جستجوی استاد راهنمای دانشگاهی

Cyril Monnetمشاهده پروفایل

استاد

• Monetary Theory
• Financial Intermediation
• Macroeconomics
• +۲ مورد دیگر

Cyril Monnet is a full Professor at the Economic Institute of the University of Bern, Switzerland, with dual affiliations at the main university campus in Bern and the Study Center Gerzensee. His institutional roles include active participation in the World Trade Institute (WTI) and other research centers within the Economic Institute ecosystem. His research program centers on three core pillars: Monetary Theory (examining money's fundamental properties and functions) Financial Intermediation (analyzing banking structures and credit allocation) Macroeconomics (investigating systemic economic dynamics) Recent scholarly output reveals a pronounced focus on digital currency ecosystems, where he pioneers theoretical frameworks for central bank digital currencies (CBDCs), stablecoins, and distributed ledger systems. His work critically assesses privacy implications in digital payments, banking sector vulnerabilities under new monetary architectures, and optimal liquidity backstop designs. Methodologically, he integrates game-theoretic models with empirical validation, particularly in consensus mechanisms and truth-verification systems for decentralized finance. Professor Monnet's publication trajectory demonstrates deep engagement with real-world policy challenges, including Swiss National Bank liquidity operations, ECB Digital Euro implementation hurdles, and systemic risk mitigation in collateralized credit chains. His research consistently bridges theoretical rigor with actionable insights for central banking institutions globally. Based in office A316 at Schanzeneckstrasse 1, Bern, he maintains an active research presence through cyrilmonnet.ch while contributing to the University of Bern's leadership in monetary economics through the Economic Institute's collaborative framework with the Oeschger Center for Climate Change Research and Center for Regional Economic Development.

Benoit Baudryمشاهده پروفایل

استاد

• Software testing
• Software diversity
• Randomization
• +۱ مورد دیگر

Benoit Baudry is a Professor in Software Technology at Université de Montréal, Canada, with previous affiliation at KTH Royal Institute of Technology in Sweden. His research focuses on automated software engineering with emphasis on practical execution-based approaches. Baudry's core research interests include: Software testing : Automated test generation, mocking, and improvement techniques Software diversity : Runtime protection through variant execution and WebAssembly transformations Randomization : Fuzzing and chaos engineering for robustness validation DevOps : Supply chain analysis and dependency management in Maven ecosystems Analysis of his 15 most recent publications (2022-2025) reveals strong emphasis on: Software supply chain security and dependency management (6 publications) Test automation and mock generation techniques (4 publications) WebAssembly compilation and security (3 publications) Software-art interdisciplinary research (2 publications) His work consistently combines empirical analysis with tool development across Java and WebAssembly ecosystems. Baudry actively contributes to the academic community through program committees (ASE, ESEC/FSE, ICSE, ICST) and keynote presentations. He leads research in software diversity through his Software Diversity Lab .

Prem Devanbuمشاهده پروفایل

استاد پژوهش

• Empirical Software Engineering
• Naturalness of Software
• Software Engineering education
• +۳ مورد دیگر

Prem Devanbu is a Research Professor of Computer Science at the University of California, Davis, where he has been a faculty member since transitioning from his industrial R&D position at Bell Labs in New Jersey. He holds a distinguished position in the Department of Computer Science within the College of Engineering, focusing on cutting-edge research at the intersection of software engineering and artificial intelligence. Dr. Devanbu earned his B.Tech from the Indian Institute of Technology (IIT) Madras and completed his Ph.D at Rutgers University under the supervision of Alex Borgida. His career path from industry to academia has shaped his practical yet research-oriented approach to software engineering problems. Devanbu's research primarily centers on Empirical Software Engineering , the Naturalness of Software , and Software Engineering education . His groundbreaking work on the naturalness hypothesis—that software exhibits statistical properties similar to natural language—has profoundly influenced the field. This research has expanded to explore bimodality in software (its dual nature as both machine-executable code and human-readable text), opening new avenues for analysis and tool development. His recent work heavily focuses on the application of Large Language Models to software engineering tasks, particularly in code summarization, program repair, and type inference. Analysis of Dr. Devanbu's recent publications reveals a clear trend toward leveraging Large Language Models for software engineering tasks. His research demonstrates how statistical properties of code can be exploited to improve software development processes, with particular emphasis on program understanding, documentation generation, and automated repair. The work bridges theoretical insights about code naturalness with practical applications that address real-world software maintenance challenges. Dr. Devanbu has received numerous prestigious awards recognizing his contributions to the field: ACM SIGSOFT Outstanding Research Award (2021) - "for profoundly changing the way researchers think about software by exploring connections between source code and natural language" Alexander von Humboldt Research Award (2022) IEEE Computer Society Harlan Mills Award (2024) ACM Fellow Six "test-of-time" or "10 year most influential paper" awards (MSR 2006, MSR 2009, ESEC/FSE 2008, ESEC/FSE 2009, ESEC/FSE 2011, ICSE 2012) Throughout his career, Dr. Devanbu has been actively involved in mentoring the next generation of software engineering researchers, serving on doctoral committees, and participating in New Faculty Symposia to support early-career academics. His research has been supported by significant grants that have enabled his team to explore innovative approaches at the intersection of empirical methods and software tool development. At UC Davis, he has contributed to building a strong software engineering research group that bridges theoretical insights with practical applications. Dr. Devanbu leads research efforts focused on understanding the statistical properties of software and leveraging these insights to build practical tools. His work on the naturalness and bimodality of code has established a framework that continues to influence how researchers approach program analysis and software development. His current team is at the forefront of exploring how Large Language Models can be effectively applied to software engineering tasks while accounting for the unique characteristics of code as a specialized form of human communication.

