جست‌وجوی استاد و پژوهشگر

Meredith Ringel Morrisمشاهده پروفایل

استاد مدعو

• Human-Computer Interaction
• Human-AI Interaction
• Accessibility
• +۶ مورد دیگر

Meredith Ringel Morris serves as Director for Human-AI Interaction Research at Google DeepMind and holds dual Affiliate Professor appointments at the University of Washington in the Paul G. Allen School of Computer Science & Engineering and the Information School. Previously, she directed Google's People + AI Research team and founded Microsoft Research's Ability group as Sr. Principal Researcher leading Interaction, Accessibility, and Mixed Reality initiatives. Her educational foundation includes a Sc.B. in Computer Science from Brown University and M.S./Ph.D. degrees in Computer Science from Stanford University, where her dissertation Supporting Effective Interaction with Tabletop Groupware pioneered surface computing research. Morris is a globally recognized leader in Human-Computer Interaction and Human-AI Interaction , with transformative contributions to accessibility technologies , social computing , and ethical AI frameworks . Her work spans collaborative web search systems (founding the field with SearchTogether), user-defined gesture methodologies adopted industry-wide, and accessible AI applications for blind users, people with dyslexia, and AAC device users. Current research critically examines generative AI's societal impact while developing human-centered interaction paradigms. Recent publications reveal accelerating focus on AI ethics , human-AGI interaction frameworks , and accessibility-specific AI applications , with generative models increasingly central to solving communication barriers for diverse user groups while addressing alignment challenges. Her scientific recognition includes: ACM Fellow designation ACM SIGCHI Academy membership Lasting Impact Award (ACM ISS 2016) Technology Review's 35 Innovators Under 35 Morris has co-taught University of Washington courses including Input and Interaction (2012) and organized seminal events like the UW-MSR Summer Institute on Expanding Accessibility Research. Her research leadership spans conference chair roles (CSCW General Chair), editorial boards (TOCHI), and steering committees (CHI/CSCW), supported by industry funding yielding over 100 publications and 20+ patents influencing Microsoft and Google product ecosystems. She founded Microsoft Research's Ability group focusing on inclusive design and currently leads Google DeepMind's human-AI interaction research, directing projects including Generative Ghosts (AI afterlives), LaMPost (dyslexia writing assistant), and alt text systems for AI-generated imagery, with ongoing collaboration through UW's dub research consortium.

Sam Bowmanمشاهده پروفایل

دانشیار

• Natural Language Processing
• Artificial Neural Networks
• Computational Semantics
• +۵ مورد دیگر

Sam Bowman is an Associate Professor at New York University in the departments of Data Science, Linguistics, and Computer Science. He is currently on a long-term leave from NYU while working at Anthropic, focusing on technical AI safety. Bowman leads the ML² Group and CILVR Lab, and previously headed the Alignment Research Group (2022-2024). His research spans natural language processing, artificial neural networks, computational semantics, and large language model safety. His work addresses critical AI safety challenges including model alignment, reward hacking, and ethical compliance. He advocates for rigorous evaluation frameworks and has developed benchmarks like SuperGLUE and BLiMP. Bowman's recent publications examine chain-of-thought reasoning, deceptive alignment, and behavioral vulnerabilities in frontier AI systems. Contact: bowman@nyu.edu | @sleepinyourhat on X.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Dr. Simon Ostermann is a Senior Researcher and Deputy Director at the Multilinguality and Language Technology (MLT) lab within the German Research Center for Artificial Intelligence (DFKI). He leads the research group on Efficient and Explainable NLP (E&E) , focusing on transparent and robust language models, particularly for low-resource languages and resource-constrained environments. Affiliation: DFKI, Saarland University Academic Role: Senior Researcher, Deputy Director, Research Group Lead His research emphasizes mechanistic interpretability , language model compression , and data-efficient learning . He contributes to projects like lorAI (Low Resource AI) and DisAI (Combating Disinformation), integrating methods from Explainable AI and multimodal learning . In teaching, he conducts seminars at Saarland University on topics such as Efficient NLP and Mechanistic Interpretability , expecting students to engage deeply with model internals and ethical deployment. Scientific contributions include organizing workshops (COIN, LowResNLP) and shared tasks (SemEval 2018, 2025), alongside advising multiple PhD and MSc students in areas like language model adaptation and XAI systems .

