Find Academic Supervisors

• AI Security
• AI Safety & Alignment
• AI & Society
• +7 more

Sahar Abdelnabi is a Principal Investigator at the ELLIS Institute Tübingen and an independent research group leader at the Max-Planck Institute for Intelligent Systems and Tübingen AI Center. She leads the COMPASS (COoperative Machine intelligence for People-Aligned Safe Systems) research group, focusing on developing safe, aligned, and steerable AI agents with emphasis on security, human aspects, and cooperative multi-agent systems. Her research spans the intersection of AI with security, safety, and sociopolitical aspects: Understanding, probing, and evaluating the failure modes of AI models, their biases, emergent risks, and misuse scenarios Designing mitigations, system defenses, white-box control methods, and reasoning enhancements to counter such risks Leveraging AI agents for scientific discovery and advancing society Dr. Abdelnabi's research has identified critical vulnerabilities in AI systems, including being the first to identify, coin, and taxonomize the indirect prompt injection vulnerability in LLM-integrated applications (2023), and proposing watermarking for generative AI (2020). Her work has received significant recognition including a Best Paper Award at ACL2025 and AISec'23 workshop. Her scientific contributions have been widely recognized: Best Paper Award at ACL2025 for "A Theory of Response Sampling in LLMs" Best Paper Award at AISec'23 workshop for "Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection" Oral Paper presentation at ICCV 2021 Spotlight Paper at NeurIPS Datasets and Benchmarks 2024 Dr. Abdelnabi actively mentors students and researchers, hiring across all levels including interns, research visitors, PhD students, and postdocs. She has served on program committees for major AI and security conferences and has presented her work at numerous international venues. Her research has influenced policy and industry practices, with findings adopted by organizations including the German Federal Office for Information Security, NIST, and OWASP. The COMPASS research group provides a dynamic environment for exploring cutting-edge questions in AI safety and security, with opportunities for collaboration across institutions and disciplines.

Christian LehmannView profile

Professor

• Industrial Robotics
• Production Automation
• Manufacturing Technology
• +3 more

Professor Christian Lehmann is a faculty member at HTW Berlin in Department 2: Engineering Sciences - Technology and Life, where he serves as Professor and Laboratory Manager for the Robotics Laboratory. He teaches across multiple programs including Mechanical Engineering (Bachelor's), Computer Science in Engineering (Master's), and Industrial Engineering (Bachelor's). His research focuses on industrial robotics, production automation, and manufacturing technology, with particular emphasis on human-robot collaboration, compliant robotics, and practical implementations in manufacturing environments. Professor Lehmann leads the Robotics Laboratory at Wilhelminenhof Campus, which houses advanced robotic systems including KUKA and Fanuc robots, drone systems, and sophisticated testing equipment for human-robot interaction. Analysis of his publication record shows consistent contributions to industrial robotics, particularly in fault-tolerant programming, compliant robot applications, and manufacturing integration. His work bridges theoretical robotics with practical industrial implementation, with numerous publications appearing in IEEE journals and conference proceedings from 2013-2019. Active supervision of student research projects and theses Management of the Robotics Laboratory with state-of-the-art equipment Multiple ongoing research collaborations with industry partners Development of practical robotics solutions for manufacturing challenges Professor Lehmann's teaching portfolio includes Fundamentals of Robotics, Production Automation, Mechatronics, and Manufacturing Technology courses across multiple engineering programs. His laboratory-based approach provides students with hands-on experience in industrial robotics systems and practical problem-solving in manufacturing environments.

