جستجوی استاد راهنمای دانشگاهی

Cristian Cadarمشاهده پروفایل

استاد

• Software Engineering
• Testing and Verification
• Computer Systems
• +۴ مورد دیگر

Cristian Cadar is Professor of Software Reliability in the Department of Computing at the Faculty of Engineering, Imperial College London. He leads the Software Reliability Group, focusing on automatic techniques for increasing the reliability and security of software systems. His research has significant impact in both academia and industry, particularly through the development of widely-used tools. His educational background includes a PhD in Computer Science from Stanford University, and undergraduate and Master's degrees from the Massachusetts Institute of Technology. Cristian's research primarily focuses on software reliability, security, and verification. His work spans symbolic execution, fuzzing, compiler testing, and automated techniques for finding and fixing software bugs. He has pioneered practical, scalable solutions that address real-world software challenges, with particular emphasis on security-critical applications. His research integrates formal methods with practical engineering approaches to create tools that are both theoretically sound and practically useful. Analysis of his recent publications reveals a strong focus on improving fuzzing techniques, enhancing symbolic execution systems, and developing better methods for compiler and infrastructure testing. His work consistently bridges the gap between theoretical foundations and practical implementation, resulting in tools that have substantial real-world impact. EuroSys Jochen Liedtke Award HVC Award BCS Roger Needham Award ACM SIGOPS Hall of Fame Award ACM CCS Test of Time Award ERC Consolidator Grant EPSRC Early-Career Fellowship Cristian has advised numerous PhD and Master's students, contributing significantly to the next generation of software engineering researchers. His research has been supported by prestigious grants including an ERC Consolidator Grant and an EPSRC Early-Career Fellowship. He has held leadership positions in major conferences, serving as Program Co-Chair for ESEC/FSE 2022 and General Chair for ISSTA 2021, demonstrating his standing in the research community. He leads the Software Reliability Group at Imperial College London, which maintains an active research agenda in software testing and verification. The group's work on KLEE and related tools continues to influence both academic research and industrial practice in software reliability engineering.

Jianwen Liمشاهده پروفایل

استاد

• Automatic Formal Verification
• Logics
• Automata Theory
• +۳ مورد دیگر

Jianwen Li is a full professor at East China Normal University (ECNU) since 2025. He previously held postdoctoral positions at Rice University (2014-2017) and Iowa State University (2017-2019). His work focuses on formal verification, logics, and automata theory, particularly in industrial applications like hardware, railway interlocking systems, and aerospace. PhD in Computer Science (2014), ECNU, advised by Jifeng He and Geguang Pu Postdoc, Rice University (2014-2017), with Moshe Y. Vardi Postdoc, Iowa State University (2017-2019), with Kristin Y. Rozier His research centers on model checking and Linear Temporal Logic (LTL) reasoning, including LTL satisfiability checking and synthesis. He developed state-of-the-art tools like CAR (Complementary Approximate Reachability) for safety verification and aalta/aaltaf for LTL/LTLf satisfiability. His publications emphasize SAT-based LTL reasoning, bug-finding algorithms, and formal verification techniques for safety-critical systems. Key trends in his work include SAT solvers for formal verification, automata theory applications, and tool development for model checking in aerospace/hardware domains. He has contributed to LTL-to-automata transformations, mission-time LTL analysis, and runtime verification through formula progression.

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.

• Formal Methods
• Cybersecurity
• Software Verification
• +۳ مورد دیگر

Tobias Nießen is a PreDoc Researcher in the Formal Methods in Systems Engineering department at TU Wien (Vienna University of Technology). His research focuses on cyber-physical systems, security verification, and formal methods applied to software engineering challenges. He is actively involved in the ARTIST project (2021–2026) , which addresses safety-critical systems and hyperproperty verification. Key Projects/Grants: ARTIST (Horizon 2020 Project) Contact & Profiles: Email: tobias.niessen@tuwien.ac.at ORCID: 0000-0002-7712-0006 Lab: Formal Methods in Systems Engineering His recent work emphasizes hyperproperty verification (e.g., differential privacy, information flow security) and backdoor detection in software systems using formal methods. He has contributed to advancing symbolic execution techniques for finding ∀∃ hyperbugs, a class of security vulnerabilities affecting multiple system executions simultaneously.

