Find Academic Supervisors

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.

Nelly BencomoView profile

Associate Professor

• Software Engineering
• Requirements Engineering
• Decision-making under Uncertainty
• +7 more

Nelly Bencomo is an Associate Professor in the Department of Computer Science at Durham University in the UK, where she joined in September 2021. Previously, she was a Senior Lecturer at Aston University and has held research positions at Lancaster University and INRIA Paris. She is a leading researcher in software engineering for autonomous and self-adaptive systems, with a focus on uncertainty quantification and decision-making under uncertainty. Her research interests span Decision-making under Uncertainty, Software Engineering for Autonomous and Self-adaptive Systems, Bayesian Inference and Machine Learning, Requirements Engineering, Model-Driven Engineering, and runtime models. She has pioneered concepts like models@run.time and requirements@run.time, exploring how systems can maintain awareness of their requirements and models during operation. Her work increasingly addresses AI ethics, human values in software, and responsible AI. Her recent publications (2021-2025) reveal a strong focus on uncertainty representation and management in adaptive systems, with growing attention to explainability, digital twins, and LLM applications in software engineering. Her work bridges theoretical foundations with practical applications in healthcare, education, and sustainable systems. Best Paper Award at International Conference on Engineering Digital Twins 2024 (EDTconf'24) Best Software Artefact at Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'24) Listed in Women for Innovation in Health Science in the North of England 10-year Most Influential Paper Awards 2019 for seminal work in self-adaptive systems Best Paper Award REFSQ 2013 Nelly has successfully supervised multiple PhD students to completion and currently mentors several postgraduate researchers. She leads the EPSRC-funded Twenty20Insight project and the Leverhulme Fellowship QuantUn, focusing on uncertainty quantification using Bayesian surprises. Her research portfolio includes significant contributions to European projects and UK research councils in the area of self-adaptive and autonomous systems. She is also involved in the Leverhulme Centre for Algorithmic Life as Project Lead in the Department of Computer Science and leads the Durham Strategic Research Fund project JusTN0W.

Lola BurgueñoView profile

Associate Professor

• Software Engineering
• Model-Driven Software Engineering
• Artificial Intelligence in Software Development
• +5 more

Lola Burgueño is an Associate Professor at the University of Malaga (UMA), Spain, where she is a member of the Atenea Research Group and part of the Institute of Technology and Software Engineering (ITIS). She completed her PhD with honors in April 2016 from the University of Málaga, following a master's degree in Software Engineering and Artificial Intelligence (2012) and a Computer Science and Engineering degree (2011). Her academic journey includes postdoctoral positions at the Open University of Catalonia (Barcelona, Spain; 2018-2022) and CEA List (Paris, France; 2018-2020), along with visiting researcher appointments at the University of Alabama (USA; 2014), Vanderbilt University (USA; 2016), and Université de Montréal (Canada; 2022). Dr. Burgueño's research spans several interconnected domains: Software Engineering and Model-Driven Software Engineering Artificial Intelligence integration in software development processes Uncertainty management during software design phases Model-based software testing methodologies Performance optimization of model transformations Digital twin technologies and Socio-Cyber-Physical Systems Her publication record shows an evolving research trajectory from foundational work in model transformations and non-functional requirements toward increasingly sophisticated integration of AI techniques with traditional software engineering approaches, with recent emphasis on ethical considerations in AI-enhanced software modeling and human-centered aspects of digital twin technologies. Professional service and recognition: PC (co-)chair for ECMFA'21, SLE'22 and JISBD'25 Active Program Committee member for ASE, ICSE, MODELS, SLE, and other premier software engineering conferences Editorial Board member of the Journal on Software and Systems Modeling (SoSyM) Steering Committee member of the ACM SIGPLAN International Conference on Software Language Engineering (SLE) Member of the WG on Diversity & Inclusion of Informatics Europe Dr. Burgueño maintains strong international collaborations across Europe and North America, regularly reviewing for top-tier journals including TSE, JSS, EMSE, and SoSyM, and contributing to the advancement of software engineering research and education globally.

