Find Academic Supervisors

Lena Maier-HeinView profile

Professor

• Machine Learning
• Biomedical Image Analysis
• Surgical Data Science
• +4 more

Lena Maier-Hein serves as one of Helmholtz Imaging's center coordinators at the German Cancer Research Center (DKFZ), head of the Intelligent Medical Systems division, managing director of the Data Science and Digital Oncology cross-topic program, and is a full professor at Heidelberg University. She also holds the position of managing director at the National Center for Tumor Diseases (NCT) Heidelberg. Her research focuses on machine learning-based biomedical image analysis with specific emphasis on surgical data science, computational biophotonics, and validation of machine learning algorithms. As a fellow of both the Medical Image Computing and Computer Assisted Intervention (MICCAI) society and the European Laboratory for Learning and Intelligent Systems (ELLIS), she leads significant initiatives including presidency of the MICCAI special interest group on challenges and chairing the international surgical data science initiative. Her publication portfolio demonstrates strong focus on algorithm validation, surgical data science, and medical imaging AI, with recent work addressing critical issues in reproducibility, bias in medical AI, and practical clinical implementation. The articles reveal consistent themes around establishing rigorous validation frameworks, developing practical surgical AI tools, and addressing real-world deployment challenges in medical imaging. Her notable scientific recognition includes: 2013 Heinz Maier Leibnitz Award of the German Research Foundation (DFG) 2017/18 Berlin-Brandenburg Academy Prize European Research Council (ERC) starting grant (2015-2020) ERC consolidator grant (2021-2026) She serves on the editorial boards of prestigious journals including Nature Scientific Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, and Medical Image Analysis, demonstrating her leadership in establishing standards for medical imaging research. Her work bridges fundamental AI research with practical clinical applications, particularly in surgical settings where real-time decision support is critical.

Sandra ReitbrechtView profile

Professor

• German Linguistics
• German as a Foreign Language
• Pronunciation Didactics
• +9 more

Professor Sandra Reitbrecht is a W2 Professor of German Linguistics / German as a Foreign Language at the University of Bamberg since September 2025, where she leads research and teaching in her specialized field. Previously, she served as a University Lecturer at the University College of Teacher Education Vienna (2017-2025) and held research positions including Project Manager for the Sparkling Science Project 'AaMoL' and Research Associate in the FWF project on scientific text competence at the University of Graz. Her research focuses on pronunciation didactics in German as a foreign/second language, academic language competence development, text competence, and writing didactics. She has made significant contributions to understanding how reflective modeling can enhance academic writing skills in secondary education contexts. Her work bridges theoretical linguistics with practical classroom applications, particularly through the development of model videos and material-based writing tasks. Professor Reitbrecht's publication record shows a clear trend toward increasingly interdisciplinary work that combines writing studies, language education, and technology integration. Her recent publications (2023-2025) demonstrate growing interest in AI applications for language instruction, multilingual approaches to language teaching, and the transfer of writing strategies across educational contexts. Member of the International Committee of the International Association for Germanic Studies (since 07/2025) Vice President of the Austrian Association for German as a Foreign/Second Language (2017-2023) Member of the organizing committee for the 18th International Conference of German Teachers in Lübeck 2025 Member of the jury for the European Language Label 2021 and 2025 Review activities for numerous academic journals including InfoDaF and International Journal of Multilingualism As an educator, Professor Reitbrecht has coordinated the Eye-Tracking Research Group and developed professional development courses for teachers across multiple countries. Her commitment to science communication is evident through her role as a Young Science Ambassador and her participation in public outreach activities including radio interviews and workshops at the Children's University of Vienna.

