration. Prasse-Freeman's work examines how marginalized populations navigate and resist state power, with particular attention to rights frameworks in contexts where such rights are systematically denied. His book Rights Refused (Stanford University Press) analyzes Burmese subaltern political thought through extended ethnography of activism. He is currently developing a second book project exploring Rohingya political subjectivity amid dislocation and mass violence, focusing on their maneuvers within post-sovereign governmental regimes that incorporate Human Rights discourse, humanitarian care/exclusion, and biopolitical regulation. Prasse-Freeman's recent publications reveal a coherent analytical trajectory examining Myanmar's political landscape through multiple lenses including ethnic conflict, state violence, technological interventions, and resistance strategies. His 2025 article on 'Big data visuality and interstitial spaces of autonomy' challenges conventional assumptions about surveillance technologies, demonstrating how humanitarian technologies like drones and blockchain can simultaneously obscure and enhance knowledge, creating spaces for autonomy within conflict zones. His scholarship consistently bridges anthropological theory with political analysis, particularly examining how marginalized populations strategically navigate complex power structures. His teaching interests include Sociology of Language and Communication, Southeast Asia in a Globalizing World, and Modernity and Social Change, reflecting the interdisciplinary nature of his scholarly work and connecting his research expertise with pedagogical practice.

• Volunteered Geographic Information
• Spatial Big Data Analytics
• Geo-Crowdsourcing
• +۲ مورد دیگر

Dr. Yingwei Yan is a Senior Lecturer in the Department of Geography at the National University of Singapore. He serves as Director of Taught Graduate/Continuing Education and Training programmes in Applied GIS and co-chairs the ISPRS Working Group on Human Behaviour and Spatial Interactions. His research bridges geographic information science, social sensing, and computational methods to address urban environmental challenges. Education: Ph.D. in Geography, National University of Singapore Postdoctoral Research, Institute of Geography, Heidelberg University Dr. Yan's research explores volunteered geographic information, spatial big data analytics, and geo-crowdsourcing applications in social and environmental contexts. His work integrates computational techniques with geographical analysis to model urban systems, focusing on traffic emissions, land use impacts, and human-environment interactions. His recent publications demonstrate consistent focus on urban sustainability through geospatial technologies, with articles spanning carbon emission modeling, thermal environment simulation, and livability metrics. These studies employ advanced machine learning and spatial analysis techniques to inform urban planning decisions. Dr. Yan teaches foundational and advanced GIS courses including: GE2215 Introduction to GIS GE3238 GIS Design and Practices GE5227 Internet GIS He also supervises graduate research projects and internships in applied geography.

• Computational Finance
• Data Science
• Medical Imaging
• +۳ مورد دیگر

Dr. Jianliang Gao is a Teaching Fellow at Imperial College London's Business School and Early Career Researcher Institute (ECRI), with a Fellowship of the Higher Education Academy. He holds a PhD in Computing from Ulster University and has extensive interdisciplinary experience spanning finance, biomedical engineering, and computational science. Education: PhD in Computing, Ulster University MSc in Information Technology, Hong Kong University of Science and Technology BSc in Computer Science, Fujian Normal University Research Interests: Computational Finance and Data Science Medical Imaging and Neuroscience (via dHCP projects) Sensor Systems and Biomechanical Engineering Big Data Analytics and Cloud Computing Prosthetics Interface Design and Healthcare Technology Recent Research Trends: His work spans stock market prediction models, graph neural networks, medical imaging frameworks (e.g., neonatal connectome analysis), and interdisciplinary tools like virtual research environments. He collaborates with experts in BiomedIA, metabolomics, and prosthetics engineering. Teaching & Leadership: Teaches MATLAB/Finance modules at the Business School and leads workshops on reproducible research computing. Collaborates with Prof. James Sefton, Prof. Alexander Michaelides, and Prof. Timothy Ebbels across ECRI and Biostatistics. Labs/Teams: Active in the BioMedIA group (medical imaging), ReCoDE project (computational reproducibility), and sensor systems teams at the University of Southampton.