Nikita Kovalمشاهده پروفایل

پژوهشگر

• Concurrent Programming
• Kotlin Coroutines
• Concurrent Data Structures
• +۶ مورد دیگر

Nikita Koval is a Researcher at JetBrains , specializing in concurrent programming, Kotlin coroutines, and testing frameworks. His work bridges academic research with industrial application, focusing on synchronization primitives, lock-free data structures, and JVM-based concurrency tools. Academic rank: Researcher Affiliation: JetBrains Research interests include: Concurrent algorithm design and optimization Formal verification of synchronization mechanisms Testing frameworks for JVM-based languages Memory-efficient lock-free data structures Scalability in coroutine communication Distributed systems debugging Publication trends show a focus on Kotlin coroutines, JVM concurrency, and testing frameworks like Lincheck. His work spans algorithm design (union-find, queues), formal verification (CQS), and practical tools for concurrency testing (Lincheck) across conferences like PPoPP, PLDI, and ECOOP. Scientific recognition: Nikita received the Best Paper Award at OPODIS 2019 for his work on concurrent union-find algorithms. His contributions to Lincheck and CQS have been adopted into Kotlin's core libraries. Workshop leadership: He offers a 4-day corporate workshop on concurrent algorithms, covering testing, debugging, and implementation of lock-free data structures, now booked for Q3/Q4 2025. The curriculum includes hands-on Java/Kotlin coding with topics like segment queues, fetch-and-add queues, flat combining, and concurrent hash tables.

• Software Testing
• Software Verification
• Programming Languages
• +۷ مورد دیگر

Karine Even-Mendoza is a Lecturer at King's College London specializing in software reliability, with active contributions to SPLASH, ASE, and ISSTA conferences. Her research bridges traditional software engineering with emerging domains like quantum computing and green software. Her research interests focus on: Advanced testing methodologies (Fuzzing, Model Checking) Machine Learning integration for test generation Quantum software validation Energy-efficient computing solutions Recent publications (2020-2025) reveal a strategic evolution from compiler testing foundations to ML-augmented quantum and green computing research. This progression demonstrates consistent innovation in software reliability while addressing industry-critical challenges in emerging technologies. As a Lecturer, she actively supervises student projects and conference submissions. Her involvement in conference organization (SPLASH, ICTSS) and industry collaborations (eBay, IBM legacy) provide students with real-world exposure to software engineering challenges. Her research group develops practical tools like CsmithEdge and SearchGEM5, maintaining strong GitHub engagement for open-source contributions to compiler and simulator testing.

• Compilers
• Software Testing
• Formal Verification
• +۳ مورد دیگر

John Regehr is a Professor in the School of Computing at the University of Utah. His research focuses on compilers, formal verification, software testing, and embedded systems. He has contributed to tools like Alive2, YARPGen, and ARMor, which address compiler correctness, fuzz testing, and secure isolation. His work emphasizes uncovering compiler bugs, optimizing low-level code, and ensuring system reliability. Key collaborations include Eric Eide, Yang Chen, and Nuno P. Lopes. His articles span compiler verification, fuzzing techniques, and embedded system safety, reflecting a strong commitment to both academic rigor and practical impact. Research interests include compiler optimization validation, undefined behavior analysis, and program synthesis. His work on peephole optimizations and formal methods has influenced LLVM and industry practices. He also explores scheduling algorithms for real-time systems and memory safety in constrained environments like TinyOS. Notable contributions include foundational papers on compiler bug detection (e.g., 'Finding and understanding bugs in C compilers') and tools like Minotaur for SIMD optimization. His lab's work often bridges theory and practice, with applications in security, performance, and embedded software reliability.

