جست‌وجوی استاد و پژوهشگر

• Image Processing
• Pattern Recognition
• Multi-media
• +۳ مورد دیگر

Seiichiro Kamata is a Professor at Waseda University's Faculty of Science and Engineering, specifically within the Graduate School of Information, Production, and Systems. He holds a Doctor of Engineering degree from Kyushu Institute of Technology and maintains an active research laboratory focused on advanced image processing techniques. His professional affiliations include the Institute of Electrical and Electronics Engineers, Institute of Image Information and Television Engineers, Information Processing Society in Japan, and Institute of Electronics, Information and Communication Engineers. Professor Kamata's research spans image processing, pattern recognition, multi-media systems, and signal processing with particular emphasis on deep learning applications. His work bridges theoretical advancements with practical implementations across medical imaging, hyperspectral analysis, and real-time computer vision systems. Recent publications demonstrate his leadership in developing novel neural network architectures for medical diagnostics, video enhancement, and document analysis. His publication record shows consistent high-impact contributions, particularly in medical image analysis (including Alzheimer's disease classification, ADHD diagnosis, and skin lesion detection) and computer vision techniques (including real-time semantic segmentation, visual tracking, and hyperspectral image classification). The research demonstrates a clear trajectory from foundational image processing algorithms toward sophisticated deep learning frameworks. Best Paper Award at ICIPRoB2020 (2020) - Constant-Time Gaussian Filtering Best Paper Award at ICSIPA2019 (2019) - Edge-guided Hierarchically Nested Network Best Paper Award at ICIEV2018 (2018) - Deep Neural Networks with Mixture of Experts Best Paper Award at Image Media Processing Symposium (2017) Best Paper Award at ICARCV2010 (2010) - Fast Polar Harmonic Transforms Professor Kamata's research group maintains a strong publication record with 266 papers and 1,622 citations (h-index: 21) as of September 2025. His work demonstrates consistent innovation in both theoretical foundations of image processing and practical applications across medical diagnostics, robotics, and cultural heritage preservation. The laboratory continues to produce cutting-edge research with recent focus on transformer architectures, multi-modal fusion techniques, and medical AI applications.

Vadim Zaytsevمشاهده پروفایل

دانشیار

• Software Language Engineering
• Program Verification
• Conceptual Modeling
• +۵ مورد دیگر

Vadim Zaytsev is an Associate Professor at Vrije Universiteit, specializing in Formal Methods and Tools. His research spans software language engineering, program verification, and model-driven development, with a focus on legacy systems and modernization. PhD in Recovery, Convergence, and Documentation of Languages (2008) Master's in Combinatorial Test Set Generation (2004) and Modelling Distributed Systems (2003) Bachelor's in Python Programming E-Learning (2002) Zaytsev's work integrates software language engineering , program verification , and domain-specific languages to address legacy system modernization. His recent publications emphasize data enrichment , automated repair , and refactoring detection across languages like Kotlin and Python. His scientific contributions include the IFIP TC2 Manfred Paul Award (2020) and datasets for program verification, refactoring detection, and automated repair. He actively participates in editorial work, including peer-review for workshops like OOPSLE 2020.

Juliane Fluckمشاهده پروفایل

استاد

• Biomedical Informatics
• Information Management
• Semantic Data Integration
• +۵ مورد دیگر

Juliane Fluck serves as Head of the Knowledge Management Program Area and Acting Deputy Scientific Director at ZB MED Information Center for Life Sciences, while also holding a W2 Professorship for Information Management at the Faculty of Agriculture, University of Bonn. She coordinates the National Research Data Infrastructure for Personal Health Data (NFDI4Health) consortium and has led this initiative since 2020. Her research focuses on optimizing the findability, accessibility, interoperability, and reusability (FAIR) of biomedical data through semantic integration and advanced terminology services. With expertise spanning text mining, data infrastructure development, and semantic annotation, she bridges computer science and life sciences to create systems that make biomedical data semantically readable for computers. Analysis of her recent publications reveals strong emphasis on synthetic health data generation, privacy protection mechanisms, FAIR data implementation across domains including agriculture (FAIRagro), and specialized terminology services for biomedical research. Her work consistently addresses challenges in health data management, with particular attention to pandemic response data (especially COVID-19) and rare disease research. Prof. Fluck actively contributes to critical advisory roles including the Advisory Board of the Center for Cancer Registry Data at the Robert Koch Institute, Scientific Advisory Board of Health.AI, and the INA expert group for Interoperability Generator for Digital Medicine. She heads the Knowledge Management program area at ZB MED and leads multiple national initiatives including NFDI4Health, demonstrating significant leadership in Germany's research data infrastructure landscape. Her work connects academic research with practical implementation in health data management systems used by researchers and clinicians.

