Find Academic Supervisors

Raffi KhatchadourianView profile

Associate Professor

• Software Engineering
• Programming Languages
• Software Evolution
• +5 more

Raffi Khatchadourian is an Associate Professor in the Department of Computer Science at Hunter College and the Graduate Center of the City University of New York (CUNY). His research focuses on techniques for automated software evolution, particularly automated refactoring and source code recommendation systems, with the goal of easing the burden associated with evolving large and complex software through automated tools. He also conducts research on the automated analysis of Object-Oriented programs. Ph.D., Computer Science & Engineering, Ohio State University (2011) MS, Computer Science & Engineering, Ohio State University (2010) BS, Computer Science, Monmouth University (2004) Khatchadourian's research spans multiple areas of software engineering and programming languages, with particular emphasis on automated software evolution techniques. His work addresses critical challenges in refactoring legacy systems to modern language constructs, optimizing parallel processing in Java 8 streams, and addressing technical debt in machine learning systems. His recent research has expanded into deep learning program transformation, where he develops techniques to convert imperative deep learning code to more efficient graph execution models while ensuring safety. His approach combines static analysis, program transformation, and empirical validation to create practical tools that developers can integrate into their workflows. Analysis of Khatchadourian's recent publications reveals a strong focus on bridging the gap between theoretical program analysis and practical software engineering challenges. His work increasingly intersects with machine learning systems, examining both how to improve ML code through refactoring and how to ensure safety in deep learning frameworks. The research demonstrates consistent evolution from foundational work on Java language features toward more complex systems involving concurrency, deep learning, and automated program transformation. Distinguished Paper Award at SCAM '18 for work on Java 8 stream optimization EAPLS Best Paper Award at FASE '20 for study on Java 8 stream usage EAPLS Distinguished Paper Award at FASE '25 for Deep Learning refactoring work Best Paper Award nominee at IJCAI '24 for AI safety framework Khatchadourian actively mentors graduate and undergraduate students, with several advisees going on to successful academic and industry positions. His former Ph.D. student Tatiana Castro Vélez accepted a tenure-track Assistant Professor position at the University of Puerto Rico. He has supervised numerous master's theses and undergraduate research projects, often resulting in co-authored publications at top software engineering venues. His research has been supported by various grants, though specific funding details are not prominently featured in the available information. Through his work on tools like Fraglight for aspect-oriented programming and Hybridize Functions for deep learning refactoring, Khatchadourian has established a research group focused on practical program analysis and transformation. His lab develops Eclipse plugins and other IDE-integrated tools that help developers with automated refactoring, bug detection, and code optimization. The group maintains active collaborations with researchers at other institutions and contributes to open-source projects on GitHub.

Wing LamView profile

Assistant Professor

• Software Testing
• Flaky Tests
• Mobile Testing
• +4 more

Wing Lam is an Assistant Professor at George Mason University specializing in software engineering with a focus on software testing methodologies. His academic service includes program committee membership for major conferences including ASE, ICSE, ISSTA, and ESEC/FSE, as well as session chair roles across multiple tracks. His research interests center on flaky tests , mobile application testing , and continuous development optimization . Lam's work addresses critical challenges in test reliability, particularly order-dependent flaky tests and resource-related flakiness. His research bridges theoretical foundations with practical applications in modern software development pipelines, with recent expansion into AI-assisted testing methodologies. Lam's publication record shows a clear trajectory focusing on flaky test detection and mitigation, with approximately 60% of his recent work dedicated to various aspects of this problem. His research increasingly incorporates machine learning techniques for UI testing and test optimization, reflecting broader trends in the field. The consistent publication venue pattern across top software engineering conferences indicates strong recognition within the academic community. Lam has served in multiple leadership roles including Program Co-Chair for MOBILESoft Research Track and Committee Member for ASE's New Ideas and Emerging Results (NIER) Track. His involvement in workshops focused on flaky testing demonstrates his specialization in this niche area of software testing.