Nadja WulffView profile

Professor

• German as a Second Language
• German as a Foreign Language
• Multilingualism
• +6 more

Professor Nadja Wulff is a prominent scholar in German language education at the University of Education Freiburg, where she serves as Head of the University Certificate in German as a Second Language and the University Certificate in Holistic Education. Her extensive research portfolio spans vocabulary acquisition, multilingualism, language-sensitive subject teaching, and the emerging intersection of artificial intelligence and language education. Her research interests focus on critical areas in contemporary language education, including language in subject and professional training , vocabulary acquisition and teaching in German as a Second/Foreign Language , multilingualism and language comparison in classrooms , and scientific writing with AI integration . Professor Wulff approaches these topics through both theoretical frameworks and practical applications, developing innovative teaching methods and assessment tools. Professor Wulff's recent publications demonstrate a clear trend toward examining the digital transformation of language education, with particular attention to AI's role in academic writing. Her 2024-2025 work investigates how students from different countries use AI tools, the challenges they encounter, and regional differences in adoption patterns. This research has significant implications for maintaining academic integrity while leveraging technological advances. Professor Wulff leads multiple externally funded research initiatives, including: DAAD-funded University Certificate in German as a Second Language (2024-2028) Joachim Herz Foundation projects on digital scenario didactics (2025-2026) and school garden language promotion (2023-2025) The Promotionskolleg DaF/DaZ doctoral program (2017-2024) The 'Integra' project preparing refugees for academic and professional life (2020-2024) Her supervisory approach emphasizes practical application of research, with students engaged in developing educational materials, conducting empirical studies, and creating digital learning resources. Professor Wulff's collaborative work with colleagues across disciplines—particularly in physics education—demonstrates her commitment to interdisciplinary approaches to language-sensitive teaching.

Robert HaaseView profile

Lecturer

• Bioimage Analysis
• Medical Informatics
• GPU-Accelerated Image Processing
• +6 more

Dr. Robert Haase is a Lecturer and Training Coordinator at the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) under Leipzig University , with prior leadership roles at the DFG Cluster of Excellence 'Physics of Life' at TU Dresden . He specializes in Bioimage Analysis , GPU-Accelerated Image Processing , and Large Language Models (LLMs) for life sciences. His research focuses on democratizing bioimage analysis through open-source tools like CLIJ , clesperanto , and bia-bob , aiming to bridge microscopy with data science . Recent projects explore LLM-driven code generation for image analysis and interactive workflow design in platforms like napari . He leads initiatives such as the NFDI4BioImage consortium for research data management in Germany and GloBIAS , a global society for bioimage analysts. Funded by organizations including the Chan Zuckerberg Initiative (CZI) and DFG , his work emphasizes reproducibility , open science , and interdisciplinary collaboration in bioimaging. As an educator, he conducts training programs like "Large Language Models for Bioimage Analysis" and "Collaborative Working with Git" , and contributes to workshops at institutions such as EMBO , Institut Pasteur , and ScaDS.AI Summer Schools .

Stefan LessmannView profile

Professor

• Predictive Analytics
• Machine Learning
• Credit Risk Modeling
• +7 more

Stefan Lessmann is a Professor of Information Systems at the School of Business and Economics, Humboldt-University of Berlin. He completed his habilitation in predictive analytics (2012) and has served as a guest lecturer at the University of Southampton's School of Management since 2008. His academic journey began with a diploma (2002) and PhD (2007) in business administration from the University of Hamburg. Research interests: Stefan focuses on empirical prediction models to support managerial decision-making, particularly in finance, marketing, and risk management. His work spans Deep learning architectures Credit risk modeling Uplift modeling Time series forecasting Big data analytics Sentiment analysis Professional activities: He has led corporate research projects with organizations like DMC GmbH and Star Financial Systems Ltd., addressing challenges in marketing ROI, cryptocurrency forecasting, and financial risk modeling. Stefan serves as Associate Editor for Business Information Systems Engineering and has peer-reviewed for journals like European Journal of Operational Research and IEEE Transactions on Software Engineering .

