جست‌وجوی استاد و پژوهشگر

Yann-Gaël Guéhéneucمشاهده پروفایل

استاد

• Software Engineering
• IoT Systems
• Static and Dynamic Analyses
• +۴ مورد دیگر

Yann-Gaël Guéhéneuc is a Professor at Concordia University's Department of Computer Science and Software Engineering. He leads the Ptidej Team, focusing on software engineering methodologies, IoT systems, and game engine architecture analysis. His research emphasizes static/dynamic analyses, service-oriented architectures, and machine learning design patterns. Current Affiliations: Concordia University (Full-time Professor) Ptidiej Team Lead Research Interests: Specializes in IoT system testing, microservices architecture, game engine design patterns, and anti-pattern detection in multi-language systems. His work bridges theoretical software engineering principles with practical industrial applications, particularly in legacy system modernization and machine learning system design. Recent Trends in Publications: Focuses on IoT testing methodologies, machine learning architecture patterns, and service-oriented system transformations. His 2025 works advance IoT system taxonomy and game engine analysis techniques. Advising: Supervises MASc and PhD programs in Software Engineering and Computer Science Labs/Teams: Ptidej Team develops software tools for system analysis (e.g., Magnet, SyDRA)

• Signal Processing
• Computer Vision
• Machine Learning
• +۶ مورد دیگر

Miroslaw Bober is Professor of Video Processing at the University of Surrey, where he joined in 2011. He leads the Visual Media Analysis team within the Centre for Vision, Speech and Signal Processing (CVSSP) in the School of Computer Science and Electronic Engineering. His extensive industry experience includes 15 years as General Manager of the Mitsubishi Electric R&D Centre Europe and Head of Research for its Visual & Sensing Division. BSc and MSc in Electrical Engineering from AGH University of Science and Technology, Krakow, Poland (1990) MSc in Machine Intelligence with distinction from Surrey University (1991) PhD in Computer Vision from Surrey University (1995) Professor Bober's research focuses on novel techniques in signal processing, computer vision and machine learning with applications in industry, healthcare, big-data and security. His expertise particularly lies in image and video analysis and retrieval, including visual search, object recognition, and analysis of motion, shape and texture. His algorithms for shape analysis, image/video fingerprinting, and visual search are considered world-leading and have been selected for ISO International standards within MPEG, with applications used by organizations like the Metropolitan Police. His recent publication trends show a strong focus on hybrid network architectures, scene graph generation, medical imaging applications, and augmented reality publishing systems. His work spans both theoretical advancements in computer vision and practical implementations addressing real-world challenges in media, healthcare, and security domains. The research demonstrates a consistent pattern of bridging academic innovation with industrial applications, particularly in visual search technology and media analysis. Presidential Award for strengthening the TV business in Japan via innovative 'Visual Navigation' content access technology (2010) Mitsubishi Best Invention Award for Image Signature Technology (2008) Professor Bober serves as Programme Director for the MSc in Multimedia Signal Processing and Communications and holds various teaching and mentoring roles. He has secured over 30 research and industrial grants totaling more than £16M, including the BRIDGET FP-7 project (5.28 M€) as coordinator and PI, and the CODAM project (£1.05 M) as PI. His work with the BBC, Huawei, and other industry partners demonstrates strong industry-academia collaboration. As chair of MPEG technical work on Compact Descriptors for Visual Search (CDVS) and Compact Descriptors for Video Analysis (CDVA), Professor Bober leads international standardization efforts. His Visual Media Analysis team develops cutting-edge visual search and media analysis algorithms with applications across broadcast, security, and healthcare domains.