Joanna Cecilia da Silva SantosView profile

Assistant Professor

• Software Engineering
• Software Security
• Program Analysis
• +3 more

Joanna Cecilia da Silva Santos is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame. She leads the Security and Software Engineering research lab (S²E) and has established herself as a significant contributor to software security and program analysis research. Her educational background includes: Ph.D. in Computing and Information Sciences from Rochester Institute of Technology (RIT) M.Sc. in Software Engineering from RIT B.Sc. in Computer Engineering from Federal University of Sergipe (UFS) Dr. Santos's research spans multiple domains at the intersection of Software Engineering and Security. Her work encompasses Software Security, Program Analysis, Code Generation, Software Architecture, and emerging work in Quantum Software Engineering. She investigates vulnerabilities in software systems, develops static analysis techniques, and critically examines the security implications of AI-generated code. Her recent publications reveal a strategic shift toward evaluating the security risks of large language models in software development, while maintaining expertise in traditional program analysis. This dual focus positions her work at the cutting edge of both established and emerging software engineering challenges. Dr. Santos has received notable recognition: Best Paper Award at the 2017 IEEE International Conference on Software Architecture (ICSA) Distinguished Reviewer Award at ESEC/FSE 2023 Research Pitch Competition winner at JOBS Workshop @MICRO'20 CAPES Scholarship recipient for masters studies at RIT (2014) As an active member of the research community, she serves on program committees for major conferences including ICSE, FSE, and ASE across multiple years. Her leadership extends to mentoring students through the S²E lab at Notre Dame, where she guides research in security-focused software engineering.

Daniel Alencar Da CostaView profile

Senior Lecturer

• Empirical Software Engineering
• Machine Learning
• Software Engineering
• +5 more

Daniel Alencar Da Costa is a Senior Lecturer in the Information Science Department at the University of Otago's School of Computing, Dunedin, New Zealand. Previously, he worked as a Postdoctoral fellow in the Software Reengineering Research Group at Queen's University, Kingston, Canada, under the supervision of Prof. Ying Zou. His educational background includes a BSc (2007) from University Centre of Pará (Belém, Brazil), MSc (2013) and PhD (2017) from the Federal University of Rio Grande do Norte (UFRN) under Prof. Uirá Kulesza, with additional supervision from Prof. Ahmed E. Hassan during his visit to the Software Analysis and Intelligence Lab (SAIL). Dr. Da Costa's research focuses on Empirical Software Engineering, where he employs machine learning, statistical methods, and qualitative techniques to understand software development phenomena. His work aims to build empirical knowledge that helps practitioners make informed decisions about development practices, such as release cycles and CI/CD implementations. He teaches undergraduate courses including INFO 201 (Developing Information Systems 1), INFO 202 (Developing Information Systems 2), INFO 301 (Applied Project), and INFO 310 (Software Project Management). His recent publications (2023-2025) demonstrate a strong focus on continuous integration practices, defect prediction, and empirical validation of software engineering techniques. The research shows increasing sophistication in analyzing real-world development practices and creating predictive models that can be applied in industry settings. His scientific contributions include multiple publications in top software engineering venues such as Empirical Software Engineering journal and the Mining Software Repositories (MSR) conference. His work has addressed important questions about CI practices in ML projects, JIT defect prediction, and the relationship between team dynamics and software quality. Dr. Da Costa currently co-supervises Chathrie Wimalasooriya and has served on program committees for major conferences including ASE, ESEC/FSE, ICSE, and MSR. He has also contributed to the community through reviewing and committee service across multiple years (2020-2026).

Andrea De LuciaView profile

Professor

• Software Engineering
• Program Comprehension
• Software Security
• +3 more

Andrea De Lucia serves as Full Professor in the Department of Computer Science at the University of Salerno, Italy, maintaining an active research profile with office hours at Fisciano Campus (Building F2, Room 089) and correspondence via adelucia@unisa.it. His scholarly contributions span software engineering with particular emphasis on security, mobile systems, and emerging quantum applications. His research portfolio demonstrates evolving focus through distinct phases: 2018-2020 : Code smell analysis and mobile energy efficiency (e.g., Android energy consumption studies) 2021-2022 : Security vulnerability lifecycle and quantum software engineering foundations 2023-2025 : Ethical AI integration (fairness in ML engineering) and advanced exploit prediction Recent publications reveal strategic expansion into quantum-computing applications and AI ethics, maintaining core software engineering principles while addressing contemporary challenges in secure, reliable systems development. His work consistently bridges theoretical frameworks with empirical validation through large-scale studies. De Lucia actively contributes to the software engineering community as program committee member for premier conferences including ICSE, ASE, and ICSME across multiple years (2018-2026), demonstrating sustained leadership in the field.