Klim ZaporojetsView profile

Research Fellow

• Natural Language Processing
• Information Extraction
• Entity Linking
• +3 more

Klim Zaporojets is a Marie Skłodowska-Curie Postdoctoral Fellow in the Department of Computer Science at Aarhus University, where he conducts research within the Data-Intensive Systems Group. His work bridges theoretical advancements and practical applications in natural language understanding. His research focuses on information extraction systems that connect textual content with structured knowledge bases. His methodology emphasizes leveraging external knowledge sources to enhance information extraction performance, particularly in document-level contexts where entities evolve over time. His work spans temporal relation extraction, entity linking, and biomedical text mining applications. The publication record reveals a strong focus on document-level information extraction with increasing emphasis on temporal aspects and knowledge integration. Recent work explores large language model applications for graph learning and calibration challenges in LLMs, showing evolution from traditional NLP tasks to cutting-edge foundation model research. His publications appear in top-tier venues including ACL, EMNLP, CIKM, and NeurIPS. His scientific recognition includes the prestigious Marie Skłodowska-Curie Postdoctoral Fellowship, supporting his research at Aarhus University. His work has produced several influential datasets including DWIE, TempEL, and BioDEX that have become benchmarks in document-level information extraction. Zaporojets maintains active collaborations with researchers at Ghent University (evidenced by his ugent.be email address) and has contributed to multiple interdisciplinary projects spanning computational linguistics, healthcare informatics, and knowledge representation. His technical contributions include open-source implementations of his research, demonstrating commitment to reproducible science.

Wing-Kwong ChanView profile

Associate Professor

• Software Engineering
• Deep Learning
• Blockchain Systems
• +4 more

Wing-Kwong Chan is an Associate Professor in the Department of Computer Science at City University of Hong Kong. With a background that includes industry experience as a software engineer, Dr. Chan returned to academia and has established himself as a leading researcher in software engineering with a focus on emerging technologies. Dr. Chan received his BEng, MPhil, and PhD all from The University of Hong Kong. His academic journey began with a hardware-oriented Computer Engineering degree before shifting to software engineering for his graduate studies. His research interests center on software engineering, particularly the technical aspects interfacing with machine learning, blockchain, and GPU technologies. He addresses challenges in program analysis and concurrency, with recent work focusing on deep learning model verification and robustness. His publications span top venues including TOSEM, TSE, ICSE, ESEC/FSE, and ASE. Dr. Chan's recent publications demonstrate a strong trend toward integrating software engineering principles with deep learning systems, particularly in verification, testing, and robustness of AI models. His work bridges theoretical software engineering concepts with practical applications in emerging technologies. Best Paper Award from COMPSAC'04 Best Paper Award from COMPSAC'08 Best Paper Award from COMPSAC'10 Best Paper Award from QSIC'11 Best Paper Award from QRS'16 Best Paper Award from ISET'18 CityU President's Award 2017 Dr. Chan has successfully advised numerous PhD and MPhil students, with alumni dating back to 2006. He has secured substantial research funding through multiple Hong Kong Research Grants Council projects, ITF grants, and international collaborations. His current research focuses on patch robustness certification for deep learning models, reflecting his ongoing commitment to advancing software engineering practices for emerging technologies. As Program Leader for the MSc in E-Commerce program from the CS Department, Dr. Chan also contributes significantly to academic administration and curriculum development at City University of Hong Kong.

• Natural Language Processing
• Machine Learning
• Deep Learning
• +5 more

Mary Ann Tan is a PhD student and Junior Researcher at Karlsruhe Institute of Technology (KIT) and FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, working within the Information Service Engineering group and the Institute of Applied Informatics and Formal Description Methods (AIFB). Her academic background includes: PhD candidate at KIT/FIZ Karlsruhe (2020–present) MSc in Computational Linguistics, Ludwig-Maximilians University, Munich (2018–2020) MSc in Computer Science (NLP specialization), De La Salle University, Manila (2002–2004) BSc in Computer Science, De La Salle University, Manila (1997–2001) Tan's research integrates Natural Language Processing, Knowledge Graphs, and Deep Learning to solve challenges in Cultural Heritage digitization. She develops methods for cross-lingual embeddings, knowledge graph refinement, multimodal search, and transformer-based workflows – transforming legacy cultural data into structured, AI-processable formats. Her work bridges technical AI innovation with practical heritage preservation needs, emphasizing under-resourced languages and multimodal cultural artifacts. Her 11 publications (2021-2025) reveal a cohesive trajectory: starting with bibliographic knowledge graphs (2021), advancing to multimodal art search and audio ontologies (2022-2023), and recently focusing on LLM integration for cultural data and mathematical semantics (2024-2025). This progression demonstrates increasing technical sophistication while maintaining consistent application to cultural heritage challenges. As an active collaborator in large international projects (e.g., the 50+ author Semantic Web and Creative AI report), Tan contributes to team-based research while developing her independent expertise. Her industry experience in software engineering informs her practical approach to research implementation within the Information Service Engineering group.