Jeff Huangمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Concurrency
• +۴ مورد دیگر

Jeff Huang is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on software engineering, programming languages, concurrency, and runtime verification, with notable contributions to static analysis, race detection, and vulnerability mitigation in concurrent systems. Education: Postdoc, Computer Science, University of Illinois at Urbana-Champaign (2013-2014) Ph.D., Computer Science, Hong Kong University of Science and Technology (2012) B.E., Electrical Engineering, National University of Defense Technology, China (2008) Research Interests: Huang's work bridges theoretical foundations and practical applications in concurrency debugging, static analysis tools, and cybersecurity for smart contracts. He emphasizes scalable solutions for pointer analysis, race detection, and vulnerability detection in distributed systems and blockchain technologies. Recent Trends in Publications: His recent work explores AI-driven program execution (e.g., SGLang), blockchain security (e.g., Smart Contract analysis), and dynamic/static analysis techniques for memory safety. These studies underscore advancements in automated tools for securing concurrent and distributed systems. Awards: 2023 ACM SIGSOFT Distinguished Paper Award 2019 DARPA Young Faculty Award 2016 NSF CAREER Award 2013 ACM SIGSOFT Outstanding Doctoral Dissertation Award Advising & Grants: Huang has advised PhD students including Bozhen Liu and Peiming Liu, who have contributed to OpenMP race detection and pointer analysis tools. His grants include NSF awards for pointer analysis as a service and DARPA funding for young faculty research. Labs & Teams: He leads the O2 Lab, focused on concurrency verification and cybersecurity, collaborating with industry partners like Coderrect Inc. and DOE on scalable static analysis frameworks.

Yue Liمشاهده پروفایل

دانشیار

• Program Analysis
• Programming Languages
• Static Program Analysis
• +۳ مورد دیگر

Yue Li is an Associate Professor at the School of Computer Science, Nanjing University, where they co-run the PASCAL Research Group with Tian Tan. Their work focuses on static program analysis techniques and tools for programming languages, software engineering, security, and hardware verification. PhD in Computer Science from UNSW Sydney (2016) Postdoctoral research at Aarhus University (Denmark) and UNSW Sydney B.Eng and M.Eng from Northwestern Polytechnical University (2010, 2012) Research interests center on Program Analysis and Programming Languages , with a focus on: Pointer analysis for database-backed applications Context sensitivity optimization Reflection analysis in Java/Android Operational semantics for hardware languages Distributed dataflow analysis frameworks Developer-friendly static analysis tools Key publication trends (2016-2025) span static analysis , pointer precision , reflection handling , and tool frameworks across conferences like OOPSLA, PLDI, ICSE, ISSTA, and journals including TOPLAS and IEEE TSE. Notable artifacts include Tai-e and Chianina systems. 2025: ICSE Best Artifact & Distinguished Paper Awards 2024: IEEE TSE Publication on Generic Sensitivity 2023: OOPSLA Distinguished Artifact, SPLASH/ECOOP committees 2021: National Youth Talent Support Program, ZiJin Scholar 2016: ECOOP Distinguished Paper, CGO Best Paper As co-PI of PASCAL Research Group, they lead projects on precision-guided analysis, microservice systems, and cloud-based dataflow frameworks, with teaching awards for SICP and Software Analysis courses.

Felix Naumannمشاهده پروفایل

استاد

• Data Profiling
• Data Quality
• Data Integration
• +۴ مورد دیگر

Professor Felix Naumann is Chair for Information Systems at the Hasso Plattner Institute (HPI) at the University of Potsdam in Germany, where he leads the Information Systems research group. He is also Coordinator for MSc. Data Engineering and for the Data and AI track for MSc. Computer Science, and Speaker of the Research School on Data Science and Engineering. His extensive academic career includes visiting positions at CIRES Centre in Brisbane (2024-2025), SAP's Innovation Center (2020), AT&T Research (2016), and QCRI (2012). Professor Naumann's research focuses on data profiling, data cleansing, data integration, and data quality assessment with over 200 scientific publications. His work spans theoretical foundations and practical applications, with significant contributions to data quality metrics, metadata extraction, and AI-driven data preparation techniques. His research group develops prominent systems like Metanome for data profiling and Metis for data quality assessment. His recent publications demonstrate continued innovation in data management, with a growing intersection between traditional database research and AI applications. The 15 most recent papers show increasing focus on data quality for AI applications (KITQAR), multimodal data analysis (MELArt), and practical data cleaning frameworks that bridge database systems with machine learning pipelines. GI Dissertationspreis 2000 for best computer science PhD thesis IBM Research Division Award, 2002 Distinguished ACM member since 2021 Distinguished Reviewer Award - SIGMOD 2023 Best paper award at EDBT 2024 for Tasheeh paper Professor Naumann has successfully advised over 30 PhD students who now hold prominent positions at institutions like MIT, Google, Snowflake, and universities worldwide. His research has been funded by major grants including DFG Nachwuchsforschergruppe (2003-2008), IBM SUR Grant (2007), and DFG Forschergruppe Stratosphere (2010-2016). He leads the Information Systems research group at HPI, which includes PostDocs, PhD students, and student assistants working on projects like Metanome, Metis, KITQAR, and Janus, focusing on data profiling, quality assessment, and change exploration in data systems.

