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Pavlos PetoumenosView profile

Assistant Professor

• Compiler Optimization
• Runtime Systems
• Energy Efficient Computing
• +6 more

Pavlos Petoumenos is a Lecturer (Assistant Professor) in the Department of Computer Science at the University of Manchester and a Research Fellow of the Royal Academy of Engineering. He leads research within the Advanced Processor Technology (APT) group, focusing on compiler optimization and energy-efficient computing. Previously, he worked as a Research Associate and Senior Researcher at the University of Edinburgh under Hugh Leather. His research centers on compilers, runtime systems, and development tools designed to help programmers write fast, energy-efficient programs with minimal effort. With electronic systems consuming 10% of global electricity (projected to reach 20% by 2030), his work addresses the critical challenge of bridging the complexity gap between programmers and modern energy-efficient hardware. Key research areas include compiler optimization, machine learning applications in compilation, code size reduction, and quantum-classical programming integration. His recent publications reveal strong trends in applying machine learning to compiler optimization, particularly through active learning techniques and deep learning models. The research spans multiple domains including function merging, branch fusion, loop transformations, and energy profiling, with significant contributions to quantum programming language design. His work consistently targets real-world applications in multicore processors and interactive mobile environments. Award highlights include: Best Paper Award at CGO 2019 Distinguished Paper Award at ISSTA 2018 Best Paper Award at PACT 2017 Best Paper Award at CGO 2017 Best Paper Award at IISWC 2014 Royal Academy of Engineering Research Fellowship Petoumenos actively contributes to the research community through mentoring PhD students, organizing the biennial International Workshop on Code Optimisation for Multi and Many-Cores (COSMIC), and co-hosting the Compucast podcast where he serves as chief editor. His collaborations span multiple institutions including the University of Edinburgh, University of Lancaster, and University of St Andrews. He has secured significant research impact through tools like BenchPress and F3M that address practical challenges in compiler design and optimization. He leads the Advanced Processor Technology group and contributes to the Compucast podcast, which features interviews with computer science researchers, academic and industry news, and technical discussions. The podcast represents a collaboration between multiple UK universities and serves as an important outreach platform for computer science research.

Abhik RoychoudhuryView profile

Professor

• Program Analysis
• Software Testing
• Software Security
• +5 more

Abhik Roychoudhury is a Provost's Chair Professor of Computer Science at the National University of Singapore (NUS), where he has been working since 2001. He serves as the Editor-in-Chief of ACM Transactions on Software Engineering and Methodology (TOSEM) and chairs the FSE Steering Committee. Professor Roychoudhury leads the TSUNAMi center, a five-year research effort funded by the National Research Foundation in trust-worthy software, and is the Lead Principal Investigator of the Singapore Cyber-security Consortium. Professor Roychoudhury received his Ph.D. in Computer Science from the State University of New York at Stony Brook in 2000. His academic journey has been primarily at NUS, where he has built a distinguished career in software engineering research. His research focuses on software testing and analysis, software security, and trust-worthy software construction. Professor Roychoudhury's research group has developed scalable techniques for testing, debugging, and repair of programs using systematic semantic analysis. His work on automatic program repair has been particularly influential, contributing to the vision of self-healing software. Current research directions include integrating large language models with traditional program analysis techniques for autonomous software engineering, as exemplified by the AutoCodeRover project. Professor Roychoudhury's recent publications demonstrate a strong focus on automated program repair, with increasing integration of large language models and AI techniques. His work spans both theoretical foundations and practical applications, with numerous contributions to top software engineering conferences. The research shows a clear trajectory toward more autonomous software engineering systems that can understand code intent, generate repairs, and verify their correctness. IEEE TCSE New Directions Award (2022) ICSE 2023 Most Influential Paper Award ACM Distinguished Speaker (2013-2019) Fellow of the ACM Professor Roychoudhury has successfully mentored numerous Ph.D. students who have gone on to academic positions at prestigious institutions worldwide, including University College London, Max-Planck Institute, and University of Melbourne. His research is supported by significant grants, including a five-year program on Automated Program Repair at NUS funded by the National Research Foundation, and collaborations with industry partners through the Singapore Cyber-security Consortium. The TSUNAMi center represents a major research initiative with substantial funding for trust-worthy software research. Professor Roychoudhury leads an active research group focused on software analysis and repair. The group has developed several notable systems including AutoCodeRover, an autonomous software engineer that can resolve GitHub issues with minimal LLM cost. The group collaborates extensively with industry, as evidenced by the recent acquisition of AutoCodeRover by Sonar. Current research directions include next-generation fuzzing of stateful systems and the integration of AI techniques with formal methods for more reliable software.