Hung Hoang Viet NguyenView profile

Researcher

• Computational Social Science
• Political Methodology
• Comparative Politics
• +7 more

Hung Hoang Viet Nguyen is a Researcher at the Deutsches Institut für Erwachsenenbildung (DIE) in the department of Organization and program planning , where he has worked since 2024. Concurrently, he is completing his Doctorate in Political Science at the Bremen International Graduate School of Social Sciences (2019–2025). Education: Doctorate of Philosophy in Political Science, Bremen International Graduate School of Social Sciences (2019–2025) Master of Arts/Social Sciences in European Studies, University of Flensburg/University of Southern Denmark (2016–2018, double degree) Bachelor in International Relations, Ho Chi Minh City University of Social Sciences and Humanities (2011–2015) Research Focus: Nguyen specializes in computational approaches to political science, including text analysis, NLP, and machine learning. His work examines party systems, welfare policies, and methodological innovations in social science research. Key domains include: Quantitative analysis of political representation and social policies Machine learning applications for party positioning Cross-national studies of pandemic policy feedback Reproducibility frameworks for social sciences Publications: His recent articles demonstrate strong emphasis on computational reproducibility, political heuristics, and pandemic policy analysis, frequently employing advanced statistical modeling and cross-national datasets.

Michael PradelView profile

Professor

• Software Engineering
• Program Analysis
• Bug Detection
• +5 more

Michael Pradel is a full professor in the Computer Science Department at the University of Stuttgart and faculty member at CISPA Helmholtz Center for Information Security (effective September 2025), where he leads the Software Lab. He is also affiliated with the International Max Planck Research School for Intelligent Systems and the Stuttgart ELLIS Unit, reflecting his interdisciplinary research approach. His research interests focus on software engineering, particularly program analysis, bug detection, and the application of machine learning to developer tools. Pradel's recent work increasingly explores LLM-based approaches for program repair, code analysis, and automated software development, as evidenced by projects like RepairAgent and ExecutionAgent. Pradel's publication record shows a clear trend toward integrating AI techniques with traditional software engineering methods, with recent papers focusing on LLM applications for program repair, change validation, and quantum software analysis. His work bridges theoretical foundations with practical tool development, as seen in frameworks like DyLin for Python analysis and LintQ for quantum programs. Ernst-Denert Software Engineering Award Emmy Noether grant (1.3 million Euro) by the DFG ERC Starting Grant (1.5 million Euro) Multiple ACM SIGSOFT Distinguished Paper Awards ACM Distinguished Member recognition Pradel actively mentors PhD students, with recent graduates including Matteo (specializing in quantum software) and Luca (focusing on software evolution). His group has received significant funding and maintains strong industry connections, including past sabbaticals at Facebook. He serves in leadership roles for major conferences, including PC co-chair for FSE 2027, demonstrating his standing in the software engineering community. The Software Lab maintains active collaborations with institutions worldwide, including CMU, Google, KAIST, and several European universities.

• Radiation Oncology
• Medical Physics
• Brachytherapy
• +5 more

Dae Yup Han, Ph.D., DABR is an Assistant Professor in the Department of Therapeutic Radiology at Yale School of Medicine and a faculty member of the Yale Medical Physics Residency Program. He is board-certified in Therapeutic Medical Physics by the American Board of Radiology and is affiliated with the Yale Cancer Center and the DNA Damage and Genome Integrity Program. Assistant Professor of Therapeutic Radiology, Yale School of Medicine Faculty member, Yale Medical Physics Residency Program Member, Yale Cancer Center Member, DNA Damage and Genome Integrity Program Member, Center for Immuno-Oncology Dr. Han's research focuses on the development and clinical translation of technologies that integrate imaging, computation, and radiation delivery. His work spans multi-modality image-guided target tracking systems for adaptive radiotherapy, intravascular brachytherapy treatment planning systems, and novel brachytherapy applicators with directional dose modulation. His research interests include Automation, Brachytherapy, Image Processing, Machine Learning, and various forms of Radiotherapy. Analysis of Dr. Han's publications reveals a consistent focus on direction-modulated brachytherapy technologies, particularly for cervical and breast cancers, with recent work expanding into dosimetry quality assurance and proton therapy film response. His publications demonstrate a trajectory from theoretical design to clinical implementation, with increasing emphasis on practical applications in radiation therapy. Board-certified in Therapeutic Medical Physics by the American Board of Radiology (2021) Dr. Han serves as an Associate Editor for Medical Physics journal and as a reviewer for several prestigious journals. He actively collaborates with academic, clinical, and industry partners to advance innovation in radiation oncology. His work represents a cross-disciplinary approach to advancing radiation oncology through engineering, physics, and medical expertise. Dr. Han's laboratory work focuses on developing and implementing advanced radiation therapy technologies, with particular emphasis on brachytherapy innovations and image-guided radiation delivery systems that can be translated to clinical practice.