Silvia Cascianelliمشاهده پروفایل

پژوهشگر

• Computer Vision
• Document Analysis
• Handwritten Text Recognition
• +۴ مورد دیگر

Silvia Cascianelli is an AI and Computer Vision Researcher at the University of Modena and Reggio Emilia (UNIMORE). She actively contributes to the computer vision and document analysis communities through research, conference organization, and academic mentorship. She serves as Area Chair for major computer vision conferences including CVPR2025, BMVC2025, and ECCV2024, demonstrating her standing in the field. Her research focuses on several key areas within computer vision and document analysis: Image Generation : Developing efficient and lightweight methods for image generation with desired characteristics, particularly using diffusion models Handwriting Imitation : Creating algorithms for generating images of text with specific content and handwriting styles, along with evaluation methods Document Understanding : Extracting information from 2D and 3D document images, ranging from modern documents to historical artifacts like carbonized Roman papyri Dr. Cascianelli's work shows a clear progression toward more sophisticated generative models and evaluation frameworks, with recent publications focusing on diffusion models for handwritten text generation, efficient token reduction for multimodal tasks, and innovative approaches to historical document analysis. Her research bridges theoretical advancements with practical applications across diverse document types. Her scientific contributions have been recognized through invitations to serve as Area Chair for top-tier computer vision conferences (CVPR, ECCV, BMVC) and opportunities to organize specialized workshops including VisionDocs at ICCV, AI4DH at ECCV, and ADAPDA at ICDAR. Area Chair at CVPR2025 Area Chair at BMVC2025 Area Chair at ECCV2024 Organizer of VisionDocs Workshop at ICCV2025 Organizer of AI for Digital Humanities Workshop at ECCV2024 Organizer of ADAPDA Workshop at ICDAR2024 Dr. Cascianelli actively mentors the next generation of researchers: Vittorio Pippi - PhD Student at UniMoRe (National PhD program in AI) Fabio Quattrini - PhD Student at UniMoRe (ICT program) Carmine Zaccagnino - Research Intern at UniMoRe (formerly MSc student) Kostantina Nikolaidou - PhD Student at Luleå University of Technology Pau Torras Coloma - PhD Student at Computer Vision Center, Universitat Autònoma de Barcelona Bram Vanherle - CV Engineer at Colruyt Group Smart Innovation (formerly PhD student) She is actively involved in several research initiatives including the AI Governance Lab where she serves as a lecturer, and collaborates with institutions worldwide. Her current projects focus on advancing diffusion models for image generation, improving handwritten text recognition systems, and developing novel methods for document understanding across historical and contemporary contexts.