Gözde Gül Şahinمشاهده پروفایل

استادیار

• Natural Language Processing
• Semantics
• Procedural Language Understanding
• +۵ مورد دیگر

Gözde Gül Şahin is an Assistant Professor in the Computer Science and Engineering Department at Koç University's College of Engineering, where she leads research at the intersection of natural language processing and machine learning. She is affiliated with KUIS AI (Koç University Artificial Intelligence Research Center) and serves as Principal Investigator for a major Tübitak-funded project on procedural language understanding. Previously, she was a postdoctoral researcher at the Ubiquitous Knowledge Processing Lab (UKP) at Technical University of Darmstadt, working with Prof. Iryna Gurevych, and completed her PhD at Istanbul Technical University under the supervision of Prof. Eşref Adalı. Dr. Şahin's research focuses on pushing the boundaries of natural language processing by building machine learning models and open-access tools for world's languages, with particular emphasis on semantics, procedural language understanding, and the abstraction and reasoning capabilities of large language models. Her work bridges theoretical linguistics with practical NLP applications, especially for low-resource and morphologically complex languages like Turkish. She has made significant contributions to multilingual probing frameworks, grammatical error correction for Turkish, and understanding how machines can learn from small data through projects like PuzzLing Machines. Her recent publications reveal a consistent research trajectory toward understanding procedural language and improving model generalizability across domains. She has developed frameworks for evaluating multilingual representations (LINSPECTOR), created benchmarks for Turkish language understanding (Cetvel), and investigated how language models process step-by-step instructions. Her work often addresses the gap between high performance on standard NLP tasks and real-world applicability, with a strong focus on making NLP more accessible for underrepresented languages. Scientific Awards Tübitak 2232 B-International Fellowship for Outstanding Researchers (2022) Dr. Şahin actively mentors students, having supervised bachelor's students like Max Eichler (now PhD student at UKP/TU Darmstadt) and pre-doctoral researchers like Haritz Puerto. Her current Tübitak-funded project supports 2 Masters students, 2 PhD students, and one postdoctoral researcher working on "Automatic Learning of Procedural Language from Natural Language Instructions for Intelligent Assistance." She has served as Area Chair for COLING 2022 and organized workshops at major NLP conferences including EMNLP. She leads the GGLab at Koç University, which focuses on developing NLP models and datasets for world's languages, with particular attention to Turkish and other low-resource languages. The lab's work spans multiple dimensions of language understanding, from grammatical error correction (GECTurk) to procedural reasoning and multilingual instruction tuning.

• Heavy Ion Collision
• Nuclear Physics at High Density
• Nuclear Symmetry Energy
• +۳ مورد دیگر

Dr. Yongsun Kim is a Professor at the Department of Physics and Astronomy , Sejong University. Holding a Ph.D. from MIT (2013) and a B.S. from the University of Illinois Urbana-Champaign (2007), he conducted postdoctoral research at Korea University (2013-2017) and UIUC (2017-2018). His work bridges high-energy physics and nuclear structure , focusing on heavy-ion collisions , quark-gluon plasma , and Higgs boson phenomenology . Research Interests Heavy Ion Collisions at LHC/RHIC Nuclear Symmetry Energy in Rare Isotopes High-Density Nuclear Physics Detector Development for sPHENIX and ECCE Scientific Contributions With over 1073 research outputs , his recent publications include measurements of W+W- production, studies of top quark entanglement , and searches for exotic Higgs decays . His work spans CMS detector upgrades, charm hadronization, and QCD investigations . Scientific Awards No specific awards mentioned in the provided data.

