جستجوی استاد راهنمای دانشگاهی

Lu Renquanمشاهده پروفایل

استاد

• Networked Control Systems
• Intelligent Decision and Collaborative Control
• Unmanned Autonomous Systems
• +۳ مورد دیگر

Professor Lu Renquan is a distinguished faculty member at Guangdong University of Technology's School of Automation, serving as Director of the Institute of Science, Technology and Humanities and holding the rank of Professor and PhD Supervisor. With over 20 years of academic leadership, he directs major research initiatives in networked control systems and autonomous technologies. His educational foundation includes: Master in Materials Engineering, Lanzhou University of Technology (1998-2001) Ph.D. in Control Science and Engineering, Zhejiang University (2001-2004) Postdoctoral Research, Zhejiang University (2005-2007) Visiting Scholar, Universiti Kuala Lumpur (2007) Professor Lu's research pioneers Networked Control Systems, Intelligent Decision and Collaborative Control, and Unmanned Autonomous Systems. His work integrates robust control theory with industrial applications, driving innovations in autonomous vehicle coordination and data-driven manufacturing. With over 140 SCI publications including 60+ IEEE Transactions papers and 9 Automatica articles, his research demonstrates significant theoretical depth and practical impact across engineering disciplines. His scientific recognition includes: Ministry of Education Natural Science Award First Prize (2019, lead recipient) Three additional Ministry of Education/National awards (2011-2016) National Outstanding Youth Fund recipient State Council Special Allowance recipient Professor Lu actively mentors students while leading substantial research programs. His current portfolio includes a national key R&D project on manufacturing data spaces, Guangdong's Unmanned Autonomous System Collaborative Control Team, and direction of the Provincial Key Laboratory for Intelligent Decision and Collaborative Control. He teaches core courses in robust control and automatic systems while guiding student teams in national competitions like the 'Challenge Cup'. He directs the Guangdong Provincial Key Laboratory of Intelligent Decision and Collaborative Control, which focuses on advancing collaborative algorithms for unmanned systems and industrial automation through interdisciplinary research in networked control theory and big data applications.

Kun Yangمشاهده پروفایل

استاد

• Regional climate model development
• Land data assimilation
• Climatic change research
• +۶ مورد دیگر

Dr. Kun Yang is a Professor at Tsinghua University's Department of Earth System Science in Beijing, China. His research focuses on regional climate model development, land data assimilation, climatic change research, and renewable energy resource assessment and prediction, with particular emphasis on Tibetan Plateau climate studies. Dr. Yang's educational background includes: Ph.D. in Hydrometeorology from The University of Tokyo (1997-2000) M.S. in Environmental Hydraulics from Tsinghua University (1994-1997) B.S. in Fluid Mechanics and Fluid Machinery from Tsinghua University (1989-1994) Professor Yang's research primarily investigates the complex interactions between land surface processes and atmospheric systems, with special focus on the Tibetan Plateau - often referred to as the 'Third Pole.' His work spans regional climate modeling, soil moisture-precipitation coupling, land data assimilation techniques, and renewable energy assessment. The Tibetan Plateau serves as a critical region for understanding Asian monsoon systems and global climate patterns, making his research highly significant for both regional and global climate science. Professor Yang has received notable recognition for his research contributions: Highly Cited Researcher by Clarivate (2021-present) Highly Cited Chinese Researcher by Elsevier (2020-present) With an impressive publication record of over 300 peer-reviewed articles and more than 20,000 citations, Professor Yang maintains active research funding supporting his work in climate modeling and earth system science. His research group at Tsinghua University collaborates extensively with institutions across China and internationally, contributing to major climate research initiatives including the Global Energy and Water Exchanges (GEWEX) program. Professor Yang leads an active research laboratory at Tsinghua University focused on advancing regional climate models, particularly for the challenging terrain of the Tibetan Plateau. His lab develops innovative approaches to land data assimilation and investigates critical questions about climate change impacts on water resources and energy systems in high-altitude environments.