Jens BechtholdView profile

Professor

• Mechanical Engineering
• Automation Technology
• Automotive Systems
• +5 more

Prof. Dr.-Ing. Jens Bechthold is a Professor in the Department of Mechanical Engineering and Automation Technology at South Westphalia University of Applied Sciences (Soest campus), a position he has held since 2010. He chairs the Mechanical Engineering Examination Committee and maintains industrial collaborations through past roles at Winergy AG and Ipsen International GmbH. His affiliations include: Full-time Professor at South Westphalia University (2010-present) Former Development Engineer at Ipsen International GmbH (2007-2010) Former Calculation Engineer at Winergy AG (2006-2007) His research spans mechanical engineering , automation , and sustainable technology , with specialized focus areas: Electric mobility and automotive system design Experimental validation of simulation models Wind turbine gearbox dynamics and vibration analysis Additive manufacturing/3D printing applications He directs the Laboratory for Design Engineering and 3D Printing Lab , facilitating hands-on student projects. He actively supports the R4 Team Westfalen, a student group competing in humanitarian rally events. His 13 publications demonstrate consistent output in mechanical systems validation, CAD education, and renewable energy technology, with recent work exploring electric vehicle innovation. Professor Bechthold teaches courses including Machine Elements, Automotive Technology, Fatigue Strength, and Design for 3D Printing. He holds a European patent for industrial furnace gas processing systems.

Andreas WübbekeView profile

Professor

• Software Engineering
• Agile Development Models
• Digitalization in Agriculture
• +2 more

Prof. Dr. Andreas Wübbeke is a Professor of Software Engineering at the Faculty of Electrical Engineering, University of Applied Science Southern Westphalia (since 2022). With extensive industry experience in tech leadership roles at CLAAS E-Systems and Wincor Nixdorf, his research bridges academic rigor and practical applications. Research Focus: His work spans Agile methodologies, VR application testing, digital agriculture, and cloud systems. Recent projects include AI-driven classification of agricultural operations and sustainable energy solutions. He leads the South Westphalia Software Engineering Lab, mentoring thesis students through structured programs. Professional Engagement: Active in academic communities, he serves on committees for conferences (PROFES, GIL) and journals. His invited keynotes address topics like smart farming connectivity and agile transformations. Education & Background: PhD in Computer Science (Databases/Information Systems), University of Paderborn (2010) Business Computer Science degree, University of Paderborn (2007)

Jan-Hinrik SchmidtView profile

Research Fellow

• Social Media
• Digital Communication
• Political Communication
• +4 more

PD Dr. Jan-Hinrik Schmidt is a Senior Researcher for Digital Interactive Media and Political Communication at the Leibniz Institute for Media Research | Hans-Bredow-Institut (HBI) in Hamburg, where he has worked since November 2007. Since 2019, he has headed the Hamburg section of the Research Institute Social Cohesion (RISC), focusing on the role of media in social cohesion. He habilitated at Universität Hamburg in June 2020 with a thesis on 'Practices and Social Order of the Social Web.' His educational background includes sociology studies at Otto-Friedrich-University Bamberg and West Virginia University Morgantown, USA. His doctoral work focused on the institutionalization of local online offerings (2004), followed by postdoctoral research on online networking practices. Dr. Schmidt's research primarily examines social media's impact on information circulation, opinion formation, and science communication. His work explores how digital platforms transform public discourse, with particular interest in social cohesion, public service media, and the societal implications of algorithmic curation. Recent projects include the Social Media Observatory database and research on public perceptions of ZDF's educational function. His scholarly output shows a clear trajectory from early work on Web 2.0 developments toward contemporary research on AI in social media, fake news, and the contribution of public service media to social cohesion. The publications reveal an interdisciplinary approach combining communication sociology, political science, and media studies to understand digital transformation's societal impacts. At HBI, he contributes to multiple research initiatives including the Social Media Observatory, which provides systematic observation of the media-based public sphere, and projects examining public service media's integration-related functions. His work often involves collaborative research with colleagues like Uwe Hasebrink and Dieter Storll.