John M. Richardsonمشاهده پروفایل

استاد مدعو

• Educational technologies
• Digital culture
• Media literacy
• +۷ مورد دیگر

John M. Richardson serves as an Adjunct Professor at the University of Ottawa's Faculty of Education while simultaneously working as Chair of the Department of English at Ashbury College. With over 25 years of experience teaching secondary English and Drama, he brings practical classroom expertise to his academic work. His current teaching responsibilities at uOttawa include courses on technology integration, English teaching methods, inclusive education, and curriculum planning. His educational background spans multiple institutions: Doctorate of Education in Educational Technology from University of Calgary (2015) Masters of Arts in Literacies from University of Ottawa (2010) Bachelor of Education in Secondary English and Drama from Dalhousie University (1995) Masters of Arts in English Literature from University of Toronto (1990) Bachelor of Arts (Hons) in English Literature and Theatre from Princeton University (1988) Richardson's research focuses on the intersection of digital culture and education, particularly examining how technology shapes identity formation among teenagers, the implications for classroom teaching, and the evolving nature of educational practices in digital environments. His work bridges theoretical frameworks with practical classroom applications, drawing from his dual perspective as both university educator and practicing high school teacher. His extensive publication record demonstrates consistent engagement with contemporary educational issues, with recent work addressing AI in education, cellphone policies, climate change education, and digital wellbeing. The progression of his scholarship reflects growing concerns about technology's impact on learning and identity, while maintaining focus on practical solutions for educators. As a frequent contributor to the Ottawa Citizen and other media outlets, Richardson regularly translates academic insights into accessible commentary on current educational challenges. His media presence includes appearances on CBC Radio's flagship arts program 'Q' as a youth culture commentator. Richardson actively participates in academic conferences internationally, having presented at venues including the University of Cambridge and University of Salford. His collaborative work with Indigenous educators demonstrates commitment to truth and reconciliation in educational contexts.

• Biomedical Engineering
• Medical Imaging
• Neural Networks
• +۴ مورد دیگر

Nicha Dvornek is an Assistant Professor at Yale School of Medicine in the Department of Radiology & Biomedical Imaging and affiliated with the Image Processing & Analysis Group and Yale Biomedical Imaging Institute. She holds a PhD, MPhil, and MS from Yale University, and a BS from Johns Hopkins University. Her research focuses on autism spectrum disorder, biomedical engineering, and neuroimaging, particularly using machine learning for analyzing fMRI and PET data. Education: • PhD, Yale University (2012) • MPhil, Yale University (2009) • MS, Yale University (2007) • BS, Johns Hopkins University (2006) Her work applies advanced machine learning techniques to medical imaging, including rotation-equivariant networks, GANs for motion correction, and transformer-based models for fMRI analysis. She has received recognition through awards like the James Hudson Brown Fellowship and Best Paper Award at MLMI 2019. Scientific Awards: Best Paper Award, International Workshop on Machine Learning in Medical Imaging (2019) James Hudson Brown – Alexander Brown Coxe Postdoctoral Fellowship (2014) Nicha actively contributes to clinical research through trials related to autism and medical imaging. She is part of Yale’s Bioimaging Sciences division, advancing tools for image processing and analysis.

Prem Devanbuمشاهده پروفایل

استاد پژوهشی

• Empirical Software Engineering
• Naturalness of Software
• Software Engineering education
• +۳ مورد دیگر

Prem Devanbu is a Research Professor of Computer Science at the University of California, Davis, where he has been a faculty member since transitioning from his industrial R&D position at Bell Labs in New Jersey. He holds a distinguished position in the Department of Computer Science within the College of Engineering, focusing on cutting-edge research at the intersection of software engineering and artificial intelligence. Dr. Devanbu earned his B.Tech from the Indian Institute of Technology (IIT) Madras and completed his Ph.D at Rutgers University under the supervision of Alex Borgida. His career path from industry to academia has shaped his practical yet research-oriented approach to software engineering problems. Devanbu's research primarily centers on Empirical Software Engineering , the Naturalness of Software , and Software Engineering education . His groundbreaking work on the naturalness hypothesis—that software exhibits statistical properties similar to natural language—has profoundly influenced the field. This research has expanded to explore bimodality in software (its dual nature as both machine-executable code and human-readable text), opening new avenues for analysis and tool development. His recent work heavily focuses on the application of Large Language Models to software engineering tasks, particularly in code summarization, program repair, and type inference. Analysis of Dr. Devanbu's recent publications reveals a clear trend toward leveraging Large Language Models for software engineering tasks. His research demonstrates how statistical properties of code can be exploited to improve software development processes, with particular emphasis on program understanding, documentation generation, and automated repair. The work bridges theoretical insights about code naturalness with practical applications that address real-world software maintenance challenges. Dr. Devanbu has received numerous prestigious awards recognizing his contributions to the field: ACM SIGSOFT Outstanding Research Award (2021) - "for profoundly changing the way researchers think about software by exploring connections between source code and natural language" Alexander von Humboldt Research Award (2022) IEEE Computer Society Harlan Mills Award (2024) ACM Fellow Six "test-of-time" or "10 year most influential paper" awards (MSR 2006, MSR 2009, ESEC/FSE 2008, ESEC/FSE 2009, ESEC/FSE 2011, ICSE 2012) Throughout his career, Dr. Devanbu has been actively involved in mentoring the next generation of software engineering researchers, serving on doctoral committees, and participating in New Faculty Symposia to support early-career academics. His research has been supported by significant grants that have enabled his team to explore innovative approaches at the intersection of empirical methods and software tool development. At UC Davis, he has contributed to building a strong software engineering research group that bridges theoretical insights with practical applications. Dr. Devanbu leads research efforts focused on understanding the statistical properties of software and leveraging these insights to build practical tools. His work on the naturalness and bimodality of code has established a framework that continues to influence how researchers approach program analysis and software development. His current team is at the forefront of exploring how Large Language Models can be effectively applied to software engineering tasks while accounting for the unique characteristics of code as a specialized form of human communication.