• Network and system measurements
• Performance analysis
• Security
• +12 more

Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University working with 5G and Internet evolution. She completed her PhD in computer science in 2017 at the Simula Research Lab and Universitetet i Oslo under the supervision of Dr. Ozgu Alay and Prof. Michael Welzl. Her PhD dissertation focused on increasing robustness in multipath transport with MPTCP. Dr. Ferlin's educational background includes a PhD in Computer Science from the Simula Research Lab and Universitetet i Oslo (2017). Her doctoral research centered on enhancing robustness in multipath transport protocols, specifically focusing on MPTCP (Multipath TCP). She also completed undergraduate work that contributed to a book project with Prof. Friedrich Oehme on electronics and circuit technology. Dr. Ferlin's research spans multiple domains at the intersection of networking, systems, and performance engineering. Her primary interests include network and system measurements, performance analysis, security, and congestion control. She investigates how networks like the Internet evolve, examining technology development, adoption patterns, and their impacts on various entities. Additionally, she explores ways to harmonize security and privacy while making them more usable and assessable. Her work particularly focuses on transport layer and multipath transport protocols, examining their performance and security aspects. She also investigates application and transport layer performance, automation, and monitoring. Her research extends to network programming in both Linux kernel and user space, mobile broadband networks from 2G to 5G, and their intersection with the Internet. She is deeply engaged in observability, distributed and system performance monitoring, and automation. Analysis of Dr. Ferlin's recent publications reveals a strong focus on next-generation networking technologies. Her work spans multiple domains including 5G/6G networks, transport protocols (particularly QUIC and MPTCP), network virtualization, container orchestration, and the application of machine learning to networking problems. She has increasingly incorporated large language models into network configuration and automation research. Her publications demonstrate a consistent emphasis on performance measurement, optimization, and security across diverse networking environments from the edge to the cloud. Dr. Ferlin has received notable recognition for her research contributions: Best paper award at IEEE ICIN'21 for 'Learning-based Incast Performance Inference in Software-Defined Data Centers' Applied Networking Research Prize (ANRP)'25 winner for 'NetConfEval: Can LLMs Facilitate Network Configuration?' Dr. Ferlin is actively involved in mentoring the next generation of networking researchers. She has co-supervised numerous Master's and PhD students across multiple institutions including Karlstad University, KTH, TU Berlin, University of Oslo, and universities in Brazil. Her students have worked on diverse topics including NAT64 performance comparison, system tracing visualization, network observability, ML applications to multipath transport, FEC integration with QUIC, high-performance networking for 5G, congestion control, shared bottleneck detection, multipath IoT applications, and container runtime performance. She is also involved in several significant research projects including Vinnova's SEMLA (Securing Enterprises via Machine-Learning-based Automation), Horizon Europe's CODECO (Cognitive Decentralised Edge Cloud Orchestration), and the Knowledge Foundation of Sweden's DRIVE (Data-driven Latency-Sensitive Mobile Services for a Digitized Society). Dr. Ferlin serves as Workshop Chair for ACM SIGCOMM '25, is a member of the ACM/IRTF Applied Networking Research Workshop (ANRW) steering committee, and co-chairs the Internet Congestion Control Research Group (ICCRG) at the IRTF. She previously served as Associate Technical Editor for IEEE Communications Magazine and has been active on numerous program committees for major networking conferences including SIGCOMM, CoNEXT, IMC, and PAM.

Zhongxin LiuView profile

Assistant Professor

• Intelligent Software Engineering (AI4SE)
• Code Intelligence
• Vulnerability Detection
• +5 more

Zhongxin Liu is an Assistant Professor at the College of Computer Science and Technology , Zhejiang University , China. He earned his Ph.D. from the same institution in 2021. His research focuses on Intelligent Software Engineering (AI4SE) , leveraging software "big data" to improve code understanding, generation, and security through machine learning techniques. Published in top-tier venues: TSE, TOSEM, ICSE, FSE, ASE, ISSTA Active in academic service: Reviewer for TSE, TOSEM, ASEJ, etc. Visiting Professor at University of Stuttgart (2024-2025) His recent work explores Large Language Models (LLMs) for code intelligence, security hardening, and vulnerability detection. Papers emphasize cross-domain applications, zero-shot learning, and API/code dependency analysis. Scientific awards include: ACM SIGSOFT Distinguished Paper Awards (ASE 2018, 2019, 2020; ISSTA 2025) Zhejiang University Qizhen Scholar (2021) CCF TCSE Doctoral Dissertation Award (2023) Recruiting undergraduate interns, graduate students (MS/Ph.D.), and postdocs for code intelligence research. Contact: liu_zx@zju.edu.cn .

