جست‌وجوی استاد و پژوهشگر

Gabriele Bavotaمشاهده پروفایل

دانشیار

• Software Quality
• Empirical Software Engineering
• Mining Software Repositories
• +۳ مورد دیگر

Gabriele Bavota is an Associate Professor at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He leads the SEART (Software Engineering Advanced Research Team) group and serves as Principal Investigator for the DEVINTA ERC starting grant focused on developer intelligence through mining software artifacts. Dr. Bavota's research spans Software Quality, Empirical Software Engineering, and Mining Software Repositories. His work has evolved from foundational studies on code smells and technical debt to cutting-edge research at the intersection of artificial intelligence and software development. He has made significant contributions to understanding API usage patterns, software quality metrics, and developer behavior through empirical studies of large software repositories. His recent publications reveal a strong focus on AI-assisted software development, with extensive research examining code generation, code summarization, and code review automation using large language models. He has also expanded his research to include quality assurance in game development (detecting game stuttering and low engagement events) and voice user interface testing. His work consistently bridges theoretical insights with practical applications for software developers. ACM SIGSOFT Distinguished Paper Award for API compatibility research (MSR 2019) ACM SIGSOFT Distinguished Paper Award for Hugging Face model documentation study (ICPC 2024) ACM SIGSOFT Distinguished Artifact Award for deep learning fault taxonomy (ICSE 2020) As an active member of the software engineering research community, Dr. Bavota serves on program committees for major conferences including ICSE, ASE, FSE, and MSR. He has held leadership roles such as Program Co-Chair for ICSME 2023 and Vision/Reflection Track Co-Chair for ICSE. His SEART research group develops practical tools like the SEART Data Hub that streamline large-scale source code mining and preprocessing for empirical software engineering research.

Tse-Hsun Chenمشاهده پروفایل

دانشیار

• Software Engineering
• Performance Engineering
• DevOps & AIOps
• +۳ مورد دیگر

Tse-Hsun (Peter) Chen is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He serves as Director of the SPEAR lab (Software Performance, Analysis, and Reliability lab), which focuses on improving the quality of large-scale software systems through research in log analysis and AIOps, software performance analysis, software testing, and mining software repositories. His research group maintains extensive collaborations with industry partners including ERA Environmental, Ericsson, Microsoft, and BlackBerry. Dr. Chen received his PhD and MSc in Computer Science from Queen's University and his BSc in Computer Science from the University of British Columbia. Dr. Chen's research addresses critical challenges in modern software engineering, including leveraging Large Language Models to assist developers with development, debugging, and maintenance; helping developers debug production systems by utilizing rich software data; providing optimization suggestions by analyzing user usage data; improving software quality assurances in DevOps environments; and mining software development history for useful developer suggestions. His work spans Software Engineering, Performance Engineering, DevOps & AIOps, Software Testing, and Mining Software Repositories, with a strong emphasis on practical applications that bridge academic research and industrial practice. His recent publications (2024-2025) demonstrate a pronounced shift toward integrating Large Language Models into various aspects of the software engineering lifecycle, particularly in log analysis, fault localization, code generation, and performance testing. This trend reflects the growing importance of AI in software engineering research and practice. Gina Cody Research award (2022) Ranked as one of the most active software engineering researchers worldwide by an independent study published in JSS Dr. Chen has successfully advised numerous PhD and Master's students, many of whom have secured prestigious academic positions. Several of his graduated PhD students now hold tenure-track assistant professor positions at institutions including York University, University of Alberta, DePaul University, and IIT Gandhinagar. His SPEAR lab has developed research tools that have been integrated into industrial practice for ensuring the quality of large-scale enterprise systems. The SPEAR lab, under Dr. Chen's leadership, has established itself as a leading research group in software engineering, with particular expertise in software performance analysis, log analysis, and AI applications for software engineering. The lab maintains strong industry connections and has produced numerous high-impact publications in top-tier software engineering venues including ICSE, FSE, ASE, and TSE.

Tien N. Nguyenمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۹ مورد دیگر

Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.

