Stack Overflow Analysis Professors and Supervisors

Richard Mayمشاهده پروفایل

پژوهشگر

Hochschule HarzGermany

Richard May is a researcher and Ph.D. candidate at the Doctoral Center Engineering and Information Technologies (IWIT) , affiliated with Harz University of Applied Sciences . His work bridges software engineering , data security , and healthcare applications through roles as a scientific assistant and industry professional. Supervised by Prof. Thomas Leich and Prof. Gunter Saake, his research focuses on configurable systems in Industry 4.0 and health informatics . Education : Master of Engineering in Technical Innovation Management Bachelor of Science in Media Informatics (specialized in Computer Science) with a semester abroad in Medical Informatics Research Interests : He explores security vulnerabilities in configurable systems , particularly enterprise resource planning (ERP) software and industrial cloud systems . Key areas include: Integration of security into software product line engineering AI incident analysis in safety-critical systems Human-computer interaction for healthcare conversational agents Variability modeling for industry 4.0 applications Article Trends : His 2022-2025 publications show growing focus on AI safety in manufacturing and healthcare, vulnerability analysis of configurable systems, and practitioner-driven insights on cybersecurity challenges. Key journals include ACM , Springer , and JMIR Publications . Academic Experience : Head of User Experience & Design at eGuest & ePassGo GmbH (4 years 10 months) Scientific Assistant at Harz University of Applied Sciences (since 2020) Research at Fraunhofer Institute for Production Technology (2 years 4 months) Startup development and academic teaching experience Labs & Teams : Collaborates with research teams at IWIT Doctoral Center, Harz University of Applied Sciences, and Otto von Guericke University Magdeburg. Works closely with Prof. Thomas Leich and Prof. Gunter Saake.

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Yasutaka Kameiمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Yasutaka Kamei is a Full Professor at Kyushu University's Graduate School and Faculty of Information Science and Electrical Engineering, where he leads the POSL Lab (Process-Oriented Software Laboratory). He was promoted from Associate Professor to Full Professor in January 2024 after serving as Associate Professor from March 2015 to December 2023. He is also an InaRIS Fellow (2023-2033), receiving 10 million yen annually for his research on 'New paradigm for software development styles based on machine-human interaction.' Dr. Kamei's research focuses on Empirical Software Engineering (ESE) and Open Source Software Engineering (OSSE), with particular expertise in software reliability, testing, defect prediction, code review analysis, and mining software repositories. His work bridges empirical methods with practical software engineering challenges, emphasizing how data-driven approaches can improve software quality assurance processes. His recent publications demonstrate a strong trend toward understanding human factors in software development processes, analyzing modern code review practices, investigating technical debt, and exploring the application of large language models in software engineering tasks. His research spans both traditional software engineering challenges and emerging AI-driven approaches to software development. IPSJ/ACM Award for Early Career Contribution to Global Research (2019) Best industry paper award at ESEM 2018 Distinguished Paper Award at MSR 2014 InaRIS Fellow (2023-2033) Dr. Kamei actively serves the software engineering community as Tutorials Chair for ASE 2025 and has been a program committee member for numerous top-tier conferences including ICSE, FSE, ASE, ESEC/FSE, SANER, and MSR. He has secured multiple competitive grants from MEXT (Ministry of Education, Culture, Sports, Science and Technology) to support his research on test case generation, automated software testing for deep learning systems, technical debt engineering, and mining software repositories. His POSL Lab at Kyushu University serves as a hub for empirical software engineering research, focusing on data-driven approaches to improve software development processes and outcomes through rigorous analysis of software repositories and developer activities.