Graeme BestView profile

Lecturer

• Robotics
• Field Robotics
• Multi-Robot Systems
• +5 more

Graeme Best is a Lecturer in robotics at the University of Technology Sydney's School of Mechanical and Mechatronic Engineering within the Faculty of Engineering and Information Technology. He joined UTS in April 2022 after working as a Postdoctoral Scholar at Oregon State University from 2018 to 2022, where he collaborated with Carnegie Mellon University and University of Washington on projects funded by DARPA, ONR, NSF, and NAVFAC. Dr. Best completed his PhD titled "Planning Algorithms for Multi-Robot Active Perception" at the University of Sydney's Australian Centre for Field Robotics. His educational background includes a BE (Electrical and Computer Systems) and BSc (Computer Science) from Monash University, both completed in 2014. His research focuses on developing fundamental algorithms for multi-robot systems, with particular emphasis on active perception, where robots plan their motion to obtain high-value observations. His work spans from theoretical algorithm development to full-scale system demonstrations across diverse environments including subterranean spaces, marine settings, precision agriculture, and planetary exploration. He has pioneered approaches such as decentralized Monte Carlo tree search, self-organizing maps for active perception, and spatiotemporal optimal stopping to address challenges like online planning, decentralized coordination, long planning horizons, and unreliable communication. Dr. Best's publication record shows a consistent trajectory of high-impact research in top robotics venues, with recent work focusing on behavior trees for intent communication, multi-room exploration, and resilient multi-sensor systems. His publications demonstrate strong interdisciplinary connections between theoretical computer science, control theory, and practical field robotics applications. Google Research Scholar Program Award (2023) DARPA Subterranean Challenge: Winner for the Tunnel Circuit (2019) DARPA Subterranean Challenge: Second Place for the Urban Circuit (2022) IEEE ICRA Best Paper Award on Multi-Robot Systems (2021) RSS Best Systems Paper Award, Finalist (2022) Dr. Best actively contributes to the robotics community through editorial roles at major conferences including IEEE ICRA, IROS, and MRS. He currently serves as Program Director for UTS's undergraduate Mechatronics major and teaches courses related to robotics software and algorithms. His funded research includes projects like the Multi-Robot Mission Control System for Maritime Autonomous Systems and the Online Behaviour Tree Synthesis for Adaptive Multi-Agent Coordination project funded by Google. He leads the UTS Motorsports Autonomous Vehicle team and supervises capstone projects, demonstrating his commitment to translating research into practical student experiences. His work on the DARPA Subterranean Challenge, where his team won the "Most Sectors Explored" award, exemplifies his ability to bridge theoretical research with real-world field applications.

