Find Academic Supervisors

Ben JannView profile

Professor

• Statistical Methods
• Data Visualization
• Social Inequality
• +2 more

Ben Jann is a Professor and current Department Head at the University of Bern in the Department of Social Sciences . His methodological expertise spans statistical software development, decomposition analysis, and data visualization. Role: Department Head Institution: University of Bern Department: Department of Social Sciences Research Interests : Jann specializes in creating Stata modules for advanced statistical analysis, including geospatial mapping (geoplot), robust regression (robreg10), and inequality decomposition (oaxaca-blinder methods). His substantive work examines social inequality through economic and sociological lenses, analyzing how living costs and taxation systems impact income distribution in Switzerland. He also investigates educational transitions and gender disparities in STEM occupational choices through longitudinal studies like TREE2. Recent Publications Trends : Jann’s 15 most recent articles (2025-2023) demonstrate his focus on statistical software development for social science applications. Key contributions include methods for marginal odds ratios (2023), two-level model variance decomposition (2025), and list experiment analysis (2025). His empirical work connects statistical methodology to pressing social issues like gender wage gaps (2021) and teacher content knowledge in developing countries (2021).

• Sensor network data analysis
• Crowdsourced data analysis from citizen science initiatives
• Statistical modeling and analysis of spatio-temporal data
• +3 more

Francesco Finazzi is a full professor of statistics (SECS-S/02) at the Department of Economics of the University of Bergamo and maintains a research affiliation with the School of Mathematics and Statistics at the University of Glasgow. He is internationally recognized as the founder of the Earthquake Network citizen science initiative (www.sismo.app), which pioneered the first globally implemented smartphone-based seismic early warning system. His research expertise spans sensor network data analysis, crowdsourced data from citizen science initiatives, statistical modeling of spatio-temporal data, and parallel scientific software development. He has made significant contributions to developing statistical methodologies for earthquake parameter estimation using smartphone sensor networks and has advanced techniques for analyzing environmental time series data. His recent publications reveal a strong research trajectory focused on earthquake early warning systems, spatio-temporal environmental modeling, and statistical approaches to crowdsourced seismic data. His work uniquely bridges statistics, seismology, and computer science to develop practical, real-world early warning solutions that leverage mobile technology and citizen participation. Finazzi has secured substantial research funding as Principal Investigator for the University of Bergamo in major Horizon 2020 projects, including RISE (Real-time earthquake risk reduction for a ReSilient Europe, 2019-2023, €8,000,000) and TURNkey (Towards more Earthquake-resilient Urban Societies through a Multi-sensor-based Information System, 2019-2022, €7,999,948). He leads the Earthquake Network initiative, a groundbreaking citizen science project that has created the first operational smartphone-based seismic early warning system worldwide. This platform not only provides early warnings but also facilitates rapid impact assessment and supports search and rescue operations following seismic events.

Talia RingerView profile

Assistant Professor

• Verification
• Proof Engineering
• Dependent Type Theory
• +3 more

Talia Ringer is an Assistant Professor at the University of Illinois at Urbana-Champaign , focusing on making program verification using interactive theorem provers more accessible through improved proof engineering tools and practices. Her research addresses challenges in maintaining proofs as programs evolve and advancing formal verification. Ph.D., University of Washington (2021), NSF GRFP and P.E.O. Fellow B.S., Mathematics and Computer Science, University of Maryland Former software engineer at Amazon Her research interests include: Program Verification Proof Engineering Dependent Type Theory Interactive Theorem Provers (Coq) Key trends in her publications include: Automated proof generation and repair using large language models Tool development for Coq and other proof assistants Formal verification of software and data structures Integration of identifiers and type equivalences in verification Test input generation for software reliability Scientific awards and honors: NSF Graduate Research Fellowship Program (GRFP) P.E.O. Scholar Award She has advised numerous researchers through her work and founded the SIGPLAN-M Mentoring Program and Computing Connections Fellowship . No specific student names are listed in the scraped text.

