جست‌وجوی استاد و پژوهشگر

Thalia Anagnosمشاهده پروفایل

استاد

• Earthquake Engineering
• Structural Analysis
• Seismic Hazard and Risk Assessment
• +۳ مورد دیگر

Dr. Thalia Anagnos is a former Professor of Civil Engineering and Vice Provost for Undergraduate Education at San José State University (retired 2023). She specializes in earthquake engineering, structural analysis, and engineering education. Her research focuses on seismic hazard assessment, infrastructure resilience, and loss estimation methodologies. Education: Ph.D. in Civil Engineering from Stanford University. Key roles include Past-President of EERI, former editor of Earthquake Spectra, and leader of NEES Education/Outreach programs. Awards include SJSU Outstanding Professor (2012) and EERI Honorary Membership (2018). Research highlights include developing substation equipment fragility databases and leading regional building inventory projects for seismic risk analysis. She has contributed to national initiatives like HAZUS loss estimation frameworks and educational programs such as the Engineering Leadership Pathway Scholars (ELPS). Awards: 1991 Distinguished Alumnus (Stanford), 2000 McCoy Family Award, 2013 Applied Materials Teaching Award. Grants and projects include NSF-funded initiatives on earthquake engineering education, leadership development for engineering students, and public outreach through partnerships like the Golden Gate Bridge educational project.

• Digital Manufacturing
• Sustainability Engineering
• Additive Manufacturing
• +۵ مورد دیگر

Professor Jonathan Corney holds the Chair of Digital Manufacture in the School of Engineering at the University of Edinburgh. His research focuses on advanced manufacturing technologies, including digital twin applications, smart factory optimization, sustainability engineering, and additive manufacturing. He leads projects addressing human factors in industrial environments, predictive analytics for production processes, and intellectual property challenges in modern manufacturing systems. His academic background encompasses mechanical engineering with specialization in CAD/CAM systems, hydroforming technology, and patent analysis for design innovation. Corney has pioneered methods like the Economic and Environmental Impact Assessment for Sustainability (EENIAS), and developed decision support frameworks for energy-efficient scheduling and garment reprocessing in circular economies. Key research themes include: Smart factory design through real-time worker movement analysis Cyber-physical systems for supply chain optimization Machine learning applications in manufacturing process control Human-centric automation and safety protocols His recent work emphasizes predictive modeling using spatio-temporal graph networks, digital twin integration in assembly processes, and sustainable manufacturing practices. Over 150 peer-reviewed articles demonstrate his contributions to near-net-shape manufacturing, intellectual property management, and crowdsourced design methodologies.

Timothy A. McKayمشاهده پروفایل

استاد

• Data Science
• Learning Analytics
• Physics and Astronomy Education
• +۲ مورد دیگر

Timothy A. McKay serves as the Arthur F. Thurnau Professor of Physics, Astronomy, and Education at the University of Michigan's College of Literature, Science, and the Arts (LSA), where he also holds the administrative role of Associate Dean for Undergraduate Education. His dual expertise bridges astrophysics research and educational innovation, with significant contributions to both observational cosmology and learning analytics. His educational background includes: B.S. in Physics from Temple University (1986) Ph.D. in Physics from the University of Chicago (1992) McKay's research spans two interconnected domains. In observational cosmology, he pioneered work with major astronomical surveys including the Sloan Digital Sky Survey (SDSS), Robotic Optical Transient Search Experiment (ROTSE), and Dark Energy Survey (DES), focusing on galaxy clusters, cosmic rays, and large-scale structure. Since 2015, he has strategically shifted toward learning analytics, applying data science to transform STEM education. His innovative projects include E 2 Coach (a personalized student support system) and the NSF-funded REBUILD initiative, which creates intergenerational research teams to develop evidence-based teaching practices across physics, chemistry, astronomy, biology, and mathematics. Analysis of his publication trajectory reveals a deliberate pivot from astrophysics to educational research around 2015. While his early work centered on galaxy clusters and cosmological phenomena, recent publications (2020-2024) overwhelmingly focus on systemic equity gaps in STEM education, data-driven interventions, and multi-institutional collaborations. This evolution demonstrates how his data science methodology transitions seamlessly between cosmic structures and educational ecosystems. His scientific recognition includes: Prestigious Arthur F. Thurnau Professorship (awarded for exceptional undergraduate teaching) McKay directs the NSF-funded REBUILD project and the Digital Innovation Greenhouse, securing substantial research funding while mentoring undergraduate and graduate students in interdisciplinary teams. His work with the Big Ten Academic Alliance (CIC) has generated cross-institutional studies on grading patterns, performance disparities, and student support systems, with practical applications implemented across multiple universities. He actively collaborates with faculty across STEM disciplines to develop scalable educational technologies. His research infrastructure includes the Digital Innovation Greenhouse (an educational technology incubator) and REBUILD project teams, which integrate undergraduates, graduate students, postdocs, and faculty in evidence-based educational research. These teams operate at the intersection of data science and pedagogy, developing tools that analyze institutional datasets to personalize student support while maintaining rigorous scientific methodology.

