جست‌وجوی استاد و پژوهشگر

Song Zhipingمشاهده پروفایل

استاد

• Corporate Governance
• State-owned Enterprise Reform
• Strategic Management
• +۴ مورد دیگر

Dr. Song Zhiping serves as Professor of Management Practice at Cheung Kong Graduate School of Business (CKGSB), leveraging 40 years of frontline enterprise management experience. He previously chaired China National Building Material Group and China National Pharmaceutical Group, transforming both into Fortune Global 500 companies while pioneering national SOE reforms. His educational foundation includes a PhD in Engineering Management from Huazhong University of Science and Technology. Research centers on state-owned enterprise innovation through frameworks like 'State-owned Enterprise Market Management' and 'Three Essence Management,' emphasizing mixed-ownership models that blend SOE scale with private-sector agility. His cement industry integration strategies became Harvard Business School case studies, demonstrating practical applications of his 'corporate competitiveness = SOE strength + private vitality' formula. Recent 2025 publications reveal expanding analytical scope from entrepreneurial resilience and film globalization to RMB internationalization and unicorn valuation sustainability, reflecting his adaptive approach to evolving global business landscapes. Major recognitions include: Special Government Allowance by the State Council National Model Worker Golden Globe Award for Outstanding Chinese Entrepreneur Yuan Baohua Gold Award for Enterprise Management Ishikawa Kano Award CCTV Economic Person of the Year Award Fortune's Chinese Entrepreneur of the Year Award Lifetime Achievement Award for the 70th Anniversary of New China As Chairman of China Association of Public Companies and World Cement Association, he shapes corporate governance standards and industry consolidation strategies. His advisory influence extends through CAPCO policy recommendations and Harvard-recognized cement integration models. Professional leadership spans the China Economic Reform Society and World Cement Association, driving cross-sector initiatives in SOE modernization and industrial consolidation.

Smita Krishnaswamyمشاهده پروفایل

دانشیار

• Unsupervised Machine Learning
• Manifold Learning
• Deep Learning
• +۷ مورد دیگر

Smita Krishnaswamy is an Associate Professor of Genetics and Computer Science at Yale University with joint appointments in both departments. She is affiliated with multiple interdisciplinary programs including the Applied Mathematics Program, Computational Biology and Bioinformatics Program, Yale Center for Biomedical Data Science, Yale Cancer Center, and the Wu Tsai Institute. Her research bridges computational methods development with biomedical applications, focusing on unsupervised machine learning approaches for high-dimensional data analysis. Associate Professor of Genetics, Yale School of Medicine Associate Professor of Computer Science, Yale University Affiliated Faculty, Applied Mathematics Program Affiliated Faculty, Computational Biology and Bioinformatics Member, Yale Center for Biomedical Data Science Member, Yale Cancer Center Member, Wu Tsai Institute Dr. Krishnaswamy's research focuses on developing unsupervised machine learning techniques, particularly manifold learning and deep learning methods, to analyze high-dimensional biomedical data. Her lab creates algorithms for non-linear dimensionality reduction, data geometry learning, denoising, imputation, and inference of multi-granular structures from complex datasets. These methods are applied to diverse data types including single-cell RNA-sequencing, mass cytometry, electronic health records, and connectomic data across multiple biological systems. Her work spans several key application areas including immunology and immunotherapy, cancer research, neuroscience, developmental biology, and health outcomes analysis. The lab employs approaches from geometric deep learning, multiscale graph signal processing, and topological data analysis to extract meaningful biological insights from complex datasets. Recent publications demonstrate the lab's leadership in developing methods for spatial transcriptomics, brain-state trajectory modeling, and organ donation prediction. Excellence in Science Early-Career Investigator Award from FASEB (2022) Yale Cancer Center Class of '61 Cancer Research Award (2025) Dr. Krishnaswamy maintains active collaborations across Yale and secures research funding supporting her work in computational biomedicine. She advises students through multiple programs including Genetics, Computer Science, and the Biological and Biomedical Sciences Graduate Program, fostering interdisciplinary training at the intersection of computation and biomedicine. The Krishnaswamy Lab operates at the forefront of computational biomedicine, developing mathematical approaches that enable new biological discoveries from complex datasets.