Vikram S. AdveView profile

Professor

• Compilers
• Programming Languages
• Parallel Computing
• +6 more

Vikram S. Adve is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. He co-founded and co-leads the Center for Digital Agriculture and directs the USDA-funded AIFARMS Institute , focusing on AI applications in sustainable agriculture. His research bridges compilers, parallel systems, and AI to address challenges in edge computing and digital farming. Research interests span compiler technologies (LLVM, HPVM), parallel programming models , software reliability , and AI-driven agriculture . Key projects include: CropWizard : Generative AI for agricultural decision-making. HPVM/ApproxHPVM : Compiler IR for edge devices. Hydride/MISAAL : Automated retargetable compiler construction. Recent publications (2018-2025) emphasize compiler optimizations, approximate computing, binary analysis, and AI for systems. Trends show convergence of compiler techniques , heterogeneous computing , and AI applications in agriculture and edge devices. Adve actively recruits students for projects funded by USDA, Intel, Amazon, and Illinois DPI. He leads the HPVM compiler team and digital agriculture initiatives , integrating cross-disciplinary research across CS, engineering, and agronomy.

Michael L. ScottView profile

Professor

• Parallel Computing
• Operating Systems
• Programming Languages
• +7 more

Michael L. Scott is the Arthur Gould Yates Professor of Engineering in the Department of Computer Science at the University of Rochester's Hajim School of Engineering and Applied Sciences. He received his Ph.D. from the University of Wisconsin-Madison in 1985 and has been a faculty member at Rochester since 1985, serving as Department Chair multiple times (1996-99, 2007, 2017, 2020-2024). He is a Fellow of the ACM, IEEE, and AAAS, and recipient of numerous awards including the Edsger W. Dijkstra Prize in Distributed Computing. Dr. Scott's research focuses on parallel and distributed systems, with particular expertise in synchronization mechanisms, transactional memory, and persistent memory systems. His work spans theoretical foundations to practical implementations, with numerous influential publications and open-source systems like RSTM and Ralloc. His research has addressed critical challenges in concurrent programming, memory management, and system reliability. His publications show a consistent focus on improving the reliability and performance of concurrent systems, with recent work centered on persistent memory technologies. The trajectory of his research demonstrates a progression from fundamental synchronization algorithms to sophisticated systems addressing modern hardware challenges. His publications span top venues in systems, architecture, and programming languages. His scientific honors include: ACM Fellow (2006) IEEE Fellow (2010) AAAS Fellow Edsger W. Dijkstra Prize in Distributed Computing (2006) University of Rochester's Goergen Award for Teaching (2001) Hajim School Lifetime Achievement Award (2018) IEEE TCCA/HPCA Test of Time Award (2022) Dr. Scott has advised over 25 Ph.D. students who have gone on to successful careers in academia and industry at institutions including Lehigh University, Google, Intel, Facebook, and NVIDIA. His textbook 'Programming Language Pragmatics' is a standard reference in the field, now in its 5th edition. He also co-authored 'Shared-Memory Synchronization,' a comprehensive treatment of the field. He spent the 2014-2015 academic year as a Visiting Scientist at Google. His research group, the Rochester Concurrent Systems Group, has developed numerous influential systems including RSTM (a software transactional memory system), Ralloc (a persistent memory allocator), and Montage (a system for persistent data structures). His work often bridges theoretical correctness with practical performance considerations.

Julia LawallView profile

Researcher

• Operating Systems
• Domain-Specific Languages
• Program Transformation
• +4 more