• Quantum Information Science
• Quantum Communication Theory
• Theoretical Quantum Cryptography
• +۳ مورد دیگر

Mario Berta is a Professor of Physics at RWTH Aachen University’s Institute for Quantum Information, with an honorary Visiting Reader position at Imperial College London’s Department of Computing. His research focuses on mathematical aspects of quantum information science, including quantum communication theory, cryptography, and algorithms. He leads a group funded by the ERC Starting Grant QEntropy, exploring entropy’s role in quantum information. He actively recruits PhD/postdoc researchers and organizes workshops like the Mathematics of Quantum Information conference at RWTH Aachen and Beyond IID 13 in Munich. Education: PhD in Theoretical Physics from ETH Zurich. Prior roles include Senior Research Scientist at Amazon Web Services’ quantum computing division and Postdoctoral Researcher at Caltech’s IQIM. He has pioneered quantum Gibbs sampling algorithms for the Fermi-Hubbard model and contributed to quantum error correction and complexity theory. His work bridges theoretical foundations with practical implementations, emphasizing resource analysis and algorithm optimization. Research interests span quantum algorithms’ computational complexity, entanglement theory, and information-theoretic security. He explores topics like quantum channel coding, hypothesis testing, and distributed quantum protocols under communication constraints. His group’s activities include organizing international workshops and collaborations with institutions like ML4Q and EPSRC. Funding sources include the European Research Council, RWTH’s Exploratory Research Space, and the EPSRC. He advocates for open-access science, as seen in his German-language article Algorithmen für neue Hardware . His work aims to advance quantum technologies through rigorous mathematical frameworks and experimental feasibility analysis.

• Circular Construction
• Circular Economy in Construction
• Sustainable Construction Management
• +۳ مورد دیگر

Dr. Sven Mackenbach serves as a Postdoctoral Researcher and Senior Engineer at RWTH Aachen University's Institute of Construction Management, Digital Engineering and Robotics in Construction (ICoM) since January 2023. Holding a doctorate in circular construction from the same institution, he leads the development of the "Circular Construction" research area, oversees multidisciplinary projects, and teaches Sustainable Construction Management to engineering students. His academic credentials include: PhD in Circular Construction, RWTH Aachen University (2023) Mackenbach's research integrates circular economy principles with digital construction technologies, focusing on strategic management frameworks for sustainable building practices. His expertise spans modular construction methods, BIM-based sustainability assessment, and digital twinning applications for infrastructure management. He bridges theoretical research with practical implementation through sustainability consulting for construction stakeholders, addressing real-world challenges in resource efficiency and regulatory compliance. His publication portfolio reveals a decisive shift toward data-driven circular construction solutions, with increasing emphasis on BIM-ontology integration for deconstruction planning, public participation systems, and sewer infrastructure management. Recent works demonstrate sophisticated methodological approaches to quantify ecological, economic, and social sustainability metrics without specialized expertise. As a scientific supervisor, Mackenbach mentors research assistants in project execution while contributing to curriculum development through his specialized course instruction. His leadership in research project coordination indicates substantial grant management experience, though specific funding sources aren't detailed in available materials. Within ICoM's collaborative environment, he contributes to cross-disciplinary teams advancing digital engineering and robotics applications in construction, particularly through the KaSyTwin sewer management project and BIPV facade digitalization initiatives.

• Embedded Systems
• Compilers
• Hardware-Software Co-Design
• +۵ مورد دیگر

Prof. Dr. rer. nat. Rainer Leupers is a faculty member at RWTH Aachen University, chairing the Department of Software for Systems on Silicon. His research focuses on embedded systems, hardware-software co-design, virtual prototyping, and security in computing-in-memory architectures. He has published extensively on RRAM accelerators, logic locking, and neuromorphic security. Chair of Software for Systems on Silicon Research in hardware security and deep learning accelerators Recent publications on cross-tool virtual frameworks and thermal side-channel attacks His work bridges system-level modeling with practical security implementations, emphasizing reliability and performance in heterogeneous computing environments. Key trends in his 2025-2023 articles include compute-in-memory optimization, neural network inference efficiency, and security vulnerabilities in emerging hardware. Awards and formal recognitions are not explicitly detailed in the provided materials. He has not directly mentioned advising students or research grants in the given text fragments. The chair's contact information includes an office at ICT Cube 1, Electrical Engineering, Aachen, with direct email and website links.

