Nobuko Yoshida is a Professor in the Department of Computer Science at the University of Oxford. Her research centers on theoretical foundations of concurrent and distributed systems, with a focus on type theory and programming languages. Affiliation: University of Oxford Research Interests: Theory of Computing, Concurrency, Type Theory, Programming Languages, Distributed Systems Conference Involvement: Active in POPL, ECOOP, ICFP, OOPSLA, PPDP, and ST30 tracks Her recent work analyzes multiparty session types, asynchronous/synchronous communication models, timed protocols, and type refinements for distributed systems. Key contributions include advancements in protocol specification, endpoint projection, and formal verification of concurrent systems. Yoshida has authored or co-authored publications at leading venues including ECOOP, ST30, and OOPSLA. Her articles emphasize compositional reasoning for multiparty sessions, hybrid protocol designs, and robustness in distributed environments with crash-stop failures. These works intersect with subfields such as session type theory, formal verification, and specification-agnostic implementation techniques.
Luca Olivieri is a Researcher at the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. His research focuses on blockchain technology, software verification, and cybersecurity, with particular emphasis on static analysis of smart contracts and distributed ledger systems. He is affiliated with the Research Institute for Complexity and teaches courses on computer programming and software verification at both undergraduate and doctoral levels. His research interests include blockchain interoperability, smart contract security, privacy-preserving technologies, and compliance with regulations such as the EU Data Act and GDPR. He has contributed to the development of static analysis tools like GoLiSA and MichelsonLiSA, which address challenges in ensuring determinism, data integrity, and vulnerability detection in blockchain ecosystems. Recent work explores cross-chain verification, phantom reads in Hyperledger Fabric, and the integration of general-purpose programming languages (e.g., Go, Java) into blockchain development. His publications appear in venues such as ACM SAC, IEEE Access, and the International Journal on Software Tools for Technology Transfer, reflecting a strong focus on both theoretical advancements and industrial applications. Collaborations with peers like Agostino Cortesi, Fausto Spoto, and Pietro Ferrara highlight his engagement in interdisciplinary research addressing cutting-edge topics in blockchain software engineering and formal verification.
Chao Peng is a Principal Research Scientist at ByteDance where he leads the Trae Research team (ByteDance Software Engineering Lab), conducting cutting-edge research on AI agents for software engineering. He also serves as a Part-time Postgraduate Student Mentor at Fudan University's School of Computer Science, bridging industry research with academic mentorship. PhD in Informatics (2021), University of Edinburgh, UK MSc in High Performance Computing and Data Science (2017), University of Edinburgh, UK BEng in Computer Science and Technology (2016), Xuzhou University of Technology, China Dr. Peng's research focuses on the intersection of software testing, program analysis, and large language models. His work explores how AI agents can revolutionize software engineering practices, with particular emphasis on automated bug detection, code generation, and testing frameworks. He has pioneered approaches for evaluating LLM performance in software engineering contexts and developing agent-based systems that enhance developer productivity while maintaining code quality and security. His recent publications demonstrate a clear trend toward integrating large language models with traditional software engineering practices. The research spans code generation evaluation, security vulnerability detection, automated bug reproduction, and issue localization. These works collectively advance the field of AI-assisted software development by addressing practical challenges in reliability, security, and efficiency of AI-generated code. Distinguished Reviewer for FSE'25 School of Informatics Scholarship (fully-funded PhD scholarship) Outstanding Graduate Scholarship at Xuzhou University of Technology Multiple China National Scholarships Honours Spot Bonus at ByteDance Certificate of Achievement for HPCAC Student Cluster Competition Dr. Peng actively mentors students through his role at Fudan University and previously at the University of Edinburgh, where he served as sub-supervisor for MSc projects and teaching assistant for software testing courses. His research has attracted significant industry attention, leading to multiple collaborations between ByteDance and academic institutions. He frequently serves on program committees for major software engineering conferences including ASE, FSE, and ICSE, demonstrating his leadership in the field. As leader of the Trae Research team at ByteDance Software Engineering Lab, Dr. Peng oversees research on AI agents for software engineering, including the application and evaluation of AI agents and training LLMs for agent-based systems. The lab's work focuses on practical systems that predict, detect, diagnose, and fix bugs across various software systems, with particular emphasis on real-world applications and measurable impact on developer productivity.