Ronnie de Souza SantosView profile

Assistant Professor

• Human Aspects of Software Engineering
• Software Development Process
• Software Project Management
• +7 more

Dr. Ronnie de Souza Santos is a Tenure-Track Assistant Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. With over 50 peer-reviewed publications and more than $600,000 in research funding, his work focuses on human aspects of software engineering, particularly software fairness, equity, diversity and inclusion (EDI) in the software industry, and software development processes. Dr. de Souza Santos received his Ph.D. in Computer Science from the Federal University of Pernambuco (Brazil) in 2019, with additional academic recognition from the University of Toronto in 2022. He also holds a Master's degree in Computer Science and a Bachelor's degree in Information Systems. Before entering academia, he worked for over eight years as a software practitioner specializing in software testing and agile development at leading software companies. His research spans multiple critical areas including software fairness debt, hybrid work environments, and diversity in software engineering. Recent publications analyze algorithmic bias in AI systems, characterize challenges in software testing tasks, and explore the financial dimensions of fairness in software projects. His work consistently integrates human factors with technical aspects of software engineering. Best Paper Award at CHASE 2023 Distinguished Paper Nomination at ICSE 2022 Best Paper Award at EASE 2016 Distinguished Reviewer Awards from ICSE and CHASE As director of the SE-ALL (Software Engineering for All) Lab, Dr. de Souza Santos mentors multiple graduate and undergraduate students working on projects related to fairness testing, algorithmic bias, and inclusive software development. His leadership extends to conference roles including General Co-Chair of Fairness'2025, Program Co-Chair at CHASE 2025, and Diversity, Equity and Inclusion Chair at ICSE 2024, ICSME 2024, and ICST 2025. He has also been invited to give keynotes on EDI in software engineering at organizations including Google.

Esther GuerraView profile

Professor

• Model-Driven Engineering
• Software Engineering
• Domain-Specific Languages
• +7 more

Esther Guerra is a Full Professor in the Department of Computer Science at the School of Engineering, Autonomous University of Madrid. She joined the university in 2010 after previously serving as a professor at the Universidad Carlos III de Madrid from 2004 to 2010. Her academic career spans over two decades with consistent contributions to software engineering research and education. Dr. Guerra's research focuses on Model-Driven Engineering (MDE), Domain-Specific Languages, and software testing. She has made significant contributions to language engineering, particularly in language product lines, metamorphic testing, and the development of tools for model-based engineering. Her work bridges theoretical foundations with practical applications, especially in chatbot development, low-code platforms, and augmented reality systems. She leads the MISo research group which actively develops tools like Merlin, MetaDepth, and Wodel-Test. Her publication record shows a consistent trajectory of high-impact research, with recent work focusing on adaptive modeling languages, chatbot engineering, and test amplification frameworks. She has published in top-tier journals including ACM Transactions on Software Engineering and Methodology, Journal of Systems & Software, and Software and Systems Modeling. As an educator, Dr. Guerra has taught numerous courses including Software Analysis and Design Project, Automated Software Development, and Model-Driven Software Development. She has supervised numerous undergraduate and master's theses focused on practical applications of model-driven techniques. Dr. Guerra is actively involved in the academic community, serving on program committees for major conferences including ASE, MODELS, ICSE, and SPLASH. She has contributed to the organization of several workshops and symposia focused on software engineering topics.

Elisabeth MaywegView profile

Assistant Professor

• Digital Knowledge Management
• Higher Education
• Educational Technology
• +4 more

Elisabeth Mayweg serves as Junior Professor for Digital Knowledge Management in Higher Education at Humboldt University of Berlin's Institute of Educational Sciences, holding a position sponsored by the Commerzbank Foundation and associated with the Einstein Center Digital Future. Previously, she conducted postdoctoral research at the University of Münster's Center for Teaching in Higher Education, worked on the SUPPORT project at Freie University Berlin, substituted for Educational Psychology at Hannover University during Winter 2017/18, and held a DFG research scholarship at Columbia University's Teachers College. Her academic credentials include a 2011 PhD and 2018 habilitation in psychology completed under Prof. Regina Jucks at the University of Münster. PhD: 2011 Habilitation: 2018 (University of Münster) Mayweg's research investigates digitalization's impact on higher education teaching-learning processes, with particular focus on synchronous/asynchronous media communication (chats, forums, blogs) and their effects on student learning outcomes. Her work bridges educational psychology and digital pedagogy, examining collaborative argumentation, critical thinking development, and information evaluation skills in online environments. Current projects address pandemic-era digital learning challenges and postdigital sovereignty concepts. Analysis of her 2021-2023 publications reveals three dominant research trajectories: (1) pandemic-related digital learning adaptations in German higher education, (2) collaborative engagement mechanisms for online information processing, and (3) teacher training interventions for critical evaluation of digital resources. Her interdisciplinary approach integrates educational technology, psychology, and media studies to address contemporary challenges in digital knowledge management. Mayweg's research is supported through the Commerzbank Foundation-sponsored professorship and previously by the German Research Foundation (DFG) during her Columbia University fellowship. She actively contributes to the Einstein Center Digital Future's initiatives and previously participated in HIIG's Knowledge & Society research program. Her collaborative projects include the EU-funded DIALLS initiative examining cultural literacy through digital teacher collaborations. Her laboratory work centers on digital learning environments at the Einstein Center Digital Future, where she develops and evaluates tools for collaborative knowledge construction. Current team projects focus on OER-based cultural literacy resources and pandemic-responsive teaching frameworks, with strong partnerships between Humboldt University, HIIG, and international education researchers.

