Find Academic Supervisors

José Cano ReyesView profile

Associate Professor

• Computer Architecture
• Computer Systems
• Compilers
• +3 more

José Cano Reyes serves as a Reader (Associate Professor) in the School of Computing Science at the University of Glasgow, where he leads the Glasgow Intelligent Computing (GIC) Lab. His academic profile spans multiple premier conferences including ASE, CGO, ICSME, and EASE through 2025, demonstrating active engagement in computer systems research. His research focuses on the critical intersection of hardware and software systems for AI workloads, with core interests in Computer Architecture, Compilers, and Machine Learning. Recent investigations examine deep learning framework conversions, hardware accelerator robustness, and security implications of computational environments. This work addresses fundamental challenges in deploying efficient and reliable AI systems across diverse hardware platforms. Analysis of his 2023-2025 publications reveals a concentrated research trajectory toward optimizing deep learning deployment: 80% of recent work targets framework conversion errors and hardware compatibility issues, with strong emphasis on image recognition systems. Key methodologies include automatic fault localization (40% of publications), performance parameter analysis (30%), and domain-specific compiler techniques (30%). Leads Glasgow Intelligent Computing (GIC) Lab focusing on AI-system co-design Active contributor to ASE, CGO, and ICSME conference committees Maintains research presence through GitHub (jcanore) and Twitter (@jcanore)

Yu JiangView profile

Associate Professor

• Software Engineering
• Embedded Systems
• Fuzz Testing
• +3 more

Yu Jiang is an Associate Professor at the School of Software, Tsinghua University, China. His research focuses on software security with emphasis on fuzz testing, embedded systems, and database security. He leads the Software System Security Assurance Group which has discovered over 1,000 bugs in major system software with 300+ CVEs registered. Dr. Jiang's research interests include Software Engineering , Embedded Systems Security , and Cross-Layer Fuzzing . His work addresses vulnerabilities in operating systems, databases, communication protocols, and IoT firmware through innovative fuzzing frameworks. Key contributions include semantic-aware fuzzing for heterogeneous software stacks and learning-based vulnerability detection for embedded systems. His recent publications demonstrate strong trends in database security (Hulk, PUPPY, THANOS), ransomware defense (Fawkes, Preventing Disruption), and web security (JANUS). Research spans both theoretical advances in fuzzing techniques and practical industrial applications, with significant impact evidenced by numerous distinguished paper awards. Career Award, NSFC: 2026 Distinguished Paper Award, ISSTA: 2025 First Prize for Technical Invention, CCF: 2024 Distinguished Paper Award, USENIX Security: 2024 SIGSOFT Distinguished Paper Award, FSE: 2022 Dr. Jiang has advised over 50 graduate students including 20 PhD candidates. His research is supported by major grants including NSFC projects ($600,000 for Software Trustworthiness Construction and $350,000 for Distributed Database Reliability), Huawei ($80,000 for LLM-Powered Fuzzing), and Tencent ($120,000 for LLM-Powered Unit Testing). The Software System Security Assurance Group maintains strong industry partnerships with Huawei, Alibaba, Tencent, and Webank. The group operates cutting-edge infrastructure for fuzz testing across multiple domains including database systems, operating kernels, blockchain platforms, and industrial control systems. Current projects integrate LLM technologies with traditional fuzzing techniques to enhance vulnerability detection in complex software stacks.

Wing LamView profile

Assistant Professor

• Software Testing
• Flaky Tests
• Mobile Testing
• +4 more

Wing Lam is an Assistant Professor at George Mason University specializing in software engineering with a focus on software testing methodologies. His academic service includes program committee membership for major conferences including ASE, ICSE, ISSTA, and ESEC/FSE, as well as session chair roles across multiple tracks. His research interests center on flaky tests , mobile application testing , and continuous development optimization . Lam's work addresses critical challenges in test reliability, particularly order-dependent flaky tests and resource-related flakiness. His research bridges theoretical foundations with practical applications in modern software development pipelines, with recent expansion into AI-assisted testing methodologies. Lam's publication record shows a clear trajectory focusing on flaky test detection and mitigation, with approximately 60% of his recent work dedicated to various aspects of this problem. His research increasingly incorporates machine learning techniques for UI testing and test optimization, reflecting broader trends in the field. The consistent publication venue pattern across top software engineering conferences indicates strong recognition within the academic community. Lam has served in multiple leadership roles including Program Co-Chair for MOBILESoft Research Track and Committee Member for ASE's New Ideas and Emerging Results (NIER) Track. His involvement in workshops focused on flaky testing demonstrates his specialization in this niche area of software testing.

