جست‌وجوی استاد و پژوهشگر

• Formal Methods
• Software Engineering
• Program Verification
• +۷ مورد دیگر

Carlo A. Furia is an Associate Professor at the Software Institute within the Faculty of Informatics at Università della Svizzera italiana (USI). He leads the ATOM research group and is actively involved in advancing formal methods in software engineering. His work bridges theoretical rigor with practical applicability, particularly in verification, automated repair, and empirical analysis of software systems. PhD in Computer Science, Politecnico di Milano Master of Science in Computer Science, University of Illinois at Chicago Laurea in Computer Science and Engineering, Politecnico di Milano His research focuses on making formal methods practical through automation, combining diverse techniques, and conducting thorough empirical evaluations. He is particularly interested in using Bayesian data analysis to assess software engineering data. His work spans program verification (e.g., AutoProof), contract inference, API usability, and multilingual program analysis. His recent publications highlight trends in automated program repair, JVM bytecode analysis, Android security, and empirical methodologies. These works reflect a consistent emphasis on correctness, reliability, and empirical validation in software development. Scientific service includes: Associate Editor, Empirical Software Engineering (EMSE) journal Program Committee member, FM 2026, FormaliSE 2026, ASE 2025, iFM 2025 He has advised students and leads the ATOM group, which develops tools for software analysis. He teaches courses such as Software Analysis, Programming Fundamentals, and Software Design & Modeling. Current research directions include improving empirical evaluation rigor and enhancing verification at lower code levels like bytecode.

Michael Pradelمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Program Analysis
• +۹ مورد دیگر

Michael Pradel is a full professor at the University of Stuttgart, specializing in software engineering, programming languages, and machine learning. He will join CISPA as a faculty member from September 2025 while retaining his Stuttgart position. His research focuses on: Neuro-symbolic software analysis Web application analysis Dynamic analysis and test generation Quantum software testing Machine learning for code Recent publications address: LLM-based program repair (RepairAgent, Treefix) WebAssembly analysis (Wasm-R3, LintQ) Python security and analysis (DyLin, DyPyBench) Quantum program analysis (LintQ) Scientific awards: Ernst-Denert Software Engineering Award Emmy Noether grant (1.3M Euro) ERC Starting Grant (1.5M Euro) 3x ACM SIGSOFT Distinguished Paper Award at FSE ACM Distinguished Member Best Paper/Distinguished Paper Awards at ISSTA, ASE, ASPLOS, MSR Key contributions include: DeepBugs for name-based bug detection Getafix for automated bug fixing LintQ for quantum program analysis DyLin for Python dynamic analysis Neuro-symbolic developer tools

Yannic Nollerمشاهده پروفایل

استاد

• Automated Program Repair
• Machine Learning Analysis
• Software Testing
• +۳ مورد دیگر

Yannic Noller is a Professor at the Faculty of Computer Science at Ruhr University Bochum (RUB), leading the Software Quality group. Previously, he held positions as Assistant Professor at Singapore University of Technology and Design (SUTD) and Research Assistant Professor at National University of Singapore (NUS). His research focuses on automated software engineering, including program repair, machine learning analysis, and software testing. He earned his Ph.D. from Humboldt-Universität zu Berlin under Prof. Lars Grunske, with a thesis on hybrid differential software testing. Education: Ph.D. in Computer Science (2016-2020, Humboldt-Universität), M.Sc. (2013-2016, University of Stuttgart), B.Sc. (2010-2013, University of Stuttgart). Research interests include automated program repair techniques, machine learning model analysis, and intelligent tutoring systems for programming education. Notable contributions include HyDiff (hybrid differential analysis tool) and CPR (concolic program repair). Awards include the Distinguished Artifact Reviewer at ISSTA'2021 and multiple scholarships for academic excellence. Teaching includes courses on software engineering, requirements engineering, and automated software engineering.