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Araz Jabbariمشاهده پروفایل

دانشیار

Laval UniversityCanada

Dr. Araz Jabbari serves as an Associate Professor in the Department of Organizational Information Systems at Laval University's Faculty of Business Administration. His research examines how individuals and organizations engage with digital technologies and navigate digital transformation, with particular focus on conceptual modeling practices in increasingly complex digital environments. Dr. Jabbari maintains an active research agenda that bridges theoretical frameworks with practical applications, contributing significantly to information systems scholarship through publications in top-tier journals and conferences. Dr. Jabbari's academic foundation includes: Ph.D. in Information Systems from Queensland University of Technology MBA in IT Management from Multimedia University B.Sc. in Mathematics from University of Urmia Dr. Jabbari's research centers on conceptual modeling in the digital era, investigating how traditional modeling approaches must evolve to address contemporary challenges. His work explores how individuals understand and use multiple conceptual modeling scripts, ontological overlap in combined models, and the transition from representation to mediation in digital environments. Recent research increasingly addresses AI-specific challenges, including information quality frameworks for AI systems and the faithfulness of machine learning training data. His scholarship examines digital transformation from both technical and organizational perspectives, with growing emphasis on how data, intelligence, and transparency reshape coordination practices. Dr. Jabbari's publication trajectory demonstrates progression from foundational conceptual modeling research to cutting-edge investigations at the intersection of information systems, organizational theory, and artificial intelligence. His work shows increasing engagement with interdisciplinary topics, particularly how digital systems fundamentally reshape organizational practice. The research evolution reflects the field's broader shift from viewing information systems as representations to understanding their mediating role in digital ecosystems. Dr. Jabbari's scholarly impact is evident through: Publications in premier journals including MIS Quarterly, Journal of the Association for Information Systems, and Decision Support Systems Presentations at leading conferences such as HICSS and AMCIS Development of influential frameworks like 'Modeling 4.0' that reshape conceptual modeling discourse 297 citations across 29 publications indicating research influence As an educator, Dr. Jabbari teaches system analysis and design, business analysis, and digitization courses that likely integrate his research expertise. His student supervision approach probably emphasizes both theoretical rigor and practical application, preparing students to address real-world digital transformation challenges. His collaborative research style, evident in numerous international co-authorships, suggests opportunities for students to engage with a global research network. The Department of Organizational Information Systems at Laval University provides a rich academic environment where Dr. Jabbari contributes to shaping how future information systems professionals understand and design digital solutions for complex organizational challenges.

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Thomas Ströderمشاهده پروفایل

مدرس

University of Applied Sciences for BusinessGermany

Thomas Ströder is a Lecturer in Business Informatics at FHDW University of Applied Sciences since 2022, specializing in software engineering and formal methods. Previously, he served as Head of Full Stack Development at METRO (2016-2019) and Site Manager at IT-P GmbH (2020-2022), combining academic research with industrial leadership in software development organizations. His educational background includes: Diploma in Computer Science with Business Administration minor from RWTH Aachen University (2004-2010), featuring an exchange semester at UNSW Sydney (2008) funded by the Studienstiftung des Deutschen Volkes PhD in Computer Science from RWTH Aachen University (2019) focused on automatic quality assurance and software synthesis Ströder's research bridges theoretical computer science and industrial software engineering through core interests in program verification, formal methods, and software architecture. His work emphasizes practical applications of automated reasoning for termination analysis, memory safety, and complexity bounds in real-world systems, while extending to organizational development and data-driven decision processes in agile environments. This dual focus enables translation of formal verification techniques into enterprise software solutions. His 15 publications (2009-2018) reveal a consistent trajectory in program termination analysis and verification, primarily through the AProVE framework. Early work established foundations in term rewriting and logic program analysis, evolving toward memory-safe C program verification and bitvector arithmetic handling. Publications in top venues like Journal of Automated Reasoning and TACAS demonstrate specialization in bridging theoretical formal methods with practical software engineering challenges, particularly in pointer arithmetic and memory manipulation contexts. Professional activities include research collaborations with Microsoft Research Cambridge (2013) and leadership in building in-house software development organizations at METRO, reflecting strong industry-academia integration in his career trajectory.

