جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Data Analytics
• Artificial Intelligence
• +۳ مورد دیگر

Maria-Cristina Marinescu serves as a Lecturer in the Department of Mathematics and Data Analytics at IQS School of Management. With a strong foundation in computational methods and data analysis, she contributes significantly to both teaching and research within the institution. Her academic profile demonstrates consistent scholarly activity with 44 documented scientific production items spanning over two decades. Dr. Marinescu's research interests center on Machine Learning, Data Analytics, and Artificial Intelligence, with notable applications in epidemic modeling, wireless sensor networks, and programming models. Her work bridges theoretical computer science with practical applications addressing real-world challenges in public health, digital well-being, and medical diagnostics. The fingerprint analysis of her work shows strong emphasis on Machine Learning (100%), Learning Systems (100%), and Transportation Models (100%), with substantial contributions to Programming Models (84%) and Wireless Sensor Networks (77%). Her recent publications reveal a trend toward interdisciplinary research that combines computational methods with societal challenges. She has made significant contributions to epidemic modeling during the COVID-19 pandemic, developing methods for accurate incidence rate estimation in Spain and analyzing information gains from multiple epidemic model outputs. Additionally, her work extends to medical applications including research on ocular ischemia and glaucoma, as well as innovative approaches to automated metadata annotation using machine learning techniques. Dr. Marinescu actively leads and participates in multiple research projects including Uncovering patterns of unconscious reactions to fake content (as Principal Investigator), MobilePressure (focused on reducing children's smartphone exposure), and ADAMIQS: Applied Data Analytics and Modelling IQS . These projects demonstrate her commitment to addressing contemporary issues through data-driven approaches while mentoring students and collaborating with interdisciplinary research teams.

• Software Architecture
• Software Quality
• Software Evolution
• +۵ مورد دیگر

Professor Ralf Heinrich Reussner is a faculty member at Karlsruhe Institute of Technology (KIT), working at the Institute of Program Structures and Data Organization within the Software Design and Quality group. His research focuses on systematic approaches to software construction with predictable quality characteristics. His research interests include software architecture, component-based software systems, performance prediction and capacity planning, quality-centric evolution of long-living software systems, and consistency management in cyber-physical systems development. Professor Reussner has developed methods and tools for architecture- and quality-centric software evolution, with particular emphasis on predictable quality characteristics in component-based systems. His notable scientific recognition includes the prestigious Emmy Noether Junior Research Group award from the German Research Foundation (DFG), which supports outstanding early-career researchers in establishing independent research groups. Principal Investigator for multiple DFG-funded projects including material grants and priority programs Speaker of SFB 1608: Consistency in the View-Based Development of Cyber-Physical Systems Co-speaker of National Research Data Infrastructure for and with Computer Science Former speaker of SPP 1593: Design for Future - Managed Software Evolution Scientific coordinator of SPP 1593 Professor Reussner has led research on cross-domain maintainability assessments, integrated observation and modeling techniques for software adaptation, and formalization of consistency concepts in collaborative research centers. His work bridges theoretical computer science with practical software engineering applications, particularly in the domain of evolving software systems within elastic platform technologies.

Minxue Panمشاهده پروفایل

استاد

• Software Modelling and Verification
• Software Analysis and Testing
• Cyber-Physical Systems
• +۵ مورد دیگر

Minxue Pan is a Professor and PhD supervisor at the Software Institute, State Key Laboratory for Novel Software Technology, Nanjing University, China. His research focuses on the dependability of complex software systems, with expertise spanning software modeling and verification, software analysis and testing, cyber-physical systems, mobile computing, and intelligent software engineering. Ph.D. in Computer Science and Technology from Nanjing University (2014), supervised by Prof. Xuandong Li B.Sc. from Nanjing University Studied at UC Berkeley's Department of Electrical Engineering and Computer Sciences (2009-2010) under Prof. Edward A. Lee Professor Pan's research interests center on improving software dependability through innovative approaches to modeling, analysis, and testing. His work spans traditional software systems, mobile applications (particularly Android), cyber-physical systems, and the application of AI techniques to software engineering problems. He has made significant contributions to GUI testing, vulnerability detection, and deep learning applications in software engineering. His recent publications (2024-2025) demonstrate a strong focus on applying advanced machine learning techniques to software testing and security challenges. Key trends include leveraging large language models for test migration, enhancing fault localization with graph learning and contrastive learning, developing specialized frameworks for Android security analysis, and improving test efficacy through GUI and functional equivalence. His work consistently targets real-world industrial settings and addresses practical challenges in mobile and complex software systems. ISSTA 2020 Distinguished Paper Award ICSE 2025 Best Artifact Award Professor Pan actively advises graduate students and has developed several notable tools including Q-testing (reinforcement-learning based Android testing), ISDChecker (model checking for interrupt-driven systems), PREFEST (preference-wise testing for Android), Sketchoid (GUI code search), and PI-REC (hand-drawn draft conversion). His research is supported by extensive publication records in top-tier software engineering venues including ASE, ICSE, FSE, ISSTA, and TOSEM. He teaches undergraduate courses in Advanced Programming with C++, Software System Design, and Software Construction, as well as graduate courses in Advanced Software Design. His laboratory work focuses on developing practical solutions for real-world software dependability challenges through the State Key Laboratory for Novel Software Technology.

Marian Lingsch-Rosenfeldمشاهده پروفایل

پژوهشگر

• Software Verification
• Program Analysis
• Model Checking
• +۵ مورد دیگر

Marian Lingsch-Rosenfeld is a researcher at the Department of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), affiliated with the Software and Computational Systems Lab. Based in Office Room F 012 at Oettingenstraße 67, Munich, they actively contribute to software verification research and mentor graduate students through thesis projects. Research interests focus on software verification , program analysis , and model checking , with specific expertise in predicate abstraction, constrained horn clauses, and deductive verification techniques. Their work bridges theoretical foundations with practical verification tools, particularly CPAchecker. Recent publications demonstrate significant contributions to verification witnesses, program transformations, and loop abstraction techniques. Conference participation includes active roles in ASE, ECOOP, and VMCAI as both author and committee member for artifact evaluation. As a thesis supervisor, they guide students through advanced topics including: Constrained-Horn-Clause export for CPAchecker BMC algorithm performance improvements Software Verification Witnesses extensions Deductive verifier development Compiler optimizations impact analysis Available for consultation via office hours by appointment through meet.lrz.de, with communication primarily via university email channels.

Mehdi Bagherzadehمشاهده پروفایل

استادیار

• Software Engineering
• Programming Languages
• Concurrent Software
• +۳ مورد دیگر

Mehdi Bagherzadeh is an Assistant Professor in the Department of Computer Science and Engineering at Oakland University. His professional profile shows consistent involvement in major software engineering conferences including ASE, SPLASH, and ICSE, where he has served in various leadership roles such as Workshops Co-Chair for SPLASH (2021-2023) and Late Breaking Results Co-Chair for ASE (2022). Dr. Bagherzadeh's research focuses on the correct construction of concurrent and big data software, situated at the intersection of Software Engineering and Programming Languages. His work particularly addresses challenges in deep learning program optimization, actor concurrency models, and automated refactoring techniques. His research trajectory shows a clear evolution from foundational work on concurrent programming models (Panini, Capsule) to current applications in deep learning systems. Analysis of his publication history reveals a strong focus on practical software engineering challenges in concurrent systems, with recent work concentrating on the migration and optimization of deep learning programs. His research combines empirical studies with formal methods, addressing both theoretical foundations and practical implementation concerns in modern software systems. Through his committee service across multiple top-tier conferences including ASE, SPLASH, ICSE, and ESEC/FSE, Dr. Bagherzadeh has established himself as an active contributor to the software engineering research community. His service roles have ranged from program committee membership to organizational leadership positions.

Gary Tanمشاهده پروفایل

استاد

• computer security
• formal methods
• programming languages
• +۳ مورد دیگر

Gang (Gary) Tan is a Professor in the Computer Science and Engineering Department at Pennsylvania State University, co-directing the Institute for Networking and Security Research (INSR). His research bridges computer security, formal methods, and programming languages to develop practical solutions for software vulnerabilities and AI fairness. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University Dr. Tan specializes in applying compiler techniques and formal verification to security challenges, with seminal work on cache side-channel attacks and fairness in machine learning. His Security of Software (SOS) Group develops frameworks that integrate theoretical guarantees into real-world systems, emphasizing measurable security outcomes and ethical AI. Recent projects focus on quantifying bias in neural networks and mitigating speculative execution vulnerabilities. Analysis of his 2021-2025 publications reveals a strategic pivot toward AI security, where he pioneers methods for fairness testing (e.g., information-theoretic debugging) and repair (e.g., NeuFair). Concurrently, his security work evolves from foundational side-channel research (SpecSafe, 2021) toward hardware-software co-design solutions, demonstrating consistent innovation across theoretical and applied domains. Scientific Awards: James F. Will Career Development Professorship NSF CAREER Award Google Research Award (two instances) Distinguished Reviewer Award at 2018 IEEE Symposium on Security and Privacy Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Best Paper Award at PLDI 2024 Dr. Tan leads the SOS Group with funding from NSF (including CAREER), DARPA (ISAT study group membership), and industry partners like Google. His grants support interdisciplinary projects spanning secure compilation, fairness engineering, and hardware security, while his teaching excellence award reflects commitment to pedagogy in core systems courses. He co-directs Penn State's Institute for Networking and Security Research (INSR), fostering collaboration between systems, security, and AI researchers. The SOS Group maintains active partnerships with industry security teams and contributes to open-source tools for vulnerability detection, with recent work expanding into fairness certification for machine learning pipelines.

Shiva Nejatiمشاهده پروفایل

استاد

• Software Testing and Verification
• Cyber Physical Systems
• Applications of AI to Software Engineering
• +۴ مورد دیگر

Shiva Nejati is a Professor at the School of Electrical Engineering and Computer Science at the University of Ottawa, where he leads research in software engineering with a focus on verification and analysis of cyber-physical systems. Previously, he served as a senior scientist (2012-2019) at the SnT Center, University of Luxembourg, and as a scientist (2009-2012) at the Simula Research Laboratory. He earned his M.Sc. and Ph.D. from the University of Toronto in 2003 and 2008, respectively. His research interests span Software Testing and Verification, Cyber Physical Systems, Applications of AI to Software Engineering, and Search-based Software Engineering. Nejati's work draws on techniques from formal software modeling, meta-heuristics optimization, machine learning, system engineering, and empirical methods. He has extensively worked on testing and fault localization of cyber physical systems, particularly applied to autonomous vehicles and IoT systems, while also exploring requirements traceability, automated configuration of product line systems, and simulation modeling of CPS. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional software verification techniques, particularly for complex cyber-physical systems. His work bridges search-based testing with formal verification methods to address the challenges of testing compute-intensive models in domains like autonomous vehicles and satellite systems. The research shows increasing focus on applying machine learning to solve longstanding problems in software testing and requirements engineering. Nejati serves in significant leadership roles across major software engineering conferences, including PC Chair for ASE 2026, General Chair for CASCON 2026, and various program committee and organizing committee roles for ICSE, ISSTA, MODELS, and other top-tier conferences. He is also an Associate Editor for EMSE Journal and ASE Journal, and previously served on the IEEE Transactions on Software Engineering editorial board. He teaches advanced courses including 'AI-enabled Software Verification and Testing' and 'Software Construction' at the University of Ottawa, while leading the Sedna lab which focuses on verification, analysis, and testing of complex systems. His research is conducted in close collaboration with industry partners across telecommunication, maritime, energy, automotive, and aerospace sectors.

He Yeمشاهده پروفایل

استادیار

• AI for Software Engineering
• Fault Localization
• Automated Program Repair
• +۲ مورد دیگر

He Ye serves as an Assistant Professor at University College London (UCL), specializing in AI-driven software engineering solutions. His work bridges academic research and industry applications through EuniAI, a startup transforming research into developer tools. Research focuses on code agents for automating software tasks, with three core thrusts: Codebase context retrieval to enhance LLM capabilities Automated issue resolution systems Code agent memory construction His publications (2021-2025) demonstrate consistent innovation in fault localization and program repair. Current advising includes PhD students Zhaoyang Chu and Xiang Li (starting Fall 2025), alongside research assistants Yue Pan, Jiayi Xu, and Han Li. He co-founded EuniAI to commercialize research solutions for practical developer challenges. He actively shapes the field through workshop organization ( LMPL@SPLASH 2025 , APR@ICSE 2025 ) and program committee roles across major conferences including ASE, ICSE, and ESEC/FSE.