Dr. rer. nat.

Rüping, Stefan

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  • Publication
    Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
    ( 2022-06-18)
    Houben, Sebastian
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    Albrecht, Stefanie
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    Akila, Maram
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    Bär, Andreas
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    Brockherde, Felix
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    Feifel, Patrick
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    Fingscheidt, Tim
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    Gannamaneni, Sujan Sai
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    Ghobadi, Seyed Eghbal
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    Hammam, Ahmed
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    Haselhoff, Anselm
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    Hauser, Felix
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    Heinzemann, Christian
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    Hoffmann, Marco
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    Kapoor, Nikhil
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    Kappel, Falk
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    Klingner, Marvin
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    Kronenberger, Jan
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    Küppers, Fabian
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    Löhdefink, Jonas
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    Mlynarski, Michael
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    Mock, Michael
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    Mualla, Firas
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    Pavlitskaya, Svetlana
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    Poretschkin, Maximilian
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    Pohl, Alexander
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    Ravi-Kumar, Varun
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    Rosenzweig, Julia
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    Rottmann, Matthias
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    Rüping, Stefan
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    Sämann, Timo
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    Schneider, Jan David
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    Schulz, Elena
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    Schwalbe, Gesina
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    Sicking, Joachim
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    Srivastava, Toshika
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    Varghese, Serin
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    Weber, Michael
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    Wirkert, Sebastian
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    Wirtz, Tim
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    Woehrle, Matthias
    Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly.
  • Publication
    Grundlagen des Maschinellen Lernens
    ( 2021)
    Bauckhage, Christian
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    Hübner, Wolfgang
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    Hug, Ronny
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    Paaß, Gerhard
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    Rüping, Stefan
    Zu definieren, was die menschliche Intelligenz sowie intelligentes Handeln – und da­mit auch die Künstliche Intelligenz – ausmacht, ist außerordentlich schwer und be­schäftigt Philosophen und Psychologen seit Jahrtausenden. Allgemein anerkannt istaber, dass die Fähigkeit zu lernen ein zentrales Merkmal vonIntelligenzist. So ist auchdas Forschungsgebiet desMaschinellen Lernens(engl.machine learning, ML) ein zen­traler Teil der Künstlichen Intelligenz, das hinter vielen aktuellen Erfolgen von KI-Sys­temen steckt.
  • Publication
    Visual Analytics in the Aviation and Maritime Domains
    ( 2020)
    Andrienko, Gennady
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    Andrienko, Natalia
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    Fuchs, Georg
    ;
    Rüping, Stefan
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    Cordero Garcia, Jose Manuel
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    Scarlatti, David
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    Vouros, George A.
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    Herranz, Ricardo
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    Marcos, Rodrigo
    Visual analytics is a research discipline that is based on acknowledging the power and the necessity of the human vision, understanding, and reasoning in data analysis and problem solving. It develops a methodology of analysis that facilitates human activities by means of interactive visual representations of information. By examples from the domains of aviation and maritime transportation, we demonstrate the essence of the visual analytics methods and their utility for investigating properties of available data and analysing data for understanding real-world phenomena and deriving valuable knowledge. We describe four case studies in which distinct kinds of knowledge have been derived from trajectories of vessels and airplanes and related spatial and temporal data by human analytical reasoning empowered by interactive visual interfaces combined with computational operations.
  • Publication
    Data science in healthcare. Benefits, challenges and opportunities
    ( 2019)
    Abedjan, Z.
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    Boujemaa, N.
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    Campbell, S.
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    Casla, P.
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    Chatterjea, S.
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    Consoli, S.
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    Costa-Soria, C.
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    Czech, P.
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    Despenic, M.
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    Garattini, C.
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    Hamelinck, D.
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    Heinrich, A.
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    Kraaij, W.
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    Kustra, J.
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    Lojo, A.
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    Sanchez, M.M.
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    Mayer, M.A.
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    Melideo, M.
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    Menasalvas, E.
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    Aarestrup, F.M.
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    Artigot, E.N.
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    Petkovic, M.
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    Recupero, D.R.
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    Gonzalez, A.R.
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    Kerremans, G.R.
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    Roller, R.
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    Romao, M.
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    Rüping, Stefan
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    Sasaki, F.
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    Spek, W.
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    Stojanovic, N.
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    Thoms, J.
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    Vasiljevs, A.
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    Verachtert, W.
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    Wuyts, R.
    The advent of digital medical data has brought an exponential increase in information available for each patient, allowing for novel knowledge generation methods to emerge. Tapping into this data brings clinical research and clinical practice closer together, as data generated in ordinary clinical practice can be used towards rapid-learning healthcare systems, continuously improving and personalizing healthcare. In this context, the recent use of Data Science technologies for healthcare is providing mutual benefits to both patients and medical professionals, improving prevention and treatment for several kinds of diseases. However, the adoption and usage of Data Science solutions for healthcare still require social capacity, knowledge and higher acceptance. The goal of this chapter is to provide an overview of needs, opportunities, recommendations and challenges of using (Big) Data Science technologies in the healthcare sector. This contribution is based on a recent whitepaper (http://www.bdva.eu/sites/default/files/Big%20Data%20Technologies%20in%20Healthcare.pdf) provided by the Big Data Value Association (BDVA) (http://www.bdva.eu/), the private counterpart to the EC to implement the BDV PPP (Big Data Value PPP) programme, which focuses on the challenges and impact that (Big) Data Science may have on the entire healthcare chain.
  • Publication
    Big Data in Gesundheitswesen und Medizin
    ( 2019)
    Rüping, Stefan
    ;
    Sander, Jil
    In Medizin und Gesundheitswesen sind immer größere Mengen immer vielfältigerer Daten verfügbar, die zunehmend schneller generiert werden. Dieser allgemeine Trend wird als Big Data bezeichnet. Die Analyse von Big Data mit Methoden des maschinellen Lernens führt zur Entwicklung innovativer Lösungen, die neue medizinische Einsichten generieren und die Qualität und Effizienz im Gesundheitssystem erhöhen können. Prototypische Beispiele existieren im Bereich der Analyse klinischer Texte, der klinischen Entscheidungsunterstützung, der Analyse von Daten aus öffentlichen Datenquellen oder Wearables und in Form der Entwicklung persönlicher Assistenten. Diese Potenziale bringen aber auch neue Herausforderungen im Bereich Datenschutz und in der Transparenz bzw. Nachvollziehbarkeit der Ergebnisse für den medizinischen Experten mit sich.
  • Publication
    Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring
    ( 2018)
    Kirsch, Birgit
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    Giesselbach, Sven
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    Knodt, David
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    Rüping, Stefan
    Nowadays social media mining is broadly used in the security sector to support law enforcement and to increase response time in emergency situations. One approach to go beyond the manual inspection is to use text mining technologies to extract latent topics, analyze their geospatial distribution and to identify the sentiment from posts. Although widely used, this approach has proven to be technically difficult for end-users: the language used on social media platforms rapidly changes and the domain varies according to the use case. This paper presents a monitoring architecture that analyses streams from social media, combines different machine learning approaches and can be easily adapted and enriched by user knowledge without the need for complex tuning. The framework is modeled based on the requirements of two H2020-projects in the area of community policing and emergency response.