Pavel Panchekhaمشاهده پروفایل

استادیار

• Programming Languages
• Program Synthesis
• Verification
• +۴ مورد دیگر

Pavel Panchekha is an Assistant Professor in the School of Computing at the University of Utah, where he holds the Warnock Chair for Junior Faculty. His research spans programming languages, web browsers, and numerical analysis, with a focus on developing programming language techniques to address challenges across computer science. Dr. Panchekha received his educational training at prestigious institutions: PhD in Computer Science from the Paul G. Allen School for Computer Science and Engineering at the University of Washington, advised by Michael D. Ernst and Zachary Tatlock BS in Mathematics from MIT Panchekha's research program has two major thrusts. First, he works on web browser internals , with projects including fuzzing layout invalidation, multi-tenant garbage collection, and optimizing 2D graphics. He is also authoring a textbook on web browsers that informs much of this research. Second, he focuses on automatic numerical analysis , with projects such as automatic accuracy improvement, synthesis via term rewriting, scalable static accuracy analysis, and math library implementation. He leads the FPBench and Herbie projects, which are major deployments of his research. His scholarly output demonstrates consistent contributions across programming languages, verification, and numerical methods. Recent work shows a growing emphasis on bidirectional typing systems, layout invalidation in browsers, and robust floating-point error analysis. His publications reveal a trajectory from foundational work on floating-point accuracy (notably the Herbie tool that won a Distinguished Paper Award at PLDI 2015) toward more comprehensive systems for program synthesis, verification, and browser optimization. Panchekha has received significant recognition for his research contributions: NSF Fellowship ARCS Foundation Fellowship Adobe Research Fellowship Wissner-Slivka Foundation Fellowship 2015 PLDI Distinguished Paper Award for work on the Herbie numerical analysis and repair tool As an advisor, Panchekha mentors a substantial group of students across multiple levels. He currently advises six students: Marisa Kirisame (PhD), Bhargav Kulkarni (PhD), Yumeng He (PhD), Artem Yadrov (MS), Jesus Ponce (BS), and Jonas Regehr (BS). Previously, he has advised over twenty students including PhD candidates like Ian Briggs and numerous MS and BS students. His advising spans theoretical topics in programming languages and practical applications in web browsers and numerical computing. Panchekha leads research groups focused on programming languages applications to web browsers and numerical analysis. His work on the Herbie tool for floating-point accuracy improvement has become influential in the programming languages community, and his more recent work on browser internals is shaping how researchers understand and optimize modern web rendering engines. He is currently developing a textbook on web browsers that aims to synthesize knowledge about browser architecture and implementation.

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.

Justin Gottschlichمشاهده پروفایل

عضو هیئت علمی

• Machine Programming
• Artificial Intelligence
• Software Engineering
• +۳ مورد دیگر

Justin Gottschlich is an Adjunct Lecturer in the Computer Science Department at Stanford University, where he teaches the graduate course Machine Programming (CS 329M) . He also serves as Founder, CEO, and Chief Scientist of Merly Inc., a startup focused on machine programming systems to improve software development efficiency and quality. Previously, he led the Machine Programming Research group at Intel Labs, pioneering advancements in automating software development through a fusion of machine learning, programming languages, and systems research. His academic roles include prior positions as Adjunct Professor at University of Colorado-Boulder and Adjunct Assistant Professor at University of Pennsylvania. He has advised numerous graduate students across institutions, contributing to their research in machine programming and related fields. Gottschlich has authored dozens of research papers and holds multiple patents, with his work highlighted by prominent outlets like the Wall Street Journal and Communications of the ACM. He actively contributes to academic committees, including serving as Steering Committee Chair for the ACM SIGPLAN Machine Programming Symposium (MAPS). His research interests span machine programming, autonomous software development, formal methods, and the integration of AI into software engineering practices. Education: PhD in Computer Science from University of Colorado-Boulder Keynote Engagements: LADSIOS (2021), MIT DSAIL (2021), Penn PRECISE (2019) Labs/Teams: Intel Labs Machine Programming Research Group, Merly's R&D Team Grants & Funding: Extensive industry and academic research funding through Intel Labs and Merly

Jinhan Kimمشاهده پروفایل

پژوهشگر

• Software Engineering
• Mutation Testing
• Fault Localization
• +۴ مورد دیگر

Jinhan Kim is a Postdoctoral Researcher at the Università della Svizzera italiana (USI) in the Faculty of Informatics, working in the TAU lab under Prof. Paolo Tonella. He earned his Ph.D. from KAIST under Prof. Shin Yoo, focusing on software engineering research in mutation testing, fault localization, and deep learning system testing. His work bridges traditional software engineering techniques with AI-driven methodologies, emphasizing AI4SE and SE4AI paradigms. Education: Ph.D. in Software Engineering, KAIST, 2023 Research Interests: Mutation Testing Deep Learning System Testing Autonomous Systems Testing Adversarial Attack Detection Empirical Software Engineering Service and Leadership: Organized SBFT 2026 and DeepTest 2026 (co-located with ICSE 2026) Program Committee Member for ASE, ISSTA, Mutation, and DeMeSSAI Board of Distinguished Reviewers for TOSEM (2024–2025) Labs and Teams: Active contributor to the TAU Lab at USI, focusing on advanced software testing and AI integration.

