Machine Learning Accelerators Professors and Supervisors in Germany

Shangwen WangView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Shangwen Wang is an Assistant Professor in the School of Computer Science at National University of Defense Technology (NUDT) in Changsha, China. He earned his Bachelor's degree in June 2017, Master's degree in December 2019, and Ph.D. in December 2023, all from NUDT. During his graduate studies, he was supervised by Professor Xiaoguang Mao. From May 2022 to July 2023, he was a visiting student at Southern University of Science and Technology under Professor Yepang Liu. His educational background includes: Ph.D. in Software Engineering, NUDT (2020.3-2023.12), supervised by Prof. Xiaoguang Mao Visiting Scholar, SUSTech (2022.5-2023.7), supervised by Prof. Yepang Liu M.A. in Software Engineering, NUDT (2017.9-2019.12), supervised by Prof. Xiaoguang Mao B.A. in Software Engineering, NUDT (2013.9-2017.6) Wang's research focuses on program repair, program comprehension, mining software repositories, software maintenance and evolution, software testing, and AI for Software Engineering. His work bridges traditional software engineering techniques with modern AI approaches, particularly leveraging large language models for various software engineering tasks. He has made significant contributions to automated program repair, fault localization, vulnerability detection, and code generation. His research demonstrates a strong emphasis on empirical validation and practical applicability to real-world software development challenges. His recent publications show a clear trend toward integrating large language models with traditional software engineering tasks. The 15 most recent articles reveal a focus on applying LLMs to program repair, fault localization, vulnerability detection, and code generation, while maintaining strong empirical foundations. His work spans both theoretical advancements and practical tool development, with applications in software security, testing, and maintenance. His notable achievements include: CCF Outstanding Doctoral Dissertation (CCF优博) 2024 Outstanding Doctoral Graduates, NUDT, 2023 Multiple distinguished paper awards including ACM SIGSOFT Distinguished Paper Award (ISSTA'24) and IEEE TCSE Distinguished Paper Awards (ICSME'22, SANER'22) Prestigious scholarships from NUDT throughout his academic career As an active member of the software engineering community, Wang serves on numerous program committees for top conferences including ICSE, ASE, ESEC/FSE, and ISSTA. He has also contributed to teaching as a teaching assistant for courses such as Compiler, Python Programming, Discrete Mathematics, and C++ Programming. His research group appears to be actively mentoring students, as evidenced by his role as corresponding author on multiple student-led publications. Wang maintains an active research presence with collaborations across multiple institutions in China. His work demonstrates a clear trajectory from traditional program analysis techniques toward integrating cutting-edge AI approaches, particularly large language models, into software engineering practices.

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Masud RahmanView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.

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Michael HiltonView profile

Teaching Professor

Max Planck Institute for Security and PrivacyGermany

Michael Hilton is an Associate Teaching Professor at Carnegie Mellon University's School of Computer Science, where he serves as the Associate Department Head for Education in the Software and Societal Systems Department. He directs the Software Engineering Minor and the Software Engineering Concentration programs, and teaches various software engineering courses including Foundations of Software Engineering, Introduction to Software Construction, and Crafting Software. His research interests focus on improving the lives of software engineers, with particular emphasis on flaky tests , continuous integration , and structured editors . His work bridges both the research and educational aspects of software engineering, aiming to make software development processes more reliable and efficient while educating the next generation of software engineers. Analysis of his recent publications reveals a strong focus on flaky test detection and mitigation, with significant contributions to understanding test reliability in continuous integration environments. His research spans empirical studies of testing practices, machine learning approaches to flakiness prediction, and educational aspects of software engineering. A notable trend is his increasing focus on the intersection of software testing and education, particularly examining how students interact with modern development tools and practices. Professor Hilton is highly active in the software engineering research community, serving on program committees for major conferences including ASE, ICSE, and SIGCSE. He has contributed to numerous workshops and symposia focused on software engineering education and testing research. As an educator, he has developed and taught multiple software engineering courses covering object-oriented design, testing, concurrency, and software construction principles. He emphasizes practical, hands-on learning experiences that prepare students for real-world software development challenges. His educational research explores best practices for teaching software engineering and the impact of new technologies like generative AI on software engineering education. He maintains an active research lab focused on software engineering tools and education, collaborating with both academic and industry partners to address practical challenges in software development workflows. His current projects include investigations into flaky test patterns, continuous integration practices, and the integration of modern development tools into software engineering curricula.