• Conceptual Modeling
• Ontology
• Domain Modeling
• +۷ مورد دیگر

Tiago Prince Sales is an Assistant Professor in the field of Semantics, Cybersecurity & Services, actively contributing to foundational research in conceptual modeling, ontology, and information systems. His work bridges formal methods with practical applications in digital platforms, cybersecurity, and knowledge representation. Assistant Professor, Semantics, Cybersecurity & Services Active in international research communities (FOIS, ER, BPMDS, RCIS) Key focus: Ontology, Domain Modeling, Interoperability, Digital Platforms Recognized with multiple best paper awards His research centers on the development and application of formal conceptual models using frameworks such as OntoUML and the Unified Foundational Ontology (UFO). He investigates how domain models can enhance system design, improve interoperability, and support risk analysis in complex information systems. His work often integrates empirical insights with theoretical rigor, aiming to address real-world challenges in modeling. The recent publications highlight a strong trend toward ontological engineering in cybersecurity (e.g., phishing attack modeling), digital platform taxonomy, and FAIR data planning. These works demonstrate a consistent focus on creating reusable, semantically rich models that support both human understanding and machine processing across domains. The integration of modeling with data science and security indicates a forward-looking research agenda. Best paper award at FOIS 2023 Best paper award at ER 2022 EMMSAD 2025 Best paper award Tiago Prince Sales actively mentors and collaborates with researchers, though no formal students are listed. He has secured recognition through competitive awards and leads significant research outputs, including datasets and open models. His involvement in organizing top-tier conferences such as FOIS 2024 reflects leadership in the academic community. While specific grants are not listed, his productivity suggests external funding support. He contributes to open science through Zenodo-hosted datasets like the OntoUML Vocabulary and GO-Plan method, and participates in collaborative teams focused on knowledge graphs, FAIRification, and model-driven engineering. His role in organizing the FOIS 2024 conference underscores his integration within leading research networks in formal ontology and conceptual modeling.

Koushik Senمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Formal Methods
• +۸ مورد دیگر

Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He holds a B.Tech from IIT Kanpur and M.S./Ph.D. from UIUC. His research focuses on Software Engineering, Programming Languages, and Formal Methods, emphasizing tools like DART, CUTE, and Jalangi for improving software reliability. He leads projects such as CORVETTE and Sky Computing Lab, and collaborates with Samsung Research America on JavaScript analysis. Sen has received prestigious awards including the Sloan Fellowship and ACM SIGSOFT Impact Award. Education: B.Tech, Indian Institute of Technology, Kanpur M.S. and Ph.D., University of Illinois at Urbana-Champaign Research Interests: Software Testing, Verification, Symbolic Execution, Security, and Quantum Computing. His work bridges automated testing (e.g., concolic testing) with machine learning for bug detection and program synthesis. Projects include Hindsight Logging for ML reproducibility and quantum circuit optimization (QFAST). Awards: NSF CAREER, IFIP Manfred Paul, ACM SIGSOFT Distinguished Paper (multiple), and Sloan Fellowship. Advising: Supervised over 30 students/postdocs, leading to faculty roles at UBC, CMU, and industry positions at Google, Facebook, and Samsung. Labs/Teams: Berkeley Center for Responsible, Decentralized Intelligence (RDI), EPIC Data Lab, and Sky Computing Lab. Active in quantum computing and hardware fuzzing (RTL-FuzzLab).

• Computational Logic
• Knowledge Representation
• Artificial Intelligence
• +۵ مورد دیگر

Professor Kewen Wang is a faculty member in the School of Information and Communication Technology at Griffith University , where he has been actively working in computational logic, knowledge representation, and their applications in artificial intelligence for over 30 years. His research has led to the development of novel logics, computer languages, and systems for knowledge representation and reasoning. Broad research areas: Computational Logic, Knowledge Representation, Artificial Intelligence, Programming, Knowledge Graphs, Explainable AI, Ontology Reasoning, Rule Learning Key contributions: RLvLR (scalable rule learner for KGs), TyRuLe (typed rule learning), Drewer (Datalog+- query engine), ALBERT+CCR (CommonsenseQA model), auction design algorithms His research has been widely cited in top venues like Artificial Intelligence , JAIR , ACM Transactions on Computational Logic , and conferences AAAI (Area Chair 2022-2025), IJCAI , KR . He has secured six ARC Grants (five Discovery, one Linkage) and smaller grants from NICTA, CSIRO, and Griffith University. Editorial roles: Area Chair for AAAI (2022-2025), Associate Editor for Journal of Web Semantics , TGDK Supervision: Has supervised over 15 PhD students including Hong Wu, Peng Xiao, and Pouya Omran Teaching: Courses in Intelligent Systems, Data Structures, Discrete Mathematics, and Robotics

