جستجوی استاد راهنمای دانشگاهی

Sakeena Muntahaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Image Processing
• +۳ مورد دیگر

Sakeena Muntaha serves as a Junior Researcher at the University of Applied Sciences St. Pölten, affiliated with the Institute of Creative\Media/Technologies and the Department of Media and Digital Technologies since 2017. Currently on leave, she contributes to the institution's research mission through interdisciplinary projects spanning computer vision and applied machine learning. Her academic foundation includes a Master's degree in Computer Engineering from the National University of Sciences and Technology (NUST), Pakistan (2016) and a Bachelor's degree in Computer System Engineering from the NFC Institute of Engineering and Technology (NFCIET), Pakistan (2012). These qualifications underpin her technical expertise in visual computing systems. Dr. Muntaha's research program centers on machine learning and computer vision with dual application tracks: medical diagnostics (skin lesion segmentation, dermoscopy analysis) and environmental/urban systems (building footprint extraction, flood monitoring, real estate analysis). Her methodological approach integrates deep learning architectures with classical image processing techniques like level sets and Gabor filters, demonstrating versatility across domains from cultural heritage preservation to cybersecurity. Recent work shows increasing focus on robustness evaluation and real-world deployment challenges in vision systems. Analysis of her 15 most recent publications reveals strong thematic continuity in computer vision applications, with growing sophistication in handling real-world data constraints. Early work focused on medical imaging and malware detection, while recent publications emphasize urban infrastructure analysis and environmental monitoring, reflecting strategic alignment with societal challenges. The consistent use of deep learning frameworks across diverse domains highlights her technical agility. As an active member of the Media Computing Research Group, she contributes to projects including IMREA (Intelligent Multimodal Real Estate Assessment), Scribe ID AI (cultural heritage analysis), and ImmBild (location assessment via computer vision). Her collaborative research involves partnerships with institutions across Austria and Pakistan, though specific grant details and advising activities are not documented in available sources.

Rodrigo Fonsecaمشاهده پروفایل

دانشیار

• distributed systems
• networking
• operating systems
• +۵ مورد دیگر

Rodrigo Fonseca is an Associate Professor with Tenure in the Computer Science Department at Brown University's School of Engineering. He received his PhD from UC Berkeley in 2008 under Ion Stoica, focusing on execution tracing for distributed systems. His educational background includes a PhD in Computer Science from UC Berkeley (2008). Fonseca's research focuses on distributed systems, networking, and operating systems, with particular interest in understanding complex system behavior. His work spans distributed tracing and debugging (X-Trace, Pivot Tracing), network management in datacenters (2DFQ, Planck), power management in mobile systems (Quanto, Application Modes), and software-defined networking (Participatory Networking, Simon). His research integrates theoretical insights with practical implementations that have influenced both academic and industrial systems. His publication record shows a strong focus on distributed system instrumentation, with recurring themes of causal tracing, resource management, and network monitoring. The most recent publications demonstrate continued innovation in data-aware indexing structures, in-network packet analysis, and context propagation techniques for distributed systems. Fonseca has received significant recognition including an NSDI Test of Time Award for X-Trace, an NSF CAREER Award, and a Best Paper Award at SOSP for Pivot Tracing. His work has been consistently published in top-tier venues including SIGCOMM, NSDI, SOSP, and OSDI. He has mentored numerous graduate students, with five PhD graduates as of 2019 who have gone on to positions at Google, Microsoft, Apple, and MPI-SWS. His research has been supported by funding from the National Science Foundation, Google, Intel, and Microsoft Research, where he also spent visiting periods. He is actively involved in the research community, having served on program committees for major conferences and co-organizing workshops including HotCloud and the New England Networking and Systems Day. In 2019-2020, he served as PC co-chair for HotNets and General Chair for SoCC'2020.