• Radiation Biophysics
• Cancer Radiotherapy
• Ionizing Radiation Effects
• +۴ مورد دیگر

David J. Brenner serves as Higgins Professor of Radiation Biophysics in Radiation Oncology and Environmental Health Sciences at Columbia University Medical Center. He directs both the century-old Center for Radiological Research and the Radiological Research Accelerator Facility (RARAF), leading interdisciplinary teams focused on radiation applications in medicine and safety. BA in Physics from Oxford University (1974) MSc in Radiation Physics from University of London (1976) MA in Physics Philosophy from Oxford University (1979) PhD in Physics from University of Surrey (1980) His research spans dual aspects of radiation: therapeutic applications in cancer treatment and risk assessment across diverse scenarios. Key initiatives include advancing carbon-ion therapy for pancreatic cancer, developing safe far-UVC light for pathogen elimination, and investigating low-dose radiation risks from medical imaging to nuclear terrorism. His team leverages RARAF's unique capabilities for mechanistic studies of radiation effects. Publications reveal dominant themes in radiation biophysics, with significant contributions to biodosimetry (RABiT platform), UV disinfection technology, and cancer risk modeling. Recent work emphasizes translational applications including medical countermeasures for radiation exposure and precision radiation oncology techniques. National Academy of Sciences Nuclear and Radiation Studies Board member National Council on Radiation Protection and Measurements member Radiation Research Society Failla Gold Medal recipient (2011) Oxford University Weldon Prize for mathematical biology (2015) Robert D. Moseley Award for Radiation Protection in Medicine Brenner leads multiple NIH-funded projects including biodosimetry development and UV disinfection research. His mentorship extends through directing Columbia's Radiological Research Accelerator Facility and training programs in radiological sciences. Current lab efforts focus on carbon-ion therapy mechanisms and 222-nm UV applications against drug-resistant pathogens, with active collaborations across oncology, microbiology, and physics disciplines.

Xujie Siمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +۵ مورد دیگر

Xujie Si is an Assistant Professor in the Department of Computer Science at the University of Toronto. He is also a faculty affiliate at the Vector Institute and an affiliate member at Mila - Quebec AI Institute, holding a Canada CIFAR AI Chair. Previously, he served as an Assistant Professor at McGill University's School of Computer Science. Education: Ph.D., Computer and Information Science, University of Pennsylvania (advised by Mayur Naik) M.S., Computer Science, Vanderbilt University B.E. (with Honors), Nankai University Research Focus: His work bridges AI and program reasoning, emphasizing the integration of statistical and logical methods. Key areas include: Static analysis and verification using deep learning/reinforcement learning Neuro-symbolic systems for urban simulation (e.g., LogiCity) Automated theorem proving via LLMs and symbolic reasoning Program repair and compiler fuzzing Recent Article Trends: Recent work focuses on synergizing LLMs with symbolic reasoning (e.g., Olympiad inequality proving), advancing SAT solving with graph neural networks, and applying neuro-symbolic methods to Euclidean geometry formalization. Awards: Canada CIFAR AI Chair (2023) Lab/Teams: Leads research teams exploring program analysis, neuro-symbolic AI, and formal verification at the University of Toronto and Vector Institute.

• Software Testing
• Deep Learning Systems
• Mutation Testing
• +۴ مورد دیگر

Nargiz Humbatova is a postdoctoral researcher at the Testing Automated (TAU) research group within the Software Institute (SI) at Università della Svizzera italiana (USI). She holds a PhD from USI (2023), an MSc in Advanced Computing from the University of Bristol, and a BSc in Mathematics from Moscow State University. Her research focuses on mutation testing of deep learning systems, fault localization, and program repair, with a particular emphasis on real-world fault analysis and testing methodologies for AI systems. Education: PhD in Informatics, Università della Svizzera italiana (2023) MSc in Advanced Computing, University of Bristol BSc in Mathematics, Moscow State University Research interests include mutation testing techniques, test input prioritization, deep learning fault benchmarks, and the application of large language models (LLMs) to fault localization and repair. She has contributed to the ERC-AdG project PRECRIME, dedicated to testing AI-based systems. Her work emphasizes practical validation through empirical studies and real-world fault injection. Her publications span topics such as mutation testing pipelines (e.g., muPRL), spectral analysis of neural activation values, and the development of tools like DeepCrime for deep learning testing. These articles highlight advancements in evaluating and improving the robustness of AI systems through rigorous testing frameworks. Labs/Teams: Member of the TAU (Testing Automated) research group at USI’s Software Institute, collaborating on interdisciplinary projects at the intersection of software engineering and artificial intelligence.

