جست‌وجوی استاد و پژوهشگر

Ritu Chaturvediمشاهده پروفایل

دانشیار

• Data Mining
• Predictive Modeling
• Educational Data Mining
• +۵ مورد دیگر

Ritu Chaturvedi is an Associate Professor at the School of Computer Science, University of Guelph. Her research focuses on data mining and predictive modeling with applications in educational technology, e-commerce, and social media analysis. She holds a faculty position emphasizing interdisciplinary approaches to computational challenges in learning systems and digital environments. Her research interests span Intelligent Tutoring Systems (ITS) Educational Data Mining Sentiment Analysis Recommendation Systems Natural Language Processing Machine Learning Methodologies Recent work includes developing algorithms for personalized learning systems, analyzing social media sentiment during global health crises, and enhancing cross-platform e-commerce recommendations. Her publications demonstrate expertise in data-driven solutions for instructional design, consumer behavior modeling, and privacy policy optimization. Advising focuses on graduate students exploring adaptive learning technologies and data integration strategies. Her lab's projects often involve open-source implementations of recommendation engines and educational analytics 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.

Dongsheng Luoمشاهده پروفایل

استادیار

• Data Mining
• Deep Learning
• Natural Language Processing
• +۵ مورد دیگر

Dr. Dongsheng Luo is an Assistant Professor at the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU). He holds a Ph.D. in Information Science and Technology from Pennsylvania State University (2022) and a B.Eng. in Computer Science and Technology from Beihang University (2017). His research focuses on developing interpretable AI tools for complex data analysis, particularly in graph mining, deep learning, and natural language processing, with applications in neuroscience, healthcare, and environmental systems. Education: 2022: Ph.D., Penn State University 2017: B.Eng., Beihang University His research interests emphasize explainable AI, robust graph neural networks, and multimodal integration. Recent work includes frameworks for medical plan generation, causal discovery, and flood forecasting. He investigates methods to enhance model reliability and transparency, such as confidence-aware explanations and fidelity evaluation. Collaborations span biomedical informatics, climate science, and IoT systems. Publications highlight contributions to graph explainability, time series analysis, and molecular modeling. His work bridges theoretical advances with practical applications in healthcare, environmental monitoring, and communication systems.

Alex Jungمشاهده پروفایل

دانشیار

• Machine Learning Theory
• Statistical Signal Processing
• Networked Data Analysis
• +۱ مورد دیگر

Alex Jung is Associate Professor at Aalto University's Department of Computer Science, researching fundamental conditions for successful machine learning applications. His work spans theoretical foundations of learning systems, distributed optimization, and statistical signal processing. He holds Austria's highest academic distinction (Promotio Sub Auspiciis Res Publicae) and leads Helsinki Institute for Information Technology research groups. Research develops clustered federated learning frameworks, explainable empirical risk minimization, total variation methods for networked data, and applications in energy systems and medical imaging. Recent work includes high-resolution wind mapping for Finland and reinforcement learning for electrical machine design. Publications include textbooks on machine learning fundamentals and award-winning papers on distributed learning. Dr. Jung serves on editorial boards and develops educational resources including the Aalto Dictionary of Machine Learning.

• Statistical learning in high dimensions
• Statistical learning on networks
• Functional data analysis
• +۱۲ مورد دیگر

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur, Nice, France. He holds a Chair in Artificial Intelligence and serves as Director of the Institut 3IA Côte d'Azur, while also leading the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. Université Côte d'Azur: Full Professor of Statistics (2019-present) Institut 3IA Côte d'Azur: Director (2019-present) Inria: MAASAI Team Leader (2019-present) His research focuses on statistical learning methodologies for high-dimensional data, network analysis, functional data modeling, and deep latent variable approaches. Key applications include medical diagnosis through radiomics, ecological monitoring, astrophysics data analysis, and humanities data science. Recent publications emphasize dynamic co-clustering techniques for count data streams, deep latent position models for network analysis, and discriminative clustering frameworks using generalized mutual information. His work integrates Bayesian sparsity, probabilistic PCA, and robust classification methods. Software development remains a core activity with tools like HDclassif, FunLBM, and Linkage.fr platform for network analysis with textual edges. He has mentored over 20 PhD/postdoc researchers in statistical learning and AI applications.