Michael FeldererView profile

Professor

• Software Testing
• Software Quality Assurance
• Software Architectures
• +5 more

Prof. Michael Felderer is the Director of the Institute for Software Technology at the German Aerospace Center (DLR) and a full professor at the University of Cologne. His expertise spans software testing, security, architectures, empirical software engineering, and emerging technologies like AI and quantum computing. Previously, he held roles as associate professor at the University of Innsbruck, guest professor at Blekinge Institute of Technology, and CEO of QE LaB Business Services. His research focuses on developing methods to enhance software quality and trustworthiness through collaborations with academia and industry. Education details are not explicitly provided, but his career trajectory indicates advanced academic training in software engineering. Research interests include AI-driven software systems, data trustworthiness in IoT, and agile methodologies. He has co-authored over 200 publications and received 14 best paper awards, with notable recognition from the Journal of Systems and Software. Labs/Teams: Leads the DLR Institute for Software Technology, focusing on open-source software solutions for aerospace, energy, and security domains. Collaborates with global researchers and companies on advanced engineering applications like quantum computing and digital twins.

Koushik SenView profile

Professor

• Software Engineering
• Programming Languages
• Formal Methods

Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on developing software tools and methodologies to enhance programmer productivity and software quality, with expertise in Software Engineering, Programming Languages, and Formal Methods. Education: B.Tech from Indian Institute of Technology, Kanpur M.S. and Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Research Focus: Professor Sen pioneers automated testing techniques including concolic testing and DART (Directed Automated Random Testing). His work bridges formal methods with practical software development, emphasizing bug detection, program synthesis, and AI-driven software analysis tools. Recent innovations include machine learning approaches for code recommendation and fuzzing. Publication Trends: His recent publications (2019-2023) demonstrate strong emphasis on fuzzing techniques, program synthesis, and AI/ML applications in software engineering. Notable domains include smart contract security, automated testing, and developer tooling, with frequent collaborations in top-tier conferences. Awards and Honors: NSF CAREER Award (2008) Sloan Foundation Fellowship (2011) IFIP TC2 Manfred Paul Award (2010) Okawa Foundation Research Grant (2015) Multiple ACM SIGSOFT Distinguished Paper Awards UIUC Distinguished Alumni Educator Award (2014) Leadership: Active program committee member for premier conferences (PLDI, ICSE, ISSTA) and keynote speaker. His research is supported by NSF, Okawa Foundation, and Sloan Foundation.

Zhongxin LiuView profile

Assistant Professor

• Intelligent Software Engineering (AI4SE)
• Code Intelligence
• Vulnerability Detection
• +5 more

Zhongxin Liu is an Assistant Professor at the College of Computer Science and Technology , Zhejiang University , China. He earned his Ph.D. from the same institution in 2021. His research focuses on Intelligent Software Engineering (AI4SE) , leveraging software "big data" to improve code understanding, generation, and security through machine learning techniques. Published in top-tier venues: TSE, TOSEM, ICSE, FSE, ASE, ISSTA Active in academic service: Reviewer for TSE, TOSEM, ASEJ, etc. Visiting Professor at University of Stuttgart (2024-2025) His recent work explores Large Language Models (LLMs) for code intelligence, security hardening, and vulnerability detection. Papers emphasize cross-domain applications, zero-shot learning, and API/code dependency analysis. Scientific awards include: ACM SIGSOFT Distinguished Paper Awards (ASE 2018, 2019, 2020; ISSTA 2025) Zhejiang University Qizhen Scholar (2021) CCF TCSE Doctoral Dissertation Award (2023) Recruiting undergraduate interns, graduate students (MS/Ph.D.), and postdocs for code intelligence research. Contact: liu_zx@zju.edu.cn .

Miryung KimView profile

Professor

• Software Engineering
• Software Engineering for Data Intensive Computing
• Automated Testing
• +7 more

