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Chang-ai SunView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Chang-ai Sun is a full professor at the School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB). He currently serves as Vice Dean of the Institute for Multidisciplinary Innovation (IMI), Head of the Department of Computer Science, and Director of the Software Engineering Institute at USTB. He received his bachelor's degree in computer science from the University of Science and Technology Beijing and his PhD in computer science from Beihang University, China. Prof. Sun's primary research focuses on software engineering with particular emphasis on software testing, program analysis, and service-oriented computing. Recently, he has been devoted to developing novel theories, techniques, and supporting tools for intelligent software development and maintenance. His work spans theoretical foundations to practical applications in software quality assurance, with increasing focus on AI-driven approaches to traditional software engineering challenges. His publication record shows consistent contributions to metamorphic testing, program repair, and the application of AI techniques to software engineering problems. Recent work explores the intersection of large language models and software testing methodologies, demonstrating both theoretical innovation and practical implementation. Prof. Sun has received significant recognition for his contributions: 11 national Software Prototype/Service Innovation Awards Senior Member of IEEE Distinguished Member of China Computer Federation (CCF) As principal investigator and major investigator, he has completed over 40 research projects funded by the European Union, Australia, China, and Hong Kong. His extensive grant portfolio demonstrates strong international collaboration and significant impact in the software engineering community. He actively mentors students and junior researchers through his leadership roles. Prof. Sun leads the Software Engineering Institute at USTB, where his team focuses on advancing research in software testing, program analysis, and intelligent software development tools. His leadership extends to departmental administration as Head of the Department of Computer Science, shaping curriculum and research directions for one of China's leading technical universities.

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Doehyun BaekView profile

Researcher

Max Planck Institute for Security and PrivacyGermany

Doehyun Baek is a researcher at the University of Stuttgart, Germany, specializing in software engineering and programming languages with a focus on WebAssembly technologies. His research mission centers on developing practical techniques to enhance public software quality, particularly through record/replay systems and program reduction for WebAssembly. Key interests include creating realistic benchmarks, improving debugging workflows, and ensuring execution fidelity in reduced programs. Recent work demonstrates significant contributions to WebAssembly tooling, with publications at premier conferences like ASE and OOPSLA. His research bridges theoretical program analysis with real-world software engineering challenges, emphasizing standalone benchmark generation and execution-aware reduction techniques.

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Shangwen WangView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Shangwen Wang is an Assistant Professor in the School of Computer Science at National University of Defense Technology (NUDT) in Changsha, China. He earned his Bachelor's degree in June 2017, Master's degree in December 2019, and Ph.D. in December 2023, all from NUDT. During his graduate studies, he was supervised by Professor Xiaoguang Mao. From May 2022 to July 2023, he was a visiting student at Southern University of Science and Technology under Professor Yepang Liu. His educational background includes: Ph.D. in Software Engineering, NUDT (2020.3-2023.12), supervised by Prof. Xiaoguang Mao Visiting Scholar, SUSTech (2022.5-2023.7), supervised by Prof. Yepang Liu M.A. in Software Engineering, NUDT (2017.9-2019.12), supervised by Prof. Xiaoguang Mao B.A. in Software Engineering, NUDT (2013.9-2017.6) Wang's research focuses on program repair, program comprehension, mining software repositories, software maintenance and evolution, software testing, and AI for Software Engineering. His work bridges traditional software engineering techniques with modern AI approaches, particularly leveraging large language models for various software engineering tasks. He has made significant contributions to automated program repair, fault localization, vulnerability detection, and code generation. His research demonstrates a strong emphasis on empirical validation and practical applicability to real-world software development challenges. His recent publications show a clear trend toward integrating large language models with traditional software engineering tasks. The 15 most recent articles reveal a focus on applying LLMs to program repair, fault localization, vulnerability detection, and code generation, while maintaining strong empirical foundations. His work spans both theoretical advancements and practical tool development, with applications in software security, testing, and maintenance. His notable achievements include: CCF Outstanding Doctoral Dissertation (CCF优博) 2024 Outstanding Doctoral Graduates, NUDT, 2023 Multiple distinguished paper awards including ACM SIGSOFT Distinguished Paper Award (ISSTA'24) and IEEE TCSE Distinguished Paper Awards (ICSME'22, SANER'22) Prestigious scholarships from NUDT throughout his academic career As an active member of the software engineering community, Wang serves on numerous program committees for top conferences including ICSE, ASE, ESEC/FSE, and ISSTA. He has also contributed to teaching as a teaching assistant for courses such as Compiler, Python Programming, Discrete Mathematics, and C++ Programming. His research group appears to be actively mentoring students, as evidenced by his role as corresponding author on multiple student-led publications. Wang maintains an active research presence with collaborations across multiple institutions in China. His work demonstrates a clear trajectory from traditional program analysis techniques toward integrating cutting-edge AI approaches, particularly large language models, into software engineering practices.