Mike Barthمشاهده پروفایل

استاد

• Automation Technology
• Control Systems
• Cyber-Physical Systems (CPS)
• +۵ مورد دیگر

Prof. Mike Barth is a Professor for Networked Secure Automation Technology at the Karlsruhe Institute of Technology (KIT), affiliated with the Department of Electrical Engineering and Information Technology (ETIT) and the Institute for Control Systems (IRS). His academic background includes a doctorate from Helmut Schmidt University (2011) and a master's degree from Pforzheim University (2008). He previously held roles as a researcher at ABB and as a professor at Pforzheim University, focusing on blended learning and Industry 4.0 integration. Education: PhD in Automation Technology, Helmut Schmidt University (2011) M.Sc. in Product Development, Pforzheim University (2008) Diploma in Mechanical Engineering, Pforzheim University (2006) Research Interests: Automation technology, control systems, Industry 4.0, cyber-physical systems, digital twin engineering, cybersecurity, and IoT protocols. Teaching: Courses include System Modeling, Cyber Physical Production Systems, and Digital Twin Engineering. His research emphasizes secure automation architectures, decentralized systems, and model-based engineering. He chairs multiple committees including IFAC TC3.1 and the VDI/VDE Society for Measurement and Automation. Over 50+ publications span topics like simulation models, industrial security, and robotic integration. Labs/Teams: Leads the IRS Automation Technology team, focusing on innovation in control systems and digital twin applications.

Masaru Shibataمشاهده پروفایل

استاد

• Relativistic Astrophysics
• Gravitational Waves
• Numerical Relativity
• +۳ مورد دیگر

Prof. Masaru Shibata is a leading figure in computational relativistic astrophysics, currently serving as Director at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) since 2018 and Professor at Kyoto University's Yukawa Institute for Theoretical Physics since 2009. His career spans multiple prestigious institutions including University of Tokyo and Osaka University. PhD in Physics, Kyoto University (1994) Graduate studies in Physics, Kyoto University (1989-1993) Undergraduate in Science, Tokyo Institute of Technology (1985-1989) As a Professor with primary focus on Relativistic Astrophysics , Shibata's research investigates gravitational wave sources , neutron star mergers , black hole formation , and multimessenger astrophysics . His work combines general relativistic simulations , magnetohydrodynamic modeling , and neutrino radiation studies to understand high-energy cosmic phenomena. Recent publications (2024-2025) demonstrate expertise in supermassive star collapse , binary neutron star merger dynamics , and black hole-torus systems . These studies employ advanced numerical relativity techniques with applications to gravitational wave astronomy and gamma-ray burst modeling . 2025 Japan's Medal of Honor (Shiju-houshou) 2018 Nishina Memorial Prize 2013 International Society of General Relativity and Gravitation Fellow 2010 JSAP Excellent Young Researchers Prize 2008 Physical Society of Japan Outstanding Paper Award 2003 Nishinomiya-Yukawa Memorial Prize Shibata contributes to both theoretical frameworks and computational methodology in relativistic astrophysics, maintaining active collaborations with international research teams while leading computational projects at his dual institutions.

Barbara Namerمشاهده پروفایل

استاد مدعو

• Translational Pain Research
• Neurophysiology
• Peripheral Nervous System
• +۶ مورد دیگر

Barbara Namer is an Adjunct Professor at Friedrich-Alexander University Erlangen-Nuremberg and leads the IZKF-funded "Neuroscience: translational pain research" group at RWTH Aachen University Hospital. Her career spans 20+ years in neurophysiological pain research with clinical translations. Doctor of Medicine (2000-2004), Erlangen-Nuremberg Venia Legendi (Habilitation) in Physiology (2010) Adjunct Professor title (2018) Her research focuses on nociceptor mechanisms in diabetic neuropathy, migraine pathophysiology, and TRPA1 channel dynamics . Computational modeling and human microneurography techniques are central to her work. Key publication trends show expertise in peripheral nerve sensitization , diabetic pain mechanisms , translational pain modeling , and ion channel pharmacology . She has received multiple DGSS and German Neurology Society awards for her pain research. 2019 - DGSS Poster Award 2015 - DGSS Poster Award 2010 - EFIC Grünenthal Grant 2003 & 2018 - German Neurology Society Awards National and international collaborations include research stays in Norway and Sweden, with extensive grant funding from DFG and IZKF projects.

Raffi Khatchadourianمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Software Evolution
• +۵ مورد دیگر

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.