• Snow Science
• Avalanche Research
• Glaciology
• +6 more

Ingrid Reiweger is an Associate Professor at the University of Natural Resources and Life Sciences, Vienna (BOKU), where she conducts research at the Institute of Mountain Risk Engineering within the Department of Civil Engineering and Natural Hazards. Her work focuses on snow physics, avalanche dynamics, and mountain risk management, bridging fundamental research with practical safety applications. Dr. Reiweger's research spans snow mechanics, glaciology, and cryospheric sciences, emphasizing both theoretical modeling and field applications. Her work combines physics-based approaches with innovative measurement techniques to understand snowpack stability, avalanche formation mechanisms, and risk assessment methodologies. Recent projects highlight her expertise in developing sensor technologies for snow monitoring and analyzing the impacts of changing snow conditions on avalanche hazards. Analysis of her recent publications reveals a strong emphasis on practical applications of snow science, particularly in developing monitoring systems using drones and sensor arrays, optimizing avalanche simulation parameters, and addressing specific challenges like glide-snow avalanches in construction sites. Her research demonstrates a clear trajectory from fundamental snow physics toward increasingly applied studies that directly inform mountain safety practices. International Snow Science Workshop (ISSW) Avalanche Divas Award (2018) Marie Heim-Vögtlin Grant of the Swiss National Science Foundation (2014) Multiple ISSW Avalanche Divas travel grants (2016, 2010) Young Scientists Award at Swiss Geoscience Meeting (2009) Dr. Reiweger actively supervises graduate students and leads multiple research projects funded by national companies and federal ministries. Her work often involves transdisciplinary collaboration with practitioners in avalanche control, mountain rescue services, and winter tourism. She maintains a strong international research network, evidenced by her ongoing summer research visits to the WSL Institute for Snow and Avalanche Research SLF in Davos.

K. PsarakisView profile

Researcher

• Cloud Computing
• Stream Processing
• Distributed Systems
• +8 more

K. Psarakis is a researcher in the field of Data-Intensive Systems, focusing on cloud computing, stream processing, and distributed dataflows. Their work bridges theoretical and practical challenges in cloud-native applications and scalable systems. Institution: Affiliated with Data-Intensive Systems Research Focus: Cloud transaction management, autoscaling, geospatial data platforms, and fault tolerance Research Trends Recent publications like Styx and CheckMate highlight innovations in transactional stateful functions and checkpointing protocols. Psarakis also contributes to geospatial data federation (e.g., Topio ) and schema matching techniques ( Valentine ). Collaborations Collaborates with researchers such as G. C. Christodoulou, M. Fragkoulis, and A. Katsifodimos on projects involving open-source platforms and cloud-native systems.

Vincenzo RiccioView profile

Assistant Professor

• Software Testing
• Machine Learning
• Deep Learning Systems
• +2 more

Vincenzo Riccio serves as an Assistant Professor at the University of Udine, Italy, following a postdoctoral position at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. His academic career centers on bridging software engineering with artificial intelligence through rigorous testing methodologies for machine learning systems. He earned his Ph.D. in Computer Science from Università degli Studi di Napoli “Federico II” in 2019, establishing the foundation for his specialized research in AI validation. His educational trajectory reflects deep technical expertise in both theoretical and applied aspects of software engineering. Riccio's research focuses intensely on test automation for machine learning-based applications, particularly deep learning systems. He investigates critical challenges including test input generation, fault detection in neural networks, and reliability validation for safety-critical domains like autonomous driving. His approach combines search-based techniques, mutation analysis, and empirical studies to develop practical testing frameworks that address the 'oracle problem' and input validity issues inherent in AI systems. Analysis of his 11 publications from 2020-2025 reveals a progressive research trajectory: starting with foundational work on behavioral exploration (2020), advancing to specialized tools like DeepHyperion and DeepMetis (2021), and maturing into domain-specific applications for autonomous vehicles (2022-2024) and generative AI testing (2025). His work consistently targets real-world applicability through empirical validation and tool development. He actively contributes to the academic ecosystem as a member of the EMSE journal's editorial board and as a reviewer for premier venues including ICSE, ASE, and ISSTA. His community leadership extends to organizing workshops such as DeepTest and SBFT, where he chairs sessions and coordinates tool competitions to advance the field of AI testing.

