جست‌وجوی استاد و پژوهشگر

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +۷ مورد دیگر

Prof. Dr. Chunyang Chen is a full professor in the Department of Computer Science at the Technical University of Munich (TUM) in Heilbronn, Germany, and an adjunct professor at Monash University, Australia. He is a core member of the Munich Data Science Institute, board member of Heilbronn Data Science Center, and fellow at Fortiss. His research spans Software Engineering, Deep Learning, and Human-Computer Interaction (HCI), focusing on AI/ML, NLP, and program analysis applications. PhD from Nanyang Technological University (NTU), Singapore His research explores three primary domains: AI-Driven Mobile App Development : Focused on requirement elicitation, UI design, code generation, and GUI testing using LLMs, NLP, and lightweight program analysis. Software Repository Mining : Involves library/API migration, collaborative editing, query reformulation, and thesaurus construction. Deep Learning Security : Examines robustness and security in mobile apps, including on-device model attacks, backdoor attacks, phishing attacks, and defense mechanisms. His recent publications highlight trends in LLM applications for software testing, bug replay, accessibility, and dataset construction. Awards include Best Paper Honorable Mention at CHI 2024, DECRA, ACM SIGSOFT Early Career Award, multiple Distinguished Paper Awards, and the Chinese Government Award for Outstanding Self-financed Students Abroad. PhD students: Shengcheng Yu, Yanqi Su, Mark Huasong Meng, Wenchao Gu, Zhen Tao, Kai Huang, Sidong Feng, Yujin Huang, Jian Gu Master/Bachelor students: Dustin Minh Duc Vu, Jason Siu, Ruiqi Dong, Minh Nguyen, An-Chieh Chang, Chao Wu, Yujin Huang, Ishan Joshi, Xinyan Li Chunyang Chen has supervised numerous students across institutions including TUM, Monash University, Chinese Academy of Sciences, ANU, and others. His teaching portfolio includes courses on Generative AI, software engineering, and programming foundations. He actively contributes to academic service as a guest editor, program committee member, and reviewer for top-tier conferences and journals.

Chanchal K. Royمشاهده پروفایل

استاد

• Code Clone Detection and Analysis
• Software Evolution and Maintenance
• Software Analytics Research
• +۳ مورد دیگر

Chanchal K. Roy is Professor of Software Engineering/Computer Science at the University of Saskatchewan and Co-Director of the Software Research Lab. He leads an NSERC CREATE graduate program on Software Analytics Research and co-leads the Data Management group for an NSERC CFREF project on Food Security, with over 170 publications cited 6,000+ times. His research centers on software clone detection using the widely adopted NICAD system, software evolution, empirical studies, and AI-driven software analytics. Recent work integrates large language models for code generation, clone detection in the AI era, and developer interactions with tools like ChatGPT, emphasizing practical applications in maintenance and analytics. Analysis of his 15 most recent publications reveals a strong trend toward AI/ML integration in software engineering: 12 of 15 articles (2025) explore LLMs, quantum computing, or deep learning for tasks like bug localization, code snippet generation, and feature-toggle analysis. Key themes include empirical validation of AI tools, Stack Overflow data mining, and cross-domain frameworks for Society 5.0. His scientific awards include: Most Influential Paper Awards (SANER 2018, ICPC 2018) Outstanding Young Computer Science Researcher Award (CS-Can/Info-Can, 2018) New Researcher Award (University of Saskatchewan, 2019) New Scientist Research Award (College of Arts and Science, 2019) As lead of the NSERC CREATE program and CFREF data group, he mentors graduate students in software analytics while securing major grants. He actively serves on program committees for ASE, ICSE, and FSE, reviewing journals and organizing workshops on clone detection and empirical methods. His lab focuses on real-world applications in food security data management and software evolution. The Software Research Lab, co-directed by Roy, drives projects like NICAD and the NSERC CREATE initiative, emphasizing open-source contributions and industry collaboration. Current efforts include quantum-SE integration and AI-augmented maintenance tools under the CFREF food security mandate.