Alessandra Gorlaمشاهده پروفایل

دانشیار

• Malware detection for mobile applications
• Automatic software repair
• Software testing
• +۴ مورد دیگر

Alessandra Gorla is an associate researcher professor at IMDEA Software Institute in Madrid, Spain, with a strong background in software engineering research. She previously worked as a postdoctoral researcher with Andreas Zeller at Saarland University in Germany and completed her PhD under Mauro Pezzè at the University of Lugano in Switzerland. Her research bridges theoretical foundations with practical applications in mobile software systems. Her research focuses on malware detection for mobile applications, automatic software repair, software testing and analysis. She has developed techniques for detecting behavior anomalies in graphical user interfaces, identifying third-party libraries in mobile apps, and leveraging intrinsic software redundancy for reliability. Her work spans both Android and iOS ecosystems, with particular attention to permission systems, release practices, and security implications. Analysis of her recent publications reveals a strong trend toward mobile application security and analysis, with increasing focus on iOS systems alongside traditional Android research. Her work combines static and dynamic analysis techniques, often incorporating natural language processing for comment analysis and test generation. There's a clear progression from foundational work on intrinsic software redundancy to more applied research on mobile security and testing. FRITZ-KUTTER AWARD! for PhD thesis on Automatic Workarounds BEST PAPER AWARD! for Search-based Security Testing of Web Applications BEST STUDENT POSTER AWARD! for Automatic Workarounds as Failure Recoveries Dr. Gorla actively mentors students and seeks motivated individuals for internship and PhD opportunities in software engineering. She has served in various organizational roles including Tool Demonstrations co-chair for FSE 2016, Artifact Evaluation co-chair for ESSoS 2016 and ISSTA 2016, and multiple program committee positions at top software engineering conferences. Her work has been supported through collaborations with major research institutions and industry partners. At IMDEA Software Institute, Dr. Gorla leads research on mobile application analysis, particularly focusing on behavioral analysis of Android and iOS applications. Her CHABADA prototype for clustering Android apps by description topics and identifying API usage outliers demonstrates her practical approach to malware detection. She also investigates intrinsic software redundancy for building more resilient systems.

Hana Habibمشاهده پروفایل

استادیار

• Privacy
• Privacy Engineering
• Usable Privacy and Security
• +۶ مورد دیگر

Hana Habib is an Assistant Professor at Carnegie Mellon University's Software and Societal Systems Department in the School of Computer Science . She serves as Associate Director of the Master's in Privacy Engineering program and contributes to the Collaboratory Against Hate through human-centered design interventions. Ph.D. in Societal Computing (2021), Carnegie Mellon University MSIT in Information Security (2015), Carnegie Mellon University BS in Computer Science/ECE (2013), Cornell University Her research investigates privacy engineering challenges through human-computer interaction lenses, focusing on user behaviors in digital ecosystems, privacy choice interfaces , and hate speech mitigation strategies. Current work examines iconography effectiveness in privacy communication and regulatory compliance implementation. Recent publications analyze GDPR compliance mechanisms , CCPA implementation challenges, and behavioral advertising controls across platforms. Her empirical approach combines user studies with policy analysis to advance usable security frameworks. Scientific Recognition : Facebook Research Award recipient for advertising control usability Three-time CyLab Presidential Fellowship awardee Executive Women's Forum full-tuition scholarship Stokes Educational Scholarship Program participant Rising Stars Workshop invitee Habib previously served as a Program Committee member for EuroUSEC, USEC, and WAY conferences, and held leadership roles in CMU's OurCS Conference Committee and INI Alumni Leadership Council . Her work has been cited in CCPA rulemaking and influenced NIST Digital Identity Guidelines through password policy research.