Yang LiuView profile

Professor

• Formal Methods
• Software Engineering
• Security
• +7 more

Yang Liu is a Full Professor and University Leadership Forum Chair at the School of Computer Science and Engineering, Nanyang Technological University (NTU) in Singapore. He serves as Programme Director for HP-NTU Digital Manufacturing Corp Lab, Deputy Director of the National Satellite of Excellence of Singapore, and Cluster Director in Cybersecurity at Energy Research Institute @NTU. His research spans Cybersecurity , Software Engineering , and Artificial Intelligence . He leads research in malware modeling and detection, vulnerability analysis using machine learning and program analysis, formal verification of security systems, program specification learning, performance analysis, Android system security, and AI security, robustness, fairness, and explainability. His notable work includes the Process Analysis Toolkit (PAT) for model checking and the Deep-Series tools for deep learning testing. Professor Liu has published extensively in top-tier conferences including ASE, ICSE, FSE, ISSTA, and S&P. His research demonstrates strong trends toward integrating AI/ML techniques with traditional software engineering and security approaches, particularly focusing on large language models for code analysis, vulnerability detection, and program repair. Recent publications show a growing emphasis on blockchain security, smart contract analysis, and addressing security challenges in AI systems. NRF Investigatorship (Class 2020) ACM's Distinguished Speaker Nanyang Research Award (Young Investigator) Microsoft Asia Research Fellowship 20 Year ICFEM Most Influential System Award for PAT Multiple ACM SIGSOFT Distinguished Paper Awards Professor Liu actively advises students and has seen notable student achievements, including Singapore Data Science Consortium research award winners and AISG PhD Fellowship recipients. His research is supported by numerous grants including a $900,000 NTU-NAP grant for Formal Verification on Cloud and a $471,000 grant for Vulnerability Detection in Binary Code. He leads the HP-NTU Digital Manufacturing Corp Lab and contributes to RollsRoyce@NTU Corporate Lab research on complex business systems simulation.

Xin ZhangView profile

Assistant Professor

• Programming Languages
• Software Engineering
• Program Analysis
• +6 more

Xin Zhang serves as an Assistant Professor in the Department of Computer Science and Technology within the School of Electronics Engineering and Computer Science at Peking University. His academic profile demonstrates deep engagement with programming languages and software engineering research communities through active participation in major conferences including ASE, SPLASH/OOPSLA, PLDI, and ICSE. Dr. Zhang's research focuses on the synergistic relationship between program analysis and machine learning. He investigates how ML/AI techniques can enhance traditional program analysis methods while simultaneously developing program analysis approaches to improve the interpretability, fairness, robustness, and safety of AI systems. His work spans probabilistic program analysis, abstraction refinement techniques, Bayesian modeling for program semantics, and applications of graph neural networks to static analysis problems. His publication record shows consistent contributions to top venues from 2016 through 2025, with recent work emphasizing Bayesian program analysis, abstraction refinement methods, and the intersection of formal methods with machine learning. The trajectory of his research demonstrates increasing sophistication in combining traditional program analysis techniques with modern AI approaches. Dr. Zhang actively contributes to the academic community as a program committee member for major conferences including ASE, SAS, PLDI, and SPLASH. His service includes reviewing, session chairing, and committee participation across multiple venues, reflecting his standing in the programming languages and software engineering communities.