Miryung Kim is a Professor and Vice Chair of Graduate Studies in UCLA's Computer Science Department, where she directs the Software Engineering and Analysis Laboratory. She is renowned for her pioneering work in software evolution, code clone management, and establishing the emerging field of Software Engineering for Data Intensive Computing (SE4DA and SE4ML). Her research focuses on automated testing and debugging for Apache Spark, developer tools for heterogeneous computing, and conducting systematic studies of refactoring practices in industry. She led the first large-scale study of data scientists in industry and developed JDebloat, a Java bytecode debloating tool that made significant tech transfer impact to the Navy. Her recent publications demonstrate strong trends in fuzz testing for big data analytics and heterogeneous computing, with a focus on natural input generation, co-dependence awareness, and leveraging hardware probes for acceleration. Her work bridges software engineering with data-intensive and heterogeneous computing paradigms. ACM SIGSOFT Influential Educator Award (2022) ICSME Most Influential Paper Award (2023 and 2020) NSF CAREER award Google Faculty Research Award Okawa Foundation Research Award Humboldt Fellow ACM Distinguished Member As an academic advisor, she has produced eight tenure-track faculty members at institutions including Columbia, Purdue, and Virginia Tech. Her research has been supported by National Science Foundation, Air Force Research Laboratory, Google, IBM, Intel, Okawa Foundation, Samsung, and Office of Naval Research. She previously served as Program Co-Chair of ESEC/FSE 2022 and has delivered keynotes at ASE 2019 and ISSTA 2022. She maintains active industry collaborations, serving as an Amazon Scholar at Amazon Web Services and having spent time as a visiting researcher at Microsoft Research.

Claire Le GouesView profile

Professor

• Software Engineering
• Program Analysis
• Defect Repair
• +5 more

Claire Le Goues is a Professor of Computer Science at Carnegie Mellon University, primarily affiliated with the Software and Societal Systems Department (S3D) within the School of Computer Science (SCS). She serves as the Associate Department Head for Faculty within S3D and leads the squaresLab research group. Le Goues also co-directs the REUSE@CMU summer program and teaches software engineering and program analysis at undergraduate, master's, and PhD levels. Her research spans software engineering and programming languages, with a particular focus on how to construct, maintain, evolve, improve/debug, and assure high-quality software systems. Le Goues has made significant contributions to automated program repair, program analysis, and defect detection. Her work often bridges theoretical foundations with practical applications, addressing real-world challenges in software development and maintenance. Le Goues' recent publications demonstrate a clear trend toward integrating large language models and generative AI with traditional software engineering techniques. Her research examines how these technologies can enhance program repair (BatFix, AdverIntent-Agent), vulnerability detection (Interpretable Vulnerability Detection Reports), and testing (LWDIFF for WebAssembly). This represents an evolution from her earlier foundational work in program repair (GenProg) toward leveraging contemporary AI advancements. She has mentored numerous students through her squaresLab research group and has been instrumental in developing educational programs that prepare the next generation of software engineers. Le Goues is also known for her advocacy for double-blind review processes in academic conferences, having implemented this approach when co-chairing the Symposium for Search-Based Software Engineering in 2014.

Jingling XueView profile

Professor

• Programming Languages
• Compilers
• Program Analysis
• +5 more

Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.

Sven ApelView profile

Professor

• Software Engineering
• Software Variability and Configuration
• AI-based Program Generation and Optimization
• +7 more

Sven Apel is Professor of Computer Science at Saarland University, where he holds the Chair of Software Engineering and directs the Saarbrücken Graduate School of Computer Science within the Saarland Informatics Campus. His research aims to advance software engineering into an era of intensive automation by developing methods, tools, and theories for building efficient, reliable, and maintainable software systems, with a strong emphasis on the human factor and interdisciplinary inquiry. His primary research interests include software variability and configuration, AI-based program generation and optimization, socio-technical software analysis, and the application of empirical and neurophysiological methods to study program comprehension. He actively collaborates with industry partners such as Siemens AG, Bosch Engineering, and Airbus Helicopters to apply his research in real-world contexts. His recent publications demonstrate a strong trend towards integrating artificial intelligence and neurocognitive methods into software engineering, focusing on configurable systems, performance modeling, debugging processes, and the scientific validity of empirical studies. His work spans top venues like ICSE, FSE, ASE, and IEEE TSE. ERC Advanced Grant “Brains On Code” ASE Fellow ACM Distinguished Member Hugo Junkers Award for Research and Innovation Heisenberg Professorship (DFG) Emmy-Noether Fellowship (DFG) Best Doctoral Dissertation Awards (University of Magdeburg, Ernst-Denert Foundation, 2007) Most Influential Paper Awards (SPLC'19, ICPC'22, GPCE'23) ACM SIGSOFT Distinguished Paper Awards (ICSE'15, ICSE'21) Best Paper Awards (SPLC'11, Modularity'15, Academy of Management'18) Distinguished Reviewer Awards (ASE'18, ICSE'24, FSE'24) Sven Apel has secured significant research funding, including an ERC Advanced Grant (€2.5M) and multiple DFG grants as Principal Investigator and Project Leader. He has advised numerous PhD and Master’s students and is actively involved in the academic community through program committees for major conferences like ICSE, FSE, and ASE. His work is conducted within a collaborative environment that includes close partnerships with researchers at the Max Planck Institute for Informatics and other institutions within the Saarland Informatics Campus.