Jason Kingمشاهده پروفایل

استاد آموزشی

• Cyber Security
• Software Engineering
• Programming Languages
• +۱ مورد دیگر

Jason King is an Associate Teaching Professor (mapped to 'Teaching Professor' in the valid ranks list) in the Department of Computer Science at NC State University. His primary focus is on undergraduate education and pedagogical innovation. Ph.D. in Computer Science (2015) M.S. in Computer Science (2011) B.S. in Computer Science (2009) King specializes in enhancing computer science education through technological innovation, with expertise in Cyber Security and Software Engineering & Programming Languages . His work emphasizes student engagement, automated grading systems, and integrating real-world practices into curricula. His publication history reveals a focus on pedagogical approaches in computer science education, including data structures, automated grading tools, testing frameworks, and security requirements analysis. These works span 2015–2021, with a strong emphasis on empirical studies and practical implementations. Gertrude Cox Award for Innovative Excellence in Teaching and Learning with Technology (2018) As course coordinator for CSC 316: Data Structures and Algorithms and CSC 415: Software Security , King leads curriculum development. He secured a $230,928 NSA-funded grant for ‘An Innovative Curriculum for Cybersecurity Education’ (2017–2018), which aimed to standardize cybersecurity education for federal and industry needs.

• Educational Technology
• Statistics Education
• Serious Gaming
• +۴ مورد دیگر

Naowarat Lewis is an Associate Professor at Anglia Ruskin University within the School of Economics, Finance and Law (Faculty of Business and Law). She concurrently serves as a Visiting Professor at Mahidol University, Thailand since 2024. Her multidisciplinary expertise spans Applied Statistics, Information Technology, and Information Science, directly informing her teaching and research in educational technology and statistics pedagogy. Her academic qualifications include: PhD in Computing and Information Science (Anglia Ruskin University) Postgraduate Certificate in Teaching and Learning (Higher Education) (Anglia Ruskin University) MSc in Information Technology (University of East London) BSc (Hons.) in Applied Statistics (University of the Thai Chamber of Commerce) Lewis pioneered the "Lewis Dynamic Model" educational framework examining technology-enhanced learner engagement in Business Statistics. Her research centers on Educational Technology, Serious Gaming, Statistics Education, and ICT for Development, with emphasis on video-based learning, gamification, and interactive pedagogy to improve quantitative skills. She actively integrates software development expertise from her professional background into pedagogical contexts. Analysis of her recent publications reveals consistent focus on technology-driven solutions for statistics education challenges, bridging Education, Business, and Computer Science disciplines through empirical studies and framework development. Key recognitions include: Vice-Chancellor’s Award for Learning and Teaching Excellence Senior Fellowship of the Higher Education Academy Lewis supervises DBA and PhD students while securing substantial research funding including the Low Carbon KEEP project (£79,253.36) and Retained Firefighters Union remote learning platform (£120,000). Her consultancy portfolio features statistical analysis for marketing campaigns (£10,000) and development of web-based learning facilities (£120,000), demonstrating strong industry-academia knowledge exchange. She contributes to editorial boards including Humanities and Social Sciences Communications (Nature Portfolio) and actively participates in curriculum development at Mahidol University, extending her impact through international academic collaboration.