Joshua Apteمشاهده پروفایل

استادیار

• Air Pollution
• Environmental Justice
• Environmental Engineering
• +۵ مورد دیگر

Joshua Apte is an Assistant Professor jointly appointed in the School of Public Health and the Department of Civil and Environmental Engineering at the University of California, Berkeley. His research focuses on air pollution, environmental justice, and climate change mitigation, with interdisciplinary applications ranging from atmospheric chemistry to urban planning. Education: ScB in Environmental Science from Brown University (2004), MS and PhD in Energy and Resources from UC Berkeley (2008, 2013). Prior to Berkeley, he held an Assistant Professorship at the University of Texas at Austin (2015–2020). Research interests emphasize air pollution exposure assessment , risk equity analysis , and environmental sensors . His work explores disparities in pollution exposure linked to historical redlining, urban infrastructure, and socioeconomic factors. Recent studies highlight persistent inequities despite regulatory improvements in California and global trends in particulate matter reduction. Publications focus on high-resolution air quality mapping, health impacts of PM2.5, and policy implications of pollution sources. Collaborative projects include calibrating low-cost sensors in India and evaluating electrification benefits in transportation sectors. No scientific awards are explicitly mentioned in the provided text. Advising and grants details are not included, though his dual appointments suggest involvement in interdisciplinary research teams. Active participation in environmental health sciences and civil engineering collaborations is evident through his joint departmental affiliations.

• Computational Geometry
• Data Structures
• Approximation Algorithms
• +۱ مورد دیگر

Joachim Gudmundsson is Professor and Head of School of Computer Science at the University of Sydney, holding SOAR and ARC Future Fellowships. His research develops algorithms for computational geometry problems including spatio-temporal analysis, graph spanners, geometric approximation, and trajectory mining. Key research domains: Spatio-temporal pattern analysis Graph spanners and approximation algorithms Trajectory clustering and map matching Geometric network optimization Multi-robot coordination algorithms Recent publications demonstrate strong focus on trajectory analysis algorithms under Fréchet distance, geometric graph decompositions, and pattern formation in multi-robot systems. Work increasingly addresses dynamic and real-world graph structures. Recognized with prestigious fellowships: SOAR Fellow (Sydney Research Accelerator) ARC Future Fellow (Australian Research Council)

• Data Analysis
• Data Mining
• Machine Learning
• +۶ مورد دیگر

Heikki Mannila is a Professor of Computer Science at Aalto University, with a focus on algorithms for data analysis, data mining, and machine learning. He previously held the title of Academy Professor (2004–2008) and served as Vice President for Research and Education at Aalto University (2009–2012) and President of the Academy of Finland (2012–2022). His research emphasizes the interplay between theoretical computer science and practical applications in fields like environmental science, linguistics, and paleontology. Education: Ph.D. in Computer Science, University of Helsinki, 1985 Research Interests: Algorithmic methods for data analysis and mining Machine learning applications across disciplines Interdisciplinary collaborations in paleontology, linguistics, and environmental science House of AI initiative for AI-driven multidisciplinary research Recent Work: Recent publications explore multilinear transforms, Hadamard decomposition problems, and applications in recommendation systems and environmental data analysis. His work bridges theoretical foundations and real-world problem-solving. Awards: Academy Professor of Finland (2004–2008) Grants & Labs: Initiator of the House of AI at Aalto University Past leadership roles in national research policy and funding Collaborations: Worked with institutions like TU Wien, Max Planck Institute, Microsoft Research, and Nokia Research, emphasizing cross-sectoral innovation.

Bennett A. Landmanمشاهده پروفایل

استاد

• Medical Imaging
• Biomedical Engineering
• Machine Learning
• +۶ مورد دیگر

Bennett A. Landman is the Stevenson Chair of Engineering and a Professor of Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He holds additional professorships in Computer Science, Biomedical Engineering, Radiology, Psychiatry, Biomedical Informatics, and Neurology. Landman leads the Vanderbilt Lab for Immersive AI Translation (VALIANT) and has been instrumental in advancing medical image processing and AI-driven personalized medicine. Education: B.S. & M.Eng. in Electrical Engineering and Computer Science from MIT (2001-2002) Ph.D. in Biomedical Engineering from Johns Hopkins University (2008) Research Focus: Landman's work centers on applying image-processing technologies to large-scale medical imaging studies, particularly in neuroimaging and radiology. His research integrates machine learning, big data analytics, and AI to improve diagnostic accuracy and personalized healthcare. Key areas include diffusion MRI harmonization, functional MRI analysis, and automated medical image segmentation. Recent Trends in Articles: Landman's latest publications focus on spatial smoothing techniques in white matter imaging, super-resolution eye atlases, and scalable medical data processing. His work emphasizes cross-disciplinary collaboration and the development of robust preprocessing methods for multi-site datasets. Awards: Fellow of the SPIE (2021) Grants & Advising: Secured grants from NIH, NSF, and DoD. Advised over 340 co-authors across institutions, resulting in 450+ peer-reviewed publications and 24,000+ citations. Served as editor for IEEE Transactions on Medical Imaging and co-chair of SPIE Medical Imaging conferences. Labs & Teams: Director of VALIANT and founding director of Vanderbilt's Center for Computational Imaging. Active in the Vanderbilt Brain Institute and Kennedy Center, advancing interdisciplinary research in surgery, engineering, and neuroscience.

