جست‌وجوی استاد و پژوهشگر

Alex Endertمشاهده پروفایل

دانشیار

• Visual Analytics
• Human-centered AI
• Information Visualization
• +۳ مورد دیگر

Alex Endert is an Associate Professor and Associate Chair of Operations and Special Initiatives in the School of Interactive Computing at Georgia Tech. He directs the Visual Analytics Lab, focusing on interactive visual tools to aid in analyzing data and AI systems. His research addresses domains like cybersecurity, manufacturing safety, and intelligence analysis, supported by NSF, DOD, and industry grants. He holds a PhD from Virginia Tech (2012), winning top dissertation awards. His lab develops systems like SafetyLens and CAVA, and explores AI ethics in education and human trafficking contexts. Education: Ph.D. in Computer Science, Virginia Tech (2012) Affiliations: GVU Center, College of Computing Research Interests: Visual Analytics, Human-centered AI, Ethical AI, Data Interpretation, and Collaborative Visualization. His work emphasizes tools that empower domain experts while mitigating cognitive biases. Recent projects include DeepSee for marine ecosystems, NL4DV for NLP-driven visualization, and Trust Junk for calibrating trust in AI systems. Key Publications (2020–2025): Includes seminal work on cognitive bias mitigation (Lumos), causal inference visualization (CausalVis), and LLM-based analytics (NL4DV-LLM). Over 100+ peer-reviewed papers with 15+ recent articles highlighted above. Awards & Grants: NSF CAREER Award (2018), IEEE Dissertation Prize (2013), and sustained industry funding. Teaching: Courses on Information Visualization (CS4460/7450) and Visual Data Analysis (CS8803). Lab & Impact: The Visual Analytics Lab hosts 13 alumni and current PhD students (e.g., Adam Coscia, Arpit Narechania), fostering interdisciplinary projects with partners like NASA and Caltech. Ongoing work includes ethical AI applications and AI-driven education systems.

Eugene Wuمشاهده پروفایل

دانشیار

• Data Management
• Database Systems
• Human-Computer Interaction (HCI)
• +۶ مورد دیگر

Eugene Wu is an Associate Professor of Computer Science at Columbia University, affiliated with the Data Science Institute and co-chair of the Center for Data, Media, and Society. His research focuses on interactive data systems, visualization, and human-data interaction. Wu holds a Ph.D. from MIT and B.S. from UC Berkeley, with postdoctoral experience at AMPLab. Education: Ph.D., Computer Science, MIT B.S., Computer Science, University of California, Berkeley Postdoc, AMPLab Research Interests: Eugene's work bridges data systems, visualization, and human-computer interaction. Key areas include: - Interactive data interfaces and visualization tools - Large Language Models (LLMs) for document processing and prompt development - Lineage tracking and explainable systems - Privacy-aware query processing and scalable analytics. His recent projects address task-based dataset search, complaint-driven debugging, and systems for LLM agents. Recent Articles Trends: Publications emphasize LLM integration (e.g., Prompt Editor, Cocoon), scalable systems (FaDE, ConnectorX), and foundational contributions to lineage and visualization (Smoke, Physical Visualization Design). Awards: NSF CAREER Award VLDB 10-Year Test of Time Award (2018) SIGMOD Best Demo Award (2016, 2025) Amazon Research Award Advising & Grants: Advises Ph.D./postdoc students on database and ML systems. Leads NSF-funded projects on entity matching and interactive debugging. Co-organizes SIGMOD panels and workshops on database research directions. Labs & Teams: Leads the WuLab research group and collaborates with industry partners like Celonis and Adobe. Active in the Data Science Institute and Center for Data, Media, and Society.