Christoph Csallnerمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Automated Bug Finding
• +۵ مورد دیگر

Christoph Csallner is a Professor in the Computer Science and Engineering Department at the University of Texas at Arlington (UTA), where he leads research in software engineering, program analysis, and mobile software development. Prior to joining UTA, he worked at Google and Microsoft Research. He directs the SERC lab (Software Engineering Research Center) and has established himself as a leading researcher in automated bug finding, reverse engineering, and Simulink analysis. University of Texas at Arlington, Professor, Computer Science and Engineering Department SERC Lab Director Former researcher at Google and Microsoft Research Dr. Csallner earned his Diplom-Informatiker degree from Universität Stuttgart, Germany, and both his M.S. and Ph.D. in Computer Science from Georgia Tech. His educational background in both European and American institutions has informed his interdisciplinary research approach that bridges theoretical foundations with practical software engineering challenges. Dr. Csallner's research spans multiple areas of software engineering with particular expertise in program analysis, automated bug finding, and mobile software engineering. His work on reverse engineering mobile application user interfaces with REMAUI has been particularly influential in the field. His recent research focuses on Simulink analysis with projects like SLNET, ScoutSL, and EvoSL, which have created foundational datasets and tools for the model-based development community. His work on PSDoodle and D2S2 has advanced mobile app screen search through innovative sketch-based interfaces. His research consistently bridges theoretical program analysis with practical applications for real-world software development challenges. Dr. Csallner's publications demonstrate a consistent pattern of innovation in software testing, analysis, and reverse engineering. His recent work shows a strong focus on mobile application analysis, Simulink model processing, and the application of machine learning techniques to traditional software engineering problems. He has successfully built bridges between formal methods and practical software development tools. Best Paper Award - IEEE ISSRE 2010 ACM SIGSOFT Distinguished Paper Awards - ISSTA 2006, 2012 Best Paper Award - PPREW 2014 Best Paper Award - ASE 2007 ACM SIGSOFT Distinguished Paper Award - ASE 2015 Distinguished Referee Award - ASE 2019 Distinguished Reviewer Award - TOSEM 2011-2012 Dr. Csallner has successfully advised numerous Ph.D. and Master's students who have gone on to prominent positions at companies including Meta, Google DeepMind, BNSF Railway, Bloomberg, and Salesforce. His research has been supported by significant funding from the National Science Foundation, MathWorks, the Alzheimer's Association, and the Texas National Security Network. His work on fake news detection received media coverage from major outlets including the Dallas Morning News, NBC DFW, and WBAP/KLIF. Dr. Csallner directs the Software Engineering Research Center (SERC) lab at UTA, where his team develops innovative tools for software analysis and testing. Notable projects include JCrasher, Check 'n' Crash, DSD-Crasher, Pex/DySy, Dsc, and REMAUI/Pixel to App. His recent work focuses on PSDoodle, TreeVada, ScoutSL, and EvoSL, demonstrating his lab's continued innovation in software engineering research. The SERC lab maintains strong industry connections, particularly with MathWorks, and has produced numerous open-source tools that have influenced both research and practice in software engineering.

Derek Dreyerمشاهده پروفایل

استاد

• Programming Language Theory
• Type Systems
• Formal Verification
• +۵ مورد دیگر