Sukyoung Ryuمشاهده پروفایل

استاد

• Programming Languages
• Program Analysis
• Static Analysis
• +۵ مورد دیگر

Sukyoung Ryu is a Professor in the Department of Computer Science at KAIST's College of Computing, South Korea. Her research centers on programming languages and program analysis, with significant contributions to WebAssembly, JavaScript semantics, and language specification. She leads the Programming Languages Research Group (PLRG) at KAIST. Her research spans multiple critical areas including executable specification engineering, static analysis techniques, and language interoperability. Recent work focuses on WebAssembly conformance testing, C-to-Rust translation, and bridging real-world implementations with formal semantics. Her group develops practical tools like SpecTec for mechanized language specifications and JISET for JavaScript semantics extraction. Analysis of her 15 most recent publications reveals strong trends in executable specifications (7 papers), WebAssembly tooling (5 papers), and language translation (4 papers). Key methodological approaches include record-replay debugging, filtered-simulation for binary lifting, and algebraic data type transformations for memory safety. Ryu actively contributes to the programming languages community through leadership roles including Steering Committee Chair for APLAS, Program Co-Chair for SPLASH/OOPSLA, and committee positions at POPL, PLDI, and ICFP. She frequently organizes workshops on real-world language specification (RPLS) and presents keynotes on programming language research for social good. Her mentoring activities include co-chairing New Faculty Symposia and participating in RTFM panels on faculty mentoring. She advises students through KAIST's programs and supervises research in the PLRG lab, which focuses on building practical language tools with formal foundations. Current projects emphasize WebAssembly standardization, secure language interoperability, and specification-based testing frameworks.

Diego Garbervetskyمشاهده پروفایل

دانشیار

• Static Analysis
• Dynamic Analysis
• Reverse Engineering
• +۷ مورد دیگر

Diego Garbervetsky is an Associate Professor at the Computer Science Department, School of Sciences, University of Buenos Aires, and a Researcher at ICC/CONICET. He also serves as Director of the Institute of Research in Computer Sciences (ICC). His academic career spans software engineering, programming languages, and formal methods with a focus on program analysis and verification. His research interests include: Static and dynamic program analysis Reverse engineering and compiler optimizations Program understanding and validation Testing and verification of programs featuring rich protocols Automatic symbolic resource analysis (gas consumption, dynamic memory, energy, etc.) Garbervetsky's recent work focuses on smart contract analysis and verification, particularly for Solidity on Ethereum blockchain. His research bridges theoretical program analysis with practical applications in security-critical domains. He has developed several tools including Contractor for behavior validation, JConsume2 for heap memory analysis, and BudaPest for automated software verification. His scientific contributions have been presented at top-tier conferences including ICSE, FSE, ISSTA, and PLDI. Garbervetsky has served on numerous program committees for major software engineering conferences and has advised multiple PhD and undergraduate students through their research.