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Siân Brookeمشاهده پروفایل

استادیار

Technical University of Berlin (TU Berlin)Germany

Dr. Siân Brooke serves as an Assistant Professor and MacGillavry Fellow at the Digital Interactions Lab (DIL), University of Amsterdam. Her interdisciplinary work bridges data science and critical social research to investigate gender and intersectional equality in technology interactions, with a focus on ethical AI and human-centered computing systems. Her research centers on gender dynamics in technology , inclusive platform design , and systemic bias mitigation in digital environments. Brooke employs mixed-methods approaches combining large-scale data analysis of platforms like GitHub and Stack Overflow with ethnographic studies and controlled experiments. Key themes include how programming styles reflect gender differences without impacting quality, how online labor markets perpetuate discrimination through design choices, and how internet memes reinforce toxic masculinity in physical tech spaces like hackathons. Analysis of her recent publications reveals consistent focus on exposing and redesigning discriminatory mechanisms in technology. Her 2024 work demonstrates how platform interventions (community composition, identity flairs) can reduce hiring discrimination in online labor markets, while her GitHub study proves gendered programming styles exist but don't correlate with code quality. A unifying thread across all work is the development of concrete design solutions for more equitable technology ecosystems. Dr. Brooke's recognition includes: NWO Veni Grant (€320,000) for "Human-Centered Code: Building Accessible IDEs for Neurodiverse Women in Computing Education" Leverhulme Early Career Fellowship examining gender in collaborative computing environments She actively mentors through hiring a PhD candidate for her NWO project (closing November 2024) and serves on the Open Tech Fund Advisory Council guiding global internet freedom initiatives. Her commitment to research transparency led to implementing Open Science Badges as Associated Editor for Data and Ethics, recognizing reproducible practices in scholarly work. Brooke leads the Digital Interaction Lab's research on technology equity, including the "Mock-Freelancer.com" experimental platform testing anti-discrimination interventions in online labor markets. Her lab collaborates across disciplines to develop accessible computing environments and policy recommendations for inclusive technology design, with recent work featured in LSE Research explaining gender disparities in programming.

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Lina Gongمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Lina Gong is an Associate Professor at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. She holds a Ph.D. in Computer Software and Theory from China University of Mining and Technology (2020) and completed a research visit at Queen's University's Software Analysis and Intelligence Lab (SAIL) under Prof. Ahmed Hassan (2019-2020). Her research focuses on leveraging machine learning to extract insights from software repositories, with emphasis on: ML-enabled defect prediction techniques Code pre-trained models for vulnerability detection Identifier normalization and issue classification Empirical studies of software quality attributes Her recent publications (2023-2025) demonstrate strong trends in applying transformer architectures to code analysis, with increasing focus on supply chain security and cross-platform UI translation. Key venues include IEEE TSE, ACM TOSEM, and ASE. Scientific recognition includes: National Natural Science Foundation of China (2022-2025) Natural Science Foundation of Jiangsu Province (2022-2025) Key National Laboratory Foundation (2022-2023) Excellent Ph.D. Student Award (CUMT) She actively mentors graduate students (14 advisees: 1 doctoral, 13 master's) and serves on program committees for ASE, APSEC, and SANER. Her research is supported by multiple competitive grants focusing on ML applications in software engineering.

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Xin Xiaمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Xin Xia is a Qiushi Distinguished Professor at the College of Computer Science and Technology, Zhejiang University. Previously, he served as the Chief Expert and Director of the Software Engineering Application Technology Lab at Huawei Technologies, China from 2021 to 2025. His academic career spans software engineering research with a focus on AI applications in the field. Ph.D. from Zhejiang University (2014) Supervised by Prof. Xiaohu Yang and Prof. Jianling Sun Visiting student at Singapore Management University (2012-2014) under Prof. David Lo Xin Xia's research primarily focuses on applying data science techniques to software engineering problems. His work spans AI for Software Engineering, Mining Software Repositories, Empirical Software Engineering, and Large Language Models for code understanding and generation. He employs data mining, information retrieval, natural language processing, search-based algorithms, and program analysis to transform software engineering data into automated tools and insights. His recent publications show a strong trend toward leveraging Large Language Models for various software engineering tasks, including code generation, vulnerability detection, and test generation. He has been exploring how to make these models more effective, reliable, and practical for real-world software development scenarios, with a particular focus on Java and Python ecosystems. ACM SIGSOFT Early Career Researcher Award (2022) ACM Distinguished Member 16 best or distinguished paper awards, including nine ACM SIGSOFT Distinguished Paper Awards Recipient of the IEEE Transactions on Software Engineering 2021 Best Paper Award Runner-Up Xin Xia has advised numerous students who have gone on to publish in top software engineering venues. His research has been supported by grants from both academic institutions and industry partners, particularly during his time at Huawei. He actively collaborates with researchers worldwide, especially with David Lo at Singapore Management University. At Zhejiang University, Professor Xia leads research in the intersection of AI and Software Engineering. His work has practical applications in improving developer productivity through automated tools that analyze software repositories and provide actionable insights.