Roee Shragaمشاهده پروفایل

استادیار

• Data Integration
• Data Discovery
• Human-in-the-loop Machine Learning
• +۷ مورد دیگر

Roee Shraga is an Assistant Professor in the Computer Science department and Data Science program at Worcester Polytechnic Institute (WPI). He previously held a Postdoctoral Research Associate position at the Data Lab within Khoury College of Computer Sciences at Northeastern University in Boston, MA. Ph.D. in Data Science, Technion – Israel Institute of Technology (2020) M.E. in Data Science, Technion (2017) B.Sc. in Industrial Engineering and Management (Information Management Engineering), Technion (2015) Shraga’s research focuses on Data Integration , Data Discovery , Human-in-the-loop Machine Learning , Information Retrieval , and Large Language Models (LLMs) . His work bridges data management, machine learning, and human-computer interaction to improve data quality in AI systems. Recent publications include advancements in LLM-driven data integration , semantic table union search , and contrastive entity resolution . Key contributions include the Explain-Da-V framework for semantic data versioning and FlexER for intent-aware entity resolution. Scientific recognition includes: iiWAS 2024 best paper award TaDA@VLDB24 best paper award Council for Higher Education (VATAT) scholarship Leonard & Diane Sherman Interdisciplinary Fellowship (2017) Daniel Excellence Scholarship (2019) Miriam & Aaron Gutwirth Memorial Fellowship (2020) He leads NSF-funded research on Human-in-the-loop AI (CRII grant) and co-organizes the HILDA workshop. His teams have won multiple Outstanding MQP Awards for undergraduate research.

Wei Chenمشاهده پروفایل

استاد

• Data Visualization
• Visual Analytics
• Biomedical Image Computing
• +۵ مورد دیگر

Wei Chen is a Professor at the State Key Laboratory of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou, China. His academic journey began and ended at Zhejiang University, where he earned both his Bachelor's (1996) and Ph.D. (2002) in Computer Science, followed by a visiting Ph.D. position at the Fraunhofer Institute in Germany (2000-2002) and a visiting scholar role at Purdue University (2006-2008). Research Focus: Visualization, Visual Analytics, Biomedical Image Computing, Big Data Analytics. Academic Leadership: Served on editorial boards of journals like IEEE Transactions on Visualization and Computer Graphics, Journal of Visual Informatics, and as Chair of IEEE Pacific Visualization Symposium. Impact: Over 110 IEEE/ACM Transactions and CCF-A papers, four Chinese textbooks on visualization, and two monographs on big data technology. His recent publications (2023-2024) span domains like visual analytics for health insurance fraud detection, quantum circuit visualization, federated learning visualization, urban mobility modeling, and AI-enhanced cultural heritage preservation. A thematic trend shows integration of deep learning, large language models, and human-in-the-loop systems with visual interfaces. Scientific Awards include the Zhejiang Province Natural Science First Prize (2021, 2017), CCF Technology Invention First Prize (2020), Wu Wenjun Artificial Intelligence Science and Technology Progress First Prize (2023), and multiple Best Paper/Poster recognitions at IEEE VIS and Pacific Visualization conferences. He actively mentors graduate students and leads the Visual Analytics Group , contributing to national and international standards in visualization research. His editorial and organizational roles in top conferences further solidify his influence in shaping the field.

Jean-Baptiste Massonمشاهده پروفایل

پژوهشگر

• Computational Neuroscience
• Bayesian Decision Theory
• Biophysics
• +۴ مورد دیگر

Jean-Baptiste Masson is a tenured Researcher at the Pasteur Institute since 2008. His work bridges Neuroscience , Biophysics , and Machine Learning , focusing on understanding decision-making processes in biological systems and developing innovative 3D visualization tools like DIVA and Genuage for medical imaging and single-molecule analysis.

• Semantic Web
• Web Services Composition
• Formal Concept Analysis
• +۶ مورد دیگر

Nizar Messai is a Researcher at the University of Tours , affiliated with the Faculty of Science and Technology and Polytechnic School of the University of Tours (EPU) . He works within the Laboratory of Fundamental and Applied Computer Science (LIFAT, UR 6300) , focusing on semantic technologies and distributed systems. Research Interests : Semantic Web Services Composition Formal Concept Analysis (FCA) for Data Mining Blockchain Applications in Trustworthy Systems Context-Aware IoT Mashups Linked Open Data Integration Scientific Contributions : Developed semantic similarity measures for service discovery Created the LDS Java library for LOD-based similarity Explored blockchain for decentralized choreography execution Integrated attention mechanisms in travel mashup systems Applied FCA to clinical decision support systems Collaborations & Labs : Active member of the LIFAT (UR 6300) laboratory, contributing to projects at the intersection of computer science, semantic technologies, and biomedical informatics.