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Ajitha RajanView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Ajitha Rajan is a Professor (Personal Chair of Software Testing & Verification) at the School of Informatics, University of Edinburgh. She joined the university in December 2012 as a Reader (equivalent to Associate Professor in American terms) and was promoted to Professor in 2024. Prior to her position at Edinburgh, she held postdoctoral positions at Oxford University and Laboratoire d'Informatique de Grenoble in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under Professor Mats Heimdahl. Her research focuses on two primary directions: Automated Software Testing (including test input generation, test oracles, and coverage measurement) and Biomedical AI (particularly cancer survival models and interpretability for biological sequences and medical images). Her work has applications in safety-critical systems, blockchains, embedded systems, and medical diagnostics. She has made significant contributions to explainable AI for healthcare applications, especially in lung cancer detection and cancer survival analysis. Her recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of software engineering and biomedical applications. She has numerous publications in top venues including ICSE, ASE, and healthcare-focused conferences. Her work increasingly focuses on making AI systems more interpretable and trustworthy, particularly in medical contexts where model decisions can have life-or-death consequences. ACM SIGSOFT Distinguished Reviewer Award, ISSTA 2025 Best Paper Award at ICHI 2025 Promoted to Professor (Chair in Software Testing & Verification) 2024 SICSA Best Supervisor Award 2024 ACM Distinguished Paper Award 2008 Professor Rajan actively supervises PhD students in both software testing and biomedical AI domains. She leads several funded projects including MANIFEST (a cancer immunotherapy response research platform), a Huawei Joint Lab project on RobustCheck, a Royal Society Industry Fellowship on AutoTest, and the KATY project on clinical knowledge for personalized medicine. Her research group includes current PhD students working on explainable AI for medical image analysis, scenario-based testing for autonomous driving, and protein design applications.

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Wolfgang KratschView profile

Research Professor

Frankfurt University of Applied SciencesGermany

Prof. Dr. Wolfgang Kratsch serves as Research Professor for Applied AI at Augsburg University of Applied Sciences, Director of the FIM Research Institute for Information Management, and holds a leading position in Fraunhofer FIT's Business Information Systems division. He co-founded and manages the Center for Process Intelligence, driving industry-academia collaboration in digital transformation. His educational background includes B.Sc. and M.Sc. in Business Informatics from the University of Augsburg (2017), followed by a summa cum laude doctorate in data-driven management of process networks from the University of Bayreuth (2020). University of Augsburg: B.Sc./M.Sc. Business Informatics (2017) University of Bayreuth: PhD in Data-Driven Process Network Management (2020) Dr. Kratsch's research centers on data-driven process management , focusing on data extraction, quality assurance, and AI-driven context-sensitive process optimization. His methodology emphasizes design science research yielding prototype implementations for immediate practical use. Key domains include process mining, robotic process automation, and generative AI integration in business workflows, with strong industry applicability. Core Methodology: Design Science Research Technical Focus: Event Log Generation, Object-Centric Process Mining Application Areas: Manufacturing, Healthcare, Transportation His publication trajectory (2021–2025) reveals accelerating integration of generative AI with process mining , particularly in unstructured data extraction (text/video) and automated process improvement. Recent works emphasize practical industry solutions in manufacturing error analysis, airport operations, and medical monitoring, demonstrating consistent collaboration with industrial partners like Munich Airport. No scientific awards were explicitly mentioned in the source material. Dr. Kratsch actively contributes to academia through teaching at Augsburg and Bayreuth Universities, industry project leadership, and startup mentorship. His spin-off credium GmbH (founded 2020) built a 15-person AI/data science team, reflecting his entrepreneurial approach to translating research into market solutions. Current projects prioritize practical AI deployment in serial production and process intelligence systems. Teaching: Lectures/seminars at Augsburg & Bayreuth Universities Startup Experience: credium GmbH (Data Science/AI focus) Industry Projects: Manufacturing optimization, airport operations He leads the FIM Research Institute and Center for Process Intelligence, directing multidisciplinary teams in developing process mining prototypes. His labs focus on bridging academic research with industrial deployment, particularly in video-based process monitoring and generative AI for business process design.

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Franziska UhingView profile

Professor

Kiel University of Applied SciencesGermany

Prof. Dr. Franziska Uhing serves as a Professor for Interactive Media at Kiel University of Applied Sciences, where she leads the Bachelor's program in Multimedia Production. Her teaching responsibilities encompass Dynamic Website Design, Media Use and Reception Research, Interactive Applications, and Intrapreneurship and Innovation Management. Her research focuses on seven primary areas: Information Systems : Design and implementation of information management infrastructures Interaction Design : User-centered design principles for digital interfaces Media Didactics : Educational applications of media technologies Social Entrepreneurship : Business models addressing societal challenges AI-supported Image Analysis : Medical diagnostics acceleration through machine learning User Experience : Human-robot interaction in surgical environments Information Visualization : Creative representation of complex data structures Prof. Uhing supervises Bachelor's theses with current projects including "Information visualization of study course content" (framed as "The study regulations as an Insta channel"), "Influence of AI-controlled analysis software on acute care of stroke patients" (measuring diagnosis speed variations), and collaborative research with Fraunhofer IMTE on user experience in robot-assisted surgery. She mandates thesis seminar attendance and certificate presentation for supervision eligibility. Office hours occur Fridays 09:30-10:00 (by appointment with advance registration) and thesis seminars Fridays 10:00-11:00. Contact via telephone +49 431 210-4514 or mobile 0175/1738336.

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