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Nate FosterView profile

Professor

• Programming Languages
• Networking
• Databases
• +5 more

Nate Foster is a Professor of Computer Science at Cornell University and currently serves as the Associate Dean for Research in the Ann S. Bowers College of Computing and Information Science. He is also a Visiting Researcher at Jane Street and served as a Visiting Professor at École Polytechnique Fédérale de Lausanne during the 2023-24 academic year. His research uses ideas from programming languages to solve problems in networking, databases, and security. BA in Computer Science, Williams College (2001) MPhil in History and Philosophy of Science, University of Cambridge (2008, all work completed in 2003) PhD in Computer and Information Science, University of Pennsylvania (2009) Foster's research focuses on developing languages and tools that make it easy for programmers to build secure and reliable systems. His current work centers on the design and implementation of languages and tools for programmable networks, particularly using the P4 language. His past work includes bidirectional languages (also known as 'lenses'), database query languages, data provenance, type systems, mechanized proof, and formal semantics. His research group at Cornell has made significant contributions to network verification, software-defined networking, and formal foundations for programmable data planes. Analysis of Foster's recent publications reveals a strong focus on network verification and programming language foundations for networking. His work consistently applies formal methods to practical networking problems, with a particular emphasis on the NetKAT and P4 languages. Over the past five years, his research has evolved toward more complex network verification techniques, including infinite state verification, active learning of network models, and dependently-typed approaches to network programming. His work bridges theoretical computer science with practical networking systems, making formal methods accessible to network engineers. ACM Fellow (2025) ACM SIGPLAN Robin Milner Award (2023) ACM SIGCOMM Rising Star Award (2018) NSF CAREER Award (2013) Alfred P. Sloan Fellowship (2012) Multiple distinguished paper awards across top conferences including POPL, PLDI, and SIGCOMM Foster has advised numerous PhD and Master's students who have gone on to prominent positions in both industry and academia, with many continuing work in programming languages and networking. He has led multiple significant research grants including an NSF CAREER Award and has been involved in the P4 Language Consortium, serving as Chair of the P4 Language Governing Board. His work has been supported by various organizations including NSF, DARPA, and industry partners like Intel and Jane Street. Foster is also active in the programming languages research community, serving on numerous program committees and as Vice Chair of DARPA's Information Science and Technology (ISAT) study group. Foster leads a vibrant research group at Cornell focused on programming languages for networks, with collaborators from academia and industry. His group has developed several influential tools and frameworks including NetKAT, Petr4, and KATch. They maintain strong connections with the P4 community and work closely with industry partners to ensure their research has practical impact on real-world networking systems.

• Network Security
• Android Security
• Malware Analysis
• +6 more

Vijay Laxmi is a Professor in the Department of Computer Science and Engineering at Malaviya National Institute of Technology (MNIT) Jaipur, India. With over a decade of active research publication from 2014-2024, Dr. Laxmi has established themselves as a prominent researcher in network security, Android security systems, and Network-on-Chip architectures. Their work demonstrates consistent collaboration with Manoj Singh Gaur and numerous doctoral students at MNIT Jaipur. Dr. Laxmi's research interests span Network Security, Android Security, Malware Analysis, Network-on-Chip Architectures, Routing Protocols, Side-Channel Attacks, Wireless Networks, and Mobile Security. Their work bridges theoretical security frameworks with practical implementations, particularly in mobile and embedded systems. Recent publications indicate a growing focus on AI-based security approaches including GAN applications for fuzzing and deep learning for image dehazing. The research trajectory shows increasing sophistication in security analysis techniques, evolving from basic malware detection to advanced side-channel attack analysis and sophisticated network security protocols. Recent publications demonstrate expertise in both theoretical frameworks and practical implementations with applications in real-world security challenges. Dr. Laxmi has mentored numerous graduate students including Vineeta Jain, Anugrah Jain, Sonal Yadav, Mohit Singh, and Gaurav Singal, who appear as co-authors across multiple publications. Their collaborative network extends to researchers at international institutions, indicating strong academic connections beyond their home institution.