Yuhong NanView profile

Associate Professor

• Software Security
• Privacy Leakage Analysis
• IoT Security
• +3 more

Yuhong Nan is an Associate Professor in the School of Software Engineering at Sun Yat-sen University, China, specializing in software security and privacy leakage analysis for emerging platforms including IoT, mobile systems, and blockchain. Previously a Post-doctoral Research Associate at Purdue University under Prof. Dongyan Xu, she builds practical security tools to detect and mitigate vulnerabilities in real-world systems. Dr. Nan earned her PhD from Fudan University in 2018 supervised by Prof. Min Yang. Her academic journey spans rigorous research in security engineering with emphasis on empirical validation and tool development for complex platform ecosystems. Her research program focuses on uncovering systemic security flaws through innovative analysis techniques. Key contributions include vulnerability detection in smart contracts (e.g., state dependencies, reentrancy), privacy leakage analysis in mobile/IoT ecosystems, and countermeasures against deceptive UI patterns. She employs hybrid approaches combining static/dynamic analysis, machine learning, and large-scale empirical studies to develop deployable security solutions. Analysis of her 15 most recent publications (2023-2025) reveals dominant themes in blockchain security (60%), particularly smart contract/DApp vulnerabilities, with significant work in mobile privacy (30%) and cross-platform threats (10%). Her methodology consistently leverages fine-grained static analysis, semantic enrichment, and feedback-driven fuzzing, yielding tools like SmartAxe and Midas that have influenced industry practices. Dr. Nan actively mentors graduate researchers with 17 advisees including Tencent-employed graduates, and serves as a trusted reviewer for premier journals (IEEE TDSC, TMC, TOPS) and conference committees (ASIACCS, ICICS). Her leadership in security communities bridges academic research with practical defense mechanisms. At Sun Yat-sen University, she directs a high-output research group that collaborates with industry partners to address evolving threats in decentralized systems, maintaining her position among top publishing authors in USENIX Security, CCS, and NDSS venues through rigorous technical innovation.

Claire Le GouesView profile

Professor

• Software Engineering
• Program Analysis
• Defect Repair
• +5 more

Claire Le Goues is a Professor of Computer Science at Carnegie Mellon University, primarily affiliated with the Software and Societal Systems Department (S3D) within the School of Computer Science (SCS). She serves as the Associate Department Head for Faculty within S3D and leads the squaresLab research group. Le Goues also co-directs the REUSE@CMU summer program and teaches software engineering and program analysis at undergraduate, master's, and PhD levels. Her research spans software engineering and programming languages, with a particular focus on how to construct, maintain, evolve, improve/debug, and assure high-quality software systems. Le Goues has made significant contributions to automated program repair, program analysis, and defect detection. Her work often bridges theoretical foundations with practical applications, addressing real-world challenges in software development and maintenance. Le Goues' recent publications demonstrate a clear trend toward integrating large language models and generative AI with traditional software engineering techniques. Her research examines how these technologies can enhance program repair (BatFix, AdverIntent-Agent), vulnerability detection (Interpretable Vulnerability Detection Reports), and testing (LWDIFF for WebAssembly). This represents an evolution from her earlier foundational work in program repair (GenProg) toward leveraging contemporary AI advancements. She has mentored numerous students through her squaresLab research group and has been instrumental in developing educational programs that prepare the next generation of software engineers. Le Goues is also known for her advocacy for double-blind review processes in academic conferences, having implemented this approach when co-chairing the Symposium for Search-Based Software Engineering in 2014.

Jinqiu YangView profile

Associate Professor

• software reliability
• quality assurance of machine learning systems
• autonomous vehicles
• +5 more

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. Her research focuses on improving software reliability and quality assurance, particularly in the context of machine learning systems and autonomous vehicles. She leads active research projects in software testing, automated program repair, and mining software repositories, with strong connections to both academic and industrial applications. Her research interests span software reliability, quality assurance of machine learning systems including autonomous vehicles, software testing, automated program repair, text analytics of software artifacts, and mining software repositories. She has developed novel approaches for testing deep learning libraries, evaluating robustness in autonomous driving systems, and tracking the evolution of static code warnings. Her work bridges traditional software engineering with emerging challenges in AI systems, addressing critical issues of reliability and safety in complex software environments. Yang's recent publications (2021-2025) demonstrate a clear trajectory toward AI/ML system reliability, with increasing focus on autonomous vehicles, concept drift detection, and security aspects of large language models. Her work spans both theoretical foundations and practical applications, often involving empirical studies of real-world systems and development of practical tools to address identified challenges. ACM SIGSOFT Distinguished Paper Award Dr. Yang actively mentors graduate students and is currently recruiting Master's and PhD candidates. She has secured significant research funding including NSERC Discovery Grants (2019-2025), Gina Cody Research and Innovation Fellowship (2024-2026), and participation in the NSERC CREATE Program SE4AI (2021-2026). Her research is supported by multiple grants including NOVA – FRQNT-NSERC PROGRAM (2024-2027) and Volt-Age Seed Grant (2024-2026). She leads research in the O-RISA Lab at Concordia University, focusing on reliability and security aspects of intelligent software systems. Her team collaborates with industry partners including IBM, where she previously worked at IBM Watson Research Lab and IBM CAS, bringing practical experience to her academic research.

