جست‌وجوی استاد و پژوهشگر

Chao Zhangمشاهده پروفایل

دانشیار

• Software Security
• System Security
• Data Security
• +۶ مورد دیگر

Chao Zhang is a Tenured Associate Professor at Tsinghua University, specializing in software security, system security, data security, and AI security. He leads the VUL337 research group and serves as the coach of the Blue-Lotus CTF team. His educational background includes a Ph.D. in Computer Science from Peking University (2008-2013), a B.S. in Mathematical Science from Peking University (2004-2008), and a postdoctoral position at UC Berkeley (2013-2016). Dr. Zhang's research focuses on Software Security , System Security , Data and AI Security , Program Analysis , and Vulnerability Discovery . His work spans binary code analysis, fuzzing techniques, blockchain security, and AI security. His recent publications demonstrate a strong emphasis on developing novel frameworks for vulnerability detection, binary code analysis, and securing AI systems against adversarial attacks. His publication trends show a consistent focus on practical security solutions with increasing attention to AI security challenges. Over the past decade, he has published extensively in top security conferences including IEEE S&P, USENIX Security, CCS, NDSS, and ISSTA, with a significant acceleration in publications since 2020. Tencent CSS TSec Professional Prize (2nd place, 2019) Tencent CSS TSec Breakthrough Prize (1st place, 2018) DARPA Cyber Grand Challenge CFE, 2nd in exploiting (2016) DARPA Cyber Grand Challenge CQE, 1st in defense (2015) Microsoft BlueHat Prize Contest's Special Recognition Award (2012) 5th place in Defcon CTF 2017 2nd place in Defcon CTF 2016 5th place in Defcon CTF 2015 Dr. Zhang leads the VUL337 research group at Tsinghua University, which focuses on vulnerability discovery and security analysis. He also serves as the coach of the Blue-Lotus CTF team and is a member of the V group of LiST. His research has received significant attention in the security community, with numerous publications in top-tier security venues and practical contributions to vulnerability discovery and mitigation techniques.

Shangwen Wangمشاهده پروفایل

استادیار

• Program repair
• Program comprehension
• Mining software repositories
• +۳ مورد دیگر

Shangwen Wang is an Assistant Professor in the School of Computer Science at National University of Defense Technology (NUDT) in Changsha, China. He earned his Bachelor's degree in June 2017, Master's degree in December 2019, and Ph.D. in December 2023, all from NUDT. During his graduate studies, he was supervised by Professor Xiaoguang Mao. From May 2022 to July 2023, he was a visiting student at Southern University of Science and Technology under Professor Yepang Liu. His educational background includes: Ph.D. in Software Engineering, NUDT (2020.3-2023.12), supervised by Prof. Xiaoguang Mao Visiting Scholar, SUSTech (2022.5-2023.7), supervised by Prof. Yepang Liu M.A. in Software Engineering, NUDT (2017.9-2019.12), supervised by Prof. Xiaoguang Mao B.A. in Software Engineering, NUDT (2013.9-2017.6) Wang's research focuses on program repair, program comprehension, mining software repositories, software maintenance and evolution, software testing, and AI for Software Engineering. His work bridges traditional software engineering techniques with modern AI approaches, particularly leveraging large language models for various software engineering tasks. He has made significant contributions to automated program repair, fault localization, vulnerability detection, and code generation. His research demonstrates a strong emphasis on empirical validation and practical applicability to real-world software development challenges. His recent publications show a clear trend toward integrating large language models with traditional software engineering tasks. The 15 most recent articles reveal a focus on applying LLMs to program repair, fault localization, vulnerability detection, and code generation, while maintaining strong empirical foundations. His work spans both theoretical advancements and practical tool development, with applications in software security, testing, and maintenance. His notable achievements include: CCF Outstanding Doctoral Dissertation (CCF优博) 2024 Outstanding Doctoral Graduates, NUDT, 2023 Multiple distinguished paper awards including ACM SIGSOFT Distinguished Paper Award (ISSTA'24) and IEEE TCSE Distinguished Paper Awards (ICSME'22, SANER'22) Prestigious scholarships from NUDT throughout his academic career As an active member of the software engineering community, Wang serves on numerous program committees for top conferences including ICSE, ASE, ESEC/FSE, and ISSTA. He has also contributed to teaching as a teaching assistant for courses such as Compiler, Python Programming, Discrete Mathematics, and C++ Programming. His research group appears to be actively mentoring students, as evidenced by his role as corresponding author on multiple student-led publications. Wang maintains an active research presence with collaborations across multiple institutions in China. His work demonstrates a clear trajectory from traditional program analysis techniques toward integrating cutting-edge AI approaches, particularly large language models, into software engineering practices.