Julia Lawall is a Senior Research Scientist (Directrice de Recherche) at Inria-Paris, where she leads research in the Whisper group. She has made significant contributions to the fields of programming languages, operating systems, and software engineering, with a particular focus on program transformation and Linux kernel development. Her work bridges theoretical computer science with practical software engineering challenges. Dr. Lawall's research primarily centers on the design and implementation of domain-specific languages for operating system problems, program transformation techniques, and automated software evolution. Her most notable contribution is the Coccinelle framework, which has been instrumental in automating the evolution of Linux device drivers for over a decade. Her work spans from theoretical foundations in optimal reduction of the lambda calculus to practical tools that address real-world software maintenance challenges in large-scale systems like the Linux kernel. Her publication record demonstrates consistent contributions across multiple domains, with recent work focusing on Android API evolution, Linux kernel bug detection, and program transformation techniques. The trajectory of her research shows a progression from theoretical programming language concepts to increasingly practical applications in system software maintenance and evolution. EuroSys Test of time award for 'Documenting and Automating Collateral Evolutions in Linux Device Drivers' at EuroSys 2008 Best paper award for 'Diagnosys: Automatic Generation of a Debugging Interface to the Linux kernel' at ASE 2012 Most Influential ICFP Paper Award for foundational work on lambda calculus Best Reviewer at GPCE 2020 and Distinguished Reviewer at ASE 2020 Dr. Lawall has been actively involved in the academic community, serving as program co-chair for numerous prestigious conferences including ASE 2019, FSE 2026, and EuroSys 2025. She has also contributed to community initiatives as the Linux kernel coordinator for Outreachy (2015-2018) and as a member of the advisory board for Software Heritage. Her leadership extends to editorial roles, including associate editor for Higher-Order and Symbolic Computation and membership on the editorial board of Science of Computer Programming. She leads the Whisper research group at Inria-Paris, which focuses on program transformation techniques and their applications to system software. The group has developed several influential tools including Coccinelle, Coccinelle4J, LiLiput, Prequel, and JMake, which have had substantial impact on both academic research and industrial practice in software maintenance and evolution.

Ben HermannView profile

Professor

• Programming Languages
• Security
• Static Analysis
• +6 more

Ben Hermann is a Professor for Secure Software Engineering at Technische Universität Dortmund, Germany, with research focusing on the intersection of programming languages and security. His work primarily centers on vulnerability detection using static analysis, risk assessment of software libraries, security guarantees in type systems, and language-based security. His academic journey includes a doctoral degree from Technische Universität Darmstadt in 2016, followed by postdoctoral work in Eric Bodden's Secure Software Engineering Group at the Heinz Nixdorf Institute. Since 2020, he has held a tenure-track professorship at TU Dortmund, after serving as an interim Professor for IT Security at Paderborn University from October 2019 to September 2020. Hermann's research portfolio demonstrates consistent output in static program analysis frameworks, particularly with contributions to Soot and PhASAR. His work spans vulnerability detection, analysis reusability, and research quality in computer science, with emphasis on artifact evaluation. His recent publications show progression from framework development toward security applications and research methodology. Hermann has been actively involved in numerous conferences including ASE, ICSE, ECOOP, and SPLASH, often serving in program committees and as session chair. His research group at TU Dortmund includes students like Maximilian Krebs who presented work on energy consumption prediction of programs. He maintains an active presence in the academic community, advocating for research quality and reproducibility, and serves in various organizational roles including Doctoral Symposium Chair and Diversity & Inclusion Chair for conferences.

Caleb StanfordView profile

Assistant Professor

• Programming Languages
• Formal Methods
• Distributed Systems
• +4 more

Caleb Stanford is an Assistant Professor in the Department of Computer Science at the University of California, Davis. He specializes in programming languages and formal methods with applications to distributed systems, data processing, and computer security. His research focuses on developing practical tools and theoretical foundations for secure and reliable systems. Research interests span programming languages theory, formal verification techniques for distributed systems, Rust ecosystem security, stream processing verification, and automata theory extensions. Specific projects include securing Rust crates through automated auditing tools, extending derivative-based abstractions for regular expressions, and developing verification frameworks for dataflow programming. His publications demonstrate consistent focus on formal methods applications, with recent emphasis on Rust program safety (2025), stream processing verification (2020-2024), and incremental graph algorithms (2023). Article keywords frequently include formal verification, programming languages, distributed systems, and automata theory. Dr. Stanford actively recruits students for research positions: Undergraduates should demonstrate excellence in relevant coursework Prospective PhD students must apply through UC Davis admissions Current openings focus on Rust security, formal methods, and distributed systems verification He leads the Guided Incremental Digraphs (GID) research project and collaborates with UCSD on Rust security initiatives. Community contributions include serving as Artifact Evaluation Co-Chair for POPL 2024 and committee member for PLDI and OOPSLA conferences.