Baishakhi RayView profile

Associate Professor

• Software Engineering
• Security
• Artificial Intelligence
• +3 more

Baishakhi Ray is an Associate Professor of Computer Science at Columbia University, working at the intersection of AI, Software Engineering, and Security. She received her Ph.D. from the University of Texas, Austin, and has established herself as a leading researcher in applying artificial intelligence to software engineering challenges. Her educational background includes a Ph.D. from the University of Texas, Austin, which provided the foundation for her research career at the forefront of AI and software engineering. Dr. Ray's research focuses on leveraging artificial intelligence to solve fundamental challenges in software engineering and security. Her work spans multiple areas including code generation with large language models, vulnerability detection, software testing, and program analysis. She has pioneered approaches that combine deep learning with traditional software engineering techniques to create more robust, secure, and efficient software development processes. Her research has practical implications for improving code quality, enhancing software security, and accelerating development cycles through AI assistance. Her recent work demonstrates a strong emphasis on semantic-aware code generation, execution reasoning, and addressing hallucinations in code language models. She has also made significant contributions to evaluating the functionality and security of AI-generated code, identifying critical challenges in the practical adoption of AI for software development. Dr. Ray has received numerous prestigious awards recognizing her contributions to the field: IEEE TCSE Rising Star NSF CAREER award IBM faculty award VMware Faculty award Distinguished Paper awards at FSE'17, ASE'22, and ISSTA'23 ICSME Most Influential Paper award Publications featured in CACM Research Highlights As an Amazon Visiting Academic and active participant in major software engineering conferences, Dr. Ray has established herself as a thought leader in AI for software engineering. Her research has been widely covered in trade media, indicating its relevance and impact on industry practices. She has mentored numerous students through their research and has been instrumental in shaping the next generation of researchers in this interdisciplinary field. Her work demonstrates a consistent focus on bridging theoretical advances with practical applications, ensuring that her research has tangible benefits for the software development community. The trajectory of her publications shows an evolving research agenda that has successfully adapted to the rapidly changing landscape of AI and its applications to software engineering.

Antonio MastropaoloView profile

Assistant Professor

• Artificial Intelligence
• Natural Language Processing
• Software Engineering
• +6 more

Dr. Antonio Mastropaolo is an Assistant Professor of Computer Science at William & Mary, USA. His research lies at the intersection of Artificial Intelligence, Natural Language Processing, and Software Engineering, with a strong emphasis on the automation of SE-related practices. He promotes explainability, efficiency, and optimization from both model-centric and output-centric perspectives. His research interests focus on the reliability and efficiency of AI systems for software engineering. He investigates robustness and adaptability of foundation models like GitHub Copilot, as well as documentation and summarization of code components. His work addresses critical challenges in AI-driven software development including transparency, scalability, and developer productivity. His publication portfolio shows a strong trend toward neurosymbolic approaches that combine neural learning with symbolic reasoning. Recent articles explore quantization of large code models, code summarization optimization, and resource-efficient AI for software engineering. His work spans both theoretical foundations and practical applications, with emphasis on empirical validation of AI techniques in real-world SE contexts. Distinguished Reviewer Award for service on FSE'25 program committees Distinguished Reviewer Award at ASE 2024 Distinguished Paper Award for 'Unveiling ChatGPT's Usage in Open Source Projects' at MSR'24 Distinguished Paper Award for 'How do Hugging Face Models Document Datasets, Bias, and Licenses?' at ICPC'24 Dr. Mastropaolo advises PhD students, with Saima recently starting her PhD journey with a publication in FORGE 2025. He received an NSF Grant (#2451058) in April 2025 for research on efficient and responsible AI for software engineering. His service includes committee membership for major conferences including ASE, ICSE, ICSME, and FSE across multiple tracks including Research Papers, NIER, and Tool Demonstrations.

Jingling XueView profile

Professor

• Programming Languages
• Compilers
• Program Analysis
• +5 more

Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.