• Natural Language Processing
• Text Analysis
• Speech Processing
• +۴ مورد دیگر

Mark Cieliebak is a Professor for Speech and Text Processing at the Zurich University of Applied Sciences (ZHAW), School of Engineering, where he leads the Natural Language Processing (NLP) research group at the Centre for Artificial Intelligence. He has held this position since December 2018, following prior roles as a lecturer at ZHAW (2012-2018). His educational background includes a Dr. sc. techn. in Computer Science from ETH Zürich (1999-2003) and a Diplom in Computer Science from Universität Dortmund (1990-1999). His research focuses on Natural Language Processing , Text and Speech Analysis , and Machine Learning applications . Primary domains include dialogue systems evaluation, low-resource speech recognition (particularly Swiss German), automated text generation assessment, and NLP for social media analysis. He also explores educational applications through flipped classroom methodologies and machine learning operations. Cieliebak's publications (2020-2024) demonstrate strong emphasis on NLP evaluation frameworks, speech corpus development for dialects, and generative AI validation. Recurring themes include robustness testing, bias mitigation in automated metrics, and multilingual processing challenges. His work frequently involves large-scale dataset creation and ensemble learning techniques. Scientific Awards: SwissNLP Award 2023 Best Paper Award - Honorable Mention at EMNLP 2020 ZHAW Teaching Award 2015 He leads numerous research projects including: Unified Model for Text Generation Evaluation End-to-End Swiss German Speech Translation Holistic Analysis of Misinformation in Social Networks Automated News Processing (AutoNews) Chatbot Development for Language Learners He co-founded SpinningBytes AG (2015-present) and maintains extensive industry collaborations. As head of the NLP research group, he oversees doctoral candidates and master's students, though specific advisee names aren't listed.

Yi Meiمشاهده پروفایل

دانشیار

• Evolutionary Computation
• Combinatorial Optimisation
• Genetic Programming
• +۱۰ مورد دیگر

Dr. Yi Mei is an Associate Professor and Associate Dean (Research) at the Faculty of Engineering, Victoria University of Wellington, New Zealand. He serves as Programme Director for Computer Science & Computer Graphics within the School of Engineering and Computer Science and is affiliated with the Centre for Data Science and Artificial Intelligence (CDSAI). Dr. Mei's research focuses on evolutionary computation for combinatorial optimisation , with specific expertise in genetic programming , automatic algorithm design , explainable AI , and multi-objective optimisation . His work bridges theoretical foundations with practical applications in transportation, healthcare, manufacturing, and aquaculture. His publication portfolio shows a clear trajectory toward increasingly complex real-world applications of evolutionary computation, with recent work focusing on emergency medical dispatch optimization , dynamic transportation systems , and explainable AI for routing problems . The research demonstrates strong interdisciplinary connections between evolutionary computation, machine learning, and domain-specific optimization challenges. Victoria University of Wellington Ki te Pae - Research Excellence Award 2024 Multiple Best Paper Awards at ACM Genetic and Evolutionary Computation Conference (GECCO) from 2022-2024 HUMIES Silver Award at GECCO 2023 IEEE Transactions on Evolutionary Computation Outstanding Associate Editor (2024, 2025) Fellow of Engineering New Zealand and IEEE Senior Member Dr. Mei has successfully supervised numerous PhD and Master's students whose research has received recognition, including the ACM SIGEVO Dissertation Award. He leads significant research projects funded by MBIE Endeavour Smart Idea Fund ($1M NZD), New Zealand Royal Society Catalyst Leaders Fund ($150K NZD), and industry partners like Honda Research Institute Europe. His service contributions include editorial roles for top journals including IEEE Transactions on Evolutionary Computation and leadership positions in IEEE Computational Intelligence Society. Dr. Mei leads the Evolutionary Computation for Combinatorial Optimisation Group (ECCO) and the Evolutionary Computation and Machine Learning Research Group (ECRG) at Victoria University of Wellington. He also chairs the IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation (TESCO), fostering international collaboration in evolutionary computation applications.

Marcus Deiningerمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Business Process Modeling
• +۲ مورد دیگر

Prof. Dr. Marcus Deininger serves as Professor of Computer Science at Stuttgart University of Applied Sciences within the Faculty of Surveying, Computer Science and Mathematics since 2004, concurrently holding the position of Dean of Studies for Software Technology since 2007. His academic leadership spans multiple degree programs including Bachelor of Applied Mathematics and AI, Bachelor of Computer Science, and Master's in Software Technology. His educational foundation includes a Doctorate in Computer Science from the University of Stuttgart (1995), preceded by significant industry experience: development and project management roles at debis Systemhaus (1996-2001) and T-Systems (2001-2005) provided practical grounding in enterprise IT solutions before his academic transition. Deininger's research manifests primarily through pedagogical contributions in software engineering education, with concentrated expertise in Java programming ecosystems, BPMN-based business process modeling, and UML-driven system analysis. His scholarly identity centers on translating complex technical concepts into accessible educational frameworks, evidenced by his extensive publication record focused on student skill development. Analysis of his 12 publications (2009-2024) reveals consistent emphasis on practical computer science education, evolving from foundational programming guides toward specialized exam preparation materials across software engineering domains. This trajectory demonstrates sustained commitment to curriculum development rather than theoretical research. His professional recognition includes membership in the German Informatics Society (GI), reflecting peer acknowledgment within Germany's primary computing professional organization. As Dean of Studies and thesis supervision guide author, Deininger shapes academic journeys through structural program oversight and pedagogical frameworks. His collaborative works with Thomas Kessel across 10+ publications indicate sustained research partnerships, while leadership in three distinct degree programs underscores institutional impact beyond narrow technical specialization. Though specific research laboratories aren't documented, his collaborative publication pattern suggests integration within computer science education communities, with recent works (2024) confirming active engagement in evolving pedagogical approaches for contemporary programming challenges.