Clancy Wilmottمشاهده پروفایل

استادیار

• Geospatial Representation
• Critical cartography
• Media geographies
• +۵ مورد دیگر

Clancy Wilmott is an Assistant Professor at the University of California, Berkeley, with office located at 551 McCone Hall. Their research focuses on critical examinations of space, mapping technologies, and cultural representations, with teaching responsibilities including undergraduate courses on geospatial technologies and power dynamics. Research Interests: Their work spans critical cartography, media geographies, and the sociopolitical dimensions of spatial data, with emphasis on cultural memory, landscape interpretation, and digital experiences. Specific domains include the politics of representation, textuality-visuality relationships, and lived digital environments. Courses Taught: GEOG 10AC: Worldings: Regions, Peoples and States GEOG 80: An Introduction to Geospatial Technologies: Mapping, Space and Power GEOG 85: Mapping: Space, Cartography and Power

Joel Wanekمشاهده پروفایل

مدرس

• Critical Environments
• Geospatial Representation
• Racial Geographies
• +۷ مورد دیگر

Joel Wanek is a Lecturer in the Department of Geography at the University of California, Berkeley, where he integrates film, sound, and geospatial practices into his teaching and creative non-fiction work. His research interests span Critical Environments, Geospatial Representation, Racial Geographies, and the interplay of spatial poetics with sound and image. Education: M.F.A. in Experimental and Documentary Arts from Duke University (2011–2013), B.A. in Photography from Webster University (1992–1996). Teaching: Courses include Sonic Geographies (GEOG 126), Geographic Film Production (GEOG 127), and Visual Geography (GEOG 189). Wanek’s creative practice merges experimental/documentary film, field recording, and spatial inquiry. His work interrogates urban soundscapes, racial dynamics, and the immaterial possibilities of media. Key projects include the long-form audio documentary series Here Is Where We Meet (2019–2023), focused on Lake Merritt’s ecological and social layers, and Reveil 8 (2021), a live dawn chorus broadcast. Scientific and Artistic Recognition 2023: Collegium Grant and UC-AFT Local 1474 Grant at UC Berkeley 2018: Individual Artist Grant from the City of Oakland 2014: Multiple festival awards for Sun Song , including Best Cinematography at the Ann Arbor Film Festival 2013: Vtape Award for Best Video at Images Festival His films have screened globally at venues such as the International Film Festival Rotterdam, ICA London, and the Ann Arbor Film Festival. Wanek’s professional background includes video journalism for Emmy Award-winning series and collaborations with institutions like the ACLU and NY Times.

• Medical Imaging
• Artificial Intelligence in Healthcare
• Deep Learning
• +۳ مورد دیگر

Dr. David Ng is an academic researcher affiliated with the Faculty of Science at the University of Sydney. His work focuses on advancing artificial intelligence and deep learning techniques for medical imaging applications, particularly in areas such as radiomics, multimodal data fusion, and healthcare diagnostics. He contributes to innovations in MRI, CT, PET, and endoscopic imaging analysis, developing algorithms for disease detection, segmentation, and predictive modeling. His research interests span medical image reconstruction, federated learning, motion artifact reduction, and dynamic epidemiological modeling. He has published extensively on topics such as neural network architectures for biomedical applications, multimodal data integration, and AI-driven diagnostic tools. Key contributions include advancements in sleep staging via multimodal networks, scleral deformation analysis using OCT images, and automatic segmentation techniques for cardiac and tumor imaging. His work emphasizes practical applications in oncology, cardiology, and public health, aiming to improve clinical decision-making through computational methods. Dr. Ng’s research also explores the intersection of virtual/augmented reality with medical education and intervention. He collaborates on interdisciplinary projects involving biomedical engineering, computer science, and clinical practice to address real-world healthcare challenges.

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.

• Labour Economics
• Health Economics
• Education Economics
• +۵ مورد دیگر

Dr. Zhang Wei is a Grant-Funded Researcher (C) at the University of Adelaide's School of Economics and Public Policy, affiliated with the Future of Employment and Skills Research Centre. He holds a PhD in economics and has held roles including Associate Lecturer at the University of Adelaide and Senior Research Fellow at Flinders University's National Institute of Labour Studies. His research focuses on labour economics, health economics, education economics, international trade, and applied game theory. Current projects include evaluations of the National Disability Insurance Scheme, impacts of ageing populations on productivity, job mismatches, and education's role in labour markets. Education: PhD in economics (thesis on multilateral vs. bilateral trade liberalisation) Research interests span policy-relevant areas such as workforce dynamics, healthcare economics, and trade agreements. He has contributed to projects funded by ARC/NHMRC, NCVER, and government departments, addressing topics like retirement policy, VET completers' job mobility, and economic security. Supervision eligibility includes co-supervision for Masters/PhD candidates.

Max Mahdi Roozbahaniمشاهده پروفایل

مدرس

• Machine Learning
• Data Analysis
• Network Analysis
• +۳ مورد دیگر

Mahdi Roozbahani is a Lecturer at Georgia Tech's School of Computational Science and Engineering and School of Computing Instruction. He earned his Ph.D. in Computational Science and Engineering from Georgia Tech (2019), along with three master's degrees in Civil and Environmental Engineering (Georgia Tech), Geotechnical Engineering (University Putra Malaysia), and a bachelor's from Iran University of Science and Technology. His research focuses on modeling/simulation, network analysis, and machine learning. Roozbahani founded Filio, a cloud-based photo management platform through Create-X incubator. He has published over 20 peer-reviewed papers and teaches courses like Machine Learning and Data Analytics. Awards: Jean-Lou Chameau Research Excellence Award (twice) NSF IRES Fellowship (Ecole des Ponts, Paris) Top Five Featured Paper (Materials Journal, 2017) 2021 Class of 1934 CIOS Honor Roll for Teaching Excellence He advises multiple students in OMSCS/OMSA programs and leads research in computational methods for disaster assessment, educational technology, and granular material analysis. His work bridges academia and industry via Filio's tech innovations.

