"This site requires JavaScript to work correctly"
minimize Header

Prof. Dr. Patrick Glauner

Big picture of AI:

  • Methodology and technology
  • Commercialization
  • Legal, economic and political issues

Faculty of Computer Science

Professors

Professor

Internship Coordinator of the Artificial Intelligence and Künstliche Intelligenz Undergraduate Programs

K 005

0991/3615-453

consulting time

Fridays from 1 to 2pm during the lecture period and by agreement. Please get in touch by email in advance.

Sortierung:
Contribution
  • Patrick Glauner
  • A. Boechat
  • L. Dolberg
  • R. State
  • F. Bettinger
  • Y. Rangoni
  • D. Duarte

Large-scale detection of non-technical losses in imbalanced data sets.

In: Proceedings of the 2016 Seventh IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2016) [September 6-9, 2016; Minneapolis, MN, USA] .

  • (2016)

DOI: 10.1109/ISGT.2016.7781159

show abstract

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.
Journal article
  • Patrick Glauner
  • J. Meira
  • P. Valtchev
  • R. State
  • F. Bettinger

The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey.

In: International Journal of Computational Intelligence Systems vol. 10 pg. 760-775

  • (2017)

DOI: 10.2991/ijcis.2017.10.1.51

show abstract

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future.
Contribution
  • Patrick Glauner
  • N. Dahringer
  • O. Puhachov
  • J. Meira
  • P. Valtchev
  • R. State
  • D. Duarte

Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations.

In: Proceedings of the 2017 IEEE International Conference on Data Mining Workshops (ICDMW 2017) [November 18-21, 2017; New Orleans, LA, USA].

  • (2017)

DOI: 10.1109/ICDMW.2017.40

show abstract

Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data due to the latter's propensity to suggest a large number of unnecessary inspections. In this paper, we propose a novel system that combines automated statistical decision making with expert knowledge. First, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data. The methodology used is specifically tailored to the level of noise in the data. Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. This work has resulted in appreciable results on a real-world data set of 3.6M customers. Our system is being deployed in a commercial NTL detection software.
Contribution
  • Patrick Glauner
  • P. Valtchev
  • R. State

Impact of Biases in Big Data.

In: Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) [April 27-29, 2018; Bruges, Belgium]. pg. 645-654

  • (2018)

show abstract

The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year's US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems.
Contribution
  • M. Thurner
  • Patrick Glauner

Digitalization in Mechanical Engineering.

In: Innovative Technologies for Market Leadership: Investing in the Future. (Future of Business and Finance) pg. 107-117

  • Eds.:
  • P. Plugmann
  • Patrick Glauner

Springer

  • (2020)
Contribution
  • S. Mund
  • Patrick Glauner

Autonomous Driving on the Thin Trail of Great Opportunities and Dangerous Trust.

In: Innovative Technologies for Market Leadership: Investing in the Future. (Future of Business and Finance) pg. 153-165

  • Eds.:
  • P. Plugmann
  • Patrick Glauner

Springer

  • (2020)
Contribution
  • Patrick Glauner

Unlocking the Power of Artificial Intelligence for Your Business.

In: Innovative Technologies for Market Leadership: Investing in the Future. (Future of Business and Finance) pg. 45-59

  • Eds.:
  • P. Plugmann
  • Patrick Glauner

Springer

  • (2020)
Contribution
  • L. Trestioreanu
  • Patrick Glauner
  • J. Meira
  • M. Gindt
  • R. State

Using Augmented Reality and Machine Learning in Radiology.

In: Innovative Technologies for Market Leadership: Investing in the Future. (Future of Business and Finance) pg. 89-106

  • Eds.:
  • P. Plugmann
  • Patrick Glauner

Springer

  • (2020)
Contribution
  • Patrick Glauner

Staying Ahead in the MOOC-Era by Teaching Innovative AI Courses.

In: Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, PMLR. pg. 5-9

  • (2021)

show abstract

As a result of the rapidly advancing digital transformation of teaching, universities have started to face major competition from Massive Open Online Courses (MOOCs). Universities thus have to set themselves apart from MOOCs in order to justify the added value of three to five-year degree programs to prospective students. In this paper, we show how we address this challenge at Deggendorf Institute of Technology in ML and AI. We first share our best practices and present two concrete courses including their unique selling propositions: Computer Vision and Innovation Management for AI. We then demonstrate how these courses contribute to Deggendorf Institute of Technology's ability to differentiate itself from MOOCs (and other universities).
Contribution
  • Patrick Glauner

Artificial Intelligence in Healthcare: Foundations, Opportunities and Challenges.

In: Digitalization in Healthcare. Implementing Innovation and Artificial Intelligence (Future of Business and Finance) pg. 1-15

  • Eds.:
  • P. Plugmann
  • Patrick Glauner
  • G. Lerzynski

Springer Nature Switzerland AG [S.l.]

  • (2021)
Contribution
  • Patrick Glauner

Everyone Needs to Acquire Some Understanding of What AI Is.

In: Decisively Digital: From Creating a Culture to Designing Strategy. pg. 267-281

  • Eds.:
  • A. Loth

John Wiley & Sons Canada, Limited

  • (2021)
Contribution
  • U. Hutschek
  • T. Abele
  • P. Plugmann
  • Patrick Glauner

Efficiently Delivering Healthcare by Repurposing Solution Principles from Industrial Condition Monitoring: A Meta-Analysis.

In: Digitalization in Healthcare. Implementing Innovation and Artificial Intelligence (Future of Business and Finance) pg. 171-176

  • Eds.:
  • P. Plugmann
  • Patrick Glauner
  • G. Lerzynski

Springer Nature Switzerland AG [S.l.]

  • (2021)

show abstract

As people get older, home care and hospital care services need to scale while maintaining humane quality standards. Qualified workers in sufficient quantities are the most important factor on the road to the future of healthcare. Therefore, automation and digital solutions are to become indispensable in order to enable both sufficient quantity and quality of care services. Such technologies can be particularly helpful when monitoring dependent persons. Our interdisciplinary team conducted a meta-analysis of the state of the art of industrial condition monitoring. We discovered 15 technological principles that look promising to find repurpose in the healthcare sector. We also propose vitally needed healthcare use cases derived from these principles. The outcomes of our analysis provide the opportunity to quickly and cost effectively deliver new products and services in healthcare.
Book
  • Patrick Glauner
  • P. Ramin

Digