جستجوی استاد راهنمای دانشگاهی

Ulrike Padoمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Artificial Intelligence in Education
• +۳ مورد دیگر

Prof. Dr. Ulrike Pado is a Professor of Computer Science at Stuttgart University of Applied Sciences (HFT Stuttgart) since 2012, holding a PhD in Computational Linguistics from Saarland University. She serves as Speaker of the Research Centre ZedFLoW (Centre for Digital Transformation in Research, Education and Business) since 2019 and Vertrauensdozentin for the German Academic Scholarship Foundation since November 2024, specializing in AI for university education and natural language processing. Research Interests Her work centers on applying Natural Language Processing and Machine Learning to educational contexts, including automated spoken language assessment, LLM-powered tutoring systems, and adaptive testing frameworks. Current projects involve TransferBot (research-access chatbot), IMPETUS (bwDigiFellowship-funded LLM tutors), and KNIGHT (AI-driven adaptive teaching tools), all developed through HFT Stuttgart's Laboratory for Natural Language Processing which she directs. Scientific Awards Fellow for Innovation in Digital Teaching at Universities (2024-2025) by Stifterverband and MWK Baden-Württemberg Advising and Grants As Vertrauensdozentin, she provides dedicated mentoring for Studienstiftung des deutschen Volkes scholarship recipients. Her research receives funding through bwDigiFellowship 24/25 (IMPETUS project) and institutional HFT Stuttgart support for KNIGHT, focusing on scalable AI solutions for educational transformation. Laboratory She leads the Laboratory for Natural Language Processing at HFT Stuttgart, which develops NLP methodologies specifically for educational applications including language skill assessment and intelligent tutoring systems.

Stephan Bohnمشاهده پروفایل

مدرس

• Organizational Change
• Institutional Change
• Digital Platform Economy
• +۷ مورد دیگر

Stephan Bohn is an associated researcher in the Innovation, Entrepreneurship & Society group at the Alexander von Humboldt Institute for Internet and Society (HIIG) and a lecturer at Freie Universität Berlin and the University of Salzburg. His academic focus spans organizational change, digitalization, and sustainability, with expertise in institutional theory and machine learning methodologies. Education: PhD in Organization Studies, Friedrich Schiller University Jena (research on sustainability implementation in tech firms) Research Interests: Bohn investigates institutional and organizational transformations driven by digitalization, open-source ecosystems, and green innovations. His work integrates qualitative methods with computational approaches like topic modeling to analyze fields such as platform economies, energy transitions, and digital organizing. Key themes include corporate political responsibility, sustainable entrepreneurship, and the societal impact of disruptive technologies. Publications Overview: His recent articles (2018–2025) explore institutional complexity, digital marketplaces, and sustainability transitions. Methodologically, they emphasize topic modeling, frame analysis, and comparative studies, often addressing German and European contexts in energy, mobility, and platform governance. Awards: Fellow, Transatlantic Doctoral Academy on Corporate Responsibility Projects & Leadership: Bohn leads the 'Digital Organizing' task force at HIIG and has directed projects on corporate accountability in platform economies (e.g., 'Increase corporate political responsibility') and sustainable digitalization (e.g., 'Digitalisation and Climate'). He has collaborated on DFG-funded initiatives examining institutional complexity in energy transitions. Academic Engagement: He regularly presents at EGOS and Academy of Management conferences, organizes interdisciplinary workshops (e.g., on digital rainforest conservation), and contributes to policy debates via media outlets like LSE Impact Blog.

Zhe Liuمشاهده پروفایل

استادیار

• Software Engineering
• Mobile Testing
• Deep Learning
• +۵ مورد دیگر

Zhe Liu is an Assistant Scientist at the Institute of Software, Chinese Academy of Sciences, and is affiliated with the University of Chinese Academy of Sciences. With a PhD from the University of Chinese Academy of Sciences, Liu focuses on innovative research at the intersection of software engineering, mobile testing, and artificial intelligence. Liu's research interests span multiple areas including: Software Engineering Mobile Testing Deep Learning Human-Computer Interaction Machine Learning Computer Vision UI Testing Crowd Testing Liu applies AI and lightweight program analysis technology in several key directions: AI/LLM-assisted automated mobile app development (requirement elicitation, app GUI testing, usability, bug replay) Human-machine collaborative testing (testing guides for testers) AI-empowered mining of software repositories (issue report mining) Liu has published 15 papers at top international Software Engineering and Human-Computer Interaction conferences/journals (CCF-A) with over 1,300 Google Scholar citations. The research shows a strong trend toward leveraging large language models for mobile app testing, with significant contributions in automated GUI testing, crash reproduction, and text input generation. Notable scientific achievements include: ACM Student Research Competition (SRC) 2023 Grand Finals Winners, 1st Place, Graduate Category Best Paper Honourable Mention in CHI'24 ACM Student Research Competition (SRC) at ICSE 2022, 1st Place ACM Student Research Competition (SRC) at ASE 2020, 1st Place Excellent Doctoral Dissertation of Chinese Academy of Sciences in 2024 Liu has served as a program committee member for ASE 2025 and has been involved in multiple research projects funded by the National Key Research and Development Program of China, National Natural Science Foundation of China, and Populus Innovation Research Funding. Current research focuses on advancing the integration of large language models with mobile application testing frameworks to improve app quality and user experience.

Xiang Chenمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Mining Software Repositories
• Software Testing and Maintenance
• +۵ مورد دیگر

Xiang Chen is an Associate Professor at the Department of Software Engineering, School of Artificial Intelligence and Computer Science, Nantong University, China. He received his B.Sc. degree from Xi'an Jiaotong University in 2002 and his M.Sc. and Ph.D. degrees in computer software and theory from Nanjing University in 2008 and 2011 respectively. He is an editorial board member of Information and Software Technology and serves as a program committee member for prestigious conferences including FSE 2026 and ASE 2025. Chen is also a senior member of the China Computer Federation (CCF) and active in various academic committees. Chen's research focuses on empirical software engineering, mining software repositories, and software testing and maintenance, with particular emphasis on applying AI techniques to software engineering problems. His work spans large language models for software engineering, security vulnerability analysis, code change representation, and regression testing. He has published over 110 papers in top-tier journals and conferences including IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology. His recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models, with traditional software engineering practices. The research spans code generation evaluation, deep learning framework testing, vulnerability detection, and automated program repair, showing a consistent focus on improving software quality through innovative testing and analysis techniques. ACM SIGSOFT Distinguished Paper Award (ICSE 2021) ACM SIGSOFT Distinguished Paper Award (ICPC 2023) Top 1% CNKI Highly Cited Scholar (2024) Top 2% Scientist by Stanford University (2023-2025) NASAC 2019 Prototype Competition First Prize Chen has successfully advised numerous graduate and undergraduate students who have gone on to prestigious institutions including Nanjing University, Tsinghua University, and Zhejiang University. Many of his students have won national programming competitions and received scholarships. His research group, smartSE, actively works on projects funded by the Natural Science Foundation of China and various provincial research programs. Chen also serves as a reviewer for top journals including IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology.

Alberto Martin-Lopezمشاهده پروفایل

پژوهشگر ارشد

• Software Testing
• Test Automation
• Web Services
• +۳ مورد دیگر

Alberto Martin-Lopez is a postdoctoral fellow in the SEART research group at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. His academic journey includes a PhD from the SCORE Unit of Excellence at the University of Seville (Spain), where he also earned a Bachelor's degree in Telecommunications Engineering and a Master's degree in Software Engineering and Technology. He has held positions as a Fulbright fellow at the University of California, Berkeley and as an external lecturer at Kristiania University College in Oslo, Norway. His research focuses on software testing, service-oriented computing, and neuro-symbolic AI applications to software engineering problems. Martin-Lopez has made significant contributions to automated testing of web services, particularly RESTful APIs, developing tools and techniques for test oracle generation, test input generation, and metamorphic testing. His work bridges traditional software engineering methods with modern AI techniques to address longstanding challenges in software verification. His publications in top-tier venues like ESEC/FSE, ISSTA, and TSE demonstrate consistent impact in the software engineering community. Analysis of his recent work shows a clear trajectory toward integrating neuro-symbolic AI approaches with traditional testing methodologies, particularly for solving the oracle problem in API testing and enhancing code generation systems. His scientific achievements have been recognized with prestigious awards: First Prize of the ACM Student Research Competition at ICSE'20 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE'22 2023 Early Career Researcher Award by the Spanish Society of Computer Science and Fundación BBVA Martin-Lopez actively contributes to the software engineering community through service on program committees for major conferences including ASE, ESEC/FSE, ICSE, and ISSTA. His collaborative work spans institutions across Spain, Switzerland, the United States, and Norway, reflecting a strong international research network. At USI, he works within the SEART research group, focusing on advancing the state of the art in automated software testing through innovative combinations of traditional software engineering techniques and artificial intelligence.

Michael Pradelمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Program Analysis
• +۶ مورد دیگر

Michael Pradel is a full professor at the University of Stuttgart and a faculty member at the CISPA Helmholtz Center for Information Security. He leads the Software Lab at the University of Stuttgart and holds additional affiliations with the International Max Planck Research School (IMPRS) for Intelligent Systems and the Stuttgart ELLIS Unit. Previously, he served as an assistant professor at TU Darmstadt, a postdoctoral researcher at UC Berkeley, and a lecturer and postdoctoral researcher at ETH Zurich, where he completed his PhD. His educational background includes computer science studies at TU Dresden and engineering studies at Ecole Centrale Paris, with a master's thesis conducted at EPFL. He has taken sabbaticals at Facebook, UC Berkeley, and UCLA. Pradel's research spans software engineering, programming languages, security, and machine learning, with a focus on tools and techniques for building reliable, efficient, and secure software. He is particularly interested in neural-symbolic software analysis, analyzing web applications, dynamic analysis, and test generation. His recent work increasingly incorporates large language models for software engineering tasks including bug detection, test generation, and program repair. His research group has produced significant work in WebAssembly analysis, Python dynamic analysis, quantum program analysis, and automated program repair, with notable tools including Wasm-R3, DynaPyt, LintQ, and RepairAgent. Ernst-Denert Software Engineering Award Emmy Noether grant by the German Research Foundation (DFG) (1.3 million Euro) ERC Starting Grant (1.5 million Euro) Best/Distinguished Paper Awards at FSE (3x), ISSTA, ASE, ASPLOS, and MSR ACM Distinguished Member Multiple ACM SIGSOFT Distinguished Paper and Artifact Awards Pradel has advised numerous PhD students including Matteo (thesis on "Testing and Analysis of Quantum Software"), Luca (thesis on "Supporting Software Evolution via Search and Prediction"), and Daniel (thesis on program analysis for WebAssembly). His research has been supported by significant grants including the Emmy Noether grant and ERC Starting Grant. He serves in leadership roles for major conferences including PC co-chair for FSE 2027 and area chair for ICSE 2026. Pradel leads the Software Lab at the University of Stuttgart, which includes visiting professors Cristian Cadar and Prem Devanbu (both supported by Humboldt Research Awards). The lab maintains active collaborations with institutions including CMU, Google, KAIST, and USI Lugano, and regularly contributes to major software engineering conferences.

• Cognitive Ergonomics
• Human Factors
• Human-Technology Interaction
• +۳ مورد دیگر

Dr. Felix Dreger is a Research Associate in the Experimental Ergonomics department at the Leibniz Research Centre for Working Environment and Human Factors (IFADO) in Dortmund, Germany. He works under the leadership of Prof. Dr. Edmund Wascher in the Cognitive Ergonomics Working Group, focusing on human-technology interaction, automation systems, and work design. Dr. Dreger holds a B.Sc. and M.Sc. in Psychology from Eberhard Karls University of Tübingen, with additional academic experience at the University of Connecticut and Delft University of Technology. His educational background includes specialized training in cognitive and media sciences from the University of Duisburg-Essen. His research interests center on cognitive ergonomics, human-technology interaction, and human factors in automation and robotics. Dr. Dreger specializes in studying how humans interact with complex systems, particularly in industrial settings involving robotic collaboration, crane operations, and forestry machinery. His work examines learning processes, workload assessment, and feedback design in human-machine systems. Analysis of Dr. Dreger's publication record from 2020-2025 reveals a strong trajectory in human-robot collaboration research, with increasing focus on industrial applications. His work spans multiple domains including forestry operations, crane control systems, and multi-human multi-robot collaboration frameworks. The research demonstrates expertise in both theoretical frameworks like cognitive ergonomics and practical applications in workplace settings. Dr. Dreger has extensive international research experience, including collaborations with Virginia Tech Transportation Institute and Delft University of Technology. His work has been supported by EU-funded projects including Marie Curie Skłodowska RISE and EU Horizon initiatives like FELICE and EU-SOPRANO. His laboratory work focuses on experimental ergonomics, utilizing methodologies including hierarchical task analysis, neuroergonomics with mobile EEG, and human factors assessment in real-world industrial settings. The research team he works with at IFADO maintains strong connections with both academic institutions and industry partners to ensure practical relevance of their findings.

Phil McMinnمشاهده پروفایل

استاد

• Software Testing
• Search-Based Software Engineering
• Flaky Tests
• +۵ مورد دیگر

Phil McMinn is a Professor of Software Engineering at the University of Sheffield, UK, where he leads the Testing Research Group, one of the largest software testing groups in the UK. His research focuses on developing automated techniques for software testing to help developers maintain robust test suites that effectively find bugs. His primary research interests include Software Testing , Search-Based Software Engineering , Flaky Tests , Mutation Analysis , and Pseudo-Tested Code . Recently, his work has centered on helping developers detect and mitigate flaky software tests, as well as developing mutation analysis approaches to assess test suite quality. His research has been funded by the EPSRC and Meta. Analysis of his recent publications (2023-2025) reveals a strong focus on addressing critical challenges in software testing, particularly around test flakiness, pseudo-tested code detection, and test suite optimization. His work spans both theoretical foundations and practical tool development, with significant contributions to understanding the limitations of current testing practices and developing novel approaches to improve test reliability and effectiveness. Professor McMinn serves as an associate editor for the Software Testing, Verification and Reliability journal and has supervised ten PhD students to completion as first supervisor. He currently mentors five PhD students and a post-doctoral researcher on projects related to test flakiness, mutation analysis, and testing for autonomous systems. He teaches the first-year COM1001 Introduction to Software Engineering module and the third-year COM3529 Software Testing and Analysis module at the University of Sheffield, emphasizing team-based development, automated testing, and code quality improvement through refactoring.

Neil Walkinshawمشاهده پروفایل

استاد

• Software Engineering
• Testing
• Machine Learning
• +۴ مورد دیگر

Neil Walkinshaw is a Professor in the Department of Computer Science at The University of Sheffield, Faculty of Engineering. He serves as joint PC-chair for the International Conference on Software Testing (ICST'26) and is an associate editor for the Journal of Automated Software Engineering. His research focuses on improving the trustworthiness and reliability of software and cyber-physical systems, with particular expertise in testing "hard to test" systems that have long execution times, non-determinism, limited state observability, and large input/output spaces. He has secured funding from EPSRC (CITCOM, REGI, and STAMINA projects), InnovateUK, DSTL, and the DfT. Walkinshaw's research spans three main areas: Causal Software Engineering : Applying Causal Inference to test causal input-output relationships in software and cyberphysical systems, including scientific models, automated driving systems, and cyberphysical systems. State Machine Inference and Analysis : Developing techniques to understand sequential behavior of systems when a prior model is unavailable, including Extended Finite State Machine inference and LTSDiff algorithm for comparing state machines. Second-order uncertainty in Software Engineering : Using Subjective Logic to reason about uncertainty in empirical software engineering experiments and inferred state machines. His recent publications (2023-2025) demonstrate a strong focus on causal testing methodologies, with applications to scientific software models (CovaSim, Luo-Rudy model), cyber-physical systems (Artificial Pancreas), and automated driving simulators. His work on the CITCOM Causal Testing Framework provides an open-source implementation of these approaches. His scientific achievements include: Best Research Paper Award at EASE'25 for "Using Causal Inference to Test Systems with Hidden and Interacting Variables" Development of the CITCOM Causal Testing Framework (GitHub, MIT License) Significant contributions to state machine inference and analysis Walkinshaw actively supervises PhD students and has successfully guided seven PhD students to completion. He teaches Software Reengineering and leads the Computer Science Ambassadors module, focusing on making Computer Science more inclusive. As the school lead on inclusion in Computer Science, he works to attract more diverse student cohorts into the field. He currently collaborates with Research Software Engineers Michael Foster and Farhad Allian on the CITCOM project (2020-2025), which explores causal testing approaches for hard-to-test systems.

Lei Buمشاهده پروفایل

استاد

• Software Verification
• Software Testing
• Software Analysis
• +۸ مورد دیگر

Lei Bu is a Professor and Vice Dean of the Software Institute at Nanjing University, China. He has been with Nanjing University since 2010, progressing from Assistant Professor to his current position. His academic career includes a visiting position at Microsoft Research Asia through their StarTrack Program from 2014-2015. Professor Bu received his B.Sc. and Ph.D. degrees in Computer Science from Nanjing University, with additional research experience at Carnegie Mellon University and University of Texas at Dallas during his doctoral studies. His academic journey shows steady progression through the ranks at his alma mater. Lei Bu's research primarily focuses on software verification, testing, and analysis, with particular expertise in model checking, bounded model checking, formal methods, and cyber-physical systems. His work spans theoretical foundations to practical applications in safety-critical systems. Recent publications demonstrate growing interest in integrating machine learning techniques with traditional formal methods, as seen in works like SpecGen that leverage large language models for program specification generation. His research outputs show a consistent focus on verification techniques for complex systems, particularly addressing challenges in hybrid systems, cyber-physical systems, and IoT applications. Professor Bu has developed several verification tools including BACH (Bounded Reachability Checker for Linear Hybrid Automata) and BRICK (Bounded Reachability Checker of Numerically-Intensive C Code), which have been used in international verification competitions. 2023: Zhongchuang Software Talent Award 2022: CCF-IEEE CS Young Computer Scientist Award 2020: The Outstanding Teachers of Computing in Higher Education Award Program 2019: NASAC Young Software Innovation Award 2016: Young Talent Development Program 2014: StarTrack Program Visiting Young Faculty 2007: Full Scholarship under the State Scholarship Fund Professor Bu serves as a Principal Investigator on multiple significant research projects funded by the Natural Science Foundation of China, including a Key Program grant (2023-2027) on dynamic adjustment-control-fault tolerance theory. He has also advised numerous students and taught courses including Formal Languages and Automata, Theoretical Foundation of Software Engineering, and Preliminary Introduction to the Theory of Computation. His laboratory work focuses on developing practical verification tools for industrial applications in cyber-physical and IoT systems.

Ying Zouمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence for Software Engineering
• Software Evolution
• +۲ مورد دیگر

Dr. Ying Zou is a Professor in the Department of Electrical and Computer Engineering at Queen's University's Smith Engineering faculty in Kingston, Ontario, Canada. With an extensive publication record spanning from 2018 through 2025, Dr. Zou has established herself as a leading researcher in empirical software engineering with a growing focus on AI integration. Dr. Zou's research focuses on Software Engineering , Artificial Intelligence for Software Engineering (AI4SE) , Software Evolution , Software Analytics , and Empirical Software Engineering . Her work bridges theoretical approaches with practical applications, examining developer behavior, code quality improvement, and AI techniques for software engineering tasks. Recent publications demonstrate a clear progression from traditional empirical studies toward more AI-centric approaches, particularly in code refactoring, type inference, and performance analysis. Analysis of Dr. Zou's publication trends reveals a strategic evolution in her research focus. Early work centered on empirical studies of Stack Overflow and GitHub, while recent publications increasingly integrate large language models and AI techniques for software engineering tasks. Her research spans multiple dimensions including code quality, developer productivity, open source community dynamics, and performance optimization, with consistent methodological rigor in empirical validation. Dr. Zou has served in numerous leadership roles across major software engineering conferences including ASE, ICSE, and ESEC/FSE. She has been a Program Committee member for multiple tracks and conferences, and notably served as New Faculty Mentoring Co-Chair for ESEC/FSE 2026. Her service to the community extends to organizing conference tracks, chairing sessions, and mentoring new researchers in the field.

Dan Haoمشاهده پروفایل

استاد

• software testing
• debugging
• program comprehension
• +۷ مورد دیگر

Professor Dan Hao is a distinguished faculty member at the Institute of Software, School of Computer Science, Peking University, where he has established himself as a leading researcher in software engineering. His extensive service to the academic community includes membership on the Steering Committee for The International Conference on Automated Software Engineering (ASE) since 2021, The ACM SIGSOFT International Symposium on Software Testing and Analysis since 2025, and The International Systems and Software Product Line Conference (SPLC) from 2018-2022. He has served as Program Co-Chair for multiple major conferences including ISSTA 2027, ICSME 2025, ICST 2023, SANER 2022, and ASE 2021. Professor Hao received his Bachelor's degree from Harbin Institute of Technology in 2002 and completed his Ph.D. at Peking University in 2008, followed by post-doctoral research at the same institution until 2009. His academic journey reflects a deep commitment to advancing software engineering research and education in China. Professor Hao's research primarily focuses on software testing and debugging, program comprehension, and software maintenance. His work has significantly contributed to compiler testing, fault localization, regression testing, and automated program repair. He has pioneered approaches in compiler auto-tuning, test-case prioritization, and history-guided testing techniques. His research bridges theoretical foundations with practical applications, addressing real-world challenges in large-scale software systems, particularly in online service environments. His publication record demonstrates a consistent trajectory of high-impact research in top-tier software engineering venues. Professor Hao's work shows increasing integration of machine learning techniques with traditional software engineering problems, particularly evident in his recent publications on LLM applications for code generation, neural theorem proving, and contrastive learning for vulnerability detection. His research maintains strong connections between theoretical rigor and practical applicability in industrial settings. ACM SIGSOFT Distinguished Paper Award for PDCAT: Preference-Driven Compiler Auto-Tuning at FSE 2025 Distinguished Paper Award for Formalizing, Mechanizing, and Verifying Class-Based Refinement Types at ECOOP 2024 ACM SIGSOFT Distinguished Paper Award for History-Guided Configuration Diversification for Compiler Test-Program Generation at ASE 2019 ACM SIGSOFT Distinguished Paper Award for History-driven Build Failure Fixing: How Far Are We? at ISSTA 2019 As an advisor, Professor Hao has mentored numerous graduate students, currently supervising 9 Ph.D. students and 7 Master's students. His former students have gone on to prestigious positions at institutions including King's College London, Tianjin University, Fudan University, and major technology companies like Huawei and China Construction Bank. His academic leadership extends through editorial roles as Deputy Editor-in-Chief of Software Testing, Verification and Reliability (STVR) and membership on the editorial boards of several premier journals including ACM Transactions on Software Engineering and Methodology, ACM Computing Surveys, and Empirical Software Engineering. Professor Hao leads a vibrant research group at Peking University's Institute of Software, focusing on cutting-edge problems at the intersection of traditional software engineering and artificial intelligence. His team actively collaborates with both academic institutions and industry partners to address practical challenges in software development and maintenance processes.

Lili Weiمشاهده پروفایل

استادیار

• Software Analysis
• Software Testing
• Code Repository Mining
• +۶ مورد دیگر

Lili Wei is an Assistant Professor in the Department of Electrical and Computer Engineering at McGill University's Faculty of Engineering. She specializes in software engineering research with a focus on Android applications, smart contracts, and IoT software. Prior to joining McGill, she completed her PhD at HKUST under Prof. S.C. Cheung and was a Postdoctoral Fellow in the CASTLE group at HKUST, receiving the Hong Kong Research Grant Council Postdoctoral Fellowship. Her research interests center on software analysis, testing, and mining code repositories, particularly addressing compatibility issues in Android applications, security vulnerabilities in smart contracts, and testing methodologies for mobile software. She has made significant contributions to understanding and solving fragmentation-induced compatibility issues in Android apps, which has been a persistent challenge for developers due to the diverse ecosystem of Android devices and OS versions. Dr. Wei's publication record shows a consistent trajectory of impactful research in top software engineering venues including ASE, ICSE, FSE, ISSTA, and IEEE Transactions on Software Engineering. Her recent work has expanded into blockchain security with a focus on front-running vulnerabilities in smart contracts. Her research combines empirical studies with practical tool development to address real-world software engineering challenges. Peter Silvester Faculty Research Award in Electrical & Computer Engineering (McGill University) Distinguished Reviewer (ASE 2022) Hong Kong RGC Postdoctoral Fellowship Scheme (2020) ACM SIGSOFT Distinguished Artifact Award (ICSE 2019) Google PhD Fellowship (in Mobile Computing, 2018) Microsoft Research Asia PhD Fellowship (2018) ACM SIGSOFT Distinguished Paper Award (ASE 2016) Dr. Wei has been actively involved in the academic community, serving on program committees for major software engineering conferences including ICSE, ASE, FSE, ISSTA, and MOBILESoft. She has held leadership roles such as Tutorials Track Chair for ASE 2023, PC Chair for MOBILESoft 2023, and is currently seeking PhD students interested in software engineering research, particularly in program analysis, test generation, and mobile computing. Her teaching at McGill includes ECSE 250: Fundamentals of Software Development, ECSE 321: Introduction to Software Engineering, and ECSE 688: Automated Software Testing and Analysis.

Yulei Suiمشاهده پروفایل

دانشیار

• Program Analysis
• Software Engineering
• Machine Learning
• +۷ مورد دیگر

Yulei Sui is an Associate Professor at the School of Computer Science and Engineering, University of New South Wales (UNSW), where he conducts research at the intersection of programming languages, software engineering, and machine learning. His work focuses on developing open-source frameworks for static analysis and verification to enhance software reliability and quality. He currently serves as Program Chair for LCTES 2024, SAS 2025, and ISSRE 2025, and is a program committee member for numerous top-tier conferences including PLDI, OOPSLA, and ICSE. His educational background includes a PhD from UNSW, where he has been a faculty member since completing his studies. His research interests span program analysis, software verification, machine learning applications in software engineering, and the intersection of programming languages with natural language processing and code LLMs. He leads the development of SVF (Static Value-Flow), an open-source framework for code analysis and verification, which has gained significant traction in both academic and industrial settings. His recent publications demonstrate a strong focus on improving the precision and efficiency of static analysis techniques, particularly in areas like context-free language reachability, pointer analysis, typestate analysis, and vulnerability detection. His work often bridges theoretical advances with practical applications, resulting in tools that address real-world software reliability and security challenges. Several of his papers have received distinguished paper awards at top conferences including ICSE and FSE. ICSE Distinguished Paper Award (2025) FSE Distinguished Paper Award (2024) OOPSLA 2022 Distinguished Artifact Award Australian Research Council Future Fellowship Fellow of Engineers Australia (FIEAust) Professor Sui actively mentors PhD students and undergraduate thesis candidates, with numerous students having completed their research under his supervision. He has secured significant research funding including ARC Discovery Projects and an ARC Future Fellowship. His community service includes editorial roles for IEEE Transactions on Software Engineering and IEEE Transactions on Reliability, demonstrating his standing as a leader in the software engineering research community.

Hamid Bagheriمشاهده پروفایل

دانشیار

• Software Engineering
• Security of Mobile Devices and IoT Systems
• Formal Methods
• +۴ مورد دیگر

Hamid Bagheri is an Associate Professor in the School of Computing at the University of Nebraska-Lincoln, where he conducts cutting-edge research at the intersection of software engineering, security, and formal methods. He also serves as a faculty associate of the Institute for Software Research (ISR) at the University of California, Irvine, and co-directs the ESQuaReD Lab (Engineering Software Quality and Reliability through Deductive methods), which focuses on ensuring software quality through rigorous engineering approaches. Dr. Bagheri's research spans several critical areas including security of mobile devices and IoT systems, scaling formal verification with machine learning, software analysis and testing, dependable cyber-physical systems, and automated program repair and fault localization. His work combines theoretical rigor with practical applications, addressing real-world challenges in software security and reliability through publications in top venues like ICSE, FSE, ASE, and IEEE/ACM Transactions journals. His recent publications demonstrate a strong trend toward integrating machine learning techniques with formal methods to enhance software analysis capabilities. This includes leveraging transformers for database design optimization, neuro-symbolic approaches for certified software synthesis, and large language models for repairing formal specifications, reflecting his ability to adapt to emerging technologies while maintaining a strong theoretical foundation. Dr. Bagheri has received numerous prestigious awards including the EPSCoR FIRST Award, the NSF CISE Career Research Initiation Initiative Award, multiple Distinguished Paper Awards from top conferences, and teaching honors such as the SoC Student Choice Outstanding Teaching Award (2023-2024) and the CSE Outstanding Teaching Award (2020-2021). As an advisor, Dr. Bagheri has mentored several successful PhD students including Mohannad Alhanahnah (who joined Chalmers University as a tenure-track faculty member in Fall 2024) and Clay Stevens (who joined Iowa State University as a tenure-track faculty member in Fall 2023). His research has been supported by significant grants from NSF and other funding agencies, with recent work focusing on automated specification repair, security analysis of IoT systems, and verification of network protocols. The ESQuaReD Lab, which Dr. Bagheri co-directs, brings together a multidisciplinary team focused on addressing critical challenges in software quality and reliability through innovative engineering approaches and formal analysis techniques, with applications spanning mobile security, IoT systems, and cyber-physical systems.