Baishakhi Rayمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Baishakhi Ray is an Associate Professor at Columbia University, specializing in improving software reliability and developers' productivity for both traditional and AI-driven systems. She leads the ARiSE Lab, focusing on interdisciplinary research at the intersection of software engineering and artificial intelligence. Her research interests include software testing for AI systems, adversarial robustness, automated testing of autonomous systems, and leveraging AI techniques such as neural networks for dynamic analysis and fuzzing. Notable projects include DeepTest for autonomous car testing and NEUZZ for efficient fuzzing. Awards: VMware Early Career Faculty Award (2020), IBM Faculty Award (2019), NSF CAREER Award (2019), and multiple best paper awards including EAPLS FASE (2020) and ACM Distinguished Papers (FSE 2017, MSR 2017). Grants: NSF CAREER grant (2019-2024) for deep learning testing, NSF grants for workshops and security bug detection, and collaborative grants on persistent memory and SSL/TLS implementations. Her recent work emphasizes advancing code generation with large language models (LLMs), evaluating model robustness under data contamination, and developing tools like CodeSense and CrashFixer for code semantics and kernel debugging. The ARiSE Lab also explores causal performance debugging and transfer learning for configurable systems.

Lingming Zhangمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Machine Learning
• +۶ مورد دیگر

Lingming Zhang is an Associate Professor at the Department of Computer Science, University of Illinois Urbana-Champaign, affiliated with the Grainger College of Engineering. His research focuses on the intersection of Software Engineering, Programming Languages, and Machine Learning, with a particular emphasis on automated program repair, compiler testing, and large language model (LLM) applications in software engineering. He has published over 100 papers, achieving an h-index of 50+, and holds an ACM Distinguished Member status. Research Interests: LLM-based software testing, repair, and synthesis Fuzzing of deep-learning libraries and compilers Open-source code LLMs (e.g., StarCoder2, Magicoder) with over 1M downloads Automated program repair systems (e.g., AlphaRepair, ChatRepair, Agentless) Recent Contributions: Developed TitanFuzz for coverage-guided compiler fuzzing Released Agentless , an LLM-based coding tool adopted by OpenAI and DeepSeek Proposed SWE-RL to enhance LLM reasoning via reinforcement learning Service Roles: Program Co-Chair for ASE 2025 and LLM4Code 2025 Associate Chair for OOPSLA 2024 and Area Chair for ICSE 2025/2026 Recipient of NSF CAREER Award and ACM SIGSOFT Early Career Award Lab/Teams: Develops open-source tools like UniAPR for efficient patch validation Active in releasing industry-adopted LLM-based software engineering tools

Yintong Huoمشاهده پروفایل

استادیار

• Intelligent Software Engineering
• AI for Software Engineering (AI4SE)
• LLM for Software Engineering (LLM4SE)
• +۷ مورد دیگر