• Cardiology
• Congenital Heart Disease
• Medical Informatics
• +۴ مورد دیگر

Jason Chami is a Clinical Associate Lecturer at the Central Clinical School within the Faculty of Medicine and Health at the University of Sydney. His academic appointment focuses on clinical teaching and research in cardiology and medical informatics, with affiliations spanning the Sydney Medical School and Central Clinical School. His research interests center on cardiology, particularly congenital heart disease complexity stratification, registry systems, and medical coding accuracy. He also investigates ophthalmology (glaucoma devices), metabolism (cardiometabolic biomarkers), and neuroscience (pain pathways). His work integrates clinical data analysis with informatics approaches to improve diagnostic precision and patient outcomes. Analysis of his 2020-2025 publications reveals dominant themes in congenital heart disease research, including algorithmic risk stratification, registry optimization, and coding error reduction. Secondary streams include ophthalmology (PreserFlo MicroShunt safety studies), metabolism (Slc16a13 gene impacts), and neuroscience (neuroreceptor changes in pain models), demonstrating cross-disciplinary clinical research methodology.

Orphée De Clercqمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Readability Prediction
• +۷ مورد دیگر

Orphée De Clercq is an Assistant Professor at Ghent University, specializing in language technology for educational applications. Her research focuses on leveraging Natural Language Processing (NLP) and Machine Learning (ML) to enhance computer-assisted language learning , readability prediction , and automated writing evaluation . She also explores sentiment analysis , emotion detection , and event coreference resolution in Dutch and multilingual contexts. Education : PhD in 2015 with groundbreaking work in readability prediction and fine-grained sentiment analysis for Dutch. Research Trends in her recent publications emphasize: Readability across domains and languages Emotion Detection using transformers and affect lexica Event Coreference in cross-document news Automated Writing Evaluation through NLP Implicit Sentiment Analysis in user-generated content Cross-Lingual Transfer with multilingual datasets She co-supervises four PhD students and contributes to interdisciplinary projects like Steunpunt Toetsen , SentEMO , and NewsDNA . Her teaching includes courses on digital communication and Computer-Assisted Language Learning .

Patrick Lamمشاهده پروفایل

دانشیار

• Static Program Analysis
• Verifiable Software Specifications
• Software Engineering
• +۸ مورد دیگر

Patrick Lam is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment to the Cheriton School of Computer Science. His research focuses on applications of programming languages and static analysis to software engineering challenges, emphasizing verifiable software specifications and program understanding. Dr. Lam has held significant grants from NSERC and is recognized for his impactful work, including the First Decade High Impact Paper award for his Soot framework. Education: Doctorate in Computer Science, Massachusetts Institute of Technology, 2007 Master's in Computer Science, McGill University, 2000 Bachelor's in Joint Honours Mathematics and Computer Science, McGill University, 1999 Research Interests: His primary areas include static program analysis, verifiable software specifications, and compiler design, with a focus on linking high-level software designs to low-level implementations. He explores techniques like lightweight specifications and domain-specific languages to enhance software reliability and efficiency. Recent work also addresses empirical studies of programming practices and security through modularization. Publications: Dr. Lam's recent publications span advancements in static analysis tools (e.g., WasmWalker for WebAssembly), formal verification of code generated by AI tools like GitHub Copilot, and empirical studies on C++ immutability usage. His work bridges theoretical programming language research with practical software engineering applications, emphasizing tools for developer productivity and code reliability. Awards and Recognition: First Decade High Impact Paper recognition for "Soot – A Java Optimization Framework" (2010) Teaching and Grants: He has taught courses such as CS 447, ECE 453, and ECE 459 on software testing and performance programming. Active in grant-funded research, he secured NSERC Engage Grant (2013) and an ongoing NSERC Discovery Grant (2013–2018). Lam has also advised graduate students and contributed to the Software Engineering Program at Waterloo as its Director (2016–2019). Labs and Teams: His research group explores topics in program analysis and software engineering, with collaborations on projects like abstract debugging tools (GobPie) and static analysis frameworks (Soot). He maintains an open-source repository on GitHub, contributing to educational materials and research tools.