• Ergonomics
• Musculoskeletal Disorders Prevention
• Biomechanical Exposure Measurement
• +7 more

Mikael Forsman serves as Professor and Head of the Division of Ergonomics at KTH Royal Institute of Technology's School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). With a PhD in Medical Electronics from Chalmers University (1995), he has specialized in ergonomics research since 1994, developing both advanced research methods and practical tools for practitioners. His work spans laboratory experiments, field studies, and participative interventions at individual and organizational levels. M.Sc. (E. Eng.) from Chalmers University of Technology, Göteborg (1987) Ph.D. in Medical Electronics from Chalmers University of Technology (1995) Docent of Ergonomics, Department of Human Factors Engineering (1999) Forsman's research centers on reliable measurement methods for ergonomic risk assessment and prevention of musculoskeletal disorders. Key areas include: Development of inexpensive, user-friendly systems using microelectronics for biomechanical exposure measurement Field studies of physical and psychosocial exposures across various occupations Creation of questionnaires and investigation of correlations between work ability, musculoskeletal pain, and workload Work accident prevention and analysis Participative ergonomic interventions at multiple organizational levels Analysis of his recent publications (2023-2025) reveals three dominant research trends: (1) Development and validation of wearable sensor technologies for real-time ergonomic assessment; (2) Application of ergonomic principles in specialized fields like surgery, construction, and waste management; (3) Integration of human factors with digital technologies such as BIM. His work consistently bridges theoretical biomechanics with practical workplace solutions, emphasizing accessible methods for practitioners. Forsman has supervised approximately 20 master's theses and served as main supervisor for 3 doctoral theses with 8 additional co-supervised theses. Currently, he supervises 3 PhD students as main supervisor and 4 others as co-supervisor. He has led approximately 12 research projects and participated as co-applicant in about 20 projects funded by research councils. His educational contributions include responsibility for numerous master's level courses in ergonomics and work environment. As leader of KTH's Division of Ergonomics, Forsman directs research on biomechanical exposure measurement and practical ergonomic interventions. He actively promotes Nordic collaboration in ergonomics research, noting similarities in work life and research traditions across the region. Current projects include the Smart Workwear Consortium developing systems with real-time vibrotactile feedback for reducing postural exposure in industrial settings, and research on leadership aspects associated with digital technology adoption in construction.

Xiaofei XieView profile

Assistant Professor

• Program Analysis
• Software Testing
• Vulnerability Detection
• +4 more

Xiaofei Xie is an Assistant Professor in the School of Computing and Information Systems at Singapore Management University (SMU), where he has been employed since 2022. Prior to this position, he was a postdoctoral researcher at Nanyang Technological University in Singapore from 2018 to 2021. His research primarily focuses on program analysis, software testing, vulnerability detection, and quality assurance of AI systems. SMU is ranked No. 9 (No. 5 in Asia) in the Software Engineering category according to CSRankings. Dr. Xie's research interests span multiple critical areas in software engineering and AI systems. His work on program analysis includes detecting non-termination bugs and developing practical methods like EndWatch for real-world software. In software testing, he has made significant contributions to deep learning systems testing, autonomous driving systems testing, and smart contract security. His research on vulnerability detection encompasses various aspects of AI security, including backdoor attacks, adversarial examples, and security testing for web-based deep learning frameworks. His quality assurance work for AI systems includes developing metrics for robustness evaluation and creating testing methodologies for diverse AI applications. Dr. Xie's publication record shows a strong trend toward integrating large language models with traditional software engineering techniques. His recent work demonstrates increasing focus on testing autonomous systems, securing AI models, and applying advanced machine learning techniques to traditional software engineering problems. The research spans multiple domains including deep learning frameworks, smart contracts, autonomous driving systems, and federated learning environments. Among his notable achievements are multiple ACM SIGSOFT Distinguished Paper Awards (ASE 2019, ASE 2023, ISSTA 2022), the ACM Tianjin Doctoral Dissertation Award 2019, and the Best Paper Award at APSEC 2020. His work has been accepted to top-tier conferences including ICSE, FSE, ASE, ISSTA, and security venues like USENIX Security. Dr. Xie actively serves the academic community as a PC co-chair for ICECCS 2025 and as a program committee member for numerous prestigious conferences including ICSE, FSE, ASE, ISSTA, and AAAI. He has also organized workshops such as the Workshop on AI and Software Testing/Analysis (AISTA) and served as Guest Editor for special issues on AI security. His service demonstrates leadership in bridging software engineering with AI and security research communities.