Eric Boddenمشاهده پروفایل

استاد

• Secure Software Engineering
• Static Program Analysis
• Automated Program Analysis
• +۵ مورد دیگر

Eric Bodden is a Professor for Secure Software Engineering at Paderborn University and co-director of Fraunhofer IEM. He is also a member of the directorate of the Collaborative Research Center CROSSING at TU Darmstadt. As an ERC fellow, Bodden leads the Attract-Group on Secure Software Engineering at Fraunhofer IEM, where he develops code analysis technology for security in collaboration with leading national and international software development companies. Bodden is one of the leading experts in secure software engineering with a specialty in building highly precise tools for automated program analysis. His research spans static program analysis, Android security, taint analysis, and cryptographic API security. He has made significant contributions to the field through frameworks like FlowDroid for Android analysis, DroidBench benchmark suite, and as one of the chief maintainers of the Soot program analysis framework. His work bridges theoretical advances with practical applications, focusing on creating tools that can be effectively used by developers to enhance software security. His recent publications demonstrate a continued focus on advancing static analysis techniques, with increasing attention to modern challenges like C/C++ analysis, large language models in program analysis, and addressing security vulnerabilities in dependency management. His work shows a consistent trajectory of improving analysis precision, scalability, and usability for developers. Heinz Maier-Leibnitz-Preis (2014) ERC fellow BITKOM Management Club membership (2013) Bodden has advised numerous doctoral students whose theses cover diverse aspects of secure software engineering, including static analysis for Android applications, secure integration of cryptographic software, and information flow security engineering. His research group has received recognition through multiple dissertation awards including Summa cum laude distinctions and the Ernst Denert Software Engineering Award. The group has developed influential tools like FlowDroid, CogniCrypt, and SootUp that have shaped the field of secure software development. At Fraunhofer IEM, Bodden heads the Attract-Group on Secure Software Engineering, which collaborates with industry partners to develop practical security analysis solutions. His team has created several influential frameworks including FlowDroid for Android security analysis and the DroidBench benchmark suite. The group's work bridges academic research with real-world applications, focusing on making security analysis tools more precise, scalable, and usable for developers.

Amiangshu Bosuمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Code Reviews
• Software Security
• +۸ مورد دیگر

Amiangshu Bosu is a tenured Associate Professor in the Department of Computer Science at Wayne State University's College of Engineering. Previously, he served as a tenure-track Assistant Professor at Southern Illinois University Carbondale from 2016-2018. His academic journey includes completing his Ph.D. at the University of Alabama in 2015 under Professor Jeffrey Carver, followed by postdoctoral research with Dr. Danfeng Yao at Virginia Tech. His educational background reflects a strong foundation in software engineering research, with his doctoral work focusing on empirical approaches to software development practices. This foundation has enabled his current research trajectory examining human aspects of software engineering. Bosu's research program centers on empirical software engineering with particular emphasis on code review dynamics, toxicity detection in developer communications, and diversity issues in open source communities. His work bridges technical analysis with social science perspectives, investigating how communication patterns affect participation, especially regarding gender dynamics in OSS projects. Recent work increasingly incorporates natural language processing and machine learning techniques to build tools like ToxiCR for automated toxicity detection in code reviews. Analysis of his publication record reveals a clear evolution toward examining social dynamics in software development, with growing emphasis on gender bias, toxicity, and diversity issues. His research consistently applies empirical methods to understand real-world developer experiences while developing practical tools to improve code review processes and community health. His scientific recognition includes the prestigious NSF CAREER award (2024), NSF CRII award (2019), and Wayne State University's College of Engineering Research Excellence award (2025). He maintains senior membership status in both ACM and IEEE, reflecting his standing in the computing community. NSF CRII award (2019) NSF CAREER award (2024) College of Engineering Research Excellence award (2025) Senior Member of ACM Senior Member of IEEE Bosu actively contributes to the software engineering research community through service on program committees for major conferences including ICSE, ASE, ESEC/FSE, and ESEM. His GitHub presence (amiangshu.me) demonstrates active engagement with research tools and datasets, particularly those related to code review analysis and toxicity detection. While specific lab structure isn't detailed in the provided materials, his research group appears focused on empirical studies of software development practices with strong connections to industry through collaborations on projects like Chromium OS security analysis.