Yao LiView profile

Assistant Professor

• Program Verification
• Programming Languages
• Formal Methods
• +3 more

Yao Li is a tenure-track Assistant Professor at Portland State University specializing in program verification and programming languages. His research bridges theoretical formal methods with practical software verification tools, focusing on advancing verification for real-world systems through programming language techniques. Dr. Li earned his Ph.D. in Computer and Information Science from the University of Pennsylvania in 2022 under Stephanie Weirich. His academic journey includes undergraduate studies at Shanghai Jiao Tong University where he received multiple national awards including the China National Scholarship (2012, 2014) and the Outstanding Graduate award (2013, 2016). His research centers on two core objectives: (1) advancing verification for real-world software, and (2) making verification tools more accessible from a programming languages perspective. This manifests in work on proof assistants, functional programming semantics, lazy evaluation analysis, and recently the integration of large language models with formal verification. His publications consistently address foundational challenges in mechanized reasoning while targeting practical applications like HTTP server verification. Analysis of his 8 recent publications (2019-2025) reveals a strong trajectory in program verification, particularly for functional languages like Haskell. Key themes include semantics for lazy evaluation (2024), novel embeddings for mechanized reasoning (2022 Distinguished Paper), and verification of networked systems (2019, 2021). His latest work explores LLM-assisted verification (2025), indicating an evolving research direction at the AI-formal methods intersection. Dr. Li's honors include: Google Open Source Peer Bonus (Scala Forklift, 2018) Multiple China National Scholarships (2012, 2014) Intel Cup National Software Innovation Contest 1st Prize (2011) National Olympiad in Informatics Prizes (2007, 2008) He currently advises four PhD students (Ian Kariniemi, Yiming Lin, Grant VanDomelen, Nicholas Coltharp) and previously mentored Laura Israel (now at University of Konstanz). His grant involvement includes the NSF-funded DeepSpec REU program at UPenn (2021), where he served as mentor for the "Science of Deep Specification" Expedition. Conference service includes Program Committees for PLDI'25, POPL'25, and CoqPL'25, plus Publicity Chair roles for PLDI'24/'25. Dr. Li is actively associated with the DeepSpec research community, contributing to projects that develop deep specifications for critical systems. His work frequently involves collaboration with University of Pennsylvania's formal methods group and participation in workshops like WITS and PriSC, reflecting strong community engagement in programming language theory and verification.

Yanlin WangView profile

Assistant Professor

• Software Engineering
• Large Language Models
• Programming Languages
• +4 more

Yanlin Wang is an Assistant Professor at the School of Software Engineering, Sun Yat-sen University, where he joined in July 2022 after working as a senior researcher at Microsoft Research Asia in the Data, Knowledge, and Intelligence (DKI) group. His academic journey includes a B.S. from Zhejiang University and a Ph.D. from the University of Hong Kong under the supervision of Prof. Bruno C. d. S. Oliveira. Dr. Wang's research spans multiple domains within computer science, with particular emphasis on applying large language models to software engineering problems. His work bridges traditional software engineering with cutting-edge AI techniques, focusing on code generation, code search, and intelligent software development tools. He has made significant contributions to understanding how LLMs can be effectively applied to repository-level code completion, security analysis of smart contracts, and improving the efficiency of code generation processes. An analysis of his recent publications reveals a strong trend toward applying AI to practical software engineering challenges, particularly in the areas of code generation quality, security analysis, and efficient model deployment. His work often combines theoretical insights with practical implementations, as evidenced by multiple tools and frameworks developed by his research group, including MemoryBank for enhancing LLMs with long-term memory and AGIEval for evaluating foundation models. ACM SIGSOFT Distinguished Paper Award (ISSTA 2024) - for work on LLM code generation acceleration ACM SIGSOFT Distinguished Paper Award (ISSTA 2024) - for work on identifying code security issues ACM SIGSOFT Distinguished Paper Award (Internetware 2024) Distinguished Reviewer Award (Internetware 2024) Best Paper Award (Modularity 2016) Dr. Wang actively contributes to the academic community through service on program committees for major conferences including ISSTA, FSE, ICSE, and COLING. He serves as a reviewer for prestigious journals and conferences such as TKDE, TNNLS, and AAAI. His teaching portfolio at Sun Yat-sen University includes Principles of Compilers, AI Revolution: Decoding Large Models, and Software Source Codes Analysis, reflecting his expertise across both foundational and cutting-edge topics in computer science.