Yi LiView profile

Associate Professor

• Software Engineering
• Program Analysis
• Automated Reasoning
• +2 more

Yi Li is an Associate Professor at the College of Computing and Data Science, Nanyang Technological University (NTU), Singapore. He leads the Software Reliability and Security Lab (SRSLab@NTU) which focuses on program analysis and automated reasoning techniques for software engineering and security applications. Dr. Li received his BComp degree in Computer Science from the National University of Singapore in 2011, followed by MSc (2013) and PhD (2018) degrees in Computer Science from the University of Toronto. His educational background has positioned him at the forefront of software reliability research. His research interests span software engineering, program analysis, automated reasoning, and formal methods, with particular focus on software reliability, security, and smart contract analysis. Dr. Li's work develops practical solutions for constructing high-quality software systems that are both reliable and sustainable. His research has identified critical issues in API documentation, smart contract security, program termination, and library compatibility across multiple programming ecosystems. Dr. Li's publications demonstrate consistent contributions to software engineering research over the past five years, with recent work focusing on smart contract analysis, API documentation verification, and program termination detection. His research methodology often combines static and dynamic analysis techniques with machine learning approaches to address challenging problems in software reliability and security. Five ACM Distinguished Paper Awards at top conferences Two Best Artifact Awards recognizing research reproducibility Publications at premier venues including ASE, ISSTA, FSE, and ICSE Dr. Li actively contributes to the software engineering community through service on program committees of flagship conferences (ICSE, FSE, ASE, ISSTA) and co-chairing program committees for ICFEM'23, ICECCS'20, SEAIS'22, and ICFEM'19 Doctoral Symposium. His lab, SRSLab@NTU, has developed multiple influential tools including InvCon, DocCon, and EndWatch, while fostering research in software reliability and security for emerging technologies.

Darko MarinovView profile

Professor

• Software Engineering
• Software Testing
• Regression Test Prioritization
• +4 more

Darko Marinov is a Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign, where he conducts research focused on improving software quality through advanced testing techniques. His work addresses fundamental challenges in software testing including flaky tests, regression test prioritization, and configuration testing. Dr. Marinov's research interests span multiple areas of Software Engineering , with particular emphasis on: Software Testing and Quality Assurance Regression Test Prioritization and Selection Flaky and Non-Deterministic Tests Configuration and Variability Testing Test Suite Optimization Software Reliability and Defect Detection His extensive publication record demonstrates consistent contributions to the field, with research trends showing evolution from foundational testing techniques to addressing modern challenges in software testing, particularly focusing on reliability issues in continuous integration environments and the growing complexity of test suites in large software systems. Dr. Marinov has received numerous prestigious awards recognizing the impact of his work: Two ACM SIGSOFT Impact Paper awards (2012, 2019) ASE Most Influential Paper Award (2015) FSE Test-of-Time Honorable Mention (2024) Seven ACM SIGSOFT Distinguished Paper awards CHI Best Paper Award (2017) Elected Fellow of Automated Software Engineering (2023) His research has been generously supported by major technology companies and government agencies including NSF, Boeing, Facebook, Google, Huawei, IBM, Intel, Microsoft, Qualcomm, Samsung, and SRC. Dr. Marinov actively contributes to the academic community through conference organization, serving as Area Chair for ASE 2025 and Program Committee member for multiple conferences including FSE 2026. His mentoring excellence has been recognized through various awards, highlighting his commitment to guiding the next generation of software engineering researchers.