Christian RossowView profile

Professor

• Software Security
• System Security
• Network Security
• +15 more

Christian Rossow is Faculty at CISPA – Helmholtz Center for Information Security in Dortmund, Germany, where he leads the System Security research group. He holds dual academic appointments as a professor in the Computer Science department at Saarland University and as an honorary professor at TU Dortmund University. His research spans systems, software, and network security, with a focus on cyber attacks, defenses, and privacy. Research Interests: His primary research areas include software and system security (e.g., exploitation techniques, compiler-assisted defenses, AI-assisted (in)security), network security (e.g., DDoS mitigation, attack attribution, traffic analysis), and cybercrime (e.g., malware analysis, data-driven studies). He emphasizes foundational research with practical applications. Publication Trends: His recent work (2023–2025) shows a strong focus on microarchitectural attacks (e.g., cache side-channels, prefetcher analysis), browser and web security (e.g., sandbox escapes, CSS fingerprinting), network protocol vulnerabilities (e.g., TCP spoofing, infinite loops), and applied cryptography (e.g., ISA extensions for key management). His research consistently targets top-tier venues like IEEE S&P, USENIX Security, ACM CCS, and NDSS. Distinguished Paper Award at IEEE EuroS&P 2021 Best Student Paper Award at MIT Spam Conference 2010 Advising and Grants: He actively supervises PhD students and postdoctoral researchers, with alumni placed in industry (NVIDIA, Crowdstrike, Continental) and academia. His research is supported by major grants including EU H2020 SISSDEN, BMBF-funded BOB, DFG-funded anonymous communication, and RAMSES. He regularly serves in leadership roles in the community, including PC Chair for RAID 2022/2023 and USENIX WOOT 2018, and Track Chair for ACM CCS 2026. Labs and Teams: He leads the System Security research group at CISPA, a world-leading institution for security and privacy. The group comprises talented full-time researchers and focuses on cutting-edge research with strong individual supervision and worldwide collaborations.

Cuiyun GaoView profile

Professor

• Software Repository Mining
• Natural Language Processing
• Code Analysis
• +5 more

Cuiyun Gao is a Full Professor and PhD Supervisor at the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen. She has established herself as a prominent researcher in the intersection of artificial intelligence and software engineering. Her educational background includes a PhD from the Chinese University of Hong Kong (completed in 2018), followed by postdoctoral work at CUHK and a Research Fellowship at Nanyang Technological University. She also had a visiting period at University College London supervised by Prof. Mark Harman and Prof. Federica Sarro. Dr. Gao's research primarily focuses on Software Repository Mining, Natural Language Processing, Code Analysis, Large Language Models, Source Code Understanding, User Review Analysis, Vulnerability Detection, and Mobile Advertising Analysis . Her work bridges the gap between traditional software engineering practices and modern AI techniques, particularly in the context of code intelligence and software maintenance. Her recent publications (2024-2025) demonstrate a strong emphasis on Large Language Models for code-related tasks, including code generation, optimization, vulnerability detection, and software engineering applications. Her research shows a clear trend toward addressing practical challenges in integrating LLMs into the software development lifecycle while maintaining code quality and security. Scientific Awards: Distinguished Paper Award at ASE 2023 Best Paper Award of the Track at ICSE 2024 Distinguished Paper Award at ICSE 2024 Dr. Gao actively supervises multiple PhD and Master's students, contributing to the next generation of software engineering researchers. She has served on numerous conference committees including FSE, ISSTA, ICSE, ASE, and SANER. Her research has received significant attention in the software engineering community, with multiple papers published in top-tier venues like FSE, ICSE, ASE, and TSE. Her lab appears to be actively engaged in both theoretical research and practical applications, particularly in the context of WeChat and other industry collaborations, demonstrating strong industry-academia connections.

Xin PengView profile

Professor

• Software Analytics
• Intelligent Software Development
• Microservice
• +4 more

Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.