Vasiliki Kantereمشاهده پروفایل

استاد

• Big Data Management
• Big Data Analytics
• Machine Learning
• +۹ مورد دیگر

Dr. Vasiliki Kantere is a Professor at the School of Electrical Engineering and Computer Science within the Faculty of Engineering at the University of Ottawa . She has held previous academic positions, including: Senior Assistant Professor at the School of Electrical and Computer Engineering, National Technical University of Athens Maître d’Enseignement et de Recherche and Maître Assistante at the University of Geneva Junior Assistant Professor at the Department of Electrical Engineering and Information Technology, Cyprus University of Technology Postdoctoral Researcher at the École Polytechnique Fédérale de Lausanne Her research interests span a wide range of interdisciplinary domains, including: Big Data Management: data storage optimization, analytics workflows, and deployment on hybrid infrastructures Big Data Analytics: support for users of varying expertise and large data graphs Machine Learning: applications in resource management and query optimization Cloud Computing: cost-aware data management and service-level agreements P2P Systems: query processing and mobile peer environments Semantic Web: ontology mapping and semantic integration Distributed Systems: federated databases and grid computing Databases: query languages and semi-structured data management General: spatial data, privacy and security, and sensor networks No academic awards, funded grants, or advising roles are explicitly listed in the provided information. Her professional contributions are reflected through her extensive research collaborations and diverse academic experiences. The text does not mention any affiliated research labs or teams.

Arno Siebesمشاهده پروفایل

استاد

• Data Mining
• Pattern Mining
• Minimum Description Length Principle
• +۲ مورد دیگر

Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.

• Privacy-Preserving Algorithms
• Differential Privacy
• Federated Learning
• +۶ مورد دیگر

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (QMUL), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Libera Università di Bolzano (2018) and a Diploma in Media Informatics from the University of Munich (2014). His research focuses on developing algorithms and data structures for managing large and sensitive datasets, with a particular emphasis on privacy-preserving techniques like differential privacy and federated learning. He has held postdoctoral roles at the University of Warwick and King's College London before joining QMUL in 2023. Research Interests: Privacy-preserving algorithms, differential privacy, federated learning, data management systems, randomized algorithms, and efficient query processing. His work bridges theoretical foundations with practical applications, aiming to enable secure data sharing while preserving individual privacy. Teaching: Leads undergraduate modules in Database Systems and Operating Systems at QMUL. His teaching emphasizes foundational concepts in computer science through rigorous coursework and practical projects. Grants and Funding: Currently supervises a PhD studentship titled 'Privacy-Preserving Algorithms: Unlocking Data Sharing for Medical Sciences & Machine Learning', funded by QMUL and open to UK home students. The role involves exploring privacy-preserving frameworks for collaborative data analysis. Professional Contributions: Serves as a reviewer for top-tier conferences (NeurIPS, ICML, SIGMOD, ICDE) and journals (IEEE TKDE, Data & Knowledge Engineering). Actively involved in conference organization, including NeurIPS Area Chair (2024) and ICDT Proceedings Chair (2024). Labs and Collaborations: Affiliated with the Centre for Fundamental Computer Science at QMUL, fostering interdisciplinary research in theoretical and applied computing. Engages with industry partners on privacy-enhancing technologies and data management solutions.