Alessio GambiView profile

Researcher

• Software Engineering
• Software Testing
• Self-Driving Cars
• +4 more

Alessio Gambi is a Researcher at the Austrian Institute of Technology (AIT) within the Security & Communication Technologies department, specializing in software engineering for autonomous systems. His current work focuses on testing methodologies for self-driving cars, self-adaptive systems, and cloud environments. His research interests center on Software Testing for Autonomous Vehicles , where he develops novel techniques for scenario generation, safety validation, and uncertainty management. Key areas include search-based procedural content generation, simulation-based testing, and the integration of large language models for test learning. His work bridges theoretical advances with practical tools like Flexcrash and TEASER for real-world validation. Analysis of his recent publications (2023-2025) reveals a strong trend toward autonomous vehicle testing with increasing incorporation of AI techniques. Approximately 60% of his work addresses self-driving car validation, 25% focuses on general software testing methodologies, and 15% explores AI/LLM applications in testing. His subfield specialization shows consistent emphasis on critical scenario generation, mixed-traffic simulation, and safety monitoring. Gambi actively contributes to the software engineering community through program committee roles at major conferences including ASE (2023-2025), ICSE (2024-2026), ISSTA (2021-2025), and ESEC/FSE. He has served as session chair, workshop organizer, and track committee member across these venues, demonstrating leadership in software testing research. His professional activities include developing open-source testing tools (visible on GitHub), teaching engagements like the Database Systems course at AIT (2024), and industry collaborations through AIT's research infrastructure. Current projects focus on predictive safety monitoring and uncertainty management for automated driving systems.

Zhi JinView profile

Professor

• Requirements Engineering
• Self-Adaptive Systems
• Learning from Source Code
• +5 more

Zhi Jin is a Professor in the Department of Computer Science and Technology at Peking University, where he has been employed since 2009. Previously, he served as a professor at the Academy of Mathematics and System Sciences, Chinese Academy of Sciences from 1994-2009. He received his BS from Zhejiang University in 1984 and MS/PhD from National University of Defense Technology in 1984 and 1992 respectively. He progressed from assistant professor (1992) to associate professor (1995) to full professor (2001). His research focuses on knowledge engineering and software engineering, with special interests in knowledge graphs, self-adaptive systems, and deep learning applications. Current research directions include Self-Adaptive Software in Human-Cyber-Physical Systems, Crowd-based Requirements Engineering, and Learning from both Natural Language and Programming Language. His work bridges theoretical knowledge engineering with practical software development challenges. His recent publications demonstrate a strong trend toward applying large language models and AI techniques to traditional software engineering problems, particularly in requirements engineering, code generation, and vulnerability detection. The articles span multiple high-impact venues including ASE, ICSE, and RE, with significant focus on aerospace applications and multi-agent collaboration approaches. Scientific honors include: Winner of National Science Fund for Distinguished Young Scholars (2006) Project 973 project lead scientist (2014) Member of Discipline Appraisal Group of the Academic Degree Commission (2015) Multiple ACM Distinguished Paper Awards He serves in numerous editorial roles including Associate Editor for IEEE Transactions on Software Engineering (2018-present) and IEEE Transactions on Reliability (2019-present). He is also an Editorial Board Member for Empirical Software Engineering and Requirements Engineering Journal, and holds leadership positions in the China Computer Federation. His extensive conference service includes PC membership for ICSE, FSE, RE, and other major software engineering venues.

Reyhaneh JabbarvandView profile

Assistant Professor

• Neural Program Analysis
• Software Testing
• Bug Localization
• +4 more

Reyhaneh Jabbarvand is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, where she leads the Intelligent CAT Lab. Her research focuses on improving software quality, reliability, and maintenance through neuro-symbolic approaches that combine AI techniques with formal methods. Her research interests span Neural Program Analysis, Software Testing (with emphasis on mobile apps and autonomous software), Bug Localization, and Applied Optimization for Software Analysis. She has made significant contributions to the fields of energy testing for Android applications, neuro-symbolic approaches for code analysis, and large language models for software engineering tasks. Dr. Jabbarvand's recent publications reveal strong trends in applying machine learning to software engineering problems, particularly using neuro-symbolic methods to bridge the gap between deep learning and formal program analysis. Her work on code translation, test flakiness, and test oracle generation demonstrates her focus on practical applications of AI in software development workflows. Google PhD Fellowship in Programming Technology and Software Engineering Rising Star in EECS NSF CAREER Award Dr. Jabbarvand has received research funding from multiple sources including NSF, IBM Research, and C3.ai. She actively mentors students through her Intelligent CAT Lab and has served on numerous program committees for major software engineering conferences including ICSE, FSE, and ISSTA. She teaches courses on Advanced Topics in Software Engineering, ML for Code, and Software Engineering I. Her lab focuses on neuro-symbolic approaches to software engineering problems, bringing together PhD, undergraduate, and high school students to tackle challenges in AI-assisted software development and testing.