جست‌وجوی استاد و پژوهشگر

Nate Fosterمشاهده پروفایل

استاد

• Programming Languages
• Networking
• Databases
• +۵ مورد دیگر

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.

• Embedded Security
• Cryptography
• Hardware Security
• +۵ مورد دیگر

Christof Paar is a Professor of Embedded Security at Ruhr University Bochum's Faculty of Computer Science. His work focuses on hardware security, cryptography, and embedded systems security. He leads the Embedded Security Group within the CASE Cluster of Excellence (Cyber Security in the Age of Large-Scale Attacks), addressing cutting-edge challenges in hardware Trojans, side-channel attacks, and cryptographic engineering. His research spans FPGA and IoT security, with contributions to physical-layer security, wireless jamming defenses, and tamper-resistant systems. Research Interests: Paar's expertise lies in hardware-software co-design for security, with a focus on embedded systems. His work includes analyzing vulnerabilities in FPGAs, developing countermeasures against side-channel attacks, and exploring physical-layer security mechanisms. He also investigates the human factors in hardware reverse engineering and the implications of adversarial machine learning on security systems. Notable Work: Recent publications highlight breakthroughs in detecting hardware Trojans across CMOS generations, breaking industry-standard IP protection mechanisms (IEEE 1735), and proposing novel defenses like IRShield against adversarial wireless sensing. His group actively contributes to standards for secure embedded systems and IoT devices, emphasizing practical implementations.

Yangruibo Dingمشاهده پروفایل

استادیار

• Software Engineering
• Large Language Models
• Agentic Systems
• +۷ مورد دیگر

Yangruibo Ding is an incoming Assistant Professor in the Department of Computer Science at the University of California, Los Angeles (UCLA), and currently serves as a Postdoctoral Scientist at AWS Agentic AI. He has held significant research positions at Google DeepMind, Amazon AWS AI Labs, and IBM Research, establishing himself as a leading researcher in software engineering with a focus on large language models for code. His research focuses on developing large language models (LLMs) and agentic systems for software engineering. He specializes in training LLMs with advanced symbolic reasoning capabilities for debugging, testing, program analysis, and verification. His work aims to build efficient, collaborative agentic systems for complex software development and maintenance tasks, with particular emphasis on code generation, vulnerability detection, and execution-aware pre-training techniques. Dr. Ding's publication record reveals a strong trajectory toward enhancing code intelligence through comprehensive semantics reasoning and self-refinement approaches. His research spans multiple dimensions of software engineering including code completion, vulnerability detection, model evaluation, and cross-file context understanding, with applications across various programming languages and development environments. Dr. Ding has received numerous prestigious awards recognizing his contributions to the field: IBM Ph.D. Fellowship Award (2022-2024) ACM SIGSOFT Distinguished Paper Award (2023) IEEE TSE Best Paper Award Runner-up (2022) Ph.D. Service Award, Columbia CS (2025) NSF Student Travel Award for ESEC/FSE'23 (2023) ACM SIGSOFT CAPS Travel Grant (2023) NSF Travel Award for ICSE'22 (2022) As he establishes his research group at UCLA, Dr. Ding is actively seeking students with strong coding skills and experience in large language models, program analysis, verification, or security. He serves on program committees for major conferences including ICSE (2026), ASE (2024, 2025), and ESEC/FSE Artifacts Track (2023), and regularly reviews for top-tier conferences and journals in AI and software engineering. His research is conducted through collaborations with leading industry teams including AWS Agentic AI, Google DeepMind's Learning4Code team, and IBM Research's AI for Code team, creating a robust network of industry-academia partnerships that drive innovation in software engineering research.

Chanchal K. Royمشاهده پروفایل

استاد

• Code Clone Detection and Analysis
• Software Evolution and Maintenance
• Software Analytics Research
• +۳ مورد دیگر