Ali MesbahView profile

Professor

• Software Engineering
• AI-driven Software Analysis
• Software Testing
• +5 more

Ali Mesbah is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), where he leads the SALT lab. His research focuses on software engineering with emphasis on AI-driven software analysis, software testing, and software evolution. Previously, he was a Visiting Research Scientist at Google during 2017-2018. Dr. Mesbah received his BSc/MSc (2003) and PhD (2009) degree cum laude in Computer Science from the Delft University of Technology (TUDelft). After completing a postdoctoral fellowship with the Software Engineering Research Group at TUDelft and a Visiting Researcher position at Fujitsu Laboratories of America, he joined UBC in 2011. His research interests span software engineering with particular focus on AI-driven software analysis, software testing, software evolution, program comprehension, fault localization and repair. His work has significant applications in web application testing, JavaScript analysis, and automated program repair. He has pioneered techniques for testing modern web applications, analyzing JavaScript code, and leveraging AI for software maintenance tasks. His recent publications demonstrate a clear evolution toward integrating large language models with traditional program analysis techniques, focusing on test generation, bug repair, and understanding multi-hunk patches. His work bridges theoretical software engineering concepts with practical applications, particularly in web technologies and AI-assisted development. Amazon Research Award (2023) Killam Accelerator Research Fellowship (KARF) (2020) Killam Faculty Research Prize (2019) NSERC Discovery Accelerator (DAS) award (2016) ACM Distinguished Paper Awards at ICSE (2009, 2014) IEEE Distinguished Paper Award at ICST (2018) Best Paper Award at ESEM (2015) Best Paper Award at ICWE (2013) Dr. Mesbah has advised numerous PhD and MASc students, many of whom have gone on to positions at leading technology companies including Google, Amazon, Apple, Microsoft, and SAP. His research has been supported by various grants including the Amazon Research Award and NSERC funding. He leads the SALT lab at UBC, which focuses on software analysis, testing, and learning, with current research directions including AI-driven software engineering, web application testing, and program repair. The lab maintains active collaborations with industry partners and academic institutions worldwide.

Shing-Chi CheungView profile

Professor

• Software Engineering
• Program Analysis
• Software Testing
• +7 more

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.

Xin XiaView profile

Professor

• Software Engineering
• Mining Software Repositories
• Empirical Software Engineering
• +5 more

Xin Xia is a Qiushi Distinguished Professor at the College of Computer Science and Technology, Zhejiang University. Previously, he served as the Chief Expert and Director of the Software Engineering Application Technology Lab at Huawei Technologies, China from 2021 to 2025. His academic career spans software engineering research with a focus on AI applications in the field. Ph.D. from Zhejiang University (2014) Supervised by Prof. Xiaohu Yang and Prof. Jianling Sun Visiting student at Singapore Management University (2012-2014) under Prof. David Lo Xin Xia's research primarily focuses on applying data science techniques to software engineering problems. His work spans AI for Software Engineering, Mining Software Repositories, Empirical Software Engineering, and Large Language Models for code understanding and generation. He employs data mining, information retrieval, natural language processing, search-based algorithms, and program analysis to transform software engineering data into automated tools and insights. His recent publications show a strong trend toward leveraging Large Language Models for various software engineering tasks, including code generation, vulnerability detection, and test generation. He has been exploring how to make these models more effective, reliable, and practical for real-world software development scenarios, with a particular focus on Java and Python ecosystems. ACM SIGSOFT Early Career Researcher Award (2022) ACM Distinguished Member 16 best or distinguished paper awards, including nine ACM SIGSOFT Distinguished Paper Awards Recipient of the IEEE Transactions on Software Engineering 2021 Best Paper Award Runner-Up Xin Xia has advised numerous students who have gone on to publish in top software engineering venues. His research has been supported by grants from both academic institutions and industry partners, particularly during his time at Huawei. He actively collaborates with researchers worldwide, especially with David Lo at Singapore Management University. At Zhejiang University, Professor Xia leads research in the intersection of AI and Software Engineering. His work has practical applications in improving developer productivity through automated tools that analyze software repositories and provide actionable insights.