Carolin StraßmannView profile

Lecturer

• Human-Computer Interaction
• Virtual Agents and Social Robots
• Long-term AI Interactions
• +2 more

Dr. Carolin Straßmann is a Lecturer for Special Tasks at the Institute of Computer Science, Ruhr West University of Applied Sciences. She holds a PhD from the University of Duisburg-Essen where she previously served as a Research Assistant at the Chair of Social Psychology: Media and Communication. Concurrently, she participates in an industry placement at celano GmbH via the state program ‘Career Paths FH Professorship’. Her research centers on optimizing human-technology interaction, with emphasis on: Social effects of virtual agents and social robots Long-term AI-based interactions Nonverbal behavior and appearance dynamics Persuasive and positive impacts of innovative technologies She received notable scientific recognition including: Best Poster Award, Media Psychology Conference (2019) Best Student Paper Award, International Communication Association (2018) At her Human Factors & Gender Studies lab, she utilizes AR/VR systems, social robots, and behavioral research tools. She teaches courses in Applied Statistics, Usability Engineering, and Media Psychology, while supervising student projects.

Yintong HuoView profile

Assistant Professor

• Log analysis
• Code intelligence
• Intelligent software engineering
• +7 more

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.

Jie LiuView profile

Professor

• Big Data Intelligent Analysis
• Machine Learning Systems
• Intelligent Software Engineering
• +3 more

Jie Liu is a Researcher at the Institute of Software, Chinese Academy of Sciences and a Professor and Doctoral Supervisor at University of Chinese Academy of Sciences. He is also a Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences and an Executive Committee Member of the System Software Committee of the CCF Computer Society. His research is conducted within the Software Engineering Technology R&D Center. Dr. Liu received his Ph.D. from the University of Science and Technology of China in 2011 and his B.A. from the same institution in 2004. He has progressed through the ranks at the Institute of Software, CAS, starting as an Assistant Research Fellow (2011-2014), then Associate Research Fellow (2014-2024), and currently as a Researcher (since 2024). His research spans Big Data Intelligent Analysis Models and Systems at the intersection of AI, Software Engineering, and System Software. Specifically, his work covers three main areas: Big Data and Machine Learning Systems (statistics and AI algorithm model libraries, data quantitative analysis tools, LLM reasoning optimization, Earth Big Data); Intelligent Software Engineering (code model constraint decoding, data science agents, system log analysis agents); and Knowledge-Enhanced Intelligent Model Construction (knowledge extraction, knowledge graphs, domain AI model design). His research has resulted in innovative approaches to handling complex data analysis challenges across multiple domains. Dr. Liu's research has produced significant outcomes including EarthDataMiner, which supports SDG indicator calculations and won the 2024 Beijing Municipal Science and Technology Progress First Prize. His work on RISC-V software migration technology has been integrated into the Ruiqian tool (https://rvpt.top/), demonstrating practical applications of his research in emerging computing architectures. Beijing Science and Technology Progress Award, First Prize, 2024 2023 Surveying and Mapping Science and Technology Award, Special Prize, 2023 DASFAA Best Paper Runner-up, Second Prize, 2013 Dr. Liu has successfully guided numerous graduate students who have secured positions at major technology companies including Alibaba, ByteDance, Southern Power Grid, and Agricultural Bank of China. He has secured funding through multiple National Natural Science Foundation projects, National Key R&D Program projects, and over ten other research initiatives. His research collaborations span industry leaders like Huawei, JD.com, and TravelSky, as well as academic institutions within the Chinese Academy of Sciences. He teaches graduate courses such as 'Machine Learning Systems' and 'Cloud Computing and Big Data Technology' at University of Chinese Academy of Sciences, and has established a research group focused on developing innovative solutions at the intersection of AI and software engineering with real-world applications in earth sciences, healthcare, and intelligent systems.