• Mathematics Education
• Language Training
• Educational Technologies
• +۵ مورد دیگر

ДИАНА ИЛИЕВА ИЗВОРСКА serves as Professor and Head of the Department of Language and Specialized Training at Technical University - Gabrovo. With a Doctorate in Mathematical Sciences, she has established herself as a leading academic in mathematics education, language training, and educational technologies. Her interdisciplinary approach bridges theoretical knowledge with practical applications in technical education. Dr. Izvorska's research spans multiple interconnected domains with a consistent focus on modern educational technologies and competency-based approaches. She has made significant contributions to mathematics education methodology, particularly in critical thinking development, reflection in mathematical training, and individualized learning approaches. Her work on professional competence development in technical university students has been particularly influential, connecting mathematical foundations with language education and pedagogical innovation. Analysis of Dr. Izvorska's recent publications reveals a strong trend toward integrating information technologies with traditional educational methodologies. Her research demonstrates practical applications of theoretical concepts across various educational contexts, from elementary mathematics to advanced engineering education. She has explored computational mathematics systems in higher mathematics training, project-based learning in information technologies, and the development of information competence across different educational levels. 25 research projects led, primarily focused on educational technologies 101 total publications including 9 books and numerous scholarly articles International collaborations across Europe and Ukraine Dr. Izvorska maintains active leadership in her department while continuing to advance educational theory and practice through her scholarly work and project management.

Fang Liuمشاهده پروفایل

استادیار

• Software Engineering
• Artificial Intelligence for Software Engineering
• Program Understanding
• +۳ مورد دیگر

Fang Liu is an Assistant Professor at the School of Computer Science & Engineering, Beihang University, China. She has made significant contributions to the field of software engineering, particularly in the intersection of artificial intelligence and software development practices. Dr. Liu received her Ph.D. in Computer Science from Peking University (Sep. 2017 - Jul. 2022), supervised by Prof. Zhi Jin and Prof. Ge Li. Prior to that, she earned her B.S. in Computer Science from Chongqing University (Sep. 2013 - Jul. 2017). Her research interests focus on AI for Software Engineering, including program understanding and generation, program repair, and applications of Large Language Models to software development tasks. Dr. Liu teaches Compiler Technology as a compulsory course at Beihang University (Fall 2024) and Programming in Cangjie Language as an elective course (Spring 2025). Her recent publications demonstrate a clear trend toward leveraging large language models for various software engineering tasks, with a particular emphasis on code editing, program repair, and code translation. Her research spans both theoretical advancements in model architectures and practical applications to real-world software development challenges, with numerous publications in top-tier venues like ASE, ICSE, and FSE. Distinguished Paper Award at ICPC'20 for "A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning" Dr. Liu actively participates in the software engineering research community as a program committee member for major conferences including ASE, FSE, and ICSE. Her work has significant implications for improving developer productivity, software quality, and the integration of AI technologies into the software development lifecycle.