Chanchal K. Roy is Professor of Software Engineering/Computer Science at the University of Saskatchewan and Co-Director of the Software Research Lab. He leads an NSERC CREATE graduate program on Software Analytics Research and co-leads the Data Management group for an NSERC CFREF project on Food Security, with over 170 publications cited 6,000+ times. His research centers on software clone detection using the widely adopted NICAD system, software evolution, empirical studies, and AI-driven software analytics. Recent work integrates large language models for code generation, clone detection in the AI era, and developer interactions with tools like ChatGPT, emphasizing practical applications in maintenance and analytics. Analysis of his 15 most recent publications reveals a strong trend toward AI/ML integration in software engineering: 12 of 15 articles (2025) explore LLMs, quantum computing, or deep learning for tasks like bug localization, code snippet generation, and feature-toggle analysis. Key themes include empirical validation of AI tools, Stack Overflow data mining, and cross-domain frameworks for Society 5.0. His scientific awards include: Most Influential Paper Awards (SANER 2018, ICPC 2018) Outstanding Young Computer Science Researcher Award (CS-Can/Info-Can, 2018) New Researcher Award (University of Saskatchewan, 2019) New Scientist Research Award (College of Arts and Science, 2019) As lead of the NSERC CREATE program and CFREF data group, he mentors graduate students in software analytics while securing major grants. He actively serves on program committees for ASE, ICSE, and FSE, reviewing journals and organizing workshops on clone detection and empirical methods. His lab focuses on real-world applications in food security data management and software evolution. The Software Research Lab, co-directed by Roy, drives projects like NICAD and the NSERC CREATE initiative, emphasizing open-source contributions and industry collaboration. Current efforts include quantum-SE integration and AI-augmented maintenance tools under the CFREF food security mandate.

Muhammad Ali Gulzarمشاهده پروفایل

استادیار

• Fault Localization
• Big Data systems
• Data Intensive scalable computing
• +۵ مورد دیگر

Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.

Andrea Bastoniمشاهده پروفایل

پژوهشگر ارشد

• Real-Time Systems
• Cyber-Physical Systems
• Operating System Design
• +۵ مورد دیگر

Dr. Andrea Bastoni is a Postdoctoral Researcher and Research Fellow at the Chair of Cyber-Physical Systems in Production Engineering at Technical University of Munich (TUM), Faculty of Mechanical Engineering. He is also the CTO and co-founder of Minerva Systems , developing operating system solutions for AI-ready embedded applications. His expertise spans real-time operating systems, cyber-physical systems, and predictable system design for heterogeneous platforms. His research focuses on enhancing predictability of memory hierarchies in complex SoCs through techniques like memory bandwidth regulation and cache partitioning. This work has industrial applications in safety-critical domains such as avionics and railways, where he contributes to certifiable hypervisors and operating systems. As former Software Architect of the PikeOS hypervisor at SYSGO GmbH (2012-2020), he specialized in DO-178C, IEC 61508, and EN 50128 standards. His academic background includes a Ph.D. in Computer Engineering from the University of Rome Tor Vergata (2007-2011), where he developed LITMUS^RT as part of UNC's Real-Time Systems Group during a visiting researcher period (2009-2010). His publications reflect ongoing work on Multicore Real-Time Scheduling , Mixed-Criticality Task Isolation, and Arm DynamIQ shared unit analysis. He actively participates in program committees for conferences like RTSS, DSN, and DATE.

Daniel Großeمشاهده پروفایل

استاد

• Formal Verification
• Hardware Verification
• Electronic Design Automation
• +۳ مورد دیگر

Prof. Daniel Große serves as Professor at the Institute for Complex Systems (ICS) within Johannes Kepler University Linz, Austria, while maintaining a dual affiliation with the German Research Center for Artificial Intelligence (DFKI) in Bremen. His research centers on Electronic Design Automation (EDA), specializing in verification, debugging, and synthesis of complex hardware/software systems through advanced virtual prototyping techniques. His primary research domains include Formal Verification , Hardware Verification , and Virtual Prototyping , with significant contributions to RISC-V architecture development and embedded systems validation. Current projects like PaSVer (automotive electronics), AUTOASSERT (analog-digital verification), and VerSys (RISC-V software platforms) demonstrate his focus on bridging simulation and formal methods for safety-critical applications. Recent publications reveal accelerating trends in metamorphic testing for embedded graphics libraries and RISC-V vector extensions, alongside innovations in waveform analysis tools like Surfer. His work increasingly integrates AI-assisted verification while maintaining rigorous formal methods foundations. Prof. Große actively supervises student research, having guided Lucas Klemmer's PhD thesis and nine Bachelor theses on topics ranging from RISC-V processor design to transaction visualization. His projects including SATiSFy (autonomous vehicle security) and CONVERS (complex system design automation) secure substantial research funding from academic and industry partners. He leads the Institute for Complex Systems' development of open-source tools such as RISC-V VP++ and contributes to international standards through program committee roles at DATE, ICCAD, and RISC-V Summit Europe.

Cuiyun Gaoمشاهده پروفایل

استاد

• Software Repository Mining
• Natural Language Processing
• Code Analysis
• +۵ مورد دیگر

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.