Karine Even-MendozaView profile

Lecturer

• Software Testing
• Software Verification
• Programming Languages
• +7 more

Karine Even-Mendoza is a Lecturer in Systems & Programming Languages at King's College London, working within the Department of Informatics in the Faculty of Natural, Mathematical & Engineering Sciences. Previously, she was a Research Associate at Imperial College London's Department of Computing, where she worked in the Software Reliability Group and Multicore Programming Group. She completed her PhD at King's College London, where she also spent four years working with the Software Systems (SSY) group. Dr. Even-Mendoza's research focuses on the intersection of software testing, verification, and programming languages, with recent work increasingly incorporating machine learning and quantum computing techniques. Her work addresses critical challenges in compiler testing, system simulation validation, and the application of large language models to software engineering problems. She has developed innovative approaches like ReFuzzer for enhancing the validity of LLM-generated test programs and SearchGEM5 for improving the reliability of system simulators through search-based testing. Her publication record demonstrates a strong trajectory in top-tier software engineering venues, with a notable shift toward incorporating large language models and quantum computing in recent years. She has become particularly active in applying AI techniques to traditional software engineering challenges, bridging the gap between classical software verification methods and modern AI approaches. Her work spans both theoretical foundations and practical applications, with implementations like CsmithEdge and GrayC contributing tangible tools to the software testing community. Dr. Even-Mendoza has been actively involved in the software engineering research community, serving on program committees for major conferences including ASE, ISSTA, ECOOP, and SPLASH. She has also contributed to artifact evaluation processes, demonstrating her commitment to research reproducibility and scientific rigor in software engineering.

• Search Based Software Engineering
• Software Testing
• Evolutionary Computation
• +7 more

Giovani Guizzo is a Software Engineer at Kii working as a Blockchain Engineer and Front-end developer with React, while maintaining an active research profile in Search-Based Software Engineering. He earned his PhD in Computer Science from the Department of Informatics at Federal University of Paraná (UFPR) in Brazil under Professor Silvia Regina Vergilio in 2018. His academic collaborations include significant affiliations with University College London where he has published as a researcher. His research program centers on applying evolutionary computation and search-based techniques to software engineering challenges, particularly in software testing and optimization. Guizzo's work bridges theoretical advances in multi-objective optimization with practical software engineering applications, resulting in innovative solutions for test model inference, mutant reduction strategies, and automated program repair. His approach often combines natural language processing with finite state machine generation to transform bug reports into actionable test models. Guizzo's publication record reveals a consistent trajectory of increasing impact in top-tier software engineering venues. His recent work demonstrates sophisticated integration of multi-objective evolutionary algorithms with practical software testing challenges, particularly in the areas of model inference from natural language bug reports and optimization of mutation testing processes. The evolution of his research shows progression from foundational work on design patterns in evolutionary algorithms toward more complex applications in automated software testing and repair. His achievements include: SSBSE 2021 Challenge Winner for innovative work on fitness functions in automated program repair Distinguished Artifact Award at ICSE 2021 recognizing reproducibility in genetic improvement research Microsoft Azure Research Award in 2017 supporting his work in cloud-based software engineering research Guizzo maintains active engagement with the research community through editorial roles for IET Software and Science of Computer Programming, membership on the SSBSE Steering Committee, and service on program committees for major conferences including ESEC/FSE, ASE, GECCO, and ICSE. His academic visits to University College London (3 months in 2017) and Queen Mary University (1 month in 2017) established lasting research collaborations with Dr. Jens Krinke, Dr. Federica Sarro, and Dr. John Drake that continue to produce high-impact publications. Through his interdisciplinary approach combining software engineering, evolutionary computation, and natural language processing, Guizzo has established himself as a significant contributor to the search-based software engineering community, with research that consistently addresses practical challenges while advancing theoretical foundations.

Vahid AlizadehView profile

Assistant Professor

• Empirical Software Engineering
• Software Quality
• Software Refactoring & Maintenance
• +7 more