• Artificial Intelligence
• Multi-Agent Systems
• Machine Learning
• +۱ مورد دیگر

Michael Wooldridge is Professor of Computer Science at the University of Oxford and Senior Research Fellow at Hertford College, having served as Head of Department from 2014-2021. He leads research in artificial intelligence with over 450 publications in multi-agent systems, game theory, and machine learning. His research examines computational approaches to multi-agent coordination, strategic reasoning, and trustworthy AI. Current projects explore foundations of trustworthy AI through theoretical frameworks for rational verification and equilibrium analysis in complex interactive systems. Recent publications demonstrate increasing focus on large language models and their applications in multi-agent coordination, security challenges in generative AI, and computational social systems. Research integrates theoretical work with experimental validation in complex simulation environments. Honors include: Lovelace Medal (BCS, 2020) ACM Autonomous Agents Research Award (2006) AAAI/EAAI Outstanding Educator Award (2021) European Association for AI Distinguished Service Award (2023) He currently supervises doctoral students in multi-agent reinforcement learning and game-theoretic verification. Major grants include a Turing AI World Leading Researcher Fellowship (UKRI, 2021) and ERC Advanced Grant 'Reasoning About Computational Economies' (2011). As Editor-in-Chief of Artificial Intelligence Journal and former president of IJCAI, EurAI, and IFAAMAS, he maintains extensive professional service commitments while leading the Whiteson Research Lab.

Steve Wilsonمشاهده پروفایل

استادیار

• Natural Language Processing
• Computational Social Science
• Information Literacy
• +۸ مورد دیگر

Steve Wilson is an Assistant Professor in the Department of Computer Science, Engineering, and Physics at the University of Michigan-Flint, within the College of Innovation and Technology. He holds a PhD in Computer Science & Engineering from the University of Michigan and has previously served as an Assistant Professor at Oakland University and held postdoctoral positions at the University of Edinburgh and the University of Michigan. PhD | 2019 | University of Michigan | Computer Science & Engineering M.S. | 2015 | University of Michigan | Computer Science & Engineering B.Sc. | 2013 | Taylor University | Computer Science/Systems His research focuses on understanding online communication through Natural Language Processing, with emphasis on social context, information literacy, social media narratives, and educational applications. His work bridges computer science, psychology, and social informatics to analyze human behavior in digital environments. The most recent publications highlight a strong trend in using NLP for social good, including detecting misinformation, analyzing educational discourse, improving AI ethics, and understanding social dynamics such as sarcasm, offensive language, and user profiling. His work frequently appears in top venues like ACL, EMNLP, ICWSM, and SemEval, often in collaboration with leading researchers such as Rada Mihalcea and Walid Magdy. MWIN Faculty Innovation Fellow | 2025 | UM-Flint’s Office of Economic Development and EDA University Center for Community and Economic Development UM-Flint Proposal Academy Awardee | 2025 | University of Michigan-Flint Office of Sponsored Research Projects Steve Wilson is the Principal Investigator (PI) on multiple funded research projects, including a National Science Foundation CRII grant on social media framing and an internal UM-Flint grant on AI in STEM education. He co-investigates an NSF REU site on Home Health Technology. He leads the CoCoA Lab at UM-Flint, which focuses on computational analysis of communication and context. He mentors graduate students and is actively involved in developing new courses, including a recent offering in Natural Language Processing.