Shaohua LiView profile

Assistant Professor

• Software Engineering
• Programming Languages
• Security
• +3 more

Shaohua Li is an Assistant Professor at The Chinese University of Hong Kong (CUHK), specializing in the correctness and security of critical software systems with emphasis on compilers. His research spans Software Engineering , Programming Languages , and Security , focusing on innovative compiler testing methodologies. Key areas include leveraging large language models for test generation, optimizing fuzzing techniques through prefix-guided execution, and decoupling sanitization mechanisms to reduce overhead in vulnerability detection. His work addresses fundamental challenges in ensuring reliability of systems programming infrastructure. Recent publications demonstrate a cohesive trajectory toward practical compiler validation: from empirical rustc bug analysis to SAND's low-overhead sanitization framework. The research consistently bridges theoretical formal methods with real-world implementation challenges in security-critical systems, showing particular strength in adapting AI techniques for traditional software testing problems.

Daye NamView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence
• Human-Computer Interaction
• +3 more

Daye Nam is an Assistant Professor in the Department of Informatics at the University of California, Irvine, where they design, build, and evaluate AI tools for developers using natural language processing techniques. Their work sits at the intersection of software engineering, artificial intelligence, and human-computer interaction, with a strong focus on creating useful and usable tools that make software development more accessible, efficient, and enjoyable. Education PhD in Software Engineering, Carnegie Mellon University (2018-2024) MS in Computer Science, University of Southern California (2016-2018) BS in Computer Science, Yonsei University (2012-2016) Research Interests Dr. Nam's research focuses on designing, building, and evaluating AI tools for programmers at all levels, with an emphasis on making these tools both useful and usable. Their work spans several key areas including machine learning for software engineering (ML4SE), developer experience, and human-AI interaction. They employ a user-centered approach that involves conducting empirical studies to understand programmers' needs, building and training machine learning models based on those insights, creating tools for programmers, and evaluating them using human-computer interaction methods. Their research has particular relevance to AI-powered developer tools, API documentation and discovery, and educational applications of AI for programming students. Publications and Research Trends Dr. Nam's recent publications demonstrate a clear trajectory toward understanding and improving how developers interact with AI systems. Their work increasingly focuses on empirical studies of developer-AI interaction, particularly with large language models for code generation and understanding. There's a strong emphasis on understanding trust in AI systems among developers, measuring the actual impact of AI on development speed, and designing tools that balance automation with user control. Their research methodology often combines log analysis, user studies, and the development of novel AI-powered tools that address specific developer pain points. Scientific Awards and Honors Best Tool Paper Award at ASE ACM Student Research Competition 2nd Place SIGSOFT CAPS Student Travel Award for FSE ACM SIGSOFT NSF Travel Award NSF Travel Award for ICSE SIGSOFT Best Research Award from University of Southern California Teaching and Service Dr. Nam teaches SWE 233: Intelligent User Interfaces at UC Irvine, guiding students through the design and evaluation of AI-powered interfaces for software development. They have previously served as a Teaching Assistant and Co-Instructor for Foundations of Software Engineering at Carnegie Mellon University. In terms of service, they've been on program committees for major software engineering conferences including ICSE, ASE, and FSE, and have reviewed papers for journals like TOSEM and Empirical Software Engineering. They've also been active in student support programs, organizing and mentoring for graduate applicant support initiatives.

Sven ApelView profile

Professor

• Software Engineering
• Software Variability and Configuration
• AI-based Program Generation and Optimization
• +7 more