Michael Ferdmanمشاهده پروفایل

دانشیار

• Computer Architecture
• Operating Systems
• Machine Learning
• +۷ مورد دیگر

Michael Ferdman is an Associate Professor in the Department of Computer Science at Stony Brook University, where he leads research in computer architecture and systems. His office is located in Room 343 at Stony Brook, NY 11794-2424, and he can be contacted via phone (631-632-8449) or email. Ferdman directs the Computer Architecture and Systems Laboratory (compas.cs.stonybrook.edu), focusing on next-generation server infrastructure. Ferdman's research spans the entire computing stack with emphasis on: FPGA integration for server environments (Intel HARP, Microsoft Catapult) Machine learning accelerators for convolutional neural networks Server systems optimization in the post-Moore era Network processing and software-defined networking Programming models for emerging memory technologies (HBM, 3D XPoint) Reconfigurable hardware and high-level synthesis His work addresses both performance and security challenges in modern computing infrastructure. Analysis of his 15 most recent publications (2022-2025) reveals consistent focus on: Hardware acceleration techniques (FPGAs, specialized processors) Memory hierarchy optimization and cache management Security vulnerabilities in web applications and systems Post-Moore computing architectures Parallel processing and distributed systems His research shows strong emphasis on practical implementations bridging hardware and software layers. Awards recognizing his contributions include: Graduate Teaching Award (2014) Best Paper Award at ASPLOS XVII Best Paper Finalist at HPCA XVII Three IEEE Micro Top Picks selections (2009, 2012) He teaches advanced courses including CSE 502, CSE 602, and CSE 506 at Stony Brook University.

Sanjeev Dewanمشاهده پروفایل

استاد

• Information Systems
• Economics of Information Technology
• Digital Platforms
• +۷ مورد دیگر

Sanjeev Dewan is a Professor of Information Systems and Associate Dean of Masters Programs at the Paul Merage School of Business, University of California, Irvine. He also serves as Faculty Director of the Master of Science in Business Analytics program. Prior to joining UCI in 2001, he held faculty positions at the University of Washington and George Mason University. PhD, University of Rochester MS, University of Rochester Bachelor of Technology, Indian Institute of Technology, Delhi His research focuses on the economics of digital platforms , social and mobile analytics , and the valuation of technology investments . He investigates how information technology creates business value, impacts consumer behavior, and influences firm performance. His work spans electronic markets, Web 2.0 technologies, IT productivity, and the digital divide. The most recent publications reveal a strong emphasis on empirical analysis of digital platforms , including studies on gender bias in open source communities, quality certification in the sharing economy (e.g., Airbnb), personalized ranking in app stores, and mobile health applications. His research frequently uses large-scale datasets to examine behavioral patterns, market dynamics, and the economic implications of IT innovations. Faculty Service Award for 2023-24, UCI Paul Merage School of Business Best Paper Award, INFORMS 2019 e-Business Cluster Faculty Service Award for 2016-17, UCI Paul Merage School of Business Best Paper Award, INFORMS Conference on Information Systems and Technology (2009) INFORMS Service Award (2009) INFORMS Certificate of Appreciation (2008) Beta Gamma Sigma Honor Society (1991) University of Rochester Fellowship (1985–1990) Sanjeev Dewan has advised numerous PhD students who have secured faculty positions at leading institutions such as the University of Wisconsin-Madison, HKUST, Penn State, and the University of Hong Kong. His editorial service includes senior editor roles at Information Systems Research and associate editor at Management Science . He has also chaired tracks at ICIS and served as program co-chair for PACIS. There is no indication of external grant funding in the provided text, but his sustained publication record and leadership roles suggest significant research activity and institutional support. He is actively involved in academic leadership and research dissemination, contributing to major conferences and editorial boards. His work bridges theory and practice, particularly in digital platform ecosystems and analytics-driven decision-making.

Yu Huangمشاهده پروفایل

استادیار

• Software Engineering
• Human Factors
• AI for Software Engineering
• +۷ مورد دیگر