• Spinocerebellar ataxias
• Neurodegenerative diseases
• MRI biomarkers
• +۵ مورد دیگر

Professor Thomas Klockgether is a leading researcher at the University of Bonn Faculty of Medicine, Department of Neurology, focusing on spinocerebellar ataxias and movement disorders. His work bridges clinical neurology with advanced data-driven methodologies to improve understanding and treatment of neurodegenerative conditions. His research spans five major interconnected areas: Digital assessment of movement disorders through tools like SARAhome, a video-based ataxia assessment system applied by patients at home Development of MRI biomarkers, including ultra-high field imaging and CerebNet (an AI-based cerebellum segmentation method) Patient-reported outcomes via the PROFA project's e-health app collecting real-time health economic and psychosocial data Data-driven staging models for SCA3 that identify pre-symptomatic biomarker changes occurring more than 10 years before ataxia onset Natural history studies of sporadic ataxias through the SPORTAX registry with over 500 participants Professor Klockgether leads multiple international research initiatives including the JPND-funded SCAIFIELD and ESMI projects, and the EJP RD-funded PROFA project. His groundbreaking work demonstrating that brain and spinal cord tissue loss begins more than a decade before symptom onset in SCA3 mutation carriers has significant implications for preventive therapeutic interventions. His research program aims to establish objective digital and imaging biomarkers that can serve as endpoints in future clinical trials for ataxia treatments. His laboratory develops innovative approaches to overcome limitations of conventional clinical assessment scales, particularly through AI-based automatic scoring methods for gait and speech analysis. The SPORTAX registry provides critical data on differential progression patterns between multiple system atrophy (MSA) and sporadic adult-onset ataxia of unknown etiology (SAOA).

Yintong Huoمشاهده پروفایل

استادیار

• Log analysis
• Code intelligence
• Intelligent software engineering
• +۷ مورد دیگر

Yintong Huo is an Assistant Professor at the School of Computing & Information Systems, Singapore Management University (SMU), where he leads research in intelligent software engineering. He received his PhD from The Chinese University of Hong Kong (CUHK) in 2024 under Prof. Michael R. Lyu and holds a Bachelor's degree from the University of Electronic Science and Technology of China. His research focuses on empowering AI models (particularly LLMs) for software development, testing, and operations, with two flagship projects: LogPAI (open-source AI platform for automated log analysis) and WebPAI (multimodal intelligence for automatic webpage development). His work spans log analysis, code intelligence, UI generation from prototypes, and configuration diagnostics. Huo's publication record shows strong trends in leveraging multimodal LLMs for practical software engineering challenges, with recent work on interactive webpage generation (Interaction2Code), configuration logging (ConfLogger), and log parsing (LILAC). His research bridges theoretical AI advancements with real-world system reliability needs. ICSE Distinguished Reviewer Award (2025) ISSRE Distinguished Reviewer Award (2024) IEEE Open Software Services Award (2022) ACM SIGSOFT CAPS Travel Grants National Scholarship (2019) Huo actively supervises PhD students (including Shi Ying Chang and Dan Huang) and research engineers. His lab has secured funding for multiple projects including WebPAI and LogPAI. He serves on program committees for major conferences (ASE, ICSE, FSE) and reviews for top journals. Current projects include dynamic webpage generation and configuration diagnostics, with ongoing work on small language models for logging systems. Huo leads the LogPAI and WebPAI research groups, developing open-source tools for automated log analysis and multimodal UI code generation. The LogPAI project has garnered over 3,000 GitHub stars and 70,000 downloads. His team collaborates with industry partners on AIOps challenges and is expanding into configuration diagnostics through the ConfLogger project.

Hui Liuمشاهده پروفایل

استاد

• Software Engineering
• AI-based Software Development
• LLM-based Program Generation
• +۵ مورد دیگر