Martin Gaedkeمشاهده پروفایل

استاد

• Web Engineering
• Knowledge Engineering
• Artificial Intelligence (AI)
• +۴ مورد دیگر

Prof. Dr.-Ing. Martin Gaedke is a Full Professor and Dean of the Department of Computer Science at Technische Universität Chemnitz (TU Chemnitz). He leads the research group Distributed and Self-organizing Systems (VSR) and holds roles such as President of the International Society of Web Engineering (ISWE), founder of the International AIQT Foundation, and member of the German Foundation for University Admissions' IT-advisory board. His expertise spans Web Engineering, AI-driven systems, and decentralized technologies. Education: Martin Gaedke earned a Master's degree (1997) and doctoral degree (2000) in Computer Science/Engineering from the University of Karlsruhe (now Karlsruhe Institute of Technology). Research Focus: Gaedke's work advances collaborative systems in hyper-connected societies, emphasizing Web Engineering, knowledge engineering, and AI-supported hypermedia systems. Key areas include chatbot design patterns, decentralized web trust, smart IoT systems, and end-user development tools. His contributions include pioneering work in Web Engineering since the 1990s, notably co-authoring the first Web Engineering paper at WWW6 (1997). Recent Trends in Publications: Recent research highlights dynamic code migration, GenAI-driven UIs, and ethical AI in chatbots. He emphasizes trust in decentralized systems (e.g., Solid-based social networks) and practical applications like autonomous smart home agents (WS3H). Awards: Ranked 39th globally in Knowledge Engineering (2020), honored with the AI 2000 Most Influential Scholars Honorable Mention and listed in the AI 2000 Annual List. His work is cited over 1,800 times in key areas like Web Engineering and IoT. Advising & Outreach: Gaedke has mentored numerous researchers and entrepreneurs through roles as a Certified High Performance Coach and business advisor. His lab, VSR, develops open-source tools like the Web-Based Network Simulator (WNSWE) for education and industry. Labs/Teams: The Distributed and Self-organizing Systems (VSR) group at TU Chemnitz focuses on innovative projects such as the Trusting Decentralized Web Data framework and the GenAI-Driven Multimodal UI Architecture .

Alexandra Meliouمشاهده پروفایل

استاد

• Data Management
• Data Quality
• Causality
• +۵ مورد دیگر

Alexandra Meliou is a Professor and Associate Chair of Faculty Development in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. She holds a PhD from UC Berkeley and has been recognized for research, teaching, and service with awards including the NSF CAREER Award and Google Faculty Research Award. Her work focuses on data management challenges such as data quality, fairness, and system explanations. She co-directs the DREAM Lab and actively promotes diversity in computer science. Research Interests: Her research emphasizes data quality diagnostics, fairness in algorithms, and user-facing database systems. Key projects include Themis (fairness testing), PackageBuilder (optimization queries), and SQuID (query intent discovery). She explores causal reasoning to explain data system behaviors and mitigate biases. Awards & Contributions: Beyond her technical contributions, Meliou has received teaching excellence (Lilly Fellowship) and service awards. She chairs DEI initiatives in database conferences and contributes to academic governance through roles like SIGMOD PC Co-Chair. Education: PhD (Computer Science, UC Berkeley, 2009), MS (UC Berkeley, 2005), BS (National Technical University of Athens, 2003).