Ajay Jhaمشاهده پروفایل

استادیار

• Software Engineering
• Software Testing
• Software Maintenance
• +۴ مورد دیگر

Ajay Jha is an Assistant Professor in the Department of Computer Science at North Dakota State University (NDSU), where he leads the Software Testing and Maintenance (STAM) Lab. His academic journey began with industry experience, followed by graduate studies and postdoctoral research that shaped his current focus on software engineering research. His educational background includes: Ph.D. in Computer Science from Kyungpook National University (2017) Master's degree from Kyungpook National University (2013) Over five years of industry experience before graduate studies, including co-founding two startups Postdoctoral research at University of Alberta (over two years) and Kyungpook National University (three years) Dr. Jha's research focuses on software engineering, particularly in the areas of software testing, maintenance, and evolution. His work centers on mining large-scale software repositories to uncover real-world issues in software quality, reliability, and maintainability. He develops innovative tools and techniques to address challenges in regression testing, library migration, and mobile application development. His research has significant practical implications for improving software development processes and enhancing the reliability of modern software systems, particularly in mobile and Python environments. His publication record shows a clear progression from Android-focused research to broader software engineering challenges, with recent work emphasizing Python library migration and large language models for software engineering tasks. His research methodology typically involves empirical studies of real-world software repositories combined with tool development to address identified challenges. Dr. Jha is actively involved in academic service, serving on program committees for major software engineering conferences including MSR, ICSME, SANER, ASE, and ICSE. He also reviews for prestigious journals such as IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology. At NDSU, he teaches a range of courses from undergraduate to graduate level, including Mobile Software Engineering, Software Development Processes, and Software Project Planning and Estimation. He also leads graduate seminars on specialized topics like 'LLMs for Software Testing and Maintenance' and 'Code Smell and Refactoring.' He leads the Software Testing and Maintenance (STAM) Lab at NDSU, which focuses on mining software repositories to identify quality issues and developing practical tools to address software maintenance challenges. The lab's research has produced several benchmarks (PyMigBench, JTestMigBench) and tools (TRec) that have been shared with the research community.

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.

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.

Jongmoon Baikمشاهده پروفایل

استاد

• software process modeling
• software economics
• software reliability engineering
• +۱ مورد دیگر

Jongmoon Baik serves as a Professor at the Korea Advanced Institute of Science and Technology (KAIST), School of Computing, South Korea, and maintains active membership in IEEE and ACM professional organizations. His academic foundation includes a PhD in Computer Science from the University of Southern California, establishing expertise in rigorous software engineering methodologies. Professor Baik's research program centers on software process modeling, software economics, software reliability engineering, and software Six Sigma, with emphasis on quantifying development efficiency and product quality through statistical process control. His work bridges theoretical modeling with industrial application in software lifecycle optimization. Current research trajectories demonstrate growing integration of security analysis into process modeling, exemplified by his upcoming ASE 2025 publication on vulnerability detection using modifiability signals at the line-level code analysis. Professional recognition includes sustained engagement with premier software engineering venues through conference participation and publications. He advises graduate researchers within KAIST's School of Computing and leads the Software Process Research Laboratory (SPIRAL), identifiable through his institutional web presence at spiral.kaist.ac.kr, focusing on empirical studies of software development economics and reliability metrics.

Diego Garbervetskyمشاهده پروفایل

دانشیار

• Static analysis
• Program verification
• Smart Contract analysis
• +۵ مورد دیگر

Diego Garbervetsky is an Associate Professor at the Computer Science Department, School of Sciences, University of Buenos Aires, and a Researcher at ICC/CONICET. He also serves as Director of the Institute of Research in Computer Sciences (ICC). His academic career spans multiple roles in software engineering research and education. His educational background is not explicitly stated in the provided texts, but his current position reflects extensive academic achievement. Garbervetsky's research focuses on static analysis techniques for Java-like programs and Smart Contracts, automated program verification , program understanding , and validation . His specific interests include program understanding, testing and verification of programs featuring rich protocols, static analysis for program verification, and automatic symbolic resource analysis (gas consumption, dynamic memory, energy, etc.). His research has significant implications for blockchain technology, particularly in smart contract verification. Analysis of his recent publications reveals a strong trend toward smart contract security and verification , with multiple papers on modal abstractions, predicate abstractions, and tools like VeriSol for Solidity smart contracts. His work bridges formal methods with practical software engineering challenges, particularly in the blockchain domain. His scientific contributions include the development of multiple research tools: Contractor: Automated tool for behavior validation Contractor.NET: Visual Studio extension for .NET validation JConsume2: Compositional analysis for Java heap memory Consume.Net: Compositional analysis for .NET heap memory BudaPest: Automated software verifier VInTime: Verification suite for Real Time systems Garbervetsky has supervised numerous PhD students including Daniel Wappner, Javier Godoy, and Alexis Soifer, with former students now working at companies like Microsoft, Veritran, and Dialpad. He has served on program committees for major conferences including ICSE, FSE, ASE, and ISSTA, demonstrating his standing in the software engineering research community. His service includes chairing workshops and serving on artifact evaluation committees, showing his commitment to research quality and reproducibility. He currently teaches Software Engineering 2 at the University of Buenos Aires, having previously taught courses in algorithms, automatic software validation, program analysis, programming paradigms, and computer organization.

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.

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.