In-Database Analytics Professors and Supervisors in Germany

Salam TraboulsiView profile

Researcher

Stuttgart University of Applied SciencesGermany

Dr. Salam Traboulsi is a Researcher at Stuttgart University of Applied Sciences (HFT Stuttgart), affiliated with the Competence Center for Digitalization in Research, Teaching & Economics since 2019. She holds a PhD in Computer Science from the University of Toulouse, France (2008). Her work bridges technology and urban innovation, with a focus on developing scalable solutions for modern cities. Research Focus: Dr. Traboulsi specializes in: Smart City ecosystems integrating IoT and 5G Precision technologies for urban positioning and navigation Data management frameworks for large-scale sensor networks Open-source IoT platforms for building efficiency and environmental monitoring Cloud and grid computing infrastructures Key Projects: She leads/contributes to: iCity 2: UDigiT4iCity – Developing urban digital twins using IoT building data and 5G sensor networks iCity 1 – Foundational research on intelligent urban infrastructure systems Publication Trends: Her recent work (2020-2024) emphasizes 5G-enabled urban solutions, including fleet management optimization, indoor positioning systems, and IoT analytics for smart buildings. Earlier research (2005-2013) focused on distributed computing, storage virtualization, and information retrieval systems, demonstrating consistent expertise in large-scale data infrastructure. Academic Engagement: She serves as a scientific reviewer for journals and conferences and teaches in the surveying study area at HFT Stuttgart.

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Philipp BruneView profile

Professor

Neu-Ulm University of Applied SciencesGermany

Professor Dr. Philipp Brune serves as a prominent academic leader at Neu-Ulm University of Applied Sciences (HNU), holding multiple key positions including Academic Head of TTZ Günzburg, Head of the Master of Artificial Intelligence and Data Analytics program, Head of the Centre for Secure IT Applications and Infrastructures, and Head of the Institute of Agile Product and System Development within the Faculty of Information Management. His career demonstrates a notable transition from physics research (evident in his early 2000-2004 publications) to his current focus on cutting-edge information systems and artificial intelligence. Dr. Brune's research program spans several interconnected domains with particular emphasis on facial emotion recognition systems, legacy system modernization, and secure IT infrastructure development. His work investigates fundamental challenges in AI interpretability, data quality for emotion recognition, and practical approaches to modernizing legacy applications. He has made substantial contributions to understanding the limitations of current facial expression recognition technologies and developing more robust, trustworthy alternatives. Analysis of his recent publications reveals a clear research trajectory toward explainable and trustworthy AI systems, with increasing sophistication in addressing data quality issues and model interpretability challenges. His work bridges theoretical computer science with practical applications across healthcare, marketing, and smart city technologies, demonstrating strong translational research capabilities. As an educator, Dr. Brune has developed innovative pedagogical approaches specifically tailored for non-computer science majors, with research examining student motivation in programming courses and effective methods for teaching cybersecurity concepts. His leadership in the Master of Artificial Intelligence and Data Analytics program shapes curriculum development for next-generation AI professionals. Dr. Brune directs multiple research initiatives including the Centre for Secure IT Applications and Infrastructures, which addresses critical cybersecurity challenges in modern IT environments, and the Institute of Agile Product and System Development, which explores innovative software development methodologies. His collaborative research approach is evident through extensive co-authorship networks spanning multiple institutions and disciplines.

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Janina KneippView profile

Professor

Leibniz Institute for Crystal GrowthGermany

Prof. Dr. Janina Kneipp is a Professor (W3) of Physical Chemistry at Humboldt-Universität zu Berlin, where she has led an active research group since 2012. She previously held positions as Assistant Professor at HU Berlin/BAM (2008-2012), Junior Researcher at BAM (2005-2008), and research appointments at Harvard Medical School, Princeton University, and Erasmus Universiteit Rotterdam. Education: Dr. rer. nat. (summa cum laude), Freie Universität Berlin (2002) Undergraduate/Graduate Studies in Biology & Physics, Freie Universität Berlin (1992-1998) Research Focus: Her interdisciplinary work bridges physical chemistry and biospectroscopy, with particular emphasis on: Surface-enhanced Raman scattering (SERS) for complex sample analysis Plasmonic catalysis and hot electron chemistry Multiphoton-excited vibrational spectroscopy Nanoscale biochemical mapping in plant and animal systems Development of advanced plasmonic substrates Publication Trends: Recent work demonstrates strong focus on multimodal spectroscopy applications, with studies combining SERS, hyper-Raman, IR, and synchrotron techniques to address questions in catalysis, nanoparticle-cell interactions, plant biochemistry, and biosensing. Publications frequently incorporate advanced nanomaterials, electrochemical methods, and machine learning-assisted spectral analysis. Scientific Awards: Fellow, European Academy of Sciences (2020) Caroline von Humboldt Professorship (2019) Wilhelm Ostwald Fellow, BAM (2012) Bunsen-Kirchhoff Award, GDCh (2010) ERC Starting Grant (2010) Academic Leadership: Currently advises 5 PhD students and leads multiple collaborative initiatives. Serves as Board Member of Einstein Center Catalysis (since 2019), Head of Chemistry Department (2014-2016), and Speaker of Graduate School SALSA (since 2012). Secured funding through DFG, EU networks, and ERC grants supporting spectroscopy infrastructure development. Lab & Team: Leads the KneippLab research group with 2 postdoctoral researchers, 5 graduate students, and technical staff. Research focuses on developing spectroscopic methods for interrogating biological and chemical processes at nanoscale resolution using plasmonic enhancement strategies.

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Xin ZhangView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

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.

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Jan GerkenView profile

Professor

Flensburg University of Applied SciencesGermany

Jan Gerken is a Professor of Data Science at the Faculty of Business, Flensburg University of Applied Sciences. He serves as Vice Dean of Faculty 4 (Business) and leads the Natural Language Processing & Text Analytics research group within FLAIR (Flensburg Artificial Intelligence Research). His contact information shows he is based in Room C 221 with telephone number 0461/805-1471, and his office hours are regularly announced on Stud.IP. Professor Gerken's research focuses on Text Analytics, Data Science, Machine Learning, Deep Learning, and Business Analytics. His work bridges theoretical computer science with practical business applications, particularly in public administration. He has established himself as a leading researcher in applying AI techniques to analyze textual data, with special emphasis on patent analysis and social media analytics. His publication record demonstrates a clear evolution from patent analysis and technology monitoring (2010-2015) to Business Intelligence applications in public administration (2018-2023) and most recently to Large Language Models and systematic literature review methods (2025). This progression reflects the broader field's movement from traditional text mining to contemporary AI-driven approaches. As Scientific Director of the AI Application Center (KIAZ) and leader of FLAIR, Professor Gerken plays a pivotal role in connecting academic research with practical implementation. His work with the Flensburg City Administration on Business Intelligence 2022 demonstrates his commitment to translating research into real-world solutions for municipal governance.

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