Victoria Clarkeمشاهده پروفایل

دانشیار

• Qualitative Research
• Gender Studies
• Thematic Analysis
• +۴ مورد دیگر

Victoria Clarke is an Associate Professor in the Health and Social Sciences school at the University of the West of England, Bristol, where she has been employed since 2003. She holds a PhD and is widely recognized for her work in qualitative research methods, particularly thematic analysis, which she has developed collaboratively with Professor Virginia Braun. Her research focuses on gender and sexuality, appearance and embodiment, family and relationships. Her educational background includes: Postgraduate Certificate of Education (Higher Education) from University of the West of England, Bristol (2003-2004) PhD in Women's Studies/Psychology from Loughborough University (1997-2002) BSc in Psychology from Brunel University London (1993-1997) Professor Clarke's research interests center on qualitative methodologies, particularly thematic analysis which she has helped shape through her award-winning publications. She has made significant contributions to gender studies, with a focus on LGBTQ+ experiences, body image, and family dynamics. Her work often employs innovative qualitative methods such as story completion to explore sensitive topics around sexuality, embodiment, and social relationships. She is particularly known for her collaborative work with Virginia Braun on thematic analysis methodology. Her recent publications demonstrate a continued focus on advancing qualitative research methods while applying these approaches to important social issues. There's a clear trend toward methodological refinement, with numerous publications addressing best practices in thematic analysis and qualitative research reporting. Simultaneously, she maintains strong engagement with substantive research areas including LGBTQ+ experiences, mental health, embodiment, and gender studies. Her work increasingly examines the intersection of methodology and substantive topics, particularly in health contexts. Professor Clarke has received several prestigious awards for her scholarly contributions: British Psychological Society 2022 President's Award British Psychological Society 2022 Book Award Association of Women in Psychology Distinguished Publication Award (2014) British Psychological Society Book Award (Textbook) (2013) American Psychological Association Division 44 Award for Best Book in Lesbian, Gay, and/or Bisexual Psychology (2007) Throughout her career at UWE Bristol, Professor Clarke has provided extensive supervision for undergraduate, master's, and doctoral students. Her research has been supported by various funding sources, including a Lord Chancellor's Department funded project on money management and marital commitment in new couples during her time at the University of Exeter. She has developed and led numerous research projects exploring gender, sexuality, and qualitative methodologies, often collaborating with interdisciplinary teams across psychology, health sciences, and social work. Professor Clarke leads a productive research laboratory focused on qualitative methodologies and their application to social and health psychology questions. Her lab collaborates with researchers across the UK and internationally, particularly with Virginia Braun at the University of Auckland. The lab is known for advancing story completion methodologies and reflexive thematic analysis, with active projects examining LGBTQ+ experiences, mental health, embodiment, and methodological innovation in qualitative research.

Laura Beckerمشاهده پروفایل

استادیار

• Linguistic Typology
• Quantitative Linguistics
• Coding Efficiency
• +۴ مورد دیگر

Laura Becker is an Assistant Professor at the Department of General Linguistics, University of Freiburg. Her research focuses on linguistic typology, quantitative methodology, and the role of coding efficiency in grammatical structures. She holds a PhD exploring article systems across languages and has contributed to cross-linguistic studies on syntax, morphology, and sociolinguistics. Her work emphasizes statistical rigor and computational tools, such as the glottospace R package for geospatial linguistic analysis. Recent projects include investigating phoneme inventories in Polynesian languages and socio-linguistic influences on conditional constructions. Publications highlight her expertise in morphosyntax, language contact, and corpus-based approaches. No scientific awards are explicitly mentioned in the provided materials. Dr. Becker has advised no recorded students in the available data and has not listed specific grants or labs. Her current research trends prioritize quantitative frameworks to address typological and evolutionary questions in linguistics.