Vahid Alizadeh is an Assistant Professor in the College of Computing and Digital Media (CDM) at DePaul University in Chicago, USA, where he has been serving since 2020. His academic career focuses on bridging the gap between software quality research and industrial practice through empirical studies and intelligent tool development. Education: Ph.D. in Computer Science, University of Michigan (2015-2020) M.Sc. in Electrical Engineering, University of Tehran (2011-2013) B.Sc. in Electrical Engineering, Iran University of Science and Technology (2006-2011) Research Interests: Dr. Alizadeh's research spans empirical software engineering, software quality, refactoring & maintenance, intelligent software engineering, and AI-enabled systems. His work combines rigorous empirical methods with practical tool development to address real-world software engineering challenges. He has particular expertise in developing intelligent refactoring technologies that are currently deployed by organizations impacting thousands of programmers worldwide. Research Trends: His publication record shows a consistent focus on applying AI and machine learning techniques to software engineering problems, particularly in the areas of refactoring and quality assurance. Recent work demonstrates increasing integration of AI/ML approaches with traditional software engineering practices, reflecting the broader trend toward AI4SE (AI for Software Engineering). Awards & Recognition: Excellence in Teaching Award from DePaul University (2025) $67,000 DePaul-RFUMS Grant for AI-Powered Community Pharmacy Research (2024) ACM SIGSOFT Distinguished Paper Award at MODELS Conference (2020) Distinguished University of Michigan-Dearborn Honors Scholar (2020) Outstanding Graduate Research Assistant Award (2018/2019) Professional Activities: Dr. Alizadeh has served in various capacities at major software engineering conferences including ASE 2022 as Session Chair and Virtualization Co-Chair, and MODELS 2025 as Web Co-Chair. His work has resulted in one patent and multiple invention licenses through the University of Michigan Tech Transfer Office. He actively mentors graduate students interested in empirical software engineering, software quality, and intelligent refactoring technologies.

Earl T. BarrView profile

Professor

• Software Engineering
• Program Analysis
• Automated Program Repair
• +5 more

Earl T. Barr is a Professor of Software Engineering at University College London (UCL), where he heads the System Software Engineering Group and is a member of the Centre for Research on Evolution, Search and Testing (CREST). He received his Ph.D. in Computer Science from the University of California, Davis in 2009. His educational background includes: Ph.D. in Computer Science, 2009, University of California, Davis Barr's research focuses on software engineering, particularly program analysis, automated program repair, and the application of machine learning to code (AI4Code). He is renowned for his work on dual channel analysis, which examines how code combines natural language elements (in identifiers, comments, and stylistic choices) with formal programming language. His research also explores game theory applications to software development processes and how dual channel constraints can improve type inference and code understanding. His recent publications demonstrate a strong trend toward leveraging large language models for code understanding and modification, with particular emphasis on dual channel constraints and natural type inference. His work bridges traditional software engineering techniques with modern AI approaches, showing how machine learning can enhance program analysis and repair while maintaining rigorous theoretical foundations. Notable awards include: ACM SIGSOFT Distinguished Paper Award for Automated Software Transplantation (2015) ACM SIGSOFT Distinguished Paper Award for Learning Natural Coding Conventions (2014) ACM SIGSOFT Distinguished Paper Award for Collecting a Heap of Shapes (2013) Best Paper Award for TrustDavis: A Non-Exploitable Online Reputation System (2005) Barr actively supervises numerous PhD students and postdocs, with current research focusing on AI for code, software security, and program analysis. He collaborates extensively with Santanu Dash of Royal Holloway on dual channel program analysis, and they jointly supervise PhD students through the EPSRC Centre for Doctoral Training in Cyber Security for the Everyday. His work often combines theoretical rigor with practical tool development, resulting in several publicly available research tools that influence both academic research and industry practice. He leads the System Software Engineering Group at UCL, which focuses on developing novel approaches to software engineering challenges at the intersection of human and machine aspects of programming. Current research directions include dual channel analysis for vulnerability detection, game-theoretic approaches to development processes, and AI-assisted software development.

Xiaolin XuView profile

Associate Professor

• Security
• Machine Learning
• AI Security/Privacy
• +5 more

Xiaolin Xu is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University. His research focuses on hardware and AI security, machine learning privacy, and energy-efficient deep learning. He earned a PhD from the University of Massachusetts Amherst and completed postdoctoral work at the Florida Institute for Cybersecurity Research. Education PhD, Electrical and Computer Engineering, University of Massachusetts Amherst, 2016 M.S., B.E., University of Electronic Science and Technology of China Research Interests : His work spans secure hardware design, AI model protection, and embedded systems security. Notable projects include securing FPGA-based ML accelerators and developing defenses against adversarial attacks. Recent Article Trends : Recent publications emphasize robustness in neural architectures, secure multi-party computation, and lightweight graph-based models for edge devices. Grants & Awards 2025 DAC Under-40 Innovators Award NSF CAREER Award (2023) Multiple NSF grants for hardware security research Advising : Supervises PhD students including Shijin Duan and Jiaxing, focusing on ML security and hardware-software co-design.