Chang Xuمشاهده پروفایل

استاد

• Big data software engineering
• Intelligent software testing and analysis
• Adaptive and autonomous software systems
• +۳ مورد دیگر

Chang Xu is a Professor and Ph.D. supervisor at Nanjing University, affiliated with the State Key Laboratory for Novel Software Technology, School of Computer Science, and Institute of Computer Software (ICS). He has been a full-time faculty member since 2010, when he joined as an associate professor and was later promoted to full professor in 2015. Education: Ph.D. from The Hong Kong University of Science and Technology (HKUST) in 2008 (advisor: Prof. S.C. Cheung) M.Eng. from Institute of Software, Chinese Academy of Sciences (ISCAS) in 2003 B.Eng. from University of Science and Technology of China (USTC) in 2000 Research Interests: Professor Xu's research focuses on big data software engineering, intelligent software testing and analysis, and adaptive and autonomous software systems. His recent work centers on constructing and providing runtime support for intelligent software in open environments, with emphasis on inconsistency detection and resolution for environments, and quality assurance for adaptive, concurrent, learning-based, smartphone-based, and spreadsheet-based applications. His work bridges theoretical foundations with practical applications in software engineering, particularly in program analysis, software testing, and self-adaptive systems. Scientific Awards: ACM SIGSOFT Distinguished Paper Award from ICSE 2025 Best Student Paper Award from EUROSYS 2025 ACM Distinguished Member in 2024 Best Paper Award from SOSP 2023 Best Paper Candidate from ISSRE 2022 Yangtze River Scholar by the Ministry of Education in 2021 Multiple ACM SIGSOFT Distinguished Paper Awards from conferences including ASE, ICSE National Science and Technology Progress Award (Second Class) in 2011 Academic Service and Advising: Professor Xu has served on numerous program committees for top software engineering conferences including ICSE, ASE, ESEC/FSE, and ISSTA. He is an editorial board member for several journals including Journal of Computer Science and Technology and Frontiers of Computer Science. He has supervised numerous Ph.D. and MSc students, with research topics spanning program analysis, software testing, self-adaptive systems, and more. His students have gone on to successful careers in both academia and industry. Research Groups: Professor Xu is associated with the SPAR research group at Nanjing University and the CASTLE research group at HKUST, focusing on software analysis, reliability, and testing.

Yingfei Xiongمشاهده پروفایل

دانشیار

• 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.

Bihuan Chenمشاهده پروفایل

دانشیار

• Software Engineering
• Software Supply Chain Security
• Program Analysis
• +۴ مورد دیگر

Bihuan Chen is an Associate Professor at the College of Computer Science and Artificial Intelligence, Fudan University, specializing in software engineering with focus on software supply chain security and trustworthy AI systems. His research spans multiple programming languages including JavaScript, Python, Java, and C/C++ across application and AI domains. Dr. Chen earned his B.Sc. and Ph.D. in Computer Science from Fudan University in 2009 and 2014 respectively, followed by postdoctoral research at Nanyang Technological University (2014-2017). His research interests include software supply chain risk assessment, trustworthy AI systems, and program analysis. His recent publications demonstrate strong focus on malicious package detection in NPM/PyPI ecosystems, vulnerability patch porting using LLMs, and safety verification for autonomous driving systems. The work shows increasing integration of machine learning techniques with traditional program analysis approaches, particularly evident in the 2024-2025 publications that leverage LLMs for vulnerability detection and code refinement. ACM SIGSOFT Distinguished Paper Award (FSE 2016, ASE 2018, ASE 2022, FSE 2025) IEEE TCSE Distinguished Paper Award (ICSME 2020, SANER 2023) CCF Prototype Competition Awards (2nd and 3rd Prizes) Dr. Chen has advised over 50 students including current PhD candidates and notable alumni now at Huawei, ByteDance, and other leading tech firms. His fuxi platform assesses security, legal, and maintenance risks across the software engineering lifecycle. He serves on program committees for major conferences including ICSE, FSE, ASE, and ISSTA, and as Associate Editor for the Journal of Software: Evolution and Process.

Jie M. Zhangمشاهده پروفایل

استادیار

• Software Testing
• AI for Software Engineering
• Software Engineering for AI
• +۵ مورد دیگر