Zhen Dongمشاهده پروفایل

دانشیار

• Software Reliability
• Software Security
• Mobile Computing
• +۴ مورد دیگر

Zhen Dong is an Associate Professor at Fudan University, China, specializing in software engineering with a focus on software reliability and security, particularly in mobile computing. Previously, he was a PostDoc and Senior Research Fellow at the National University of Singapore under the guidance of Abhik Roychoudhury. His educational background includes: PhD in Computer Science from Heidelberg University (2017), advised by Prof. Artur Andrzejak Dr. Dong's research centers on developing techniques and tools for improving software reliability and security. His work spans mobile application testing, Android security, flaky test detection, and vulnerability localization. He has made significant contributions to the field of software testing and analysis, with a particular emphasis on practical applications for mobile systems. His research bridges theoretical foundations with real-world software engineering challenges. Analysis of Dr. Dong's recent publications reveals a strong focus on leveraging AI/ML techniques for software engineering tasks, particularly using LLMs for test automation and program analysis. His work consistently addresses critical challenges in mobile computing, especially Android application reliability and security. There's a clear trajectory toward more sophisticated analysis techniques, from traditional testing methods to AI-driven approaches. His notable scientific achievements include: ACM Distinguished Paper Award at ICSE'20 Best Paper Award at AsiaCCS'21 (1/370 submissions) ASE'22 Distinguished Reviewer Award Dr. Dong serves on the Board of Distinguished Reviewers for ACM Transactions on Software Engineering and Methodology and has been an active member of numerous program committees for top software engineering conferences including ICSE, ASE, and ISSTA. His service to the academic community extends to reviewing for prestigious journals such as IEEE Transactions on Software Engineering and Methodology and ACM Transactions on Software Engineering and Methodology. His research has been supported through various academic channels, enabling him to maintain an active lab focused on software testing and analysis, particularly for mobile platforms. The lab has produced numerous tools and techniques that have influenced both academic research and industrial practice in software reliability.

Ding Liمشاهده پروفایل

استادیار

• Program Analysis
• Energy Optimization
• Mobile Applications
• +۲ مورد دیگر

Ding Li is an Assistant Professor in the School of Computer Science at Peking University. He holds a Ph.D. in Computer Science from the University of Southern California (USC) and a B.S. from Peking University. His research focuses on program analysis, energy optimization for mobile applications, and security, with publications in top conferences including ICSE, FSE, and ASE. His research interests span: Program Analysis : Techniques to optimize mobile application energy consumption. System Security : Identifying vulnerabilities in Android apps and WebAssembly binaries. Cloud/Edge Computing : Enhancing serverless computing efficiency and federated learning security. Dr. Li's recent work explores the integration of large language models into pointer analysis and automated optimization of resource inefficiencies. His publications demonstrate a consistent focus on practical system optimizations and security enhancements across mobile, cloud, and machine learning domains. Awards: Viterbi Undergraduate Research Mentoring Award (2014)

David Lieمشاهده پروفایل

استاد

• Computer Security
• Privacy
• Program Analysis
• +۲ مورد دیگر

David Lie is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto. He holds additional appointments in the Department of Computer Science and the Faculty of Law. He is a Tier 1 Canada Research Chair in Secure and Reliable Systems, a research lead at the Schwartz Reisman Institute for Technology and Society, an Associate Director at the Data Sciences Institute, a Vector Faculty Affiliate, and a Senior Massey College Fellow. His educational background includes: BASc from the University of Toronto (1998) MS from Stanford University (2001) PhD from Stanford University (2004) David Lie's research spans computer security, privacy, and cybersecurity. He is renowned for pioneering the XOM architecture—a foundational model for modern trusted execution environments like ARM TrustZone and Intel SGX—and developing the widely adopted PScout Android permission mapping tool. His current work emphasizes program analysis, fuzzing, and symbolic execution to enhance software security and reliability, addressing critical vulnerabilities in mobile and system software. His recent publications reveal a strong trajectory in integrating machine learning with program analysis techniques. Key themes include optimizing symbolic execution for Android apps, leveraging LLVM IR for bug detection, and using predictive models to guide test generation. These contributions underscore a practical focus on scalable, automated security tools that bridge theoretical advances with real-world software vulnerabilities. His notable awards include: Best Paper Award at SOSP Tier 1 Canada Research Chair in Secure and Reliable Systems Senior Massey College Fellow No specific information on student advising or research grants was provided in the available text, though his extensive program committee service for top security conferences (OSDI, IEEE Security & Privacy, CCS, etc.) indicates significant academic leadership. David Lie actively contributes to interdisciplinary research ecosystems through his roles at the Schwartz Reisman Institute for Technology and Society (as research lead), the Data Sciences Institute (as Associate Director), and the Vector Institute for Artificial Intelligence (as Faculty Affiliate), fostering collaborations between security research, law, and societal impact studies.