Laurence C. Smithمشاهده پروفایل

استاد

• Arctic Studies
• Hydrology
• Remote Sensing
• +۳ مورد دیگر

Laurence C. Smith is the John Atwater and Diana Nelson University Professor of Environmental Studies at Brown University, affiliated with the Institute at Brown for Environment & Society (IBES) and the Department of Earth, Environmental and Planetary Sciences (DEEPS). Previously, he served as UCLA's Geography Department Chair and Professor. His research focuses on Arctic environments, water resources, and remote sensing, with over 150 peer-reviewed publications and $16M in NSF/NASA grants. He has contributed to IPCC reports and leads NASA's SWOT mission for global water monitoring. Key awards include the Guggenheim Fellowship (2006-07), AGU Fellow (2015), and Walter P. Kistler Book Award (2012) for *The World in 2050*. Education: PhD, Cornell University (1996) MS, Indiana University (1991) BS, University of Illinois at Urbana-Champaign (1989) Research Interests: Arctic systems, hydrology, satellite remote sensing, and climate change impacts. His work bridges natural and social sciences, addressing topics like Greenland meltwater dynamics and northern resource development. Recent projects include methane emissions from Arctic lakes and river avulsion monitoring via SWOT. Awards: Phi Beta Kappa Visiting Scholar (2020-21) AGU Fellow (2015) Walter P. Kistler Book Award (2012) Guggenheim Fellowship (2006-07) Teaching & Grants: Teaches courses on water systems and scientific writing at Brown. Secured major grants from NASA and NSF, totaling over $16M. Advises graduate students in environmental studies and geosciences. Labs/Teams: Collaborates with IBES, DEEPS, and global institutions like the World Economic Forum on Arctic development and NASA's SWOT mission. Active in interdisciplinary research networks.

Cristian Román-Palaciosمشاهده پروفایل

استادیار

• Phylogenetics
• Biodiversity Drivers
• Paleoclimatic Reconstructions
• +۷ مورد دیگر

Cristian Román-Palacios is an Assistant Professor in the Department of Ecology and Evolutionary Biology at the University of Arizona, where he also serves as Coordinator and Advisor for the Master of Science in Data Science (MSDS) and Master of Science in Information Systems (MSIS) programs. He is a core faculty member in Artificial Intelligence and Machine Learning, Data Management, Analysis and Visualization, and Environmental, Health and Biological Sciences. Education: PhD in Ecology and Evolutionary Biology, University of Arizona (2020) BS in Biology, Universidad del Valle, Colombia (2015) His research lies at the intersection of phylogenetics, biodiversity modeling, and machine learning, focusing on large-scale biodiversity patterns, the impacts of climate change on species survival, and the development of statistical tools for paleoclimatic reconstructions. He employs computational and data-intensive methods to explore evolutionary and ecological questions across diverse taxa. His recent publications (2024–2025) demonstrate a strong trend toward interdisciplinary research, combining computational biology, geochemistry, climate science, and open-source software development. Key themes include biodiversity informatics (e.g., Animal Culture Database), paleoclimatic modeling (e.g., clumped isotope thermometry), reproducibility in science, and tools for collaborative research (e.g., LabOps, SSARP). His work increasingly integrates data science with biological and environmental applications. Scientific Contributions: Published over 25 peer-reviewed papers, many as first author Research featured in Science News, Popular Science, CNN, USA Today Developed open-source tools: phruta , treedata.table , SSARP , LabOps Cristian advises graduate students through the infosci-msadvise@arizona.edu email and Calendly appointments. He was previously a staff researcher at UCLA’s Tripati Lab. He leads initiatives such as the Southwest Center on Resilience for Climate Change and Health and promotes inclusive, collaborative science through online toolkits and leadership ecosystems aimed at addressing climate and social inequities. His lab, the Román-Palacios Lab, and involvement with the Data Diversity Lab reflect his commitment to open, reproducible, and equitable research practices in data-intensive biology.

• Algorithms
• Data Structures
• Pattern Matching
• +۳ مورد دیگر

Simon J. Puglisi is a Professor in the Department of Computer Science at the University of Helsinki. His research focuses on algorithms, data structures, pattern matching, and data compression, with applications in bioinformatics and genomics. He has collaborated extensively with researchers in the field, including Travis Gagie, Juha Kärkkäinen, and Andrew Turpin. His work spans theoretical computer science and practical applications in genomic data processing and efficient indexing techniques. Key research interests include genome sequencing algorithms, efficient compression methods (e.g., Lempel-Ziv and relative Lempel-Ziv), and the development of succinct data structures for handling large genomic datasets. His contributions to suffix arrays, wavelet trees, and de Bruijn graphs have advanced computational methods in bioinformatics and information retrieval. Puglisi's articles frequently address challenges in text indexing, error correction in short-read sequencing, and optimizing algorithms for scalability. He is known for his work on self-indexing techniques and the SHREC error correction method for genomic data. His research bridges theoretical algorithm design with real-world applications in high-throughput sequencing and large-scale data management.