• Drug Delivery
• Biomaterials
• Nanobiotechnology
• +۷ مورد دیگر

W. Mark Saltzman is the Sterling Professor of Biomedical Engineering and Professor of Cellular and Molecular Physiology and of Chemical Engineering at Yale University. He serves as Affiliated Faculty at the Yale Institute for Global Health and was the founding chair of Yale's Department of Biomedical Engineering for 12 years. From 2016-2022, he was Head of Jonathan Edwards College, one of Yale's residential colleges. His academic journey includes faculty positions at Johns Hopkins University and Cornell University before joining Yale in 2002 as the Goizueta Foundation Professor of Chemical and Biomedical Engineering. Professor Saltzman's research has profoundly impacted drug delivery, biomaterials, nanobiotechnology, and tissue engineering, with over 350 research papers and patents. His work integrates mathematical modeling with experimental approaches to develop innovative delivery systems. Key contributions include developing mathematical models for drug delivery system design, creating methods for brain tumor drug delivery, producing the first controlled delivery systems for nerve growth factors, and developing materials for DNA/RNA delivery. His lab's current focus spans RNA therapeutics for cancer, in utero gene editing for cystic fibrosis, and targeted nanoparticle delivery systems. His recent publications (2024-2025) demonstrate a strong emphasis on translational applications, particularly in cancer therapy and genetic disorders. These works showcase his lab's expertise in nanoparticle engineering, with research spanning from fundamental studies of nanoparticle-cell interactions to preclinical studies in nonhuman primates. The interdisciplinary nature of his work is evident in collaborations across engineering, medicine, and basic science departments at Yale. Elected member of the US National Academy of Medicine Elected member of the US National Academy of Engineering Sterling Professor of Biomedical Engineering (2025) Author of three textbooks: 'Biomedical Engineering,' 'Tissue Engineering,' and 'Drug Delivery' Professor Saltzman has mentored 43 doctoral students and 27 postdoctoral associates throughout his career, with many becoming independent researchers at leading institutions. His research group maintains active collaborations with clinicians and scientists across Yale, particularly in cystic fibrosis research, cancer therapy, and fetal medicine. The Saltzman Research Group continues to push the boundaries of drug and gene delivery technology, with recent work focusing on RNA therapeutics, in utero treatments, and precision targeting of nanoparticles to specific cell populations. The lab maintains strong connections with Yale's Cancer Center, Center for RNA Science and Medicine, and Vascular Biology and Therapeutics Program, facilitating translation of engineering approaches to clinical applications. Current research directions include developing nanoparticle systems for cancer immunotherapy, in utero gene editing, and targeted delivery to specific organ systems.

• Cancer immunotherapy
• Drug development for cancer
• Melanoma treatment
• +۲ مورد دیگر

Mario Sznol, MD is a Professor of Medicine (Medical Oncology) at Yale School of Medicine, Clinical Research Leader of the Melanoma Program, and Co-Director of the Yale SPORE in Skin Cancer. He is also co-director of the Cancer Immunology Program at Yale Cancer Center. Previously with the National Cancer Institute, Dr. Sznol has developed an international reputation in cancer drug development. His educational background includes: BA from Rice University (1979) MD from Baylor College of Medicine (1982) Residency at Baylor College of Medicine (1985) Fellowship at Mount Sinai College of Medicine (1987) Dr. Sznol's research focuses on cancer immunotherapy , drug development for cancer , and the treatment of patients with melanoma and renal cell carcinoma . He actively works to expand clinical trial opportunities at Yale Cancer Center, particularly those focusing on immunotherapy and novel agents. His work spans both clinical trials and translational research to better understand immunotherapy mechanisms and resistance patterns. His recent publications demonstrate a strong emphasis on immunotherapy outcomes for melanoma and kidney cancers, with particular attention to treatment combinations, biomarkers of response, and end-of-life considerations in the immunotherapy era. His collaborative network includes prominent Yale researchers such as Dr. Harriet Kluger, Dr. Ruth Halaban, and Dr. Lucia Jilaveanu. Notable professional contributions: Co-Director of the Yale SPORE in Skin Cancer Principal Investigator for multiple melanoma clinical trials Extensive publication record in high-impact oncology journals Active role in Yale's immuno-oncology training programs As a clinician, Dr. Sznol sees patients with melanoma and kidney cancers at Yale Medicine, emphasizing honest communication about disease, treatment options, and expected outcomes. His team at Smilow Cancer Hospital provides comprehensive support throughout the treatment process. Dr. Sznol is actively involved in mentoring through Yale's educational programs, including the K12 Calabresi Immuno-Oncology Training Program, contributing to the development of the next generation of oncology researchers and clinicians.