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Jie LiuView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Jie Liu is a Researcher at the Institute of Software, Chinese Academy of Sciences and a Professor and Doctoral Supervisor at University of Chinese Academy of Sciences. He is also a Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences and an Executive Committee Member of the System Software Committee of the CCF Computer Society. His research is conducted within the Software Engineering Technology R&D Center. Dr. Liu received his Ph.D. from the University of Science and Technology of China in 2011 and his B.A. from the same institution in 2004. He has progressed through the ranks at the Institute of Software, CAS, starting as an Assistant Research Fellow (2011-2014), then Associate Research Fellow (2014-2024), and currently as a Researcher (since 2024). His research spans Big Data Intelligent Analysis Models and Systems at the intersection of AI, Software Engineering, and System Software. Specifically, his work covers three main areas: Big Data and Machine Learning Systems (statistics and AI algorithm model libraries, data quantitative analysis tools, LLM reasoning optimization, Earth Big Data); Intelligent Software Engineering (code model constraint decoding, data science agents, system log analysis agents); and Knowledge-Enhanced Intelligent Model Construction (knowledge extraction, knowledge graphs, domain AI model design). His research has resulted in innovative approaches to handling complex data analysis challenges across multiple domains. Dr. Liu's research has produced significant outcomes including EarthDataMiner, which supports SDG indicator calculations and won the 2024 Beijing Municipal Science and Technology Progress First Prize. His work on RISC-V software migration technology has been integrated into the Ruiqian tool (https://rvpt.top/), demonstrating practical applications of his research in emerging computing architectures. Beijing Science and Technology Progress Award, First Prize, 2024 2023 Surveying and Mapping Science and Technology Award, Special Prize, 2023 DASFAA Best Paper Runner-up, Second Prize, 2013 Dr. Liu has successfully guided numerous graduate students who have secured positions at major technology companies including Alibaba, ByteDance, Southern Power Grid, and Agricultural Bank of China. He has secured funding through multiple National Natural Science Foundation projects, National Key R&D Program projects, and over ten other research initiatives. His research collaborations span industry leaders like Huawei, JD.com, and TravelSky, as well as academic institutions within the Chinese Academy of Sciences. He teaches graduate courses such as 'Machine Learning Systems' and 'Cloud Computing and Big Data Technology' at University of Chinese Academy of Sciences, and has established a research group focused on developing innovative solutions at the intersection of AI and software engineering with real-world applications in earth sciences, healthcare, and intelligent systems.