Hui Liu is a Professor in the School of Computer Science and Technology at Beijing Institute of Technology, where he leads research in AI-based software development with a focus on LLM applications. His work spans software refactoring, quality improvement, and maintenance, funded by the National Natural Science Foundation of China and the National Key Research and Development Program of China. PhD from Peking University (2008) Former graduate student at Software Engineering Institute, Peking University Distinguished member of China Computer Federation Secretary-General of CCF Technical Committee on Software Engineering Professor Liu's research centers on LLM-based program generation, evaluation and testing of large language models, software refactoring techniques, and automatic construction of software engineering datasets. His work bridges artificial intelligence and software engineering, with particular emphasis on improving code quality through empirical studies and machine learning techniques. Current projects include code contamination detection, context-aware naming recommendations, and refactoring validation using LLMs. His research has evolved from traditional code smell detection to cutting-edge applications of large language models in software development. Liu's publication record shows a strong trend toward LLM applications in software engineering, with recent work focusing on code review generation, commit message generation, and refactoring validation using large language models. His research combines empirical methods with machine learning approaches, often analyzing large code corpora from open-source projects. The work spans both theoretical foundations and practical tool development, with several contributions merged into Eclipse as part of the open-source community. ACM Distinguished Paper Award (ESEC/FSE 2023) ACM Distinguished Paper Award (ICSE 2022) RE'2021 Best Research Paper Award IET Software Premium Award (2018) New Century Excellent Talents in University (2013) Beijing Higher Education Young Elite Teacher (2013) Professor Liu actively mentors PhD and Master's students, with recent graduates including Waseem Akram (awarded Outstanding Graduate) and several students publishing at top venues. His research is supported by major Chinese funding agencies, and he serves on program committees for leading software engineering conferences including ASE, ICSE, and FSE. He maintains strong industry connections through contributions to Eclipse and studies of open-source ecosystems like Rust. Liu leads a research group focused on AI for software engineering, with active projects on code generation, refactoring, and quality improvement. The group collaborates extensively with international researchers and contributes directly to open-source tools, particularly in the Eclipse ecosystem where multiple refactoring improvements have been merged.

Antonio Ruiz-Cortésمشاهده پروفایل

استاد

• Service Oriented Computing
• Software Engineering
• Software Architecture
• +۴ مورد دیگر

Antonio Ruiz-Cortés is Professor of Software and Service Engineering at the University of Seville and an elected member of the Academy of Europe. He currently heads the Applied Software Engineering Group at the University of Seville and has served as President of the Spanish Society on Software Engineering (SISTEDES) since September 2018. His research spans multiple critical areas of modern software engineering: Service-Oriented Computing Software Architecture Business Process Management Testing methodologies Software Product Lines Automatic Model Analysis Professor Ruiz-Cortés has established himself as a leading researcher in REST API testing and verification, with multiple influential publications in top software engineering conferences. His work bridges traditional software engineering with emerging domains like quantum computing, demonstrating remarkable adaptability. He has developed practical tools including RESTest and AGORA that address real-world testing challenges faced by industry practitioners. His scientific contributions have been recognized with the Most Influential Paper award at SPLC 2017. He serves as associate editor for Springer Computing and has organized major conferences including JCIS 2008, SPLC 2017, SISTEDES 2018 and BPM 2020, demonstrating significant leadership in the academic community. Professor Ruiz-Cortés maintains active research funding supporting his work in service-oriented computing and software testing, though specific grant details aren't provided in the available information. His presidency of SISTEDES reflects his commitment to advancing software engineering research in Spain.

Austin Mordahlمشاهده پروفایل

استادیار

• Static Analysis
• Taint Analysis
• Automated Testing
• +۴ مورد دیگر

Austin Mordahl is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago. His research focuses on improving the usability and reliability of software quality assurance tools, particularly static analyses and fuzz testing. Dr. Mordahl actively seeks students interested in cutting-edge research in software engineering at UIC. Dr. Mordahl's research interests span multiple areas of software engineering with a particular emphasis on: Static Analysis and Taint Analysis for software security Automated Testing techniques including fuzz testing Improving the usability and reliability of software quality assurance tools Configurable static analysis tools and their behavior Applying machine learning to triage and configure static analysis tools Lifting static analysis to work on software product lines Improving evaluations of fuzz testing tools His recent publications demonstrate a strong focus on addressing challenges in configurable static analysis tools, with particular attention to nondeterministic behavior, configuration spaces, and automatic testing and debugging approaches. Mordahl's work bridges theoretical foundations with practical applications, aiming to make software quality assurance tools more accessible and effective for developers. Dr. Mordahl has received several prestigious awards for his work: NSF Graduate Research Fellowship Awardee (2020) Eugene McDermott Graduate Research Fellowship Awardee (2020) ICSE 2019 Student Research Competition Winner Dr. Mordahl is actively involved in mentoring and seeks students interested in software engineering research. He has served on numerous program committees for top software engineering conferences including ICSE, ASE, ISSTA, and PLDI. His service to the academic community extends to artifact evaluation committees, demonstrating his commitment to research reproducibility and rigor.