Xiang Chenمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Mining Software Repositories
• Software Testing and Maintenance
• +۵ مورد دیگر

Xiang Chen is an Associate Professor at the Department of Software Engineering, School of Artificial Intelligence and Computer Science, Nantong University, China. He received his B.Sc. degree from Xi'an Jiaotong University in 2002 and his M.Sc. and Ph.D. degrees in computer software and theory from Nanjing University in 2008 and 2011 respectively. He is an editorial board member of Information and Software Technology and serves as a program committee member for prestigious conferences including FSE 2026 and ASE 2025. Chen is also a senior member of the China Computer Federation (CCF) and active in various academic committees. Chen's research focuses on empirical software engineering, mining software repositories, and software testing and maintenance, with particular emphasis on applying AI techniques to software engineering problems. His work spans large language models for software engineering, security vulnerability analysis, code change representation, and regression testing. He has published over 110 papers in top-tier journals and conferences including IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology. His recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models, with traditional software engineering practices. The research spans code generation evaluation, deep learning framework testing, vulnerability detection, and automated program repair, showing a consistent focus on improving software quality through innovative testing and analysis techniques. ACM SIGSOFT Distinguished Paper Award (ICSE 2021) ACM SIGSOFT Distinguished Paper Award (ICPC 2023) Top 1% CNKI Highly Cited Scholar (2024) Top 2% Scientist by Stanford University (2023-2025) NASAC 2019 Prototype Competition First Prize Chen has successfully advised numerous graduate and undergraduate students who have gone on to prestigious institutions including Nanjing University, Tsinghua University, and Zhejiang University. Many of his students have won national programming competitions and received scholarships. His research group, smartSE, actively works on projects funded by the Natural Science Foundation of China and various provincial research programs. Chen also serves as a reviewer for top journals including IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology.

Fang Liuمشاهده پروفایل

استادیار

• Software Engineering
• Artificial Intelligence for Software Engineering
• Program Understanding
• +۳ مورد دیگر

Fang Liu is an Assistant Professor at the School of Computer Science & Engineering, Beihang University, China. She has made significant contributions to the field of software engineering, particularly in the intersection of artificial intelligence and software development practices. Dr. Liu received her Ph.D. in Computer Science from Peking University (Sep. 2017 - Jul. 2022), supervised by Prof. Zhi Jin and Prof. Ge Li. Prior to that, she earned her B.S. in Computer Science from Chongqing University (Sep. 2013 - Jul. 2017). Her research interests focus on AI for Software Engineering, including program understanding and generation, program repair, and applications of Large Language Models to software development tasks. Dr. Liu teaches Compiler Technology as a compulsory course at Beihang University (Fall 2024) and Programming in Cangjie Language as an elective course (Spring 2025). Her recent publications demonstrate a clear trend toward leveraging large language models for various software engineering tasks, with a particular emphasis on code editing, program repair, and code translation. Her research spans both theoretical advancements in model architectures and practical applications to real-world software development challenges, with numerous publications in top-tier venues like ASE, ICSE, and FSE. Distinguished Paper Award at ICPC'20 for "A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning" Dr. Liu actively participates in the software engineering research community as a program committee member for major conferences including ASE, FSE, and ICSE. Her work has significant implications for improving developer productivity, software quality, and the integration of AI technologies into the software development lifecycle.

Kate Hanمشاهده پروفایل

مدرس

• Scheduling and Timetabling
• Transportation Simulation and Optimization
• Metaheuristics and Evolutionary Algorithms
• +۴ مورد دیگر