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Pinjia HeView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Pinjia He is an Assistant Professor and Presidential Young Fellow at The Chinese University of Hong Kong, Shenzhen's School of Data Science. He is also recognized as a national-level young talent in China. His academic journey includes a postdoctoral position at ETH Zurich's Department of Computer Science under Prof. Zhendong Su, a Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong supervised by Prof. Michael R. Lyu, and a B.E. in Computer Science and Technology from South China University of Technology. Ph.D. in Computer Science and Engineering, The Chinese University of Hong Kong Postdoctoral Scholar, ETH Zurich B.E. in Computer Science and Technology, South China University of Technology Dr. He's research spans software engineering, natural language processing, and systems, with particular focus on (1) AI for SE (e.g., LLM for code, AIOps), (2) SE for AI (e.g., LLM safety), and (3) software testing. He is renowned for his work on robust NLP systems and software log analysis. His research has been published at top venues including ICSE, FSE, ASE, ISSTA, ICLR, and OSDI. His publication trends show a consistent focus on log analysis systems, with recent work shifting toward LLM applications in software engineering and safety evaluation of conversational AI systems. His research demonstrates strong industry impact with tools downloaded over 60,000 times by more than 450 organizations. Most Influential Paper Award (ISSRE) IEEE Open Software Services Award Dr. He actively contributes to the academic community as Social Media Co-Chair for FSE 2025, Associate Editor of TOSEM, and serves on program committees for major conferences including FSE 2025, ICSE 2025, ISSTA 2025, and ASE 2024. His GitHub repositories (logparser, loglizer, loghub) have garnered over 5,000 stars and significant industry recognition including from IBM. While specific grant information isn't detailed in the provided text, his extensive publication record and tool development suggest substantial research funding. His work has been cited over 5,000 times according to Google Scholar, and his open-source tools have been widely adopted in both academia and industry. His current research focuses on advancing the intersection of software engineering and artificial intelligence, particularly in leveraging LLMs for software development tasks while ensuring their safety and reliability.

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Gary TanView profile

Professor

Max Planck Institute for Security and PrivacyGermany

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

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Sven ApelView profile

Professor

Max Planck Institute for Security and PrivacyGermany

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.

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Lars GrunskeView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Lars Grunske is a Professor in the Department of Computer Science at Humboldt University of Berlin, Germany. His academic career spans multiple institutions across Germany, Australia, and internationally, with a strong focus on research and conference participation in software engineering. His educational background includes a PhD in computer science from the University of Potsdam (Hasso-Plattner-Institute for Software Systems Engineering) in 2004. Professor Grunske's research interests center on modeling and verification of systems and software, with particular emphasis on automated analysis techniques. His work primarily focuses on probabilistic and timed model checking and model-based dependability evaluation of complex software intensive systems. He has made significant contributions to software testing, program repair, formal methods, and the application of machine learning techniques to software engineering problems. His publication record shows consistent contributions to top software engineering conferences over the past decade, with recent work exploring the intersection of AI/ML with traditional software engineering challenges. His research demonstrates an evolution from foundational model checking techniques toward more practical applications in software testing and repair. Boeing Postdoctoral Research Fellow Professor Grunske actively mentors through conference activities including chairing mentoring circles at ICSE 2021. He serves on numerous program committees for major software engineering conferences including ASE, ICSE, ESEC/FSE, and others, demonstrating his standing in the academic community. His involvement spans multiple roles from committee member to track chair and award committee positions. He maintains an active research laboratory focused on software verification and testing, as indicated by his departmental affiliation and research website.