Simone Scalabrinoمشاهده پروفایل

استادیار

• Software Quality
• Software Testing
• Software Security
• +۲ مورد دیگر

Simone Scalabrino is an Assistant Professor at the University of Molise, Italy, where he is part of the STAKE lab. He also serves as CSO at Datasound. His academic career includes teaching courses such as Automated Software Delivery and Object-Oriented Programming at the University of Molise. Dr. Scalabrino received his Ph.D. from the University of Molise in 2019 with a thesis entitled "Automatically Assessing and Improving Code Readability and Understandability," supervised by Prof. Rocco Oliveto. He earned his Master's Degree in Computer Science from the University of Salerno in 2015 and his Bachelor's Degree in Computer Science from the University of Molise in 2013. His research interests focus on Software Quality, Software Testing, Software Security, and Empirical Software Engineering . Dr. Scalabrino's work spans multiple areas including code readability assessment, software testing methodologies, Docker container analysis, game quality assessment, and voice user interface testing. His research often combines empirical methods with machine learning techniques to address practical software engineering challenges. Analysis of his recent publications reveals a strong focus on improving software quality through various approaches. His work spans code readability assessment, Docker container analysis, game quality testing, and voice user interface evaluation. There's a clear trend toward applying machine learning techniques to traditional software engineering problems, particularly in the areas of code understanding, defect prediction, and quality assessment. His research often involves large-scale empirical studies with real-world data from open source projects and commercial applications. Scientific Awards Distinguished Reviewer Award @ FSE 2025 Distinguished Reviewer for TSE (2023) Best Reviewer Award for JSS (2022) ACM Distinguished Paper Award @ MSR 2019 ACM Distinguished Paper Award @ ASE 2017 ACM Distinguished Paper Award @ ICPC 2016 Dr. Scalabrino has been actively involved in mentoring students through research projects, though specific student names are not listed in the provided information. He has served on program committees for numerous prestigious conferences including ASE, ICSE, ICPC, and FSE. His service extends to reviewing for top-tier journals such as Transactions on Software Engineering and Empirical Software Engineering. He leads or contributes to several research projects including DevProDev, which focuses on developer-centered recommendation systems, and ATTICUS, a tele-monitoring system for ambient-assisted living. Dr. Scalabrino has also developed multiple research tools such as Code Readability Predictor, TIRESIAS, OCELOT, CLAP, and ACRyL to address various software engineering challenges.

Ajitha Rajanمشاهده پروفایل

استاد

• Software Testing
• Software Verification
• Test Automation
• +۶ مورد دیگر

Ajitha Rajan is a Professor (Personal Chair of Software Testing & Verification) at the School of Informatics, University of Edinburgh. She joined the university in December 2012 as a Reader (equivalent to Associate Professor in American terms) and was promoted to Professor in 2024. Prior to her position at Edinburgh, she held postdoctoral positions at Oxford University and Laboratoire d'Informatique de Grenoble in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under Professor Mats Heimdahl. Her research focuses on two primary directions: Automated Software Testing (including test input generation, test oracles, and coverage measurement) and Biomedical AI (particularly cancer survival models and interpretability for biological sequences and medical images). Her work has applications in safety-critical systems, blockchains, embedded systems, and medical diagnostics. She has made significant contributions to explainable AI for healthcare applications, especially in lung cancer detection and cancer survival analysis. Her recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of software engineering and biomedical applications. She has numerous publications in top venues including ICSE, ASE, and healthcare-focused conferences. Her work increasingly focuses on making AI systems more interpretable and trustworthy, particularly in medical contexts where model decisions can have life-or-death consequences. ACM SIGSOFT Distinguished Reviewer Award, ISSTA 2025 Best Paper Award at ICHI 2025 Promoted to Professor (Chair in Software Testing & Verification) 2024 SICSA Best Supervisor Award 2024 ACM Distinguished Paper Award 2008 Professor Rajan actively supervises PhD students in both software testing and biomedical AI domains. She leads several funded projects including MANIFEST (a cancer immunotherapy response research platform), a Huawei Joint Lab project on RobustCheck, a Royal Society Industry Fellowship on AutoTest, and the KATY project on clinical knowledge for personalized medicine. Her research group includes current PhD students working on explainable AI for medical image analysis, scenario-based testing for autonomous driving, and protein design applications.