Yue Wangمشاهده پروفایل

استادیار

• Text Data Mining
• Machine Learning
• Health Informatics
• +۳ مورد دیگر

Yue (Ray) Wang is an Assistant Professor at the School of Information and Library Science, University of North Carolina at Chapel Hill. He holds a PhD in Computer Science and Engineering from the University of Michigan, and prior degrees from Shanghai Jiao Tong University and Georgia Institute of Technology. His research focuses on text data mining, machine learning, and health informatics, with an emphasis on developing interactive and interpretable algorithms to reduce human effort in data analysis. He has contributed to clinical NLP, high-recall information retrieval, and computer-assisted qualitative analysis, winning awards such as the WSDM Best Paper Award (2016) and the Deborah Barreau Award for Teaching Excellence (2021). Dr. Wang’s work bridges computational methods with real-world applications, including environmental policymaking through extreme systematic reviews and NeuroBridge—a platform for neuroimaging data discovery. He teaches courses like INLS 509 (Information Retrieval) and INLS 690-270 (Data Mining). His recent research explores user-centered explainability in AI, leveraging crowdsourced experiments and in-lab studies to understand how users interact with machine predictions. He collaborates with organizations like the EPA on machine-assisted literature screening for environmental policy. Education: BS/BA in Information Security & English (Shanghai Jiao Tong University) MS in Computer Applied Technology (Shanghai Jiao Tong University) MS in Electrical and Computer Engineering (Georgia Tech) PhD in Computer Science and Engineering (University of Michigan) Awards: UNC Junior Faculty Development Award (2022) Outstanding Reviewer Award (WSDM 2022) Deborah Barreau Teaching Excellence Award (2021) WSDM Best Paper Award & Outstanding Reviewer (2016) Dow Distinguished Award (2015) Key Research Themes: User-centric AI interpretability Health informatics applications Large-scale data screening for policy Explainable retrieval models

João Correia Lopesمشاهده پروفایل

استادیار

• e-Health
• e-Science
• IoT
• +۶ مورد دیگر

João Correia Lopes is an Assistant Professor at the University of Porto 's Department of Informatics Engineering , Faculty of Engineering. He also serves as a Senior Researcher at INESC TEC Technology and Science. Correia Lopes holds a PhD in Computing Science from Glasgow University (1997) and a Diploma in Electrical Engineering and Computers from the University of Porto (1984). Research Interests: e-Health, e-Science, IoT, Web and Mobile Applications Key Contributions: Development of data curation platforms (Dendro, UPBox), FAIR data implementations, and blockchain-based solutions for media licensing and journalism.

Kritagya Raj Upadhyayمشاهده پروفایل

استادیار

• Cybersecurity
• Blockchain and Smart Contracts
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Kritagya Raj Upadhyay is an Assistant Professor in the Department of Computer Science at Middle Tennessee State University (MTSU). Specializing in Blockchain and Smart Contracts , Cybersecurity , Artificial Intelligence , Data Cooperatives , and Metaverse , his research focuses on developing architectures and algorithms that address uncertainty, trust, privacy, and security in emerging technologies. Research Interests: Blockchain for Trusted Artificial Intelligence Smart Contracts Security and Scalability Data Cooperatives for Privacy Protection Metaverse Threat Modeling Fuzzy Logic in Legal Contract Interpretation Recent Publications demonstrate expertise in blockchain applications, metaverse security, and AI integration across diverse domains. His work spans Smart Contract Vulnerability Detection , Legal Contract Automation , and Secure IoT Systems . Scientific Awards : MTSU Data Science Institute Seed Grant ($5,000) - 2024 Best Research Paper Award - IEEE TPS-ISA 2023 Outstanding Ph.D. Student Award - University of North Texas 2023 NSA Innovation Award - 2021 Dr. Upadhyay mentors graduate students like Samir Poudel (Second Best Poster Awardee 2023) and teaches courses including Blockchain and Smart Contracts , Secure Software Systems , and Computer Science I at MTSU.

Vasa Curcinمشاهده پروفایل

استاد

• Health Informatics
• Digital Health
• Stroke Research
• +۵ مورد دیگر

Professor Vasa Curcin is a Professor of Health Informatics at King’s College London and leads the Digital Health Group. He holds a joint role as Head of the Department of Population Health Sciences within the School of Life Course & Population Sciences. His research focuses on reproducibility solutions using data provenance, embedding clinical trials into routine practice, decision support systems for Learning Health Systems, and machine learning applications in mental health detection. He completed his PhD at Imperial College London on scientific workflow systems and has contributed to the development of early workflow-based phenotyping tools. He is a co-director of the EPSRC Centre for Doctoral Training in Data-Driven Health (DRIVE-Health) and leads initiatives like the Health Data Research UK’s Phenomics and Clinical Trial Informatics themes. Education: BSc Computing (King’s College London), MSc (Imperial College London), PhD in Bioinformatics (Imperial College London). Research interests span digital health innovations, stroke analytics, and AI-driven healthcare solutions. His work emphasizes data interoperability, phenotype library development, and leveraging EHR data for clinical insights. Notable projects include the Phenoflow Library, CONSULT healthcare project, and King’s CAT social media research tool. Key contributions include machine learning models for stroke mortality prediction, antimicrobial resistance analysis in ICU, and social media-based mental health monitoring. He collaborates across disciplines, integrating software engineering practices with medical research to improve patient outcomes and healthcare efficiency. Grants and collaborations include funding from UK Research and Innovation, NHS partnerships, and international initiatives addressing post-COVID syndromes and healthcare inequalities. His work aims to bridge the gap between software engineering and medical innovation through reproducible, ethical, and patient-centered approaches.