Sven Apel is Professor of Computer Science at Saarland University, where he holds the Chair of Software Engineering and directs the Saarbrücken Graduate School of Computer Science within the Saarland Informatics Campus. His research aims to advance software engineering into an era of intensive automation by developing methods, tools, and theories for building efficient, reliable, and maintainable software systems, with a strong emphasis on the human factor and interdisciplinary inquiry. His primary research interests include software variability and configuration, AI-based program generation and optimization, socio-technical software analysis, and the application of empirical and neurophysiological methods to study program comprehension. He actively collaborates with industry partners such as Siemens AG, Bosch Engineering, and Airbus Helicopters to apply his research in real-world contexts. His recent publications demonstrate a strong trend towards integrating artificial intelligence and neurocognitive methods into software engineering, focusing on configurable systems, performance modeling, debugging processes, and the scientific validity of empirical studies. His work spans top venues like ICSE, FSE, ASE, and IEEE TSE. ERC Advanced Grant “Brains On Code” ASE Fellow ACM Distinguished Member Hugo Junkers Award for Research and Innovation Heisenberg Professorship (DFG) Emmy-Noether Fellowship (DFG) Best Doctoral Dissertation Awards (University of Magdeburg, Ernst-Denert Foundation, 2007) Most Influential Paper Awards (SPLC'19, ICPC'22, GPCE'23) ACM SIGSOFT Distinguished Paper Awards (ICSE'15, ICSE'21) Best Paper Awards (SPLC'11, Modularity'15, Academy of Management'18) Distinguished Reviewer Awards (ASE'18, ICSE'24, FSE'24) Sven Apel has secured significant research funding, including an ERC Advanced Grant (€2.5M) and multiple DFG grants as Principal Investigator and Project Leader. He has advised numerous PhD and Master’s students and is actively involved in the academic community through program committees for major conferences like ICSE, FSE, and ASE. His work is conducted within a collaborative environment that includes close partnerships with researchers at the Max Planck Institute for Informatics and other institutions within the Saarland Informatics Campus.

• High-Performance Computing
• Parallel Programming
• Domain-Specific Languages
• +3 more

Wenguang Chen is a researcher affiliated with Tsinghua University and Pengcheng Laboratory , specializing in computer science and high-performance computing . His work bridges theoretical advancements with practical applications in domain-specific languages , parallel programming , and machine learning . Research Interests include: Development of modular DSLs for numerical methods (e.g., Mat2Stencil) Performance optimization in distributed and parallel systems Compiler frameworks for privacy-preserving AI (e.g., FHE-based neural network inference) Graph algorithms scaling to trillion-edge datasets Applications of Rust in memory-safe pointer analysis Recent Publications span 2014–2025, focusing on: Parallelization strategies for supercomputing Compiler automation tools Extreme-scale data processing Performance variance diagnosis in production environments

Sandra MartinView profile

Research Fellow

• Cognitive Neuroscience
• Aging and Cognition
• Neurolinguistics
• +3 more

Sandra Martin is a Postdoctoral Researcher at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, where she works in the Lise Meitner Research Group Cognition and Plasticity. Her research combines neuroimaging and neuromodulation methods with graph theory, large language models, and behavioral modeling to explore cognitive changes across the adult lifespan, with a particular focus on age-related changes in memory processes and their interaction with executive functions. Education: PhD in Cognitive Neuroscience (2018-2023) from the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig M.Sc. in European Master of Clinical Linguistics (2014) from University of Potsdam, Germany & University of Groningen, The Netherlands B.A. in Linguistics (2012) from Humboldt University Berlin, Germany Research Interests: Dr. Martin's research focuses on cognitive aging , neurolinguistics , and network neuroscience . She investigates how domain-general networks contribute to language prediction and semantic cognition across the adult lifespan. Her work often employs non-invasive brain stimulation techniques like transcranial magnetic stimulation (TMS) to understand the causal roles of specific brain regions in cognitive processes. She is particularly interested in how executive resources shape language predictability effects and how these relationships change with age. She's a strong advocate for open and reproducible research practices including preregistration and registered reports. Her recent publications reveal a strong trend toward understanding the neural mechanisms of semantic cognition and language processing across the lifespan. She combines multiple methodologies including fMRI, TMS, graph theory, and computational modeling to investigate how brain networks reorganize with age and how this affects cognitive performance. Her work has important implications for understanding cognitive aging and developing interventions for language disorders like aphasia. Scientific Awards: Doctoral fellowship from the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes) Advising and Grants: Dr. Martin has been involved in several research projects, including a DFG-funded project on 'The role of domain-general networks in natural language predictions' together with Prof. Dr. Gesa Hartwigsen (Leipzig University) and Prof. Dr. Jonas Obleser (University of Lübeck). While there's no explicit mention of students she's formally advising, her role as a postdoc suggests she likely mentors junior researchers. She actively promotes open and reproducible research practices and co-organized a symposium on Good Scientific Practice titled 'Doing Good – Scientific Practice under Review'. Labs and Teams: Dr. Martin is part of the Lise Meitner Research Group Cognition and Plasticity at the Max Planck Institute for Human Cognitive and Brain Sciences. She collaborates with researchers from Leipzig University and University of Lübeck. Her work often involves interdisciplinary teams combining expertise in cognitive neuroscience, linguistics, and clinical neurology, particularly in studying aphasia and language recovery after stroke. Her research group maintains open science practices with code and data available on GitLab and OSF.