Derek Dreyer serves as Scientific Director at the Max Planck Institute for Software Systems (MPI-SWS) and holds the position of Honorarprofessor (Honorary Professor) of Computer Science at Saarland University's Saarland Informatics Campus. With a PhD from Carnegie Mellon University, he has established himself as a leading researcher at the intersection of programming language theory and practical software verification. Dreyer's research focuses on developing formal methods that bridge theoretical foundations with real-world systems programming challenges. His work has significantly advanced the theoretical understanding of programming languages, particularly in the areas of type systems, separation logic, and concurrency. He is renowned for his contributions to the formal verification of the Rust programming language, including the influential RustBelt project. His recent publications demonstrate a consistent focus on making formal verification practical for industrial-strength codebases. The research trajectory shows increasing sophistication in handling complex systems properties while maintaining theoretical rigor. His work spans from foundational logical frameworks to applied verification techniques for specific language features and system components. As an academic leader, Dreyer has served as Program Chair for major conferences including POPL and ICFP, and has mentored numerous students and postdocs. He is known for his insightful commentary on academic life, including a widely-read blog post addressing impostor syndrome in research careers. Dreyer leads a vibrant research group at MPI-SWS that collaborates extensively with both academic and industrial partners. His team's work has influenced both theoretical developments in programming languages and practical verification tools used in industry.

Christoph Csallnerمشاهده پروفایل

استاد

• Program Analysis
• Automated Bug Finding
• Software Security
• +۴ مورد دیگر

Christoph Csallner is a Professor in the Computer Science and Engineering Department at the University of Texas at Arlington (UTA). He previously worked at Google and Microsoft Research and holds a Diplom-Informatiker degree from Universität Stuttgart, Germany, and M.S. and Ph.D. degrees in Computer Science from Georgia Tech. His research has received numerous best paper awards at top software engineering conferences including ASE, ISSTA, and ISSRE. Dr. Csallner's research interests focus on software engineering, with particular expertise in program analysis, automated bug finding, software security, and mobile software development. His work bridges theoretical foundations with practical applications, developing tools that have real-world impact in improving software quality and security. Recent research has concentrated on analyzing Simulink models for cyber-physical systems, mobile app screen search and generation, and applying deep learning techniques to software testing problems. His publications show a consistent trajectory of innovation in software testing and analysis, with recent work exploring the intersection of machine learning and software engineering. The research spans from foundational program analysis techniques to practical tools addressing challenges in mobile development and cyber-physical systems. His work on Simulink model analysis has created valuable resources for the research community, including large open-source corpora of Simulink models. Scientific awards include: Best Paper Award at IEEE ISSRE 2010 ACM SIGSOFT Distinguished Paper Awards at ISSTA 2006 and 2012 Best Paper Award at PPREW 2014 ACM SIGSOFT Distinguished Paper Awards at ASE 2007 and 2015 Distinguished Referee Award at ASE 2019 Dr. Csallner has successfully advised numerous Ph.D. and Master's students who have gone on to prominent positions at companies like Meta, Google DeepMind, and Bloomberg. His research has been funded by the National Science Foundation, MathWorks, the Alzheimer's Association, and other organizations. He leads the Software Engineering Research Center (SERC) lab at UTA, where his team develops innovative tools for software analysis, testing, and development.

David Lieمشاهده پروفایل

استاد

• Computer Security
• Privacy
• Program Analysis
• +۲ مورد دیگر

David Lie is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto. He holds additional appointments in the Department of Computer Science and the Faculty of Law. He is a Tier 1 Canada Research Chair in Secure and Reliable Systems, a research lead at the Schwartz Reisman Institute for Technology and Society, an Associate Director at the Data Sciences Institute, a Vector Faculty Affiliate, and a Senior Massey College Fellow. His educational background includes: BASc from the University of Toronto (1998) MS from Stanford University (2001) PhD from Stanford University (2004) David Lie's research spans computer security, privacy, and cybersecurity. He is renowned for pioneering the XOM architecture—a foundational model for modern trusted execution environments like ARM TrustZone and Intel SGX—and developing the widely adopted PScout Android permission mapping tool. His current work emphasizes program analysis, fuzzing, and symbolic execution to enhance software security and reliability, addressing critical vulnerabilities in mobile and system software. His recent publications reveal a strong trajectory in integrating machine learning with program analysis techniques. Key themes include optimizing symbolic execution for Android apps, leveraging LLVM IR for bug detection, and using predictive models to guide test generation. These contributions underscore a practical focus on scalable, automated security tools that bridge theoretical advances with real-world software vulnerabilities. His notable awards include: Best Paper Award at SOSP Tier 1 Canada Research Chair in Secure and Reliable Systems Senior Massey College Fellow No specific information on student advising or research grants was provided in the available text, though his extensive program committee service for top security conferences (OSDI, IEEE Security & Privacy, CCS, etc.) indicates significant academic leadership. David Lie actively contributes to interdisciplinary research ecosystems through his roles at the Schwartz Reisman Institute for Technology and Society (as research lead), the Data Sciences Institute (as Associate Director), and the Vector Institute for Artificial Intelligence (as Faculty Affiliate), fostering collaborations between security research, law, and societal impact studies.