Alexander Hambleyمشاهده پروفایل

پژوهشگر

• Human-Centered AI
• Web Accessibility
• Human-Computer Interaction
• +۴ مورد دیگر

Alexander Hambley is a Senior Research Software Engineer at the eScience Lab within the Department of Computer Science, focusing on developing computational tools for efficient research practices, particularly in web accessibility and Human-Centered AI. He contributes to the HDR UK Federated Analytics project, emphasizing FAIR principles. Previously, he served as an Associate Lecturer at The Open University and held roles at the University of Leeds. Education: PhD in Computer Science, University of Manchester (Research in web accessibility and machine learning) BSc (Hons) Computer Science, University of Nottingham (First Class) Research interests include Human-Centered AI, Web Accessibility, Human-Computer Interaction, and Open Science. His work bridges assistive technologies for visually impaired users and data-driven approaches to enhance accessibility evaluation via clustering and optimization methods. Key trends in his articles focus on workflow systems, accessibility tool development, and integrating machine learning for efficient auditing. His contributions span both technical software tools (e.g., OPTIMAL-EM) and collaborative projects like WorkflowHub Knowledge Graph. Honors include the Best Communication Paper at W4A 2022. His professional activities include founding Build Humanly, an inclusive web design agency, and affiliations with the Information Management Group and Interaction Analysis and Modelling Laboratory.

• Machine Learning
• Natural Language Processing
• Education Technology
• +۳ مورد دیگر

Jieun Han is a researcher with a focus on interdisciplinary research spanning natural language processing, education technology, healthcare informatics, and wireless communications. Her work often integrates AI tools like ChatGPT into educational settings and explores applications of robotics and UAV systems in healthcare and communication. Co-authored publications indicate collaboration with institutions in South Korea and international partners. Research interests include AI-driven education systems (e.g., EFL writing support), multimodal safety testing for vision-language models, and task planning with large language models. Technical contributions include UAV-based localization systems, VR environments for cognitive assessment, and provenance compression for large datasets. Recent publications (2023-2025) highlight innovations in automated essay scoring, chatbot integration in language learning, and beamforming algorithms for 3D sidelobe suppression. Collaborations span computer science, electrical engineering, and health informatics domains.

Michael Godfreyمشاهده پروفایل

استاد

• Software Engineering
• Software Evolution
• Empirical Studies
• +۵ مورد دیگر

Michael Godfrey is a Professor at the University of Waterloo's David R. Cheriton School of Computer Science, affiliated with the Department of Electrical and Computer Engineering. His work focuses on software engineering, empirical studies of software systems, code review practices, and open-source software ecosystems. He holds a Ph.D., M.Sc., and B.Sc. from the University of Toronto (1997, 1988, 1986). Research interests include software evolution, mining software repositories, provenance tracking, code duplication analysis, and program comprehension. He actively explores how developers interact with code reviews, documentation systems, and modern CI/CD pipelines. Recent work investigates app store software ecosystems, Bash scripting vulnerabilities, and deep learning API testing. Publications span empirical studies of developer communities (e.g., Stack Overflow analysis), architectural teaching methods, and automated testing techniques like documentation-guided fuzzing. His work bridges theory and practice, addressing challenges in large-scale systems maintenance and developer productivity. Leverages tools like Elasticsearch for repository mining and explores anomaly detection in software development. Engaged in curriculum development for software architecture education and has contributed to conferences like MSR and ICSE.