Diego Garbervetskyمشاهده پروفایل

دانشیار

• Static analysis
• Program verification
• Smart Contract analysis
• +۵ مورد دیگر

Diego Garbervetsky is an Associate Professor at the Computer Science Department, School of Sciences, University of Buenos Aires, and a Researcher at ICC/CONICET. He also serves as Director of the Institute of Research in Computer Sciences (ICC). His academic career spans multiple roles in software engineering research and education. His educational background is not explicitly stated in the provided texts, but his current position reflects extensive academic achievement. Garbervetsky's research focuses on static analysis techniques for Java-like programs and Smart Contracts, automated program verification , program understanding , and validation . His specific interests include program understanding, testing and verification of programs featuring rich protocols, static analysis for program verification, and automatic symbolic resource analysis (gas consumption, dynamic memory, energy, etc.). His research has significant implications for blockchain technology, particularly in smart contract verification. Analysis of his recent publications reveals a strong trend toward smart contract security and verification , with multiple papers on modal abstractions, predicate abstractions, and tools like VeriSol for Solidity smart contracts. His work bridges formal methods with practical software engineering challenges, particularly in the blockchain domain. His scientific contributions include the development of multiple research tools: Contractor: Automated tool for behavior validation Contractor.NET: Visual Studio extension for .NET validation JConsume2: Compositional analysis for Java heap memory Consume.Net: Compositional analysis for .NET heap memory BudaPest: Automated software verifier VInTime: Verification suite for Real Time systems Garbervetsky has supervised numerous PhD students including Daniel Wappner, Javier Godoy, and Alexis Soifer, with former students now working at companies like Microsoft, Veritran, and Dialpad. He has served on program committees for major conferences including ICSE, FSE, ASE, and ISSTA, demonstrating his standing in the software engineering research community. His service includes chairing workshops and serving on artifact evaluation committees, showing his commitment to research quality and reproducibility. He currently teaches Software Engineering 2 at the University of Buenos Aires, having previously taught courses in algorithms, automatic software validation, program analysis, programming paradigms, and computer organization.

Lei Buمشاهده پروفایل

استاد

• Software Verification
• Software Testing
• Software Analysis
• +۸ مورد دیگر

Lei Bu is a Professor and Vice Dean of the Software Institute at Nanjing University, China. He has been with Nanjing University since 2010, progressing from Assistant Professor to his current position. His academic career includes a visiting position at Microsoft Research Asia through their StarTrack Program from 2014-2015. Professor Bu received his B.Sc. and Ph.D. degrees in Computer Science from Nanjing University, with additional research experience at Carnegie Mellon University and University of Texas at Dallas during his doctoral studies. His academic journey shows steady progression through the ranks at his alma mater. Lei Bu's research primarily focuses on software verification, testing, and analysis, with particular expertise in model checking, bounded model checking, formal methods, and cyber-physical systems. His work spans theoretical foundations to practical applications in safety-critical systems. Recent publications demonstrate growing interest in integrating machine learning techniques with traditional formal methods, as seen in works like SpecGen that leverage large language models for program specification generation. His research outputs show a consistent focus on verification techniques for complex systems, particularly addressing challenges in hybrid systems, cyber-physical systems, and IoT applications. Professor Bu has developed several verification tools including BACH (Bounded Reachability Checker for Linear Hybrid Automata) and BRICK (Bounded Reachability Checker of Numerically-Intensive C Code), which have been used in international verification competitions. 2023: Zhongchuang Software Talent Award 2022: CCF-IEEE CS Young Computer Scientist Award 2020: The Outstanding Teachers of Computing in Higher Education Award Program 2019: NASAC Young Software Innovation Award 2016: Young Talent Development Program 2014: StarTrack Program Visiting Young Faculty 2007: Full Scholarship under the State Scholarship Fund Professor Bu serves as a Principal Investigator on multiple significant research projects funded by the Natural Science Foundation of China, including a Key Program grant (2023-2027) on dynamic adjustment-control-fault tolerance theory. He has also advised numerous students and taught courses including Formal Languages and Automata, Theoretical Foundation of Software Engineering, and Preliminary Introduction to the Theory of Computation. His laboratory work focuses on developing practical verification tools for industrial applications in cyber-physical and IoT systems.