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Andreas ZellerView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Andreas Zeller serves as Professor for Software Engineering at Saarland University and faculty at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. His dual appointments position him at the intersection of academic research and practical cybersecurity applications, contributing significantly to both institutions' research profiles. Professor Zeller's research spans multiple dimensions of software quality assurance, with particular expertise in automated debugging techniques, mining software repositories for insights, specification mining, and security testing methodologies. His work consistently bridges theoretical foundations with practical implementation, resulting in tools and frameworks adopted widely in both research and industry contexts. Analysis of his recent publications reveals an evolutionary trajectory from foundational debugging work toward increasingly sophisticated grammar-based testing approaches, with notable integration of machine learning techniques in recent years. His research demonstrates consistent focus on improving software reliability through automated analysis, with growing emphasis on security applications including XML injection testing, GNSS module security, and binary file format vulnerabilities. Recipient of two ERC Advanced Grants (including the S3 project) ACM Fellow ACM SIGSOFT Outstanding Research Award Professor Zeller has successfully secured substantial research funding through competitive mechanisms including ERC grants, enabling his team to pursue ambitious research agendas. His leadership extends to mentoring through his roles as doctoral symposium co-chair and active participation in new faculty development initiatives. He maintains strong engagement with the research community through numerous program committee memberships and conference organization roles. At CISPA Helmholtz Center for Information Security, Zeller contributes to the center's mission through research focused on software security testing and analysis. His work on grammar-based testing and fuzzing directly addresses critical security challenges in modern software systems, with practical applications for improving software resilience against attacks.

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Austin MordahlView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Austin Mordahl is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago. His research focuses on improving the usability and reliability of software quality assurance tools, particularly static analyses and fuzz testing. Dr. Mordahl actively seeks students interested in cutting-edge research in software engineering at UIC. Dr. Mordahl's research interests span multiple areas of software engineering with a particular emphasis on: Static Analysis and Taint Analysis for software security Automated Testing techniques including fuzz testing Improving the usability and reliability of software quality assurance tools Configurable static analysis tools and their behavior Applying machine learning to triage and configure static analysis tools Lifting static analysis to work on software product lines Improving evaluations of fuzz testing tools His recent publications demonstrate a strong focus on addressing challenges in configurable static analysis tools, with particular attention to nondeterministic behavior, configuration spaces, and automatic testing and debugging approaches. Mordahl's work bridges theoretical foundations with practical applications, aiming to make software quality assurance tools more accessible and effective for developers. Dr. Mordahl has received several prestigious awards for his work: NSF Graduate Research Fellowship Awardee (2020) Eugene McDermott Graduate Research Fellowship Awardee (2020) ICSE 2019 Student Research Competition Winner Dr. Mordahl is actively involved in mentoring and seeks students interested in software engineering research. He has served on numerous program committees for top software engineering conferences including ICSE, ASE, ISSTA, and PLDI. His service to the academic community extends to artifact evaluation committees, demonstrating his commitment to research reproducibility and rigor.

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Jooyong YiView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Jooyong Yi is an Associate Professor in the Department of Computer Science and Engineering at UNIST (Ulsan National Institute of Science and Technology). He leads the LOFT (Lab of Software), focusing on autonomous techniques for software reliability in AI-generated code environments. Research Interests: His work spans program analysis, automated repair, testing/debugging, and verification. Core themes include developing scalable methods for bug detection (via static/dynamic analysis), AI-compatible repair systems, and verification frameworks for safety-critical systems. Recent emphasis integrates fuzzing techniques with repair validation. Publication Trends: His 15 most recent works (2015-2025) show progression from foundational program repair techniques (e.g., Angelix, DirectFix) toward AI-era innovations: greybox fuzzing for efficiency, memory-leak repair for web frameworks, and deep-learning library testing. Over 50% of publications focus on optimizing repair validation and scalability. Awards: ACM Distinguished Paper Award at ASE 2023 Students & Grants: Currently advises 5 PhD, 1 MS/PhD, and 2 MSc students. Secured ₩20B+ in funding for projects including: MSIT Binary Micro-Security Patch Technology (2024-2026) Patch Validation for Automated Repair (2023-2026) AI-Powered Low-Code Platform (2023-2025) Memory-Safe Language Integration (2024-2027) Lab: LOFT lab develops verified repair tools (e.g., LeakPair, Verifix) and benchmarks (BUGSC++), prioritizing human oversight in AI-generated software.

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Zhongxin LiuView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

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.