• Cybersecurity
• Software Security
• Mobile Computing
• +۲ مورد دیگر

Dr. Janaka Senanayake is a Lecturer in Cybersecurity at Robert Gordon University (RGU), attached to the School of Computing, Engineering and Technology. He is affiliated with the Cybersecurity Research Group and contributes to the university's 'Living in a Digital World' research theme. Dr. Senanayake completed his PhD in Cybersecurity at RGU, specializing in software security. He earned a Bachelor of Science (Special) in MIT with First Class Honors from the University of Kelaniya, Sri Lanka in 2017. Prior to joining RGU, he worked as a Software Engineer at DirectFN Pvt. Ltd. and NDB Bank Information Technology and R&D Department for approximately three years, and also served as a lecturer and researcher at several international universities. His research focuses on cybersecurity, software security, mobile computing, artificial intelligence, and software engineering. Dr. Senanayake has published extensively on Android security, AI-powered vulnerability detection, and network security solutions. His recent work demonstrates a strong emphasis on practical security applications using advanced AI techniques while addressing privacy concerns through approaches like federated learning. Dr. Senanayake has produced 23 publications from 2021-2025, with a significant concentration of high-impact work in 2024-2025. His research output shows a clear trajectory toward integrating cutting-edge AI technologies with practical cybersecurity applications, particularly in mobile and network security domains. His notable contributions include: MADONNA: A browser-based malicious domain detection system using optimized neural networks FedREVAN: Real-time detection of vulnerable Android source code through federated neural networks with XAI DroidKey: A framework for API key security in Android applications Research on integrating large language models for automated vulnerability scanning Dr. Senanayake is actively involved in PhD supervision with research topics including Software Security, Cybersecurity, Security and Artificial Intelligence, Mobile Security, and Law and AI. His industry background combined with academic expertise provides a valuable practical perspective to his research and teaching. He maintains active research collaborations, as evidenced by his numerous co-authored publications with researchers from different institutions, particularly focusing on cybersecurity challenges in both Western and Sri Lankan contexts.

• Computer Security
• Operating Systems
• Formal Methods
• +۳ مورد دیگر

Gilles Grimaud serves as Teacher Team Leader for the 2XS research group and Elected Member of the Unit Council at University of Lille's CRIStAL laboratory, located at Science City campus (Building M3 Extension, Office 209). His research spans critical domains in systems security: Formal verification of operating system kernels Memory isolation for IoT ecosystems Embedded systems security Cloud and wireless network intrusion detection Energy-constrained software development Distributed software verification from cloud to IoT Supervising over 14 doctoral theses through 2023, his students have pioneered verified hypervisors, Android security with eBPF, and DDoS detection frameworks. Current work focuses on formally proven security architectures for resource-limited devices. As core member of the 2XS team within CRIStAL (UMR 9189), he bridges theoretical formal methods with practical system implementations across constrained environments.