Sébastien Bardinمشاهده پروفایل

پژوهشگر

• Program Analysis
• Formal Methods
• Security
• +۵ مورد دیگر

Sébastien Bardin is a Senior Researcher and CEA Fellow at the Software Safety and Security Laboratory of Commissariat à l'Energie Atomique (CEA) in Paris-Saclay, France, with affiliation to University Paris-Saclay. He leads significant research initiatives including the BINSEC group on binary-level security analysis (since 2012) and the QBricks group on quantum program verification (since 2020), and served as Head of the Software Quantum Program at CEA LIST from 2021-2023. His research focuses on formal methods and automatic program analysis with applications in software security. Key areas include binary-level security analyses such as vulnerability detection & assessment, reverse engineering, and malware deobfuscation, as well as quantum programming and verification. His methodological expertise spans symbolic execution, abstract interpretation, software model checking, and SMT solving. Bardin's publications demonstrate consistent impact across top venues in security and formal methods, with notable papers at ESOP, CAV, ICSE, NDSS, and S&P. His work on robust symbolic execution, directed fuzzing, and binary-level analysis has established him as a leading researcher in security-oriented program analysis. Recent publications show growing emphasis on quantum program verification alongside continued contributions to binary security analysis. Scientific Awards: ICSE 2021 ACM SIGSOFT Distinguished Paper Award RTAS 2021 Best Paper Award CAV 2021 Selected Paper CEA Fellow (2021-present) ACM Senior Member (2021-present) Bardin actively mentors PhD students and has advised numerous researchers who have received recognition including GDR Sécurité and GDR GPL PhD awards. His service to the community is extensive, with leadership roles in GDR Sécurité, RESSI, and organizing major conferences including CAV 2023 (sponsor co-chair) and FIC 2023 (scientific program chair). He also contributes to multiple program committees across security and formal methods venues.

• Macroeconomics
• Monetary Policy
• Fiscal Policy
• +۶ مورد دیگر

Kurt Mitman is a Professor at CEMFI, an Associate Professor at the Institute for International Economic Studies (IIES) at Stockholm University (currently on leave), a Research Fellow of the Centre for Economic Policy Research (CEPR), and a Research Fellow of the IZA Institute of Labor Economics. He also conducts research at the University of Oslo and serves as a Research Fellow of IZA. Previously, he served as Managing Editor of the Review of Economic Studies from December 2019 to January 2024 and is co-editor of the Journal of the European Economic Association and Macroeconomic Dynamics. PhD in Economics from the University of Pennsylvania (2014) Professor Mitman's research focuses broadly on macroeconomics, with particular interest in monetary and fiscal policy, housing, household debt and default, labor market dynamics, and wealth inequality. His work often employs heterogeneous-agent models to understand how household-level differences affect macroeconomic outcomes and policy responses. He has made significant contributions to understanding the effects of unemployment insurance, bankruptcy policies, and housing market dynamics during economic downturns. His research has identified important patterns regarding how wealth inequality amplifies business cycle fluctuations, how unemployment benefit extensions affect both job search behavior and vacancy creation, and how housing market dynamics interact with household balance sheets during recessions. His recent ERC Consolidator Grant for the HIPSTERMacro project focuses on housing, inequality, and price-setting in the macroeconomy. ERC Consolidator Grant for HIPSTERMacro research proposal Professor Mitman has served as Managing Editor of the Review of Economic Studies and is co-editor of the Journal of the European Economic Association and Macroeconomic Dynamics. His research has been supported by significant grants including the ERC Consolidator Grant. He regularly presents at major universities and conferences worldwide and co-organizes the VMACS (Virtual Macro Seminar) series. Professor Mitman is actively involved in organizing academic conferences including the SITE Session on Macroeconomics and Inequality and the Economic Growth and Fluctuations Group at the Barcelona Summer Forum. His research group focuses on macroeconomic issues with household heterogeneity, particularly examining how inequality affects business cycles and policy effectiveness.