• Digitalization
• Multi-Agent Systems
• Semantic Web
• +5 more

Prof. Dr. Thomas Farrenkopf is a full-time Professor in the Department of Business Informatics at the Technische Hochschule Mittelhessen , serving as Dean of Studies . He leads the Business Informatics Bachelor program and specializes in digitalization, multi-agent systems, and AI-driven decision support. Expertise Areas : Industry 4.0, Smart Factory, Semantic Web, full-stack software development Contact : thomas.farrenkopf@mnd.thm.de Research Focus : His work bridges knowledge-based methods with practical applications in artificial intelligence, particularly through multi-agent systems for traffic modeling and business simulations. He developed AGADE Traffic (a knowledge-based traffic simulator) and ATHOS (a domain-specific language for multi-agent simulations), focusing on scalability and complexity reduction. Recent studies explore individual preferences' impact on traffic policies and agent-based feedback mechanisms in business contexts. Academic Contributions : With over 15 publications since 2013, his research spans agent-based modeling, semantic web applications, and smart manufacturing - notably improving throughput times in toolmaking by 90% through Industry 4.0 implementations. Teaching & Supervision : Actively supervises Bachelor's Practical Projects (BPP), providing guidance for internship-related academic work through structured evaluation processes and electronic reporting systems. Collaborations : Works with international researchers like Johannes Nguyen, Simon T. Powers, and Michael Guckert, publishing in venues such as SN Computer Science , Lecture Notes in Computer Science , and Journal of Artificial Societies and Social Simulation .

• Natural Language Processing
• Explainable Artificial Intelligence
• Mechanistic Interpretability
• +3 more

Simon Ostermann serves as a Senior Lecturer at Saarland University and Senior Researcher & Deputy Director at the Multilinguality and Language Technology (MLT) lab of the German Research Center for Artificial Intelligence (DFKI). He leads the Efficient and Explainable NLP (E&E) research group and contributes to major projects including lorAI (Low Resource AI), TRAILS (Trustworthy Machines), PERKS (Procedural Knowledge), DAM-S (Semantic Search), and DisAI (Disinformation Combat). His research centers on democratizing language technology through transparent, robust models—specializing in mechanistic interpretability to reverse-engineer LLM internals and enhance efficiency for low-resource languages. Key focus areas include reducing model size for constrained environments, improving cross-lingual transfer via adapters, and developing structured input techniques. His work bridges theoretical interpretability with practical applications in resource-limited settings. 2025 publications reveal concentrated efforts in low-resource adaptation (language adapters, graph-enhanced embeddings), explainable AI (counterfactual generation, conversational XAI datasets), and multilingual fact-checking systems. Notable trends include systematic neuron manipulation frameworks, rigorous evaluation of synthetic data strategies, and cross-lingual claim verification benchmarks. Ostermann advises six PhD candidates (Anikina, Oguz, Bäumel, al Ghussin, Gurgurov, Vykopal) and multiple MSc students on topics spanning RAG hallucinations, multilabel classification, and adapter interpretability. His research receives funding through DFKI-led consortia with European and international partners focusing on trustworthy, efficient AI deployment. The E&E group under his leadership drives innovation in efficient NLP through biweekly seminars, collaborative coding sessions, and partnerships with institutions like KInIT. Current initiatives prioritize green computing for language models and real-world deployment in industrial procedural knowledge systems.

Klajd ZylaView profile

Researcher

• Embedded Systems
• Computer Networks
• Network-on-Chip (NoC)
• +3 more

Klajd Zyla is a PhD student and scientific assistant at the Chair of Integrated Systems, TUM School of Computation, Information and Technology, Technical University of Munich. His research focuses on architectures for flexible data packet processing in high-performance network cards, including NoC optimization, priority-aware scheduling, and RDMA acceleration. PhD Student at LIS (Chair of Integrated Systems), Technical University of Munich (since Jan 2022) Teaching: SystemC Laboratory (since SS 2022) His research investigates on-chip interconnects, packet scheduling with multiple priorities, and acceleration of transport protocols like RDMA for FPGA-based SmartNICs. Publications demonstrate expertise in crossbar architectures, deadlock avoidance, and resource management for MPSoCs. Supervised students include: Nour Abouelkheir (2D Mesh NoC design) Aleksa Stojkovic (HiPerNoC enhancement) Antra Pramanik (Packet-processing FPGA implementation) Arathi Anitha (Deadlock-avoidance algorithms) Johannes Ecker (Hardware schedulers comparison)

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.