Mingwei Liuمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence
• AI4SE
• +۴ مورد دیگر

Dr. Mingwei Liu is an Associate Professor at the School of Software Engineering, Sun Yat-Sen University, China. Previously, he completed postdoctoral research at Fudan University in 2024 and received both his Ph.D. (2022) and B.E. (2017) in Software Engineering from Fudan University under the mentorship of Xin Peng. Dr. Liu's research focuses on the intersection of Software Engineering (SE) and Artificial Intelligence (AI), specifically in the domains of AI4SE (AI for Software Engineering) and SE4AI (Software Engineering for AI). His primary interest lies in leveraging advanced AI technologies, particularly large language models (LLMs) and knowledge graphs (KGs), to address complex software engineering challenges and tackle system engineering problems in AI applications. His research portfolio demonstrates a strong focus on practical applications of AI in software development, with particular emphasis on code generation, vulnerability detection, test generation, and knowledge representation for software artifacts. Through his work, Dr. Liu has contributed significantly to understanding how LLMs perform in realistic software development scenarios beyond simple function-level code generation. Dr. Liu has published over twenty papers in top international journals and conferences including TSE, TOSEM, ICSE, FSE, and ASE. His research has been recognized with prestigious awards including the IEEE TCSE Distinguished Paper Award (ICSME 2018) and the ACM SIGSOFT Distinguished Paper Award (FSE 2023). IEEE TCSE Distinguished Paper Award (ICSME 2018) ACM SIGSOFT Distinguished Paper Award (FSE 2023) Dr. Liu actively contributes to the academic community as a program committee member for major conferences including ASE, ESEC/FSE, ICSME, and SANER. He leads the SYSUSELab research group, which has developed notable resources including the ClassEval benchmark for evaluating LLMs on class-level code generation tasks.

Andreas Vogelsangمشاهده پروفایل

استاد

• Requirements Engineering
• Model-Based Systems Engineering
• Empirical Software Engineering
• +۱ مورد دیگر

Andreas Vogelsang is a full Professor of Software Systems Engineering at the University of Duisburg-Essen and board member of paluno - the Ruhr Institute for Software Technology. His research focuses on requirements engineering, model-based systems engineering, empirical software engineering, and AI for software engineering, with significant contributions to the software engineering community through conference leadership and publications. His research interests center on improving requirements engineering processes using AI techniques, addressing model-based systems engineering challenges in embedded systems, and conducting empirical validation of software engineering practices. He investigates industrial adoption barriers, quality requirements interpretation, and the application of large language models to automate requirements tasks, emphasizing practical relevance and empirical evidence. Recent publications reveal a strong trend toward leveraging AI—particularly large language models—for requirements elicitation, traceability, and code generation. His work bridges theoretical advancements with industrial applications, focusing on empirical validation in domains like automotive systems and research software, while exploring explainability in AI-assisted development. Scientific awards include: Junior Fellow of the German Informatics Society (GI) (2018) Young Scientist of the Year by academics and the DHV (2019) As a board member of paluno, he drives interdisciplinary research initiatives and industry collaborations, fostering innovation in software technology through academic-industrial partnerships and contributing to the institute's strategic direction in software engineering research.

Mirela Djordjevicمشاهده پروفایل

استاد

• programming languages design
• domain specific languages
• electronic document systems
• +۳ مورد دیگر

Mirela Djordjevic is a Professor at Manhattanville University, where she has served since 2002 after prior appointments at Trinity University (2000-2002) and the University of Belgrade in Serbia (1984-1999). She holds a PhD and master's in computer science from the University of Maryland, complemented by a master's in computer science and bachelor's in mathematics from Belgrade University. Education: Numerical Mathematics, BS, Belgrade University Computer Science, MS, Belgrade University Computer Science, MS, University of Maryland Computer Science, PhD, University of Maryland Research Focus: Dr. Djordjevic's work centers on programming languages design , domain specific languages , and constructive type theory , with significant contributions to program verification and electronic document systems. Her research bridges theoretical computer science and practical software development methodologies. Publication Trends: Her scholarly output reveals dual expertise in computer science education and applied systems development. She has authored textbooks on Java, Visual Basic, and database design while publishing research on pedagogical models like the Spiral Method and theoretical foundations of type systems. This corpus demonstrates consistent focus on improving programming education and language design since the 1990s. Scientific Recognition: No major awards or fellowships are documented in available sources. Academic Service: Current teaching responsibilities include Computer Programming I/II, Systems Architecture, Database Design, and Web Applications Development. Information regarding graduate advisees, research grants, or laboratory leadership is not provided in the source material.