Yingfei XiongView profile

Associate Professor

• Software Engineering
• Programming Languages

Yingfei Xiong is an active Associate Professor at Peking University, China, specializing in software engineering and programming languages. With a consistent research trajectory from 2013 through 2026, Xiong has established themselves as a prominent figure in the software engineering research community, regularly contributing to top-tier conferences including SPLASH, ICSE, ASE, and PLDI. Dr. Xiong's research primarily focuses on program synthesis, automated program repair, and software analysis techniques. Their work bridges theoretical programming language concepts with practical software engineering applications, particularly in developing novel approaches for code generation, bug fixing, and program optimization. The research demonstrates strong interdisciplinary connections between traditional software engineering, programming languages theory, and emerging AI techniques. Analysis of Xiong's publication trends reveals a clear evolution in research focus, beginning with foundational work in API transformations and program adaptation around 2013-2016, shifting toward program repair techniques from 2017-2020, and most recently incorporating machine learning and neural approaches into program synthesis and repair (2021-2026). The work consistently addresses practical challenges in software development while maintaining theoretical rigor, with increasing integration of AI techniques in recent years. Dr. Xiong has served in various leadership roles across the software engineering conference ecosystem, including program committee membership and session chair positions at major conferences. Their extensive service demonstrates recognition by peers as a subject matter expert in software engineering and programming languages research. While specific grant information isn't detailed in the provided text, the sustained publication record suggests successful research funding.

Juan ZhaiView profile

Assistant Professor

• Software-AI Synergy
• Software Dependability
• Formal Specification Synthesis
• +4 more

Juan Zhai is an Assistant Professor in the Manning College of Information & Computer Sciences (CICS) at University of Massachusetts Amherst, where she co-directs the Laboratory for Advanced Software Engineering Research (LASER) and participates in the UMass NLP group. Her academic career spans over 7 years of active service including program committee roles at top-tier conferences like ICSE, FSE, and ASE. Her research focuses on Software-AI Synergy with core areas including: Formal Specification Synthesis for precise software behavior definition Comment Generation and Maintenance using LLMs Trustworthy AI through bias detection and framework testing Deep Learning Infrastructure Reliability Recent work demonstrates strong emphasis on practical tools for AI safety and software dependability. Her publication trends show consistent output in top software engineering venues (ASE, ICSE, FSE) with increasing focus on AI/ML conferences (ACL, CVPR, ICLR). Key themes include metamorphic testing for deep learning frameworks, bias analysis in LLMs, and formal methods for specification synthesis. She actively serves the community through: Program committees for 13 major conferences Reviewing for 5 top journals including TOSEM and TSE 40+ total reviews across SE and AI venues Juan mentors PhD students including Gehao Zhang (research focus: Software Engineering, AI Safety) and teaches graduate courses like CS520 (Theory and Practice of Software Engineering) and CS692P (Hot Topics in SE Research). She leads the LASER lab which develops tools like C2S, CPC, and DevMuT for software reasoning and AI infrastructure testing.

• Natural Language Processing
• Large Language Models
• Multilingual Models
• +5 more

Timothy Baldwin is a Professor at the University of Melbourne, School of Computing and Information Systems, with additional affiliation at Mohamed bin Zayed University of Artificial Intelligence in UAE. His research spans natural language processing, large language models, and multilingual AI systems. His research interests focus on the safety, reliability, and ethical aspects of large language models. He investigates bias evaluation and debiasing techniques, uncertainty quantification methods, fact-checking systems, and multilingual model safety. His work addresses critical challenges in making AI systems more transparent, reliable, and culturally aware, with particular attention to low-resource languages and cross-cultural differences. Baldwin's recent publications demonstrate a strong focus on evaluating and improving the safety of language models across diverse linguistic contexts, developing tools for fact verification, and understanding the internal mechanisms of large language models. His research shows increasing emphasis on practical applications with real-world impact, particularly in multilingual settings and safety-critical domains. His scientific contributions include foundational work on multilingual NLP, bias mitigation techniques, and frameworks for evaluating LLM safety across different cultural contexts. His research has been published in top-tier venues including ACL, NAACL, EMNLP, and ICLR. Baldwin actively mentors students and junior researchers, with frequent collaborations with Haonan Li, Xudong Han, and Fajri Koto, among others. His research group appears to focus on practical applications of NLP with strong ethical considerations, particularly regarding model safety and cultural sensitivity.