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Shangwen Wangمشاهده پروفایل

استادیار

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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Raffi Khatchadourianمشاهده پروفایل

دانشیار

Hunter College, CUNYUnited States

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.

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John Grundyمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

John Grundy is a Professor of Software Engineering and Senior Deputy Dean at Monash University's Faculty of Information Technology in Melbourne, Australia. He is also an Australian Laureate Fellow (2020-2026) and leads the "Human-centric Software Engineering" (HumaniSE) research lab. With over 32 years of academic experience, Professor Grundy has held numerous leadership positions including Pro Vice-Chancellor at Deakin University and Dean roles at Swinburne University and the University of Auckland. BSc(Hons), MSc, PhD and DSc degrees in Computer Science from the University of Auckland IEEE Fellow, Fellow of Automated Software Engineering, Fellow of Engineers Australia Lero Parnas Fellow (2023) Recipient of the ACM SIGSOFT Distinguished Service Award (2023) and Dean's Award for Graduate Research Student Supervision (2024) Professor Grundy's research focuses on making "Software Engineering more like traditional Engineering disciplines" through human-centric visual modeling approaches. His primary research areas include model-driven engineering, software architecture, visual languages, software security engineering, and human factors in software development. He specifically investigates how personality, emotions, gender, age, and disability impact software usage, requirements engineering, design, and testing. His current projects include the Visual Wiki platform for knowledge engineering, Marama meta-tools, and Software Process and Product Improvement initiatives. His research has significant implications for accessibility, usability, and the alignment of software applications with diverse user needs. Professor Grundy has published extensively in top software engineering venues and has supervised numerous PhD students throughout his career. IEEE Technical Council on Software Engineering Distinguished Education Award (2014) ACM SIGSOFT Distinguished Service Award (2023) CORE Distinguished Service Award (2023) Lero Parnas Fellow (2023) Dean's Award for Graduate Research Student Supervision (2024) Professor Grundy has supervised numerous PhD students and has received funding for various research projects, most notably his 5-year Australian Laureate Fellowship (2020-2026) focused on human-centric software engineering. His HumaniSE research lab brings together interdisciplinary teams to address challenges in making software systems more responsive to human needs and contexts. His lab focuses on developing new conceptual foundations and modeling techniques that incorporate human factors throughout the software development lifecycle, with applications in smart homes, digital health, and smart city solutions.

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Alexander Serebrenikمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Alexander Serebrenik is a full professor of social software engineering at the Eindhoven University of Technology in the Netherlands, working within the Department of Mathematics and Computer Science. His academic profile spans decades of interdisciplinary research bridging computer science and social sciences. Professor Serebrenik's research focuses on facilitating software evolution through understanding social aspects of development. His work integrates computer science methods (socio-technical coordination theory, natural language processing, machine learning) with organizational psychology principles. A consistent theme across his publications is empiricism - addressing software engineering challenges through observation and experimentation while balancing social and technical perspectives. His recent work increasingly emphasizes diversity, equity, and inclusion in software engineering, culminating in his 2024 book "Equity, Diversity, and Inclusion in Software Engineering: Best Practices and Insights" (APress). His publication record reveals evolving interests from socio-technical coordination to human factors, community dynamics in open source, and now DEI-focused research. Distinguished Paper Award at ICSE 2023 Distinguished Paper Award at MSR 2023 Distinguished Reviewer Award at FSE 2020 Senior member of IEEE Member of ACM As an academic leader, Professor Serebrenik has mentored PhD students including Tukaram Muske, and actively participates in doctoral symposia. His service includes roles as Diversity and Inclusion Co-Chair at multiple conferences and extensive program committee participation across the software engineering conference landscape. His research group at TU/e maintains an active program studying the social dimensions of software engineering through empirical investigations, contributing significantly to understanding how human factors influence development processes and outcomes.

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