• Global biosphere dynamics
• Crop–climate interactions
• Land-use change
• +۳ مورد دیگر

Dr. Christoph Müller is a leading scientist at the Potsdam Institute for Climate Impact Research (PIK), Germany, where he has served as working-group leader of the Land Biosphere Dynamics group since 2012. He also co-leads the Global Biosphere and Water Modeling team and acts as the scientist-in-charge for the internationally renowned LPJmL global vegetation and crop model. Additionally, he is Co-lead of the Ag-GRID initiative within the Agricultural Model Intercomparison and Improvement Project (AgMIP) and serves as Topical Editor for Geoscientific Model Development . Education Diploma in Geoecology, University of Potsdam (2002) PhD in Geoecology, University of Potsdam & International Max Planck Research School (IMPRS) (2007) Research Interests Dr. Müller’s research centres on understanding and modelling the interactions between climate, land use, and the biosphere to support sustainable food-system transformations. His work integrates global-scale vegetation and crop models with climate projections, socio-economic scenarios, and observational data to assess: Impacts of climate change and extreme events on crop yields and food security Carbon, nitrogen, and water cycles in managed and natural ecosystems Land-based climate-mitigation strategies and their co-benefits or trade-offs Adaptation options for agriculture under global change He promotes open science and reproducible modelling workflows, exemplified by the LPJmL open-source ecosystem model and associated toolkits. Publication Profile & Trends Since 2014 he has authored or co-authored more than 200 peer-reviewed articles. Recent work (2024-2025) highlights three dominant themes: (1) quantifying underestimated negative impacts of climate extremes on crop yields, (2) assessing the sustainability of large-scale land-based mitigation measures, and (3) advancing model intercomparison frameworks (e.g., AgMIP, ISIMIP) to reduce uncertainty in global yield projections. His studies increasingly integrate economic and health perspectives, examining how dietary shifts and food-system transformations can achieve climate, environmental, and social co-benefits. Scientific Awards & Recognition While no formal awards are explicitly listed, several publications have received notable recognition: “Soil quality both increases crop production and improves resilience to climate change” (Nature Climate Change, 2022) – listed among China’s top ten major advances in agricultural science in 2023. “Large potential for crop production adaptation depends on available future varieties” (Global Change Biology, 2021) – top-downloaded article. “Climate change impacts on global agriculture emerge earlier in new generation of climate and crop models” (Nature Food, 2021) – widely cited in IPCC AR6. Leadership, Grants & Collaboration Dr. Müller leads or co-leads multiple international projects and working groups: Working Group Leader – Land Biosphere Dynamics, PIK Research Department 2 Co-Lead – Ag-GRID, Agricultural Model Intercomparison and Improvement Project (AgMIP) Scientist-in-Charge – LPJmL model development and application Topical Editor – Geoscientific Model Development journal These roles involve coordinating multi-institutional consortia, securing competitive grants, and mentoring early-career researchers. Laboratory & Data Resources Dr. Müller’s “team” is essentially the LPJmL modelling group at PIK, comprising post-docs, doctoral researchers, and software engineers who maintain and extend the LPJmL code-base, develop satellite-data fusion products, and provide model-driven policy support to governments and international organisations such as the IPCC, FAO, and World Bank.

Sriramkrishnan Muralikrishnanمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• High Performance Scientific Computing
• Plasma Physics
• +۴ مورد دیگر

Sriramkrishnan Muralikrishnan is a Research Staff member at the Department of Mathematics and Education within the Jülich Supercomputing Center (JSC) at Forschungszentrum Jülich, Germany. His work focuses on developing advanced computational methods for high-performance scientific computing, particularly in plasma physics and related multi-physics applications. Dr. Muralikrishnan's research spans several key computational domains: Numerical Analysis and High-Order Methods High Performance Scientific Computing for Exascale Architectures Plasma Physics Simulations Fast Solvers and Preconditioners Parallel-in-Time Integration Techniques Performance Portable Programming His recent publications demonstrate a strong focus on particle-based computational methods, particularly Particle-in-Cell and Particle-in-Fourier techniques. His work consistently addresses challenges in energy conservation, scalability across architectures, and noise reduction in plasma simulations. A significant portion of his research involves developing performance-portable frameworks that can efficiently leverage modern supercomputing hardware from different vendors without code rewrites. Dr. Muralikrishnan is actively involved in open-source scientific software development: Lead developer of IPPL (a performance portable library for grids and particles) Developer of OPAL (an open-source particle accelerator library) His research has direct applications in plasma physics, fusion energy research, and advanced accelerator design, with a strong emphasis on making computational methods accessible through open-source development and advocating for diversity in scientific computing.

Zhongxin Liuمشاهده پروفایل

استادیار

• Intelligent Software Engineering (AI4SE)
• Code Intelligence
• Vulnerability Detection
• +۵ مورد دیگر

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 .