Dr. Yu Huang is an Assistant Professor in the Department of Computer Science at Vanderbilt University's School of Engineering, with a secondary appointment in the Department of Teaching and Learning at the Peabody School of Education. She is affiliated with the Institute for Software Integrated Systems, the Frist Center for Autism and Innovation, the Vanderbilt Lab for Immersive AI Translation (VALIANT), and the Vanderbilt LIVE Learning Innovation Incubator. Her academic journey began with a BS in Aerospace Engineering from Harbin Institute of Technology in China (2011), followed by an MS in Computer Engineering from the University of Virginia (2015), and culminated with a PhD in Computer Science and Engineering from the University of Michigan in 2021 under Professor Westley Weimer. Dr. Huang's research bridges human cognition and machine intelligence to enhance software development. Her work spans software, hardware, AI, medical imaging (fMRI/fNIRS), eye tracking, and mobile sensing through collaborations with Security, Education, Psychology, and Neuroscience researchers. She leads the MIND Lab (Mixed INtelligence Development for programming lab), investigating programming expertise formation, code comprehension processes, cognitive error patterns, and diversity in programming communities. Her innovative approach combines empirical human studies with AI model development to create more effective programming tools. Her recent publications reveal a growing emphasis on leveraging human attention data to improve code language models, analyzing cognitive biases in security contexts, and examining social factors in technical communication. The research shows strong interdisciplinary connections between neuroscience, psychology, and software engineering, with increasing applications of LLMs in developer tooling. Dr. Huang's work consistently demonstrates how understanding human cognition can inform better AI systems for programming tasks. Dr. Huang has received numerous prestigious recognitions including the 2025 ICPC Vaclav Rajlich Early Career Achievement Award and three ACM SIGSOFT Distinguished Paper Awards (ICSE 2019, FSE 2023, ICSE 2024). Her lab has earned the Best Presentation Award at GI2024, while her students have received the Richard Bennett/Dorothy Danforth Compton Prize scholarship and the C. F. Chen Best Paper award. She actively mentors a diverse team of graduate students (Yifan Zhang, Zach Karas, Zihan Fang, Yueke Zhang, Jiahao Zhang) and undergraduate researchers, with many former students advancing to top institutions (Stanford, Harvard, Duke, UC Berkeley) and organizations (NASA JPL). Her research is supported by a 4-year NSF grant, GitHub Tech for Social Good funding, and the Provost's Faculty Immersion Vanderbilt Grant, enabling comprehensive studies of human-AI collaboration in software engineering. The MIND Lab maintains a strong collaborative culture, frequently working with Professor Kevin Leach's research group and organizing retreats to locations like Radnor State Park and the Great Smoky Mountains. This environment fosters innovation at the intersection of human cognition and software engineering while supporting the professional development of emerging researchers in the field.

• Software Engineering
• Refactoring Techniques
• Technical Debt Management
• +۴ مورد دیگر

Christian Newman is an Associate Professor in the Department of Software Engineering at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and has expertise in software engineering methodologies, refactoring techniques, and source code analysis. His research focuses on improving code quality, developer practices, and automated documentation. Education: Newman holds a BS, MS, and Ph.D. from Kent State University. His academic background aligns with his current research in software engineering and empirical studies. Research Interests: His work emphasizes identifier naming standards, technical debt management, refactoring strategies, and code reuse. He explores how developers perceive and implement refactoring tools, as well as the role of large language models (LLMs) in programming education and code generation. Publications: Newman's recent work includes studies on identifier semantics, part-of-speech tagging for code analysis, and the performance of LLMs in introductory programming tasks. His research often combines empirical studies with tool development, such as SATDBailiff for technical debt tracking and TSDetect for test smell detection. Teaching & Advising: He teaches courses like SWEN-250 (Personal Software Engineering), SWEN-331 (Engineering Secure Software), and graduate-level thesis supervision. His courses emphasize secure development, software design principles, and team-based projects. Tools & Contributions: Newman has developed tools like srcSlice (static slicing), srcType (type resolution), and SCALAR (identifier analysis). These tools support software evolution, code comprehension, and empirical research in the field.

Muhammad Asaduzzamanمشاهده پروفایل

استادیار

• Software Engineering
• Mining Software Repositories
• Empirical Software Engineering
• +۲ مورد دیگر

Muhammad Asaduzzaman is an Assistant Professor in the School of Computer Science within the Faculty of Science at the University of Windsor. His research focuses on software engineering, particularly software maintenance, mining software repositories, and recommendation systems for developers. Research interests span empirical studies of software artifacts, API usage analysis, and improving developer productivity through tools like COSTER for API element identification. Recent work examines dependency management in Maven ecosystems and AI-assisted code completion. Publications show consistent focus on analyzing developer activities through platforms like Stack Overflow and GitHub. Current investigations include LLM applications for code synthesis and technical debt impact analysis.