Yunming Xiaoمشاهده پروفایل

استادیار

• Computer Systems
• Networks
• Security
• +۳ مورد دیگر

Yunming Xiao is an Assistant Professor at the School of Data Science, The Chinese University of Hong Kong (CUHK), Shenzhen. Previously, he served as a Research Fellow at the University of Michigan and earned his Ph.D. in Computer Science from Northwestern University in 2024. His research focuses on computer systems, networks, and security, with emphasis on security and privacy of Internet services and performance optimization of cloud infrastructure. His work has been recognized with the Best Paper Award at APNet and Best Student Paper Award at ACM EuroSys. Education: Ph.D. in Computer Science, Northwestern University (2024) B.Eng. in Computer Science, Beijing University of Posts and Telecommunications (2019) His research bridges cloud infrastructure and security, where projects like SwiftRDMA, Conspirator, and MegaTE demonstrate innovative approaches to resource scheduling, SmartNIC integration, and traffic engineering at scale. These works highlight his focus on solving real-world performance and security bottlenecks in distributed systems. Scientific Awards: Best Paper Award, APNet'25 Best Student Paper Award, ACM EuroSys'24 Yunming actively collaborates with industry, having interned at Google, Hewlett Packard Labs, Nokia Bell Labs, and Bytedance. He serves on TPCs for major conferences including CCS'26, IMC'26, and IEEE S&P'26.

Peter Hertkornمشاهده پروفایل

استاد

• Model-Driven Software Development
• Domain-Specific Languages
• DevOps Automation
• +۲ مورد دیگر

Prof. Peter Hertkorn is a full professor of Computer Science at Reutlingen University's Faculty of Computer Science, specializing in programming languages and databases. He holds leadership roles including Chair of the Examination Board for Media and Communication Informatics B.Sc. and BAföG Officer for the same program. His research focuses on model-driven software development, domain-specific languages, DevOps automation, interactive learning technologies, and distributed ledger technologies. He co-leads the Software Engineering and User Experience (swuxLAB) and Distributed Ledger Technologies (DLT-LAB) research groups. Education background includes a Diplom-Informatiker (master's equivalent) and Dr.-Ing. (PhD) from University of Stuttgart. Professional experience includes 5 years at T-Systems International GmbH and prior academic roles at University of Stuttgart. His teaching focuses on Software Engineering and Database Systems courses. Publications span interactive learning environments, collaborative innovation systems, and AR-based knowledge spaces. Active in academic service roles and interdisciplinary research collaborations since 2009.

Azza Abouziedمشاهده پروفایل

دانشیار

• Human-Data Interaction
• Database Systems
• Human-Computer Interaction
• +۷ مورد دیگر

Azza Abouzied is Associate Professor of Computer Science at New York University Abu Dhabi and Global Network Associate Professor at the Tandon School of Engineering. She serves as Vice Provost for Faculty Advancement and Engagement at NYUAD starting September 2024. Her research bridges database systems and human-computer interaction, focusing on intuitive tools for data querying and decision-making in uncertain, collaborative environments. PhD, Yale University (2013) MPhil, Yale University MSc, Dalhousie University BSc, Dalhousie University Her research centers on human-data interaction, designing systems that make data accessible to non-experts. She combines techniques from UI design, machine learning, and databases to build tools that simplify complex data tasks. Her earlier work focused on example-driven querying and synthetic data generation, while her recent work explores in-database prescriptive analytics and decision support in domains like disinformation mitigation and epidemic planning. Her publications span database and HCI venues, with a recurring theme of enhancing usability without sacrificing scalability. She co-founded Hadapt, a Big Data analytics platform, and has led interdisciplinary research through the Human-Data Interaction Lab and the Center for Interacting Urban Networks. Her teaching includes foundational courses such as Database Systems, Operating Systems, and Data, as well as the critical thinking course Techruption. VLDB Test of Time Award (2019) Best Paper Award in Database Systems Honorable Mention in HCI Publications Azza mentors undergraduate capstone students and advises prospective PhDs, research assistants, and postdocs. She is actively involved in academic leadership, having chaired NYUAD’s faculty council in 2024 and co-chaired the SIGMOD 2025 program. Her work emphasizes empowering users to critically engage with data and AI, both in research and education.