Adam ChlipalaView profile

Professor

• Programming Languages
• Formal Methods
• Computer Systems
• +6 more

Adam Chlipala is the Arthur J. Conner Professor of Computer Science at the Massachusetts Institute of Technology (MIT). His work bridges programming languages, formal methods, computer systems, and security, with a focus on building practical systems verified end-to-end using the Coq proof assistant. He has advised numerous PhD and Master’s students and leads research into verified compilers, hardware-software stacks, and dependent types for real-world applications through his startup Nectry. Undergraduate: Carnegie Mellon University, 2003 PhD: University of California, Berkeley, 2007 Postdoc: Harvard University, through 2011 His research revolves around verified compilers , hardware-software co-verification , and dependent types for enterprise software . Recent work explores high-performance parallel computing stacks and end-to-end machine-checked proofs for cryptographic systems. His publications span conferences like PLDI, POPL, and ICFP, emphasizing formal verification, optimization, and security. Current research trends highlight verified cryptographic code , concurrent hardware verification , and DSLs with formal guarantees . His students work on topics from state machines to network switches and parallel computing frameworks. Coq Proof Assistant Verified Compilation Cryptographic Security Dependent Types Hardware Verification High-Performance Computing Chlipala has served on program committees for CoqPL, Dafny, CPP, and PLDI. He co-created the MIT course Formal Reasoning About Programs and authored two books: Certified Programming with Dependent Types and Formal Reasoning About Programs .

Liqian ChenView profile

Professor

• Program analysis
• Program verification
• Abstract interpretation
• +3 more

Liqian Chen is a Full Professor and PhD Supervisor in the College of Computer Science and Technology at the National University of Defense Technology (NUDT) in Changsha, China. With a prolific research career spanning over a decade, Professor Chen has established himself as a leading expert in program analysis, verification, and automated program repair. His work bridges theoretical foundations with practical applications, particularly in the areas of abstract interpretation and numerical program analysis. Professor Chen's primary research interests include: Program analysis and verification Abstract interpretation Automated program repair Floating-point analysis Neural network verification His research focuses on developing rigorous theoretical frameworks for program analysis while ensuring practical applicability to real-world software systems. A significant portion of his work addresses the challenges of numerical accuracy in software, particularly in floating-point computations, and has extended these techniques to the emerging domain of neural network verification. Analysis of Professor Chen's recent publications reveals several key trends in his research trajectory. His work has evolved from foundational abstract interpretation techniques to address increasingly complex software systems, including neural networks. There's a clear progression from theoretical developments in abstract domains to practical applications in software verification and repair. His recent work demonstrates a growing emphasis on the intersection of traditional program analysis with machine learning systems, particularly in verifying neural network behavior and addressing numerical instability in AI systems. Professor Chen has received notable recognition for his contributions to the field: ACM SIGSOFT Distinguished Paper Award (ISSTA 2024) Best Paper Award (APSEC 2017) Best Paper Award Nomination (EMSOFT 2015) As an academic advisor, Professor Chen supervises numerous PhD and Master's students working on cutting-edge research in program analysis and verification. His team has developed several influential tools including AutoRNP (for automatically detecting and repairing floating-point errors), Software Apron (a library for numerical abstract domains), and F-IKOS (a static analyzer for Fortran programs). Professor Chen has secured substantial research funding to support his work, though specific grant details are not provided in the available information. He actively serves the research community through program committee roles for major conferences including ASE, SAS, and VMCAI, and as a guest editor for journals such as Automated Software Engineering Journal and Journal of Systems Architecture. Professor Chen leads a vibrant research group focused on high-confidence software technologies. His team, comprising PhD students, Master's students, and research engineers, collaborates closely on projects spanning from theoretical foundations of program analysis to practical tool development. The group maintains strong connections with both academic institutions and industry partners, facilitating the transfer of research innovations to real-world applications. Their work is characterized by rigorous theoretical underpinnings combined with practical validation on real software systems.