Andrea Buntمشاهده پروفایل

استاد

• Human-Computer Interaction
• Intelligent Interactive Systems
• Software Learnability
• +۵ مورد دیگر

Andrea Bunt is a Full Professor and Associate Head (Graduate) in the Department of Computer Science at the University of Manitoba, where she co-directs the HCI lab. She has established herself as a leading researcher in human-computer interaction with significant contributions to software learnability, rural computing, and technologies for children and families. Her work bridges theoretical and practical aspects of HCI, with strong community engagement through student supervision and collaborative projects. Dr. Bunt completed her B.Sc. at Queen's University, followed by an M.Sc. in 2001 and Ph.D. in 2007 at the University of British Columbia. Prior to joining the University of Manitoba, she was a Postdoctoral Fellow at the University of Waterloo in the Human-Computer Interaction Lab. This educational trajectory has provided her with a strong foundation for her interdisciplinary research approach. Her research interests span multiple areas within human-computer interaction, with particular focus on software learnability for diverse user groups, improving computing experiences in rural and remote communities, and designing technologies specifically for children and families. Her work on explainable AI, gender inclusivity in technology, and collaborative learning dynamics represents cutting-edge contributions to the field. Recent projects include Stream Assistant for live streamers, digital interventions for adolescent tech disengagement, and gender-inclusive approaches to online question-and-answer platforms. Analysis of her recent publications reveals a strong emphasis on user-centered approaches to technology design, particularly focusing on vulnerable or underserved populations. Her work consistently bridges theoretical HCI principles with practical applications, demonstrating how technology can be made more accessible, inclusive, and effective for diverse user groups. There's a clear trajectory toward addressing societal challenges through HCI, with increasing focus on ethical considerations in AI systems. CS-Can | Info-Can Young Researcher Award (2018) NSERC Accelerator Supplement (2015-2018) Multiple Best Paper Awards at premier conferences including CHI, Graphics Interface, and FDG Consistent recognition for methodological innovation and impactful research contributions Dr. Bunt actively mentors a diverse group of students at all levels, from undergraduate research assistants to Ph.D. candidates. Her lab receives funding from NSERC Discovery Grants and other sources to support research on intelligent interactive systems. She has successfully guided numerous students through their academic journeys, with many going on to impactful careers in academia and industry. Her collaborative approach extends to interdisciplinary partnerships across computer science, education, and social sciences. The HCI lab she co-directs serves as a vibrant research hub focusing on real-world applications of human-computer interaction principles. Current projects address critical challenges including technology use in rural communities, digital wellbeing for adolescents, and inclusive design practices. The lab fosters a collaborative environment where students and researchers work together to develop innovative solutions to complex HCI problems.

• Machine Learning
• Deep Learning
• Data Analytics
• +۳ مورد دیگر

Shafaq Khan is an Assistant Professor in the School of Computer Science at the University of Windsor. She holds a PhD in Computer Science from the University of Salford (2017). Her research spans machine learning, deep learning, data analytics, and database systems with applications in healthcare informatics, agricultural technology, educational systems, and blockchain. Recent work focuses on AI-driven healthcare transformation, privacy-preserving data methods, federated learning for disease prediction, and computer vision applications in agriculture. Additional interests include educational technology for addressing disparities, blockchain implementations in government services, and open-source search engine development. Her work demonstrates consistent integration of cutting-edge computing techniques with practical domain applications.

Abbas Heydarnooriمشاهده پروفایل

استادیار

• Software Engineering
• Artificial Intelligence for Software Engineering (AI4SE)
• Software Analytics
• +۵ مورد دیگر

Abbas Heydarnoori is an Assistant Professor in the Department of Computer Science at Bowling Green State University (USA) since 2022, and previously held a faculty position at Sharif University of Technology (Iran) from 2012 to 2022. He earned his Ph.D. in Computer Science from the University of Waterloo (Canada, 2009), and M.Sc. and B.Sc. in Software Engineering from Sharif University of Technology (2001 and 1999). His research focuses on AI-driven software engineering (AI4SE/SE4AI), leveraging data science and AI to address challenges like fault localization, bug prediction, and code comprehension. He analyzes software repositories (e.g., GitHub, Stack Overflow) to improve developer productivity and software quality. He has contributed to tools like CrowdSummarizer and ExceptionTracer, and his work spans topics such as microservices architecture, API usage analysis, and code summarization. Teaching includes graduate/undergraduate courses on AI for Software Engineering, Database Systems, and Software Engineering. His service roles include editorial board membership at Science of Computer Programming , and PC membership in conferences like MSR, SANER, and FSE. His research group actively publishes on automated code analysis, documentation generation, and developer productivity tools, with a focus on empirical and data-driven approaches.

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.

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.