• Laser Technology
• Optics
• Biophotonics
• +۴ مورد دیگر

Prof. Maria Dienerowitz is a Professor at Ernst Abbe University of Applied Sciences Jena specializing in laser technology and biophotonics. She teaches core courses including Laser Technology, Quantum Optics, and Vacuum Technology across Bachelor's and Master's programs while leading three major research initiatives: BioLOC (Carl-Zeiss-Stiftung-funded Lab-on-a-Chip system for molecular dynamics), OPTO (historical spectacle lens analysis with the German Optical Museum), and TOOLS (DFG-funded tailored optics for life sciences). Her research centers on advanced optical manipulation techniques including the ABEL trap for single-molecule studies without surface binding and holographic optical tweezers for nanoparticle control. Key focus areas encompass real-time enzyme kinetics of molecular motors like F-ATP synthase, single-molecule FRET dynamics , and nanoparticle characterization in biological contexts. This work bridges fundamental physics with biomedical applications through innovations in trapping methodology and optical instrumentation. Analysis of her 15 most recent publications reveals strong continuity in optical trapping methodologies with increasing biomedical applications since 2020. Her work demonstrates consistent contributions to single-molecule biophysics (particularly molecular motor studies), nanoparticle manipulation , and optical instrumentation development , with growing emphasis on life science interfaces evident in her Carl-Zeiss-Stiftung and DFG-funded projects. Prof. Dienerowitz currently oversees a research team comprising scientific staff members working across the BioLOC, OPTO, and TOOLS projects, including Dr. Jakub Malohlava (TOOLS), Maryam Shahrezaei (BioLoc), and Frederic Braun (TOOLS). Her laboratory infrastructure supports optical trap development, nanoparticle characterization, and molecular dynamics studies through equipment funded by the Carl-Zeiss-Stiftung and German Research Foundation.

Stefan Hendricksمشاهده پروفایل

پژوهشگر

• Sea Ice Physics
• Climate Science
• Remote Sensing
• +۵ مورد دیگر

Dr. Stefan Hendricks is a Senior Researcher at the Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (AWI) in Bremerhaven, Germany, working within the Department of Sea Ice Physics. Previously, he served in the Division of Climate Sciences at the same institution from February 2005 to June 2020. With a Doctor of Science degree in Geophysics from the University of Cologne (1997-2005), his career has focused on polar research, particularly sea ice physics and remote sensing applications. Dr. Hendricks' research interests center on sea ice thickness measurement and monitoring using satellite remote sensing techniques. His expertise spans near-surface geophysics, electromagnetics, applied geophysics, and Python programming , which he applies to Arctic and Antarctic sea ice studies. He has made significant contributions to understanding climate change impacts on polar regions through his work on sea ice dynamics, thermodynamics, and remote sensing methodologies. His recent publications reveal a strong emphasis on satellite altimetry applications for sea ice thickness retrieval, with particular focus on addressing limitations in traditional mapping approaches that neglect ice advection and deformation processes. The MOSAiC expedition has been central to his recent work, where he contributed to various aspects including airborne laser scanning of sea ice surfaces, radar altimetry studies, and analysis of sea ice deformation and snow-ice interactions. 331 publications with over 64,971 reads and 7,767 citations Extensive work with CryoSat-2 satellite data for sea ice monitoring Development of AWI CryoSat-2 Sea Ice Thickness data product Significant contributions to the MOSAiC expedition research As a Senior Researcher at AWI, Dr. Hendricks leads and contributes to numerous research projects focused on polar climate change. His work bridges observational science with practical applications for climate modeling and environmental monitoring. He collaborates extensively with international partners across multiple disciplines, contributing to our understanding of how sea ice changes impact global climate systems. His research group continues to develop innovative methods for sea ice observation using both satellite and in-situ techniques, with implications for climate prediction and polar environmental assessment.

Kun Yangمشاهده پروفایل

استاد

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

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

Maura Johnمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Genome-wide Association Studies
• Machine Learning in Biology
• +۴ مورد دیگر

Maura John serves as a Research Associate at the Chair of Bioinformatics at Hochschule Weihenstephan-Triesdorf's Straubing Campus for Sustainable Resource Use. Her research focuses on developing advanced computational methods for biological data analysis, with particular expertise in genome-wide association studies and protein structure prediction. Her primary research interests include: Genome-wide association studies with permutation-based significance thresholds that preserve population structure Development of bioinformatics tools like permGWAS2 and easyPheno Protein thermostability prediction using machine learning approaches Genomic selection methodologies for crop breeding applications Dr. John's recent publications demonstrate a strong focus on methodological improvements in computational biology, particularly addressing limitations of traditional approaches in handling skewed phenotype distributions and population structure. Her work bridges theoretical statistical methods with practical biological applications across plant genomics and protein science. Notable contributions include: permGWAS2: An improved method that maintains population structure during permutations ProLaTherm: A protein language model-based thermophilicity predictor outperforming existing methods easyPheno: A comprehensive Python framework for phenotype prediction model comparison Her research program demonstrates strong collaborative efforts with Dominik Grimm's group and other bioinformatics researchers, focusing on developing open-source tools that address critical challenges in genomic data analysis. The work has practical applications in plant breeding, protein engineering, and understanding genotype-phenotype relationships.