Raffi Khatchadourian is an Associate Professor in the Department of Computer Science at Hunter College and a member of the Doctoral Faculty of The Graduate School and University Center's Ph.D. Program in Computer Science at the City University of New York (CUNY). He leads the PONDER Lab @ CUNY and is a member of the CUNY Institute of Computer Simulation, Stochastic Modeling, and Optimization (CoSSMO). His research lies at the intersection of software engineering, programming languages, and reliable machine learning systems. He investigates how program analysis, automated refactoring, and type theory can ease the burden of correctly, efficiently, and securely evolving large and complex software. His work spans several key areas including automated refactoring of Java programs, empirical studies of software development practices, migration of imperative Deep Learning programs to graph execution, and the application of software engineering methodology to improve statistical programs and artificial intelligence. His research has been externally supported by the National Science Foundation (NSF), the Japan Society for the Promotion of Science (JSPS), Amazon Web Services (AWS), and the Verizon Foundation. Dr. Khatchadourian's recent publications demonstrate a strong focus on improving Deep Learning systems through automated refactoring techniques, analyzing technical debt in machine learning systems, and addressing concurrency challenges in modern programming languages. His work frequently appears in top-tier conferences including ICSE, ASE, ESEC/FSE, and FASE. His scientific achievements have been recognized with numerous awards including the EAPLS Distinguished Paper Award at FASE '25, EAPLS Best Paper Award at FASE '20, and a Distinguished Paper Award at IEEE SCAM '18. He has also received fellowships from JSPS and NSF EAPSI, along with the Eleanor Quinlan Memorial Award for Excellence in Teaching. As an advisor, Dr. Khatchadourian has mentored numerous PhD, Master's, and undergraduate students, including Tatiana Castro Vélez who recently accepted a tenure-track Assistant Professor position at University of Puerto Rico. He has served on multiple program committees for major conferences including ICSE, ASE, ECOOP, and GPCE, and has organized events such as the New York Seminar on Programming Languages and Software Engineering (NYPLSE). He also mentors students through NYU GSTEM and ACM SIGPLAN-M programs. Dr. Khatchadourian maintains active research collaborations across institutions and leads the PONDER Lab @ CUNY, which focuses on programming, optimization, and novel development environments for reliable software. The lab provides opportunities for undergraduate, master's, and doctoral students interested in programming languages and software engineering research.
Ajitha Rajan is a Professor (Personal Chair of Software Testing & Verification) at the School of Informatics, University of Edinburgh. She joined the university in December 2012 as a Reader (equivalent to Associate Professor in American terms) and was promoted to Professor in 2024. Prior to her position at Edinburgh, she held postdoctoral positions at Oxford University and Laboratoire d'Informatique de Grenoble in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under Professor Mats Heimdahl. Her research focuses on two primary directions: Automated Software Testing (including test input generation, test oracles, and coverage measurement) and Biomedical AI (particularly cancer survival models and interpretability for biological sequences and medical images). Her work has applications in safety-critical systems, blockchains, embedded systems, and medical diagnostics. She has made significant contributions to explainable AI for healthcare applications, especially in lung cancer detection and cancer survival analysis. Her recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of software engineering and biomedical applications. She has numerous publications in top venues including ICSE, ASE, and healthcare-focused conferences. Her work increasingly focuses on making AI systems more interpretable and trustworthy, particularly in medical contexts where model decisions can have life-or-death consequences. ACM SIGSOFT Distinguished Reviewer Award, ISSTA 2025 Best Paper Award at ICHI 2025 Promoted to Professor (Chair in Software Testing & Verification) 2024 SICSA Best Supervisor Award 2024 ACM Distinguished Paper Award 2008 Professor Rajan actively supervises PhD students in both software testing and biomedical AI domains. She leads several funded projects including MANIFEST (a cancer immunotherapy response research platform), a Huawei Joint Lab project on RobustCheck, a Royal Society Industry Fellowship on AutoTest, and the KATY project on clinical knowledge for personalized medicine. Her research group includes current PhD students working on explainable AI for medical image analysis, scenario-based testing for autonomous driving, and protein design applications.