Daniel Germanمشاهده پروفایل

استاد

• Software Engineering
• Software Evolution
• Open Source Software Development
• +۴ مورد دیگر

Dr. Daniel German is a Professor in the Department of Computer Science at the University of Victoria, part of the Faculty of Engineering and Computer Science. He holds a PhD from the University of Waterloo, specializing in software engineering and open source ecosystems. His research focuses on software evolution, open source development practices, intellectual property issues in software systems, and licensing compliance challenges in modern AI/ML environments. German has contributed extensively to understanding dependency management, developer workflows, and legal aspects of software development. His work includes seminal studies on code provenance tracking (e.g., Cregit), library dependency management, and the sociotechnical dynamics of open source communities like the Linux kernel and GitHub ecosystems. German has explored critical topics such as licensing inconsistencies in software projects, ethical implications of AI-generated code, and the integration of open source components into proprietary systems. He is actively involved in software engineering education, examining how students engage with open source projects and the challenges of maintaining code quality in large-scale distributed systems. German’s research has practical implications for both academic and industrial software development practices, addressing real-world issues like security vulnerabilities in dependency chains and the legal risks of AI training data usage.

Maria Del Rio-Chanonaمشاهده پروفایل

استادیار

• Network Science
• Agent-Based Modeling
• Large Language Models
• +۷ مورد دیگر

Maria Del Rio-Chanona is an Assistant Professor in the Department of Computer Science at University College London (UCL), where she conducts interdisciplinary research at the intersection of network science, machine learning, and economic modeling. She is also a member of the External Faculty at the Complexity Science Hub since January 2025, reflecting her ongoing engagement with complex systems research. Her academic background includes a PhD in Mathematics from the University of Oxford, where she was part of the Institute for New Economic Thinking at the Oxford Martin School, and undergraduate studies in Physics at UNAM, Mexico. Her research interests center on understanding socioeconomic transformations using computational methods. She specializes in applying Large Language Models (LLMs), Agent-Based Models (ABMs), and network analysis to study the impacts of generative AI, the net-zero transition, and global crises like the COVID-19 pandemic on labor markets, economic resilience, and public discourse. Her work often involves analyzing large-scale digital trace data, including online labor platforms and social media, to uncover behavioral and structural shifts. The recent publications reveal a consistent focus on labor market dynamics in the face of technological disruption. Her work examines how generative AI reshapes demand for freelance skills, reduces public knowledge sharing on platforms like Stack Overflow, and influences mental health discourse in relation to employment decisions. She also investigates broader economic modeling, including pandemic shock propagation and employment transitions during decarbonization. Among her scientific recognitions is the Emerging Scientific Award from the Complex Systems Society in 2023. She has collaborated with international policy organizations such as the International Monetary Fund (IMF) and the International Labour Organisation (ILO), underscoring the policy relevance of her research. Maria Del Rio-Chanona has held prestigious research positions, including as a James S. McDonnell Foundation (JSMF) Postdoctoral Fellow at the Complexity Science Hub and a Visiting Fellow at the Growth Lab, Harvard Kennedy School. These roles have enabled her to lead and contribute to high-impact interdisciplinary projects. While no current advisees are listed, she actively engages with PhD opportunities and interdisciplinary research networks. She is involved in multiple research initiatives, including the development of HuBERT, an NLP algorithm for extending the Seshat Global History database, and projects studying labor transitions in net-zero scenarios. Her work on the HiST-LLM benchmark for historical knowledge in LLMs highlights her innovative integration of AI with social science.

Chanchal K. Royمشاهده پروفایل

استاد

• Software Engineering
• Software Maintenance and Evolution
• Code Clone Detection
• +۳ مورد دیگر

Dr. Chanchal K. Roy is a Professor of Software Engineering/Computer Science at the University of Saskatchewan (USask), Canada, and Director of the NSERC CREATE SOAR program. He leads the Software Research Lab (SRLab) and is renowned for his work on code clone detection (NiCad tool) and software maintenance. His research spans software evolution, big data analytics, and quantum computing applications in software engineering. Dr. Roy holds a Ph.D. from Queen’s University, an M.Sc. from RWTH Aachen University, and a B.Sc. from Khulna University. Research interests include software clone detection, maintenance, and evolution, with emphasis on semantic analysis and cross-language clones. He has published over 240 papers (h-index 52) and attracted $6M+ in funding, including NSERC grants and CFI-JELF support. Awards include the GSA Advising Excellence Award, Outstanding Young Computer Science Researcher Award, and multiple Most Influential Paper awards. Key contributions include developing NiCad, advancing Stack Overflow search techniques, and leading collaborative projects in software analytics. His work has been featured in ACM Tech News, TechRepublic, and Stack Overflow blogs. Dr. Roy actively engages in keynotes at conferences like WCRE, IWSC, and BIM.