Vitor S. Martinsمشاهده پروفایل

استادیار

• Remote Sensing
• Water Quality
• Deep Learning
• +۵ مورد دیگر

Dr. Vitor S. Martins is an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University (MSU). He leads the Geospatial Computing for Environmental Research (GCER) Lab , focusing on satellite remote sensing, deep learning, and environmental monitoring. His research integrates Earth observation data with AI to advance digital agriculture, water resource management, and climate resilience. Education: Ph.D., Agricultural and Biosystems Engineering, Iowa State University M.Sc., Remote Sensing, Brazilian Institute for Space Research (INPE) B.Sc., Agriculture and Environmental Engineering, Federal University of Viçosa Research Interests: Remote sensing of water quality and pollution Land cover/land use change mapping Satellite and aerial image processing Soil & water conservation engineering Deep learning for geospatial data analysis Climate change impacts on environmental systems Recent Research Trends (Articles): His work emphasizes algal bloom monitoring , crop type mapping , and global water transparency using advanced satellite platforms like Sentinel-2/3 and Landsat. He also develops frameworks for burned area mapping and soil spectral analysis , leveraging cloud computing and AI-driven models. Awards & Honors: 2019 Reverent P.T. Taiganides Award (Outstanding Ph.D. Student) 2019 Harold & Katherine Guy Fellowship (Soil & Water Conservation Research) 2012 CAPES-FIPSE International Exchange Award Lab & Collaborations: The GCER Lab collaborates with multiple institutions to promote AI solutions for agriculture and water management. Projects include developing AlgaeMap for Latin American water quality monitoring and creating scalable frameworks like FieldSeg for agricultural field extraction.

• Parallel Programming
• Dependently Typed Languages
• Concurrent Systems
• +۳ مورد دیگر

Dr Adam Barwell is a Lecturer and Researcher at the School of Computer Science, University of St Andrews. His research focuses on parallel programming, dependently typed languages, and formal methods in concurrent systems. He advises PhD student Leonid Nosovitskiy and has contributed to projects involving session types, distributed systems, and type-safe refactoring techniques. His educational background includes a PhD (not explicitly detailed here), and he actively engages in academic activities such as organizing events like Doors Open @ Computer Science 2025. Key research themes include concurrency control, functional programming paradigms, and improving code security through dependent types. Recent publications emphasize advancements in parallel language design, fault-tolerant protocols, and type-driven development. Collaborations span international conferences and institutions, reflecting his contributions to both theoretical and applied computer science domains.

• Software Engineering
• Software Testing
• Program Analysis
• +۴ مورد دیگر

Alessandro Orso is the Dean and Professor of the University of Georgia College of Engineering. Previously, he served as a professor at Georgia Institute of Technology, where he directed the Scientific Software Engineering Center and held leadership roles, including interim dean of the College of Computing (2023–2024). He earned his Ph.D. and master’s in computer science and electrical engineering from Politecnico di Milano, Italy. Orso’s research focuses on software testing, program analysis, and debugging, with applications in improving software reliability and security. His work includes developing automated testing tools (e.g., AutoRestTest), methodologies for scientific software engineering, and techniques for program debloating. His research has secured funding from agencies and industry partners like Google, IBM, and Microsoft, including a $11M grant from Schmidt Futures. His scientific awards include being a Distinguished Member of the ACM and IEEE Fellow. He advocates for innovative education, such as Georgia Tech’s Online Master of Science in Computer Science and his virtual software engineering course. His publications span automated testing, API analysis, and security, reflecting his commitment to advancing both academic research and practical software solutions. Orso’s leadership includes directing the Scientific Software Engineering Center, which pioneers methodologies for scientific software improvement and trains a new generation of engineers. His career blends academic excellence with impactful industry collaborations, driving advancements in software engineering practices.