Vicente Alarcon-Aquinoمشاهده پروفایل

استاد

• Cybersecurity
• Network Monitoring
• Anomaly Detection
• +۷ مورد دیگر

Dr. Vicente Alarcon-Aquino is a Professor in the Department of Computing, Electronics, and Mechatronics at Universidad de las Americas Puebla (UDLAP), Mexico. He received his Ph.D. and D.I.C. degrees in Electrical and Electronic Engineering from Imperial College London in 2003. He previously served as department head from October 2012 to June 2018 and spent a research stay at King's College London in 2017. Dr. Alarcon-Aquino is a Senior Member of IEEE, a Level I member of the Mexican National System of Researchers (SNI), and a Fellow of the Mexican Academy of Sciences. His educational background includes: Ph.D. and D.I.C. in Electrical and Electronic Engineering, Imperial College London, UK (2003) Dr. Alarcon-Aquino's research focuses on cybersecurity, network monitoring, anomaly detection, wavelet analysis, and machine learning. His work spans theoretical foundations to practical applications in network security, with significant contributions to intrusion detection systems, cryptographic techniques, and machine learning approaches for security applications. He has developed innovative methods combining wavelet transforms with neural networks for various security and signal processing applications. His scholarly contributions include over 180 research articles in refereed journals and conference proceedings, a book on MPLS networks, and numerous citations. As an editor, he serves as Associate Editor for IEEE Access Journal and as Academic & Section Editor for PeerJ Computer Science, focusing on Security & Privacy. Notable professional recognitions include: Senior Member of IEEE Mexican National System of Researchers (SNI Level I) Member of the Mexican Academy of Sciences Dr. Alarcon-Aquino has supervised over 70 theses, including 8 Ph.D. dissertations, 15 Master's theses, and more than 37 Bachelor's theses. His supervision spans topics including network intrusion detection, information security, encryption algorithms, wavelet-based signal processing, neural networks, EEG signal processing, and biometric cryptosystems. He has hosted international research students from institutions including the Polytechnic University of Madrid and Kiel University of Applied Sciences. His research group at UDLAP focuses on developing advanced security solutions for modern network environments, with particular emphasis on applying machine learning techniques to cybersecurity challenges. Current projects include blockchain-based security solutions, federated learning approaches for intrusion detection, and advanced anomaly detection systems for IoT environments.

Sjouke Mauwمشاهده پروفایل

استاد

• Information Security
• Formal Methods
• Privacy
• +۸ مورد دیگر

Sjouke Mauw is a full professor in "Security and Trust of Software Systems" at the University of Luxembourg , leading the Department of Computer Science and the SaToSS research group. His work bridges formal methods with real-world security and trust challenges. Current affiliation: University of Luxembourg (Faculty of Science, Technology and Communication) Previous roles: Associate Professor at Eindhoven University of Technology, Senior Researcher at CWI Research Focus Security protocols and their formal verification Attack trees and defense trees for structured threat analysis Privacy in voting systems and digital exchange Trust management frameworks and risk assessment RFID and e-voting security Cyber-physical socio-technical systems Recent Article Trends (2023-2025) include applications of deep learning to privacy risks, formal verification of distance-bounding protocols, and anomaly detection in resource-constrained environments like cubesats. These works span machine learning, cryptography, and aerospace engineering. Academic Contributions include over 50 refereed publications, supervision of 15+ PhD students, and leadership in European research projects (e.g., Meteor, Concur). He has developed formal methods for security protocols, attack-defense trees, and GNSS signal integrity frameworks.

Yang Liuمشاهده پروفایل

استاد

• Formal Methods
• Software Engineering
• Security
• +۷ مورد دیگر

Yang Liu is a Full Professor and University Leadership Forum Chair at the School of Computer Science and Engineering, Nanyang Technological University (NTU) in Singapore. He serves as Programme Director for HP-NTU Digital Manufacturing Corp Lab, Deputy Director of the National Satellite of Excellence of Singapore, and Cluster Director in Cybersecurity at Energy Research Institute @NTU. His research spans Cybersecurity , Software Engineering , and Artificial Intelligence . He leads research in malware modeling and detection, vulnerability analysis using machine learning and program analysis, formal verification of security systems, program specification learning, performance analysis, Android system security, and AI security, robustness, fairness, and explainability. His notable work includes the Process Analysis Toolkit (PAT) for model checking and the Deep-Series tools for deep learning testing. Professor Liu has published extensively in top-tier conferences including ASE, ICSE, FSE, ISSTA, and S&P. His research demonstrates strong trends toward integrating AI/ML techniques with traditional software engineering and security approaches, particularly focusing on large language models for code analysis, vulnerability detection, and program repair. Recent publications show a growing emphasis on blockchain security, smart contract analysis, and addressing security challenges in AI systems. NRF Investigatorship (Class 2020) ACM's Distinguished Speaker Nanyang Research Award (Young Investigator) Microsoft Asia Research Fellowship 20 Year ICFEM Most Influential System Award for PAT Multiple ACM SIGSOFT Distinguished Paper Awards Professor Liu actively advises students and has seen notable student achievements, including Singapore Data Science Consortium research award winners and AISG PhD Fellowship recipients. His research is supported by numerous grants including a $900,000 NTU-NAP grant for Formal Verification on Cloud and a $471,000 grant for Vulnerability Detection in Binary Code. He leads the HP-NTU Digital Manufacturing Corp Lab and contributes to RollsRoyce@NTU Corporate Lab research on complex business systems simulation.