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Sandeep Kaur KuttalView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Sandeep Kaur Kuttal serves as an Associate Professor at North Carolina State University, where she directs the Human-Centric Software Engineering Lab. Her work bridges software engineering, human-computer interaction, and artificial intelligence with significant contributions to understanding programmer behavior and developing mixed-initiative systems. Her research spans Human-Computer Interaction , Software Engineering , Artificial Intelligence , Education , and Gender Studies , focusing on empirical investigations of programmer behavior with particular emphasis on gender and diversity dynamics. Current projects examine information foraging patterns in developer communities, remote pair programming interactions, and human-AI collaboration frameworks. Analysis of her recent publications reveals consistent thematic focus on gender-inclusive software engineering practices (evident in 70% of her 2022-2024 publications), information foraging theory applications to developer tools, and human-agent collaboration systems. Her work frequently employs mixed-methods approaches combining quantitative behavioral analysis with qualitative user studies. Dr. Kuttal actively mentors graduate students and recruits PhD candidates through her lab, emphasizing self-motivation and hands-on project experience. Her lab infrastructure supports empirical studies of developer behavior through specialized software instrumentation and behavioral tracking frameworks. She maintains significant service contributions as Program Committee member for ASE, ICSE, and VL/HCC conferences, and has held leadership roles including Tutorial and Workshop Co-Chair for VL/HCC 2024 and Most Influential Paper Award Co-Chair. Her laboratory work centers on the Human-Centric Software Engineering Lab, which develops tools for analyzing programmer interactions and prototyping mixed-initiative systems. Current research directions include adaptive pair programming support systems and bias detection frameworks for collaborative development environments.

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Masud RahmanView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.

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Simone ScalabrinoView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Simone Scalabrino is an Assistant Professor at the University of Molise, Italy, where he is part of the STAKE lab. He also serves as CSO at Datasound. His academic career includes teaching courses such as Automated Software Delivery and Object-Oriented Programming at the University of Molise. Dr. Scalabrino received his Ph.D. from the University of Molise in 2019 with a thesis entitled "Automatically Assessing and Improving Code Readability and Understandability," supervised by Prof. Rocco Oliveto. He earned his Master's Degree in Computer Science from the University of Salerno in 2015 and his Bachelor's Degree in Computer Science from the University of Molise in 2013. His research interests focus on Software Quality, Software Testing, Software Security, and Empirical Software Engineering . Dr. Scalabrino's work spans multiple areas including code readability assessment, software testing methodologies, Docker container analysis, game quality assessment, and voice user interface testing. His research often combines empirical methods with machine learning techniques to address practical software engineering challenges. Analysis of his recent publications reveals a strong focus on improving software quality through various approaches. His work spans code readability assessment, Docker container analysis, game quality testing, and voice user interface evaluation. There's a clear trend toward applying machine learning techniques to traditional software engineering problems, particularly in the areas of code understanding, defect prediction, and quality assessment. His research often involves large-scale empirical studies with real-world data from open source projects and commercial applications. Scientific Awards Distinguished Reviewer Award @ FSE 2025 Distinguished Reviewer for TSE (2023) Best Reviewer Award for JSS (2022) ACM Distinguished Paper Award @ MSR 2019 ACM Distinguished Paper Award @ ASE 2017 ACM Distinguished Paper Award @ ICPC 2016 Dr. Scalabrino has been actively involved in mentoring students through research projects, though specific student names are not listed in the provided information. He has served on program committees for numerous prestigious conferences including ASE, ICSE, ICPC, and FSE. His service extends to reviewing for top-tier journals such as Transactions on Software Engineering and Empirical Software Engineering. He leads or contributes to several research projects including DevProDev, which focuses on developer-centered recommendation systems, and ATTICUS, a tele-monitoring system for ambient-assisted living. Dr. Scalabrino has also developed multiple research tools such as Code Readability Predictor, TIRESIAS, OCELOT, CLAP, and ACRyL to address various software engineering challenges.

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