Marsha ChechikView profile

Professor

• Software Engineering
• Formal Methods
• Software Specification and Verification
• +7 more

Marsha Chechik is a Professor in the Department of Computer Science at the University of Toronto within the Faculty of Arts and Science. She previously served as Chair of the Department of Computer Science (2019-2022) and as Acting Dean in the Faculty of Information (July-December 2022). Professor Chechik received her Ph.D. from the University of Maryland in 1996 and has established herself as a leading researcher in software engineering with a focus on formal methods. Her research interests include software safety and security, automated verification, software product lines, and model management. Professor Chechik has authored numerous papers in formal methods, software specification and verification, computer safety and security, and requirements engineering. Her work has evolved to incorporate newer areas such as machine vision reliability analysis, normative requirements with large language models, and machine learning development lifecycle analysis. Her research consistently bridges theoretical foundations with practical applications in software engineering. Professor Chechik has served in numerous leadership roles including Program Committee Co-Chair for major conferences such as ICSE 2018, TACAS 2016, VSTTE 2016, ASE 2014, CONCUR 2008, CASCON 2008, and FASE 2009. She is currently the Area Chair for ASE 2025 in Security and Other Non-Functional Properties. She has also served as Associate Editor in Chief of Journal on Software and Systems Modeling and as associate editor of IEEE Transactions on Software Engineering (2003-2007, 2010-2013). Best paper award at RE'12 (2012) SIGSOFT Distinguished Paper at ICSE'12 (2012) Winner of Best Student Paper Award at CASCON'07 (2007) Winner of Distinguished Paper Award at ICSE'07 (2007) Professor Chechik regularly mentors PhD students, with notable advisees including Michalis Famelis, Shiva Nejati, Jocelyn Simmonds, Ou Wei, and Aws Albarghouthi. Her advising philosophy emphasizes rigorous formal methods while addressing practical software engineering challenges. She has been involved in numerous research grants focusing on software verification, safety-critical systems, and requirements engineering. Currently, her work explores the intersection of formal methods with emerging technologies like large language models and machine vision systems. Professor Chechik maintains an active research lab focused on model-based software engineering, with projects spanning software product lines, model management, and verification techniques. Her team collaborates with industry partners to address real-world software engineering challenges while advancing theoretical foundations. Current work includes developing techniques for normative requirements operationalization, machine vision reliability analysis, and automated code reconciliation using AI techniques.

Mitchell OlsthoornView profile

Assistant Professor

• Network Security
• Search-based Software Engineering
• Pen-testing
• +5 more

Mitchell Olsthoorn is an Assistant Professor in the Software Engineering Research Group (SERG) at Delft University of Technology. He is also a member of the Computational Intelligence for Software Engineering lab (CISELab) and the Delft Blockchain Lab (DBL). His academic career is built on a strong foundation from Delft University of Technology, where he earned all his degrees. Mitchell's research interests span a diverse range of topics including Network Security, Search-based Software Engineering, Pen-testing, Computational Intelligence, Software Testing, Security Testing, Fuzzing, and Blockchain. His work demonstrates a strong focus on applying computational intelligence techniques to solve complex software engineering problems, particularly in the areas of test case generation and security testing. His publication record shows a consistent research trajectory with a focus on developing innovative testing frameworks for various programming languages and blockchain technologies. His work bridges the gap between theoretical research and practical applications, with tools developed for JavaScript, Kotlin, and blockchain platforms. Cum laude distinction (top 5% in Netherlands) Mitchell actively contributes to the academic community through conference organization and program committee roles. His current position as Hot-off-the-Press Track Co-Chair for SSBSE 2025 demonstrates his growing recognition in the software engineering research community. He also serves on program committees for major conferences including ISSTA, ICST, and ASE. Mitchell maintains active research laboratories including the Computational Intelligence for Software Engineering lab (CISELab) and the Delft Blockchain Lab, where he continues to advance research in software testing and security.