Sven Apelمشاهده پروفایل

استاد

• Software product lines and configurable systems
• AI-based program generation and optimization
• Socio-technical software analysis
• +۳ مورد دیگر

Professor Sven Apel holds the Chair of Software Engineering at Saarland University's Saarland Informatics Campus in Germany. He is also the Director of the Saarbrücken Graduate School of Computer Science. His work focuses on software engineering with an emphasis on automation, human factors, and interdisciplinary approaches. Prof. Apel received his Ph.D. in Computer Science in 2007 from the University of Magdeburg. His academic journey includes: Ph.D. in Computer Science, University of Magdeburg (2007) Emmy-Noether Fellowship of the German Research Foundation Heisenberg Professorship of the German Research Foundation Prof. Apel's research centers on empowering software engineering practice to enter an era of intensive automation. His key research areas include software variability and configuration, AI-based program generation and optimization, socio-technical software analysis, and empirical and neurophysiological methods. He pays special attention to the human factor and interdisciplinary research questions, applying his findings to real-world software systems from both open-source projects and industry collaborations with partners like Siemens AG, Bosch Engineering, and Airbus Helicopters. Analysis of Prof. Apel's recent publications reveals a strong focus on configurable software systems, neurophysiological approaches to understanding programming, and the application of AI techniques to software engineering problems. His work often bridges the gap between theoretical foundations and practical applications, with many studies involving industrial collaborations. There's a noticeable trend toward interdisciplinary research combining software engineering with neuroscience, organizational studies, and machine learning. Prof. Apel has received numerous prestigious awards and honors: ERC Advanced Grant "Brains On Code" (2022) ACM Distinguished Member for "Outstanding Scientific Contributions to Computing" (2018) Multiple Most Influential Paper Awards (SPLC'18, ICPC'22, GPCE'23) Multiple Best Paper Awards (SPLC'11, Modularity'15, AOM'18) Heisenberg Professorship and Emmy-Noether Fellowship from the German Research Foundation Prof. Apel has advised numerous Ph.D., Master's, and Bachelor's students throughout his career. His research has been generously funded by multiple grants including an ERC Advanced Grant (2,500,000 Euro, 2022-2027), several DFG projects (CPEC, Congruence, Pervolution), and previous grants like SafeSPL, FeatureFoundation, and Pythia. His work has practical impact through collaborations with industry partners including Siemens AG, Bosch Engineering, and Airbus Helicopters. Prof. Apel leads research in the Chair of Software Engineering at Saarland University, where his team explores the intersection of software engineering, neuroscience, and artificial intelligence. His "Brains On Code" ERC project specifically investigates how programmers' brains process code using neuroimaging techniques. The research group maintains strong connections with both academic and industry partners, facilitating the transfer of research findings into practical applications.