Dr. Kate Han serves as a Lecturer at Salford Business School, University of Salford, specializing in computational optimization and AI-driven solutions for real-world business and transportation challenges. Her academic foundation includes a PhD in Software Engineering from Queen's University Belfast (2018), where she pioneered hyper-heuristic approaches for timetabling problems using reinforcement learning. Affiliated with the Salford Business School Research Centre, she actively contributes to UN Sustainable Development Goals for quality education, sustainable industry, and resilient cities. Dr. Han's research expertise spans: Scheduling and timetabling optimization using metaheuristics Transportation simulation with evolutionary algorithms Ensemble learning and deep neural network architectures Data-driven business process automation AI-assisted educational innovation in higher education Her work bridges theoretical AI advancements with practical applications in public transport, sustainable infrastructure, and business education transformation. Recent publications (2023-2025) reveal a strategic shift toward sustainable transportation systems and educational technology. Key trends include optimizing connected autonomous vehicle integration in multi-modal networks using evolutionary algorithms, developing novel ensemble selection techniques (VISTA/VEGAS), and pioneering generative AI applications for business education. These efforts consistently align with net-zero emission goals and digital transformation imperatives across sectors. Her scientific recognition includes: Hornory Lecturer award (2022) for honorary research contributions Dr. Han teaches advanced business technology courses including AI in Practice, Business Data Information Analytics, and Prompt Engineering for Business. She actively collaborates with government bodies and industry partners on real-world projects, though specific grant details remain undisclosed. Her research methodology emphasizes stakeholder engagement across diverse project scales, from micro-level business process automation to macro-level urban transport simulations. As a core contributor to Salford Business School's research ecosystem, she participates in international initiatives like the 2024 International Conference on Software Engineering and Cybersecurity. Her work demonstrates how computational intelligence can drive sustainable industrial innovation while advancing educational practices in the post-pandemic era.

Chunyang Chenمشاهده پروفایل

استاد

• Software Engineering
• Applied Data Analysis
• Deep Learning
• +۵ مورد دیگر

Chunyang Chen is a Professor at the School of CIT, Technical University of Munich (TUM), Germany. He maintains an active research profile in software engineering with a focus on applying data analysis and artificial intelligence techniques to software development processes. His primary research interests include Software Engineering , Applied Data Analysis , Deep Learning , and Human-Computer Interaction . Dr. Chen is particularly known for his work in applying data analysis to automated software development and mining software repositories, with significant contributions in mobile application testing, GUI testing, and leveraging Large Language Models for software engineering tasks. His recent publications (2024-2026) demonstrate a strong focus on integrating Large Language Models with software testing methodologies, particularly for mobile applications. The research trends show increasing sophistication in using multimodal LLMs, dynamic memory techniques, and retrieval augmentation to improve automated testing capabilities. Dr. Chen maintains an active presence in the software engineering research community, serving on program committees for major conferences including ASE, ICSE, and ESEC/FSE. He has also delivered keynote speeches on mobile application testing with Large Language Models, highlighting his thought leadership in this emerging area.

Mingwei Liuمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence
• AI4SE
• +۴ مورد دیگر

Dr. Mingwei Liu is an Associate Professor at the School of Software Engineering, Sun Yat-Sen University, China. Previously, he completed postdoctoral research at Fudan University in 2024 and received both his Ph.D. (2022) and B.E. (2017) in Software Engineering from Fudan University under the mentorship of Xin Peng. Dr. Liu's research focuses on the intersection of Software Engineering (SE) and Artificial Intelligence (AI), specifically in the domains of AI4SE (AI for Software Engineering) and SE4AI (Software Engineering for AI). His primary interest lies in leveraging advanced AI technologies, particularly large language models (LLMs) and knowledge graphs (KGs), to address complex software engineering challenges and tackle system engineering problems in AI applications. His research portfolio demonstrates a strong focus on practical applications of AI in software development, with particular emphasis on code generation, vulnerability detection, test generation, and knowledge representation for software artifacts. Through his work, Dr. Liu has contributed significantly to understanding how LLMs perform in realistic software development scenarios beyond simple function-level code generation. Dr. Liu has published over twenty papers in top international journals and conferences including TSE, TOSEM, ICSE, FSE, and ASE. His research has been recognized with prestigious awards including the IEEE TCSE Distinguished Paper Award (ICSME 2018) and the ACM SIGSOFT Distinguished Paper Award (FSE 2023). IEEE TCSE Distinguished Paper Award (ICSME 2018) ACM SIGSOFT Distinguished Paper Award (FSE 2023) Dr. Liu actively contributes to the academic community as a program committee member for major conferences including ASE, ESEC/FSE, ICSME, and SANER. He leads the SYSUSELab research group, which has developed notable resources including the ClassEval benchmark for evaluating LLMs on class-level code generation tasks.