Jakob BeetzView profile

Professor

• Building Information Modeling (BIM)
• Digital Twins
• Artificial Intelligence in Construction
• +3 more

Prof. Dr. Jakob Beetz serves as a University Professor at RWTH Aachen University's Faculty of Architecture, leading the Design Computation (DC) research group. His work addresses critical challenges in sustainable built environments through digital innovation, focusing on integrating knowledge, information, and data across disciplines to reduce the sector's energy and material consumption—which accounts for over one-third of global totals—while advancing climate goals under the European Green Deal. His research spans Building Information Modeling (BIM), digital twins, and artificial intelligence, with emphasis on graph-based data federation, semantic web technologies, and large language models in construction. Key interests include evidence-based planning, parametric design optimization, building physics simulation, and networked knowledge modeling. Recent projects explore federated digital twin ecosystems for infrastructure management, intelligent damage assessment systems, and AI-driven solutions for wood structure preservation, directly contributing to sustainable development targets. Analysis of his 2024-2025 publications reveals a cohesive trajectory toward decentralized data environments and AI integration in Architecture, Engineering, and Construction (AEC). His work bridges theoretical foundations in knowledge representation with practical applications in bridge maintenance, road infrastructure, and timber construction, demonstrating consistent innovation in spatial data querying, federated issue management, and ontology-based process modeling. Prof. Beetz actively supervises PhD candidates, as evidenced by DC.Promotions 2024, and drives international collaboration through events like the Forum Construction Informatics 2025 and CIB W78 conferences. His research group engages with industry standards including Industry Foundation Classes (IFC) and Common Data Environments (CDEs), emphasizing open data principles and interoperability to transform construction workflows.

Zhenchang XingView profile

Research Professor

• Software Engineering
• Responsible AI
• Human-Computer Interaction

Zhenchang Xing is a Research Professor at CSIRO's Data61 and holds a Hans Fischer Senior Fellowship at TUM-IAS. With a Ph.D. in Computer Science from the University of Alberta (2008), he previously served as Associate Professor at Australian National University and Assistant Professor at Nanyang Technological University. His research focuses on software engineering for AI systems and human-centered computing. Current projects include: Automated Software-Hardware Co-Design for AI Systems Software Supply Chain Security frameworks Data Bill of Materials (DataBOM) for verifiable data ecosystems Software Testing Knowledge Graph development Professor Xing has received 10 Distinguished Paper Awards from ACM and IEEE, including the 2005 Most Influential Paper Award for UMLDiff. His work combines software engineering with responsible AI development.

Ruben MartinsView profile

Assistant Professor

• Constraint Programming
• Program Synthesis
• Formal Verification
• +3 more

Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.

Gabriele BavotaView profile

Associate Professor

• Software Quality
• Empirical Software Engineering
• Mining Software Repositories
• +3 more

Gabriele Bavota is an Associate Professor at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He leads the SEART (Software Engineering Advanced Research Team) group and serves as Principal Investigator for the DEVINTA ERC starting grant focused on developer intelligence through mining software artifacts. Dr. Bavota's research spans Software Quality, Empirical Software Engineering, and Mining Software Repositories. His work has evolved from foundational studies on code smells and technical debt to cutting-edge research at the intersection of artificial intelligence and software development. He has made significant contributions to understanding API usage patterns, software quality metrics, and developer behavior through empirical studies of large software repositories. His recent publications reveal a strong focus on AI-assisted software development, with extensive research examining code generation, code summarization, and code review automation using large language models. He has also expanded his research to include quality assurance in game development (detecting game stuttering and low engagement events) and voice user interface testing. His work consistently bridges theoretical insights with practical applications for software developers. ACM SIGSOFT Distinguished Paper Award for API compatibility research (MSR 2019) ACM SIGSOFT Distinguished Paper Award for Hugging Face model documentation study (ICPC 2024) ACM SIGSOFT Distinguished Artifact Award for deep learning fault taxonomy (ICSE 2020) As an active member of the software engineering research community, Dr. Bavota serves on program committees for major conferences including ICSE, ASE, FSE, and MSR. He has held leadership roles such as Program Co-Chair for ICSME 2023 and Vision/Reflection Track Co-Chair for ICSE. His SEART research group develops practical tools like the SEART Data Hub that streamline large-scale source code mining and preprocessing for empirical software engineering research.