Zhou Yangمشاهده پروفایل

استادیار

• Software Engineering
• Artificial Intelligence
• AI for Software Engineering
• +۳ مورد دیگر

Zhou Yang is an Assistant Professor at the University of Alberta and Fellow at the Alberta Machine Intelligence Institute (Amii), with research focusing on the intersection of software engineering and artificial intelligence. His academic journey includes a Ph.D. from Singapore Management University, an M.Sc. in Software System Engineering from University College London, and undergraduate studies at Yangzhou University. His research interests span Software Engineering, Artificial Intelligence, AI for Software Engineering (AI4SE), Software Engineering for AI (SE4AI), Large Language Models, and Cybersecurity. Yang's work explores how human and AI collaboration can improve code writing, how AI impacts open-source communities, and how developers build software in emerging environments like VR/AR. His recent publications demonstrate strong trends in code language models, with significant contributions to ASE, ICSE, ISSTA, and top journals like TOSEM and TSE. His research addresses critical challenges including token efficiency in code generation, user perception of AI coding assistants, privacy preservation in code models, and fairness in AI systems. 2024 IEEE Computer Society Best Paper Award (1 out of 183 submissions) ACM Distinguished Paper Award from ISSTA 2024 Distinguished Reviewer Award from Internetware 2024 SMU Research Staff Excellence Award (1 of 4 university-wide) SMU Presidential Fellowship Award SMU Dean's List Award 1st Place in ACM Student Research Competition at ICSE 2024 Yang actively mentors graduate students with regular one-on-one meetings, constructive feedback, and support for top-venue publications. He provides full funding through teaching assistantships and research grants, including travel support for conferences. His lab focuses on responsible research conduct and societal implications of AI work. Though early-career, he actively builds professional networks for students through collaborations and supports diverse career paths in academia and industry. He leads research in the Alberta Machine Intelligence Institute, focusing on practical applications where software engineering principles enhance AI systems and where AI techniques solve real-world software engineering challenges.

Hongyu Zhangمشاهده پروفایل

استاد

• Intelligent software engineering
• Software analytics
• Data-driven software engineering
• +۴ مورد دیگر

Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.

• Video Quality Assessment
• Telecommunications
• Quality of Service (QoS)
• +۵ مورد دیگر

Professor Tadeus Uhl is a faculty member at the University of Applied Sciences Flensburg in the School of Information and Communication. His research focuses on telecommunications, video quality assessment, and Quality of Service (QoS) technologies. He maintains an active research profile with numerous publications spanning from 2017 to 2024. Professor Uhl's research interests center around video quality evaluation, telecommunications technologies, and network performance analysis. His work examines cutting-edge topics including 8-bit and 10-bit MP4 coding, machine learning applications for video quality assessment, and user experience with different video resolutions (1K, 2K, and 4K). He has also conducted significant research on 5G technology, VoIP systems, and Quality of Service in IoT environments. His recent publications show a clear trend toward video quality assessment using modern machine learning techniques, with a focus on real-world streaming services like Netflix. His work bridges theoretical network performance concepts with practical applications in video streaming, cellular networks, and offshore communication systems. Professor Uhl has made substantial contributions to understanding Quality of Service parameters across various communication technologies, from traditional VoIP systems to modern IoT networks and offshore wind farm communications infrastructure. His research has been published in notable venues including IEEE conferences, the Journal of Telecommunications and Information Technology, and various scientific journals focused on telecommunications and multimedia technology.

• Process Systems Engineering
• Chemical Engineering
• Optimization
• +۶ مورد دیگر

Alexander Mitsos is a Professor at Forschungszentrum Jülich in Germany, where he leads research at the intersection of process systems engineering, chemical engineering, and computational methods. His work spans optimization theory, machine learning applications, and energy systems, with a focus on developing novel methodologies for complex engineering problems across multiple domains. Dr. Mitsos's research interests center on the application of advanced optimization techniques to chemical engineering problems. His primary areas of focus include: Process systems engineering and optimization Machine learning applications in chemical engineering Energy systems and hydrogen technologies Bioprocess engineering and control systems Ammonia energy storage and carbon capture His recent publications reveal a strong trend toward integrating machine learning with traditional chemical engineering approaches. He has pioneered work on graph neural networks for molecular property prediction, reinforcement learning for control systems, and bilevel optimization for energy systems. His research demonstrates a consistent focus on developing computationally efficient methods that bridge theoretical advances with practical engineering applications, particularly in sustainability-focused domains like hydrogen technologies and carbon emission reduction. The analysis of his 15 most recent publications shows a balanced portfolio between theoretical method development (e.g., optimization algorithms) and practical applications (e.g., cement production, hydrogen compression). Dr. Mitsos has mentored numerous graduate students and postdoctoral researchers, as evidenced by his extensive publication record with junior authors. His research has been supported by various grants focused on energy transition, process optimization, and sustainable chemical engineering solutions, with significant collaborations across European institutions. The funding landscape for his work appears to emphasize sustainability transitions and industrial decarbonization, particularly in energy-intensive sectors. His work appears to be conducted within a research group focused on process systems engineering, with strong connections to both computational mathematics and practical chemical engineering applications. The group maintains laboratories for experimental validation of computational models, particularly in bioprocess engineering and hydrogen technologies, while maintaining strong theoretical foundations in optimization and control theory.