Jie M. Zhang is an Assistant Professor in the Department of Informatics at King's College London, specializing in the intersection of software engineering and artificial intelligence. Her research focuses on two main directions: AI for Software Engineering (leveraging AI technologies to automate software tasks) and Software Engineering for AI (applying SE principles to enhance AI system trustworthiness). Her educational background includes a PhD in Computer Science from Peking University, where she was supervised by Professors Lu Zhang and Dan Hao. Prior to joining King's College London, she was a Research Fellow at University College London working with Professor Mark Harman and Professor Federica Sarro. Dr. Zhang's research interests center on software testing, machine learning trustworthiness, fairness testing, bias mitigation in AI systems, and program analysis. Her work particularly examines how large language models can be utilized for code generation, test case creation, and program repair, while also developing techniques to detect and fix issues within AI models. Her recent publications demonstrate strong trends in evaluating and enhancing the trustworthiness of AI-generated code, with specific emphasis on fairness testing across various domains including autonomous driving systems, machine translation, and decision-making software. Her research increasingly focuses on the efficiency of generated code and detecting hallucinations in large language models. 2025 ACM Sigsoft Early Career Researcher Award for pioneering contributions to software engineering for AI IEEE TSE 2024 Best Paper Award for 'Stealthy Backdoor Attack for Code Models' FSE 2025 Distinguished Paper Award Royal Society International Exchange Grant recipient NMES Enterprise & Engagement Partnerships Fund recipient Dr. Zhang has served in numerous leadership roles across major software engineering conferences including as General Chair for AIware 2025, Area Chair for ASE 2025, and Steering Committee Member for ICST. She has advised multiple PhD students and received significant research funding for her work on LLMs and software engineering. Her research group collaborates with industry partners including Huawei and Facebook, and she leads projects such as ITEA GENIUS and ITEA GreenCode. She is actively involved with King's College London research hubs including the Trusted Autonomous Systems Hub, Security Hub, and Software Systems group, where her work contributes to developing trustworthy AI systems across multiple domains.

Juan Zhaiمشاهده پروفایل

استادیار

• Software-AI Synergy
• Software Dependability
• Formal Specification Synthesis
• +۴ مورد دیگر

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.

Kihong Heoمشاهده پروفایل

دانشیار

• Program Analysis
• Programming Language
• Machine Learning
• +۳ مورد دیگر

Kihong Heo is an Associate Professor in the School of Computing and Graduate School of Information Security at KAIST (Korea Advanced Institute of Science and Technology) in South Korea. His academic career includes serving as an Assistant Professor at KAIST from 2017-2019 before being promoted to Associate Professor in 2020, following his postdoctoral research at the University of Pennsylvania. He earned both his Ph.D. and B.S. in Computer Science & Engineering from Seoul National University. Dr. Heo's research focuses on developing program reasoning systems for safe and reliable software, with specific interests in AI-based program analysis systems for detecting deep semantic software bugs, general-purpose program simplification systems for secure and efficient software, and scalable program synthesis systems for automatic software generation and repair. His work bridges the gap between programming languages, program analysis, and machine learning techniques to create next-generation programming systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning techniques with traditional program analysis methods, with significant contributions in compiler validation, software security, fault localization, and program debloating. His research has practical impact, with some of his work incorporated into Facebook's Infer static analyzer. ACM SIGSOFT Distinguished Paper Award, FSE 2025 Amazon Research Award, 2024 The Soo-Young Lee Teaching Innovation Award, KAIST, 2024 Prize for Excellence in Teaching, KAIST, 2024 Best Artifact Award, ICSE 2022 ACM SIGPLAN Distinguished Paper Award, PLDI 2019 ACM SIGSOFT Distinguished Paper Award, ICSE 2019 Dr. Heo actively mentors graduate students, currently advising several Ph.D. candidates including Yeonhee Ryou, Taeeun Kim, and Sujin Jang, as well as master's students. He has served on program committees for major software engineering and programming language conferences including PLDI, ICSE, POPL, and SPLASH, demonstrating his active role in the academic community. His laboratory, the Programming Systems Laboratory at KAIST, focuses on creating innovative programming systems that leverage both semantic-based program analysis and AI techniques.

Jürgen Citoمشاهده پروفایل

دانشیار

• Software Engineering
• Explainable AI
• Performance Engineering
• +۳ مورد دیگر

Jürgen Cito is an Associate Professor with tenure at Vienna University of Technology (TU Wien), specializing in software engineering, explainable AI, and performance engineering. He leads research at the IPA Lab (as indicated by his personal website) and maintains a visiting researcher position at Google. His academic journey began with joining TU Wien as an Assistant Professor in Spring 2020, with promotion to Associate Professor announced in April 2024. His research interests span multiple critical areas of modern software development, with particular focus on developer experience, program comprehension, and the intersection of AI with software engineering practices. His work bridges theoretical foundations with practical industrial applications, as evidenced by collaborations with major technology companies. Analysis of his recent publications reveals a strong emphasis on practical tools and methodologies that enhance software quality, performance, and security. His research trajectory shows increasing focus on explainable AI techniques applied to software engineering problems, performance prediction from source code, and automated security testing approaches that leverage large language models. best teaching award for distance learning for Web Engineering (2020) Cito actively contributes to the software engineering community through numerous conference committee roles, including program committee positions at ASE, ICSE, ESEC/FSE, and other major venues. His lab appears to focus on developer tools, program analysis, and AI-assisted software engineering, with connections to both academic and industrial research environments.