Jo Leonardi-Beeمشاهده پروفایل

استاد

• Evidence Synthesis
• Meta-Analysis
• Systematic Reviews
• +۵ مورد دیگر

Professor Jo Leonardi-Bee is a faculty member at the University of Nottingham within the School of Medicine . As Professor of Evidence Synthesis and Co-Director of the Nottingham Centre for Evidence-Based Healthcare (JBI Centre of Excellence), her work focuses on quantitative evidence synthesis, Cochrane reviews, and clinical guideline development. Education: BSc in Mathematics & Chemistry, Nottingham Trent University MSc in Medical Statistics, University of Leicester PhD and PGCHE, University of Nottingham Her research spans tobacco control (smoking cessation, legislation impact, maternal exposure) and dermatology (skin cancer, corticosteroid safety). She specializes in meta-analysis techniques, systematic reviews, and health policy evaluation, with over 50 peer-reviewed publications. Recent publications highlight trends in respiratory health (alcohol/smoking effects), pregnancy outcomes (smoking cessation interventions), and allergy prevention (infant dietary guidelines). She serves as Statistical Editor for the British Journal of Dermatology and former Cochrane Skin Group editor. Teaching includes postgraduate modules in systematic reviews and developing reusable learning objects for evidence-based practice. Current funded projects include maternal tobacco control initiatives, familial hypercholesterolemia management, and infant feeding guidelines with the Health Technology Assessment (HTA) program.

Catia Pesquitaمشاهده پروفایل

دانشیار

• Semantic Web
• Biomedical Ontologies
• Knowledge Graphs
• +۷ مورد دیگر

Catia Pesquita is an Associate Professor in Computer Science at the Faculty of Sciences of the University of Lisbon , where she is also a Senior Researcher at LASIGE and leads the Health and Biomedical Informatics Research Line . With a multidisciplinary background in Biology and Computer Science, she focuses on Artificial Intelligence and Data Science applications in life and health sciences . Her research spans Semantic Web , Biomedical Ontologies , Knowledge Graphs , and Explainable AI , with significant contributions to ontology matching and semantic similarity . Education: PhD in Computer Science - Bioinformatics (2012) MSc in Bioinformatics (2008) Degree in Cell Biology and Biotechnology (2005) Current Projects: KATY (2021-2024): AI-Empowered Personalized Medicine for cancer treatments. BRAINTEASER (2021-2024): AI for ALS and MS disease progression models. Research Outputs: Developed tools like AgreementMakerLight (AML) , KGsim-benchmark , and the Epidemiology Ontology . Over 133 publications with significant citations (32,909 reads, 3,889 citations). Teaching: Lectures advanced topics in Databases , Data Integration , Bioinformatics , and Big Data . Advocacy: Vice-president of Biodata.pt , promoting biological data valorization in Portugal. Actively involved in initiatives to promote computer science careers to young women .

Luis Alberto Velasquez Zarateمشاهده پروفایل

استادیار

• Anatomic Pathology
• Surgical Pathology
• Cancer Epidemiology
• +۶ مورد دیگر

Luis Alberto Velasquez Zarate is an Assistant Professor at the Medical College of Georgia, where he holds appointments in the Department of Pathology with a subspecialty focus in Anatomic Pathology. He is actively involved in clinical research and diagnostic pathology, contributing to advancements in cancer classification and molecular diagnostics. Research Interests: His work spans anatomic pathology with emphasis on rare tumors, cancer epidemiology using population databases, and molecular techniques in cytology and sarcoma diagnostics. Key areas include endocrine, gynecologic, and soft tissue pathology, with a strong methodological foundation in SEER database analysis and immunohistochemical validation. The recent publications highlight a consistent trend in utilizing large-scale epidemiological data (SEER) to analyze incidence and survival in rare malignancies, combined with deep dives into molecular markers such as YWHAE and NKX2.2 for diagnostic precision in sarcomas and the application of FISH in pancreatobiliary lesions. This reflects a dual focus on population health and precision pathology. Service: Committee Member, Society of Pediatric Pathology (2021–Present) Education: MD in Medicine, Universidad San Martin De Porr (2009) Certified by GA Composite Medical Board (2018, 2022) Dr. Velasquez Zarate’s research contributes significantly to the understanding of rare cancers and diagnostic innovation in surgical pathology. While no formal advisees or awards are listed, his scholarly output indicates active engagement in translational pathology research. He plays a service role in a national pathology society, suggesting professional integration and contribution to the specialty.