Hui LiuView profile

Professor

• Software Engineering
• AI-based Software Development
• LLM-based Program Generation
• +5 more

Hui Liu is a Professor in the School of Computer Science and Technology at Beijing Institute of Technology, where he leads research in AI-based software development with a focus on LLM applications. His work spans software refactoring, quality improvement, and maintenance, funded by the National Natural Science Foundation of China and the National Key Research and Development Program of China. PhD from Peking University (2008) Former graduate student at Software Engineering Institute, Peking University Distinguished member of China Computer Federation Secretary-General of CCF Technical Committee on Software Engineering Professor Liu's research centers on LLM-based program generation, evaluation and testing of large language models, software refactoring techniques, and automatic construction of software engineering datasets. His work bridges artificial intelligence and software engineering, with particular emphasis on improving code quality through empirical studies and machine learning techniques. Current projects include code contamination detection, context-aware naming recommendations, and refactoring validation using LLMs. His research has evolved from traditional code smell detection to cutting-edge applications of large language models in software development. Liu's publication record shows a strong trend toward LLM applications in software engineering, with recent work focusing on code review generation, commit message generation, and refactoring validation using large language models. His research combines empirical methods with machine learning approaches, often analyzing large code corpora from open-source projects. The work spans both theoretical foundations and practical tool development, with several contributions merged into Eclipse as part of the open-source community. ACM Distinguished Paper Award (ESEC/FSE 2023) ACM Distinguished Paper Award (ICSE 2022) RE'2021 Best Research Paper Award IET Software Premium Award (2018) New Century Excellent Talents in University (2013) Beijing Higher Education Young Elite Teacher (2013) Professor Liu actively mentors PhD and Master's students, with recent graduates including Waseem Akram (awarded Outstanding Graduate) and several students publishing at top venues. His research is supported by major Chinese funding agencies, and he serves on program committees for leading software engineering conferences including ASE, ICSE, and FSE. He maintains strong industry connections through contributions to Eclipse and studies of open-source ecosystems like Rust. Liu leads a research group focused on AI for software engineering, with active projects on code generation, refactoring, and quality improvement. The group collaborates extensively with international researchers and contributes directly to open-source tools, particularly in the Eclipse ecosystem where multiple refactoring improvements have been merged.

Zhou YangView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence
• AI for Software Engineering
• +3 more

Zhou Yang is an Assistant Professor at the University of Alberta and Fellow at the Alberta Machine Intelligence Institute (Amii), with research focusing on the intersection of software engineering and artificial intelligence. His academic journey includes a Ph.D. from Singapore Management University, an M.Sc. in Software System Engineering from University College London, and undergraduate studies at Yangzhou University. His research interests span Software Engineering, Artificial Intelligence, AI for Software Engineering (AI4SE), Software Engineering for AI (SE4AI), Large Language Models, and Cybersecurity. Yang's work explores how human and AI collaboration can improve code writing, how AI impacts open-source communities, and how developers build software in emerging environments like VR/AR. His recent publications demonstrate strong trends in code language models, with significant contributions to ASE, ICSE, ISSTA, and top journals like TOSEM and TSE. His research addresses critical challenges including token efficiency in code generation, user perception of AI coding assistants, privacy preservation in code models, and fairness in AI systems. 2024 IEEE Computer Society Best Paper Award (1 out of 183 submissions) ACM Distinguished Paper Award from ISSTA 2024 Distinguished Reviewer Award from Internetware 2024 SMU Research Staff Excellence Award (1 of 4 university-wide) SMU Presidential Fellowship Award SMU Dean's List Award 1st Place in ACM Student Research Competition at ICSE 2024 Yang actively mentors graduate students with regular one-on-one meetings, constructive feedback, and support for top-venue publications. He provides full funding through teaching assistantships and research grants, including travel support for conferences. His lab focuses on responsible research conduct and societal implications of AI work. Though early-career, he actively builds professional networks for students through collaborations and supports diverse career paths in academia and industry. He leads research in the Alberta Machine Intelligence Institute, focusing on practical applications where software engineering principles enhance AI systems and where AI techniques solve real-world software engineering challenges.