Yintong Huo is a tenure-track Assistant Professor in the Department of Computer Science at Singapore Management University (SMU), School of Computing and Information Systems. He joined SMU in early 2024 after completing his PhD at The Chinese University of Hong Kong (CUHK) under Prof. Michael R. Lyu. His academic journey includes a Bachelor's degree from the University of Electronic Science and Technology of China. Education: PhD in Computer Science and Engineering, The Chinese University of Hong Kong (2024) Bachelor's degree, University of Electronic Science and Technology of China Huo's research focuses on intelligent software engineering , particularly empowering AI models (especially LLMs) for software development, testing, and operations. His work spans AI4SE, LLM4SE, AIOps, code intelligence, and multimodal software engineering . Two flagship projects define his current research: LogPAI - an open-source AI platform for automated log analysis adopted by leading tech companies, and WebPAI - a multimodal intelligence project for automatic webpage development. His research addresses critical challenges in software reliability, log analysis, and UI code generation through innovative applications of AI. His recent publications reveal a strong trend toward multimodal approaches in software engineering , combining vision and language models for UI code generation, and increasingly sophisticated applications of LLMs for log analysis and software reliability. Huo's work demonstrates exceptional impact, with multiple papers accepted at top-tier venues including ASE, ICSE, and FSE with high acceptance rates (e.g., 9.5% for ASE'25). Scientific Awards: ICSE Distinguished Reviewer Award (2025) ISSRE Distinguished Reviewer Award (2024) IEEE Open Software Services Award (2022, for LogPAI with 3k+ GitHub stars and 70k+ downloads) ACM SIGSOFT CAPS Travel Grants (ASE'23, ICSE'24, FSE'24) Nomination for Best Teaching Assistant Award (2022) National Scholarship (2019) Huo actively mentors students at multiple levels, currently supervising PhD students Shi Ying Chang and Dan Huang (co-supervised with Prof. David Lo), research engineer Minxing Wang, and visiting students including Shiwen Shan. His undergraduate mentee Truong Hai Dang will intern at Apple Inc. He maintains strong industry connections, with his LogPAI project adopted by world-leading tech companies. Huo serves on program committees for major conferences including ASE'25, ICSE'26, and FSE'26, and is recruiting fully-funded PhD students and research assistants for projects in AI4SE and multimodal software engineering. Huo leads the LogPAI and WebPAI research initiatives, which have evolved into substantial open-source projects with significant industry adoption. His team focuses on practical applications of AI in software engineering, with particular emphasis on reliability and usability in real-world systems. The research environment benefits from SMU's strong position in software engineering research, where the university ranks No. 2 globally in Software Engineering according to CSRankings (2020-2025).

John D. Kubiatowiczمشاهده پروفایل

استاد

• Computer Architecture
• Quantum Computing
• Distributed Systems
• +۷ مورد دیگر

Professor John D. Kubiatowicz is a faculty member at the University of California at Berkeley in the Department of Electrical Engineering and Computer Sciences since 1998. He holds a PhD in Electrical Engineering and Computer Science (minor in Physics) from MIT (1998), an M.S. in EECS (1993), and a double B.S. in Electrical Engineering and Physics (1987) from MIT. His research interests span Quantum Computing Architectures Distributed Systems and Storage Network Security and Peer-to-Peer Protocols Introspective and Manycore Operating Systems Edge and Fog Computing Hardware-Assisted Security He has pioneered systems like OceanStore , a global-scale distributed file system, and Tessellation , a manycore OS with continuous adaptation. The scientific awards he has received include Presidential Early Career Award (PECASE, 2000) Scientific American 50 (2002) Diane S. McEntyre Teaching Award (2003) IEEE ICRA Best Paper (2025) George M. Sprowls Award for MIT PhD thesis (1998) Okawa Research Grant (1998) Best Paper at International Conference on Supercomputing (1993) His recent publications focus on Quantum Circuit Design and Optimization Edge/Fog Computing Architectures Secure Runtime Systems Distributed Garbage Collection Manycore OS Innovations Hardware-Assisted Security Mechanisms He leads the Quantum Architecture Research Center and co-founded the SWARM Lab at Berkeley, advancing a vision of self-adapting, secure systems from the chip level to internet scale.

Yu Huangمشاهده پروفایل

استادیار

• Software Engineering
• Human-Centered AI
• Human Factors in Programming
• +۵ مورد دیگر

Yu Huang is an Assistant Professor of Computer Science at Vanderbilt University with a secondary appointment in the Department of Teaching and Learning at the Peabody School of Education. She is affiliated with the Institute for Software Integrated Systems, the Frist Center for Autism and Innovation, the Vanderbilt Lab for Immersive AI Translation (VALIANT), and the Vanderbilt LIVE Learning Innovation Incubator. Her research focuses on human-centered AI for software engineering, combining human cognition with machine intelligence to enhance software development processes. Educated at the University of Michigan (PhD, 2021), University of Virginia (MS, 2015), and Harbin Institute of Technology (BS, 2011), her work spans software engineering, human factors, AI, and medical imaging. Key projects include the MIND Lab, studying programmer expertise and cognitive processes, and the HumanAISE workshop on Human-Centered AI for Software Engineering. Huang has received significant recognition, including the 2025 ICPC Vaclav Rajlich Early Career Achievement Award and three ACM SIGSOFT Distinguished Paper Awards. Her research is supported by NSF, GitHub, and Vanderbilt initiatives. She advises numerous graduate and undergraduate students, emphasizing diversity and innovation in programming education.