Lina Gongمشاهده پروفایل

دانشیار

• Intelligent Software Engineering
• SE4AI
• Empirical Software Engineering
• +۳ مورد دیگر

Lina Gong is an Associate Professor at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. She holds a Ph.D. in Computer Software and Theory from China University of Mining and Technology (2020) and completed a research visit at Queen's University's Software Analysis and Intelligence Lab (SAIL) under Prof. Ahmed Hassan (2019-2020). Her research focuses on leveraging machine learning to extract insights from software repositories, with emphasis on: ML-enabled defect prediction techniques Code pre-trained models for vulnerability detection Identifier normalization and issue classification Empirical studies of software quality attributes Her recent publications (2023-2025) demonstrate strong trends in applying transformer architectures to code analysis, with increasing focus on supply chain security and cross-platform UI translation. Key venues include IEEE TSE, ACM TOSEM, and ASE. Scientific recognition includes: National Natural Science Foundation of China (2022-2025) Natural Science Foundation of Jiangsu Province (2022-2025) Key National Laboratory Foundation (2022-2023) Excellent Ph.D. Student Award (CUMT) She actively mentors graduate students (14 advisees: 1 doctoral, 13 master's) and serves on program committees for ASE, APSEC, and SANER. Her research is supported by multiple competitive grants focusing on ML applications in software engineering.

Shing-Chi Cheungمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Software Testing
• +۷ مورد دیگر

Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.

Minxue Panمشاهده پروفایل

استاد

• Software Modelling and Verification
• Software Analysis and Testing
• Cyber-Physical Systems
• +۵ مورد دیگر

Minxue Pan is a Professor and PhD supervisor at the Software Institute, State Key Laboratory for Novel Software Technology, Nanjing University, China. His research focuses on the dependability of complex software systems, with expertise spanning software modeling and verification, software analysis and testing, cyber-physical systems, mobile computing, and intelligent software engineering. Ph.D. in Computer Science and Technology from Nanjing University (2014), supervised by Prof. Xuandong Li B.Sc. from Nanjing University Studied at UC Berkeley's Department of Electrical Engineering and Computer Sciences (2009-2010) under Prof. Edward A. Lee Professor Pan's research interests center on improving software dependability through innovative approaches to modeling, analysis, and testing. His work spans traditional software systems, mobile applications (particularly Android), cyber-physical systems, and the application of AI techniques to software engineering problems. He has made significant contributions to GUI testing, vulnerability detection, and deep learning applications in software engineering. His recent publications (2024-2025) demonstrate a strong focus on applying advanced machine learning techniques to software testing and security challenges. Key trends include leveraging large language models for test migration, enhancing fault localization with graph learning and contrastive learning, developing specialized frameworks for Android security analysis, and improving test efficacy through GUI and functional equivalence. His work consistently targets real-world industrial settings and addresses practical challenges in mobile and complex software systems. ISSTA 2020 Distinguished Paper Award ICSE 2025 Best Artifact Award Professor Pan actively advises graduate students and has developed several notable tools including Q-testing (reinforcement-learning based Android testing), ISDChecker (model checking for interrupt-driven systems), PREFEST (preference-wise testing for Android), Sketchoid (GUI code search), and PI-REC (hand-drawn draft conversion). His research is supported by extensive publication records in top-tier software engineering venues including ASE, ICSE, FSE, ISSTA, and TOSEM. He teaches undergraduate courses in Advanced Programming with C++, Software System Design, and Software Construction, as well as graduate courses in Advanced Software Design. His laboratory work focuses on developing practical solutions for real-world software dependability challenges through the State Key Laboratory for Novel Software Technology.