Ming Wangمشاهده پروفایل

استاد

• Biostatistics
• Data Integration
• Causal Inference
• +۱۲ مورد دیگر

Ming Wang, PhD is a Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine. She also serves as Director of the Masters Program in Biostatistics . Her research focuses on statistical method development for biomedical and human health problems, including causal inference, longitudinal data analysis, and computational methods for genomic and imaging data. She collaborates on studies involving cancer, cardiovascular disease, kidney disease, neurodegenerative disorders, and child abuse. Education : PhD in Biostatistics, Emory University (2013) MSc in Biostatistics, University of Louisville (2008) BSc in Applied Mathematics, Peking University (2006) Research Interests : Methodological: Bayesian statistics, missing data, spatial statistics, risk prediction Clinical: Prostate cancer, kidney disease, cardiovascular outcomes, neurodegeneration Key Awards : Elected Member, International Statistical Institute (2021) Dean's Excellence in Teaching Award (2019) NISS/ASA Junior Faculty Presentation Award (2016) Professional Roles : Associate Editor: Biometrics , Statistics in Medicine , Journal of Biopharmaceutical Statistics Statistical Editor: Journal of the American Society of Nephrology Her work bridges statistical innovation with clinical impact, particularly in leveraging large-scale health data for precision medicine and population health strategies.

John Liagourisمشاهده پروفایل

استادیار

• Distributed Systems
• Databases
• Secure Analytics
• +۴ مورد دیگر

Dr. John Liagouris is an Assistant Professor at Boston University's Faculty of Computing and Data Sciences, appointed since July 2022. Previously, he served as an Adjunct Assistant Professor (2020–2022) and held roles at BU’s Hariri Institute for Computing, ETH Zurich’s Systems Group, UC Berkeley’s RISELab, and the University of Hong Kong. His research focuses on distributed systems, databases, and secure analytics, with a strong emphasis on privacy-preserving technologies in cloud environments. He earned a 5-year diploma in Electrical and Computer Engineering (2008) and a PhD (2015) from the National Technical University of Athens (NTUA). His academic journey includes visiting scholar positions at UC Berkeley and the University of Hong Kong, alongside research roles at the Athena Research Center in Greece. Liagouris’s research spans cryptographic cloud analytics, streaming state management, and spatial RDF data systems. His work on frameworks like Queryshield and TVA demonstrates expertise in securing distributed data processing. Recent trends in his publications emphasize multi-party computation and latency-conscious distributed systems. His contributions to fault-tolerant systems (e.g., Lineage Stash) and incremental routing logic (DeltaPath) highlight a focus on optimizing distributed workflows. Though no formal advising records are listed, his roles suggest active involvement in training researchers in distributed computing and cybersecurity. Labs and teams associated with his work include BU’s Hariri Institute and collaborations with institutions like ETH Zurich’s Systems Group, reflecting a networked approach to academic research.

Cong Gaoمشاهده پروفایل

استاد

• Data Science
• Geospatial data management
• Spatio-temporal data mining
• +۷ مورد دیگر

Cong Gao is a Professor and Head of the Division of Data Science at Nanyang Technological University's College of Computing & Data Science. He also holds a courtesy appointment with the School of Physical & Mathematical Sciences. Previously, he served as an Assistant Professor at Aalborg University, Denmark, and worked as a researcher at Microsoft Research Asia. He co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU). His educational background includes: Ph.D. in Computer Science from National University of Singapore (2004) Master of Engineering from Tianjin University, China (1999) Bachelor of Engineering from Tianjin University, China (1996) Professor Gao's research focuses on Data Science, with particular expertise in geospatial data management, spatio-temporal data mining, recommendation systems, and social media data analysis. His work has significantly impacted areas like spatial-textual indexing, point of interest recommendation, and mining social networks. He has published extensively in top venues including VLDB, SIGMOD, ICDE, KDD, and WSDM, with over 14,000 citations and an H-index of 61. His recent publications demonstrate strong trends in applying machine learning to database systems, with particular focus on spatial and trajectory data management. Key research directions include learned indexing techniques, trajectory data analysis, and integrating large language models with database systems for improved query optimization. Professor Gao has received notable scientific recognition including: Best paper runner-up award at WSDM'22 Best paper award runner-up at WSDM 2020 He has advised numerous students who have become significant contributors in their own right, including Xin Cao, Lisi Chen, Kaiyu Feng, and Kaiqi Zhao. His research has been supported by substantial grants from Ministry of Education, NRF, IAF, Singtel/NCS, Roll-Royce, Alibaba, and Microsoft, including a S$42.4 million funding over 5 years for the SCALE@NTU lab. Professor Gao leads the Data Management Research Group (DANTE) and co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU), which develops market-leading AI and data science technologies.