• formal methods for security
• programming languages
• design and verification of security-critical systems
• +۱۱ مورد دیگر

Cătălin Hrițcu is a tenured faculty member and head of the Formally Verified Security group at the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. He also serves as Adjunct Professor in the Faculty of Computer Science at Ruhr University Bochum (RUB), where he is affiliated with HGI and the CASA Cluster of Excellence. His educational background includes: PhD from Saarland University in Saarbrücken, Germany Habilitation from ENS Paris Hrițcu's research focuses on developing rigorous formal techniques for security. His primary interests span three interconnected areas: Formal methods for security: secure compilation, compartmentalization, memory safety, speculative execution defenses, information flow control, and security protocols Programming-languages techniques: program verification, machine-checked proofs, dependent types, formal semantics, and property-based testing Design and verification of security-critical systems: compilation chains, reference monitors, tagged hardware architectures, and high-assurance cryptography His recent publications reveal a strong trajectory toward addressing real-world security challenges through formal methods, with increasing emphasis on hardware security aspects like speculative execution vulnerabilities and memory safety. Hrițcu has received significant scientific recognition: ERC Starting Grant on formally secure compilation Distinguished Paper Award at CSF 2025 for FSLH: Flexible Mechanized Speculative Load Hardening Distinguished Paper Award at CSF 2021 for SSProve As an advisor, Hrițcu has mentored numerous PhD students and postdoctoral researchers who have gone on to successful academic careers. His group receives substantial funding through projects like ERC SECOMP. He has co-authored volumes of the Software Foundations textbook series and actively teaches courses at RUB. Hrițcu leads the Formally Verified Security research group at MPI-SP, which includes PhD students, postdocs, and research interns working on cutting-edge problems at the intersection of programming languages and security. The group has made significant contributions to verification tools including F* and Coq, with applications in secure compilation and cryptographic verification.

• Programming Languages
• Programming Language Design
• Programming Language Semantics
• +۱۱ مورد دیگر

Danel Ahman is an Associate Professor of Programming Languages at the Institute of Computer Science, University of Tartu, Estonia. He is a member of the Chair of Programming Languages and the Laboratory for Software Science (SWS). His academic journey includes postdoctoral positions at the University of Ljubljana and Inria Paris, as well as research internships at Microsoft Research. Education: PhD in Theoretical Computer Science, University of Edinburgh (2012-2017), thesis: "Fibred Computational Effects" MPhil in Advanced Computer Science, University of Cambridge (2011-2012), thesis: "Computational effects, algebraic theories and normalization by evaluation" BSc in Informatics, Tallinn University of Technology (2007-2010), Cum Laude Danel Ahman's research focuses on programming languages, their design and semantics. He is particularly interested in dependent and refinement types, computational effects (especially algebraic effects, effect handlers, and runners), modal types for verifying temporal resource usage, and category-theoretic denotational and operational semantics. His work bridges theoretical foundations with practical applications in verified software development, exploring how programming language features can ensure correctness while maintaining expressiveness and efficiency. His recent publications demonstrate a consistent focus on computational effects, modal types, and verified programming. The trajectory shows increasing sophistication in handling temporal resources and asynchronous effects, with several papers published in top programming languages conferences (POPL, ICFP) and journals. His work often combines theoretical category-theoretic foundations with practical implementations, particularly using the F* verification language. Scientific Awards: 2025: Estonian Research Council grant on "Explainable Verification" (co-investigator) 2024: EuroProofNet COST Action Short-Term Scientific Missions to University of Ljubljana 2019: Horizon 2020 Marie Skłodowska-Curie Individual Fellowship 2018: 1st prize at the Estonian Ministry of Education and Research dissertations competition 2012: Google prize and Citrix prize for best research dissertation at University of Cambridge Ahman actively supervises graduate students and has served on numerous program committees for programming languages conferences. His grant portfolio includes significant funding for research on programming language foundations and verification. He collaborates extensively with researchers across Europe and the US, particularly in the F* verification ecosystem and effect handler communities. He is a key member of the Laboratory for Software Science at the University of Tartu, contributing to research on programming language theory and verified software development. His work intersects with several research groups focusing on formal methods, programming language design, and software verification.