• Remote Sensing
• Geophysics
• Climate Science
• +۳ مورد دیگر

Robinson Gary is a researcher affiliated with the University of Reading, specializing in geophysical and remote sensing studies. His work focuses on radiative processes, desert climate dynamics, satellite-based environmental monitoring, and digital elevation model accuracy. Key contributions include analysis of Saharan dust storm impacts, radiative flux measurements in Niger, and thermal land surface temperature studies using satellite data. Research Interests: Remote Sensing, Climate Science, Atmospheric Science, Geomatics Publications span experimental fieldwork (e.g., RADAGAST project) and methodological advancements in geospatial data analysis. His early work established foundational methods for evaluating digital elevation models derived from contour data (1994).

Gary Tamمشاهده پروفایل

مدرس ارشد

• Visual Analytics
• 3D Computer Vision
• Point Cloud Processing
• +۲ مورد دیگر

Dr. Gary Tam is Senior Lecturer in Computer Science at Swansea University and member of the Visual Computing Group. Education: B.Eng. from Hong Kong University of Science and Technology M.Phil. from City University of Hong Kong Ph.D. from Durham University PGCtHE from Swansea University Research: Focuses on 3D computer vision, visual analytics, and geometric processing. Recent work includes point cloud compression, saliency detection, and 3D reconstruction techniques. Publications: Research demonstrates strong focus on 3D data processing (point clouds, reconstruction) and visual attention modeling, with increasing application of deep learning across domains including healthcare.

Adam Birchfieldمشاهده پروفایل

استادیار

• Power System Modeling
• Power System Resilience
• Synthetic Power Grids
• +۳ مورد دیگر

Adam Birchfield is an Assistant Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the Energy and Power Group. His research focuses on power system modeling, resilience to extreme events, synthetic grid datasets, and grid dynamics. He holds a B.E.E. from Auburn University, an M.S. from the University of Illinois at Urbana-Champaign, and a Ph.D. from Texas A&M. Recipient of the NSF CAREER Award (2025) and Thomas W. Powell Fellowship. Leads the Birchfield Research Group, emphasizing open science and synthetic grid development. Teaches courses like Power System Operations and Electromagnetic Transients. Research interests include power system computational analysis, synthetic grid validation, and interdependent infrastructure resilience. Recent work addresses cascading failures, geomagnetic disturbance impacts, and gas-electric network integration. Active in grants such as the SCIDAC-OE project with national labs and universities. Advises graduate students in topics like transformer thermal response and grid restoration strategies. Developed the Texas A&M Electric Grid Test Case Repository, a widely used open dataset for power system studies.

Vignesh Subbianمشاهده پروفایل

دانشیار

• Biomedical Informatics
• Health Systems Engineering
• Clinical Decision Support
• +۱۲ مورد دیگر

Vignesh Subbian is an Associate Professor at the University of Arizona with joint appointments in the Department of Biomedical Engineering, Systems and Industrial Engineering, Statistics, Applied Mathematics, Clinical Translational Sciences, and Medicine. He is affiliated with the College of Engineering, the BIO5 Institute, and the Center for Biomedical Informatics & Biostatistics (CB2), where he serves as Associate Director. He is also a Distinguished Fellow of the Center for University Education Scholarship (CUES) and a member of the Graduate Faculty. His research lies at the nexus of systems engineering, medicine, and informatics, focusing on clinical decision-making, digital phenotyping, explainability in AI, and health equity. His educational initiatives emphasize asset-based practices, ethics in engineering, and professional identity formation. He leads two major training programs: the NLM-funded PHIRE program and the NSF-supported eCAMINOS initiative. The recent publications highlight a strong trend in applying machine learning to critical care, particularly in acute respiratory failure and traumatic brain injury. His work emphasizes model interpretability (e.g., WindowSHAP, SLAC-Time), temporal modeling of EHR data, and ethical considerations in informatics and AI. There is a growing focus on large language models for phenotyping and inclusive language in scientific communication. Excellence at the Student Interface Award (2023, 2021, 2019) Arizona Champion, Office of the Provost (2022) Dr. Subbian leads the Health Systems Engineering & Informatics Laboratory, which is supported by the NIH, NSF, AHRQ, and BIO5 Institute. He advises students and mentors trainees through federally funded programs. His lab collaborates widely across medicine, pharmacy, and public health, and he plays a key role in national initiatives like All of Us and RECOVER.