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.

Alexandre BartelView profile

Professor

• software security
• software engineering
• system security
• +5 more

Alexandre Bartel is a Professor in the Department of Computing Science at Umeå University, Sweden, specializing in software security and software engineering. His research focuses on system security and analysis of permission-based software stacks, particularly Android. With numerous publications in top-tier security and software engineering conferences and journals, Bartel has established himself as a leading researcher in vulnerability analysis and software security. Bartel's research interests primarily center around software security, with a particular emphasis on Java and Android ecosystems. His work delves into vulnerability analysis, deserialization attacks, control flow integrity, and security mechanisms for complex software systems. He investigates how to verify security properties through efficient algorithms and examines existing software layers from a security perspective. His research bridges theoretical security concepts with practical implementation challenges in real-world systems. Analysis of Bartel's recent publications reveals a strong focus on Java deserialization vulnerabilities, control flow integrity mechanisms, and Android security. His work demonstrates a consistent trajectory from fundamental vulnerability analysis to developing practical security solutions and benchmarks. The research spans both theoretical frameworks and empirical evaluations, with significant contributions to understanding long-term security adoption patterns and developing tools for vulnerability detection. Scientific Awards: Most influential Paper ICSE N-10 Award for IccTA: Detecting Inter-Component Privacy Leaks in Android Apps Bartel actively contributes to the academic community through service roles, having served on program committees for major conferences including ASE, ESEC/FSE, ICSE, and FSE. His research has practical implications for software developers and security practitioners, particularly in the areas of vulnerability detection and security mechanism implementation. While specific grant information isn't detailed in the provided materials, his extensive publication record suggests successful funding for his research initiatives. Though not explicitly detailed in the provided information, Bartel's research likely involves collaboration with students and researchers on projects related to software security analysis. His work on benchmarks like Gleipner and CONFUZZION suggests involvement in developing tools and resources for the security research community.

Steffen BeckerView profile

Professor

• Software Engineering
• Cloud Systems
• Model-Driven Engineering
• +7 more

Steffen Becker is a Professor at the University of Stuttgart's Faculty of Computer Science, Electrical Engineering and Information Technology, affiliated with the Institute for Software Engineering's Software Quality and Architecture group. His work focuses on software engineering, cloud systems, model-driven engineering, and cybersecurity. He leads research in architectural modeling tools like Slingshot, GUI testing frameworks (ViMoTest), and hardware security analysis. Recent studies explore AI integration in testing, education, and automotive systems (CARISMA). Research interests include elasticity modeling, self-adaptive systems, and educational technology. His 2025 publications address issues like end-user hardware comprehension, FPGA security, and LLM-driven test generation. Notable tools developed include the Slingshot Simulator for cloud-native systems and Gropius for cross-component issue management. Becker contributes to both theoretical advancements and practical implementations in software quality, security, and cloud infrastructure. Key Areas: Software Architecture, Cyber-Physical Systems, Testing, Reverse Engineering Tool Developments: ViMoTest, Slingshot, Gropius Education Focus: Online programming pedagogy and curriculum innovation His work bridges foundational research with industry applications, addressing challenges in automotive computing, cloud elasticity, and human-centric security awareness. Recent efforts emphasize explainable hardware (XHW) and AI's role in qualitative analysis automation.