Erhan GençView profile

Professor

• Neuroscience
• Differential Psychology
• Cognitive Functions
• +5 more

Prof. Dr. Erhan Genç serves as a University Professor (W2) for 'Neuroscientific Foundations of Differential Psychology' at TU Dortmund University since 2023, operating under the Jülich model for research activities at IfADo (Leibniz Research Centre for Working Environment and Human Factors). He leads the 'Neuroimaging and Interindividual Differences' research group at the Institute for Occupational Safety and Health of the German Social Accident Insurance in Dortmund, Germany. His primary research focuses on: Neural basis of interindividual differences in cognitive functions and personality Application of multimodal neuroimaging methods (7T, 3T EEG/fMRI, TMS/fMRI, DWI, neurogenetics) Genç's research program investigates how variations in brain structure and function correlate with differences in cognitive abilities and personality traits. His work employs advanced neuroimaging techniques to uncover neural mechanisms underlying human intelligence and cognitive control processes, with particular emphasis on structural and functional brain connectivity. His publication record demonstrates consistent contributions to understanding the relationship between brain microstructure (particularly white matter organization and neurite architecture), brain network properties, and cognitive abilities. His recent work has established important links between polygenic scores, brain network efficiency, and intelligence measures. Methodologically, his expertise spans diffusion MRI for assessing microstructural properties of brain tissue, functional MRI for investigating brain connectivity patterns, and multimodal integration of different neuroimaging techniques to provide comprehensive insights into brain-behavior relationships.

John GrundyView profile

Professor

• Software Engineering
• Model-Driven Engineering
• Human-Centric Software Engineering
• +5 more

John Grundy is a Professor of Software Engineering and Senior Deputy Dean at Monash University's Faculty of Information Technology in Melbourne, Australia. He is also an Australian Laureate Fellow (2020-2026) and leads the "Human-centric Software Engineering" (HumaniSE) research lab. With over 32 years of academic experience, Professor Grundy has held numerous leadership positions including Pro Vice-Chancellor at Deakin University and Dean roles at Swinburne University and the University of Auckland. BSc(Hons), MSc, PhD and DSc degrees in Computer Science from the University of Auckland IEEE Fellow, Fellow of Automated Software Engineering, Fellow of Engineers Australia Lero Parnas Fellow (2023) Recipient of the ACM SIGSOFT Distinguished Service Award (2023) and Dean's Award for Graduate Research Student Supervision (2024) Professor Grundy's research focuses on making "Software Engineering more like traditional Engineering disciplines" through human-centric visual modeling approaches. His primary research areas include model-driven engineering, software architecture, visual languages, software security engineering, and human factors in software development. He specifically investigates how personality, emotions, gender, age, and disability impact software usage, requirements engineering, design, and testing. His current projects include the Visual Wiki platform for knowledge engineering, Marama meta-tools, and Software Process and Product Improvement initiatives. His research has significant implications for accessibility, usability, and the alignment of software applications with diverse user needs. Professor Grundy has published extensively in top software engineering venues and has supervised numerous PhD students throughout his career. IEEE Technical Council on Software Engineering Distinguished Education Award (2014) ACM SIGSOFT Distinguished Service Award (2023) CORE Distinguished Service Award (2023) Lero Parnas Fellow (2023) Dean's Award for Graduate Research Student Supervision (2024) Professor Grundy has supervised numerous PhD students and has received funding for various research projects, most notably his 5-year Australian Laureate Fellowship (2020-2026) focused on human-centric software engineering. His HumaniSE research lab brings together interdisciplinary teams to address challenges in making software systems more responsive to human needs and contexts. His lab focuses on developing new conceptual foundations and modeling techniques that incorporate human factors throughout the software development lifecycle, with applications in smart homes, digital health, and smart city solutions.