Shing-Chi Cheungمشاهده پروفایل

استاد

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

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

Yintong Huoمشاهده پروفایل

استادیار

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

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

• Software Engineering
• Program Repair
• Large Language Models
• +۲ مورد دیگر

Bofei Chen is a researcher at Fudan University actively contributing to software engineering research, as evidenced by their publication at the ASE 2025 conference in Seoul, South Korea. Their work bridges academic research and practical software development challenges. Research interests focus on critical areas of modern software engineering: Software Engineering Program Repair Large Language Models Software Maintenance Artificial Intelligence Recent work demonstrates a strong trend in leveraging large language models for software maintenance tasks. The ASE 2025 publication reveals a methodological shift toward task decomposition and self-correction techniques in patch porting, indicating growing integration of AI-driven approaches in traditional software engineering domains. This reflects broader industry-academia convergence in intelligent software development tools. No scientific awards were identified in the provided materials. While specific advising relationships and grant details remain undisclosed in available sources, the research output suggests active participation in collaborative academic projects. The absence of student listings or grant acknowledgments indicates either early-career status or focused independent research contributions. Laboratory affiliations and team structures were not specified in the source materials, though the research methodology implies engagement with software analysis and AI experimentation environments.

Mehdi Bagherzadehمشاهده پروفایل

استادیار

• Software Engineering
• Programming Languages
• Concurrent Software
• +۳ مورد دیگر

Mehdi Bagherzadeh is an Assistant Professor in the Department of Computer Science and Engineering at Oakland University. His professional profile shows consistent involvement in major software engineering conferences including ASE, SPLASH, and ICSE, where he has served in various leadership roles such as Workshops Co-Chair for SPLASH (2021-2023) and Late Breaking Results Co-Chair for ASE (2022). Dr. Bagherzadeh's research focuses on the correct construction of concurrent and big data software, situated at the intersection of Software Engineering and Programming Languages. His work particularly addresses challenges in deep learning program optimization, actor concurrency models, and automated refactoring techniques. His research trajectory shows a clear evolution from foundational work on concurrent programming models (Panini, Capsule) to current applications in deep learning systems. Analysis of his publication history reveals a strong focus on practical software engineering challenges in concurrent systems, with recent work concentrating on the migration and optimization of deep learning programs. His research combines empirical studies with formal methods, addressing both theoretical foundations and practical implementation concerns in modern software systems. Through his committee service across multiple top-tier conferences including ASE, SPLASH, ICSE, and ESEC/FSE, Dr. Bagherzadeh has established himself as an active contributor to the software engineering research community. His service roles have ranged from program committee membership to organizational leadership positions.

Jooyong Yiمشاهده پروفایل

دانشیار

• Program Analysis
• Program Repair
• Software Testing
• +۳ مورد دیگر

Jooyong Yi is an Associate Professor in the Department of Computer Science and Engineering at UNIST (Ulsan National Institute of Science and Technology). He leads the LOFT (Lab of Software), focusing on autonomous techniques for software reliability in AI-generated code environments. Research Interests: His work spans program analysis, automated repair, testing/debugging, and verification. Core themes include developing scalable methods for bug detection (via static/dynamic analysis), AI-compatible repair systems, and verification frameworks for safety-critical systems. Recent emphasis integrates fuzzing techniques with repair validation. Publication Trends: His 15 most recent works (2015-2025) show progression from foundational program repair techniques (e.g., Angelix, DirectFix) toward AI-era innovations: greybox fuzzing for efficiency, memory-leak repair for web frameworks, and deep-learning library testing. Over 50% of publications focus on optimizing repair validation and scalability. Awards: ACM Distinguished Paper Award at ASE 2023 Students & Grants: Currently advises 5 PhD, 1 MS/PhD, and 2 MSc students. Secured ₩20B+ in funding for projects including: MSIT Binary Micro-Security Patch Technology (2024-2026) Patch Validation for Automated Repair (2023-2026) AI-Powered Low-Code Platform (2023-2025) Memory-Safe Language Integration (2024-2027) Lab: LOFT lab develops verified repair tools (e.g., LeakPair, Verifix) and benchmarks (BUGSC++), prioritizing human oversight in AI-generated software.