Dong WangView profile

Associate Professor

• Empirical Software Engineering
• Mining Software Repositories
• AI4SE

Dong Wang is an Associate Professor at Tianjin University, China, specializing in software engineering research with a focus on empirical methods and AI applications in software development. His primary research interests include: Empirical Software Engineering Mining Software Repositories AI for Software Engineering (AI4SE) Professor Wang has established himself as a significant contributor to the software engineering research community through numerous publications at top conferences. His work spans software testing, programming tools, developer productivity, and open source software development, with particular emphasis on applying AI techniques to software engineering problems. His recent publications demonstrate expertise in unit test generation, JVM fuzzing, equivalent mutant detection, and studies of developer tools like GitHub Copilot. His contributions have been recognized through publications at premier software engineering venues including ASE, ICSE, FSE, and ICSME across multiple years from 2023-2025. Professor Wang is actively engaged in the research community, serving on program committees for numerous conferences and participating in New Ideas and Emerging Results tracks, demonstrating his commitment to advancing the field and supporting emerging research directions.

Zeyu SunView profile

Researcher

• Software Engineering
• Program Repair
• Machine Learning for Code
• +4 more

Zeyu Sun is a researcher at the Institute of Software, Chinese Academy of Sciences, with an extensive publication record in premier software engineering conferences including ASE, ICSE, and ESEC/FSE from 2020 through 2025. Their academic service includes program committee membership for multiple conferences, demonstrating recognition as a domain expert in software engineering and programming languages. Dr. Sun's research program bridges traditional software engineering with artificial intelligence, focusing on neural program repair, software testing, and compiler optimization. Their work develops innovative techniques for code understanding and generation, with particular emphasis on type-aware repair systems, syntax-guided editing approaches, and context-enriched representations of code changes. Recent publications show increasing sophistication in integrating machine learning with software engineering tasks, moving from basic program analysis to specialized architectures that maintain structural integrity of code. Analysis of publication trends reveals a consistent focus on improving software quality through AI-enhanced methods, with expanding applications across compiler design, smart contract security, and fairness testing. The research demonstrates both technical depth in core software engineering challenges and breadth across multiple application domains, reflecting an evolving research trajectory that continually identifies new intersections between AI and software development practices. Dr. Sun actively contributes to the academic community through program committee service at major conferences including ASE, ICSE, and ESEC/FSE across multiple years. This sustained service commitment indicates peer recognition as a subject matter expert. While specific grant details aren't provided in available materials, the consistent publication output across multiple funding cycles suggests successful acquisition of research support for their work in software engineering and AI for code.

Chengnian SunView profile

Associate Professor

• Software Engineering
• Programming Languages
• Program Reduction
• +5 more

Chengnian Sun is an Associate Professor in Software Engineering and Programming Languages at the University of Waterloo, Canada. His research spans program reduction, software testing, and compiler-related technologies, with a focus on developing practical tools for debugging and testing. Dr. Sun's research interests center on Software Engineering and Programming Languages , particularly in program reduction techniques, compiler testing, and software security. His work bridges theoretical foundations with practical applications, developing frameworks like Perses and Latra that have become influential in the software engineering community. His research demonstrates a consistent evolution from basic program reduction techniques to incorporating modern approaches like LLMs for compiler testing. His recent publications show a strong trend toward language-agnostic transformation frameworks and practical debugging tools , with increasing emphasis on security applications and integration of AI techniques. The research spans both theoretical foundations and practical implementations, with several tools developed becoming widely used in the software engineering community. Dr. Sun has served on program committees for major software engineering conferences including ASE, ICSE, ESEC/FSE, and ISSTA, demonstrating his standing in the research community. His extensive publication record shows consistent high-impact contributions across multiple venues, with particular emphasis on program reduction and compiler testing techniques. His work has led to the development of several influential tools including Perses (syntax-guided program reduction), Latra (template-based transformation framework), and AddressWatcher (memory leak localization). These tools have been widely adopted in both academic and industrial settings for debugging and testing purposes.

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.