Huaming ChenView profile

Senior Lecturer

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

Dr. Huaming Chen is a Senior Lecturer in the School of Electrical and Computer Engineering at The University of Sydney, Australia. His work focuses on trustworthy machine learning systems, software engineering, and software security. With numerous publications in top-tier conferences and journals, Dr. Chen has established himself as a significant contributor to the fields of AI security and software engineering. Dr. Chen's primary research interests lie at the intersection of software engineering and artificial intelligence, with a strong emphasis on trustworthy AI systems. His work spans several key areas including: Software Security for AI-enabled systems Trustworthy and Responsible AI development Computational biology applications Industrial 4.0 implementations Federated learning and privacy-preserving techniques Large language model verification and uncertainty analysis His research addresses critical challenges in ensuring AI systems are secure, reliable, and ethically sound. Dr. Chen's recent publications demonstrate a strong trend toward addressing security and trustworthiness challenges in AI systems. His work spans multiple domains including software security (particularly for AI systems), trustworthy AI development, and applications in computational biology. A significant portion of his recent work focuses on large language models, examining their vulnerabilities, verification methods, and uncertainty analysis. He also maintains active research in federated learning, adversarial machine learning, and software security techniques. Dr. Chen has received several notable awards and recognitions: 2020 IEEE CIS Student Grant for IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2017 Student and Early Career Travel Fellowship for The 16th International Conference on Bioinformatics (InCoB 2017) 2017 Student Travel Award for 2017 IEEE World Congress on Services Dr. Chen actively supervises multiple research students working on cutting-edge projects related to trustworthy AI and software security. His current students are exploring topics ranging from blockchain-based governance frameworks to open-source AI security and digital twin platforms. He also serves in numerous committee roles at top conferences including area chair for ACM MM, and PC member for ACM CCS, IJCAI, KDD, and many others. His service as a Guest Editor for journals like Computers & Security and as a Grant Reviewer for UKRI demonstrates his standing in the research community. Dr. Chen organizes workshops focused on Trustworthy and Responsible AI, reflecting his commitment to advancing the field. His research group appears to focus on practical applications of AI security techniques, with projects spanning multiple domains including healthcare, finance, and industrial systems. He maintains active collaborations with researchers across multiple institutions, as evidenced by his co-authorship on diverse publications.

Yao WanView profile

Associate Professor

• Artificial Intelligence
• Software Engineering
• Natural Language Processing
• +3 more

Yao Wan is an Associate Professor at the School of Computer Science and Technology, Huazhong University of Science and Technology (HUST) in Wuhan, China. He leads the ONE Lab, focused on empowering machines to interact with the physical world through unified natural language interfaces (Language + X paradigm). He obtained his Ph.D. from Zhejiang University and has research visiting experience at Chinese University of Hong Kong, University of Technology Sydney, and University of Illinois Chicago. His research bridges Artificial Intelligence and Software Engineering, with core interests in: Natural Language Processing for code intelligence Large Language Model applications Multimodal learning across code, vision, and UI domains Program analysis and code generation Software engineering automation His publications demonstrate strong focus on applying transformer-based models to software engineering challenges, with recent work expanding into multimodal applications. Research spans code model security, GUI generation, data visualization, and compiler understanding, predominantly using deep learning approaches. Awards: IEEE TCSE Distinguished Paper Award for SANER 2025 publication He actively mentors students through the ONE Lab and serves on program committees for top conferences including ASE, ISSTA, and ICSE. He is seeking highly-motivated undergraduate researchers to join his team. The ONE Lab conducts cutting-edge research at the intersection of programming languages and artificial intelligence, with ongoing projects in code intelligence, multimodal learning, and LLM applications for software engineering.