Zhongxin Liuمشاهده پروفایل

استادیار

• Intelligent Software Engineering
• AI for Software Engineering
• Code Intelligence
• +۵ مورد دیگر

Zhongxin Liu is a Distinguished Research Fellow (Assistant Professor) and Doctoral Supervisor at the College of Computer Science and Technology, Zhejiang University. He received his Ph.D. from Zhejiang University under the supervision of Prof. Shanping Li. His research focuses on intelligent software engineering (AI4SE), particularly using AI to help developers understand, write, change, and test code through learning from software "big data". His educational background includes: Ph.D. in Computer Science, Zhejiang University (2016-2021) Dr. Liu's research interests center around intelligent software engineering with emphasis on code intelligence, program analysis, and software testing. His work leverages machine learning, particularly large language models, to solve challenging problems in software development including code generation, bug localization, and test case generation. He has made significant methodological contributions to the field through his innovative approaches to understanding and improving the software development lifecycle. His recent publications (2024-2026) demonstrate a clear trend toward leveraging large language models for software engineering tasks, with particular focus on code generation, fault localization, and program analysis. The research spans multiple domains including vulnerability detection, smart contract development, and cross-domain code search, showing how AI can enhance traditional software engineering practices. Dr. Liu has received numerous prestigious awards for his contributions: ACM SIGSOFT Distinguished Paper Award (ISSTA 2025, ASE 2018-2020) Distinguished Paper Award (APSEC 2023) Zhejiang University Education Foundation Qizhen Scholar (2021) Distinguished Doctoral Thesis of Zhejiang University (2021) As a Doctoral Supervisor at Zhejiang University, Dr. Liu mentors graduate students in intelligent software engineering. He serves on program committees for major conferences including ICSE, ASE, and FSE. His research group actively recruits undergraduate interns, graduate students, and postdocs to work on code intelligence projects. Dr. Liu is also scheduled to be a Visiting Professor at the University of Stuttgart from October 2024 to January 2025. Dr. Liu leads a vibrant research group at Zhejiang University focused on advancing AI-powered tools for software development. His work bridges theoretical advances in machine learning with practical applications in software engineering, creating novel solutions that help developers be more productive and produce higher quality code.

Lu Xiaoمشاهده پروفایل

استادیار

• Software Architecture
• Software Economics
• Cost Estimation
• +۳ مورد دیگر

Lu Xiao is an Assistant Professor in the School of Systems and Enterprises at Stevens Institute of Technology, where she conducts research in software engineering with a focus on software architecture, software economics, cost estimation, and software ecosystems. Dr. Xiao completed her PhD in Computer Science at Drexel University in 2016 under the supervision of Dr. Yuanfang Cai. Her doctoral research focused on the relationship between software architecture and quality attributes. Her research spans several key areas in software engineering with emphasis on empirical methods. She investigates how software architecture influences quality attributes, studies software economics and cost estimation techniques, and examines the dynamics of software ecosystems. Her work frequently analyzes real-world software projects, particularly those in the Apache Software Foundation, providing insights into software maintenance patterns, testing practices, and performance issues. Dr. Xiao has developed practical tools like SAIN for software architecture infrastructure and eFish'nSea for performance education. Analysis of Dr. Xiao's publication record reveals consistent contributions to empirical software engineering, particularly in software architecture analysis, testing methodologies, and performance issues. Her research often centers on Apache projects, demonstrating methodological rigor in studying real-world development practices. She has made significant advances in understanding test refactoring, mocking frameworks, and the identification of performance bottlenecks through linguistic analysis of issue reports. Dr. Xiao has actively contributed to the software engineering research community through service on program committees for major conferences including ASE, ICSE, ESEC/FSE, and ICSA. Her work on program committees spans multiple tracks including Research Papers, Student Research Competition, and specialized workshops. As an academic advisor, Dr. Xiao guides graduate students in research on software architecture analysis, testing practices, and performance optimization. Her research methodology combines empirical analysis of large software repositories with tool development and validation, ensuring practical relevance of her findings. Dr. Xiao leads research efforts focused on understanding the relationship between software architecture and quality attributes. Her current work on bots in pull requests represents cutting-edge research into automation in open source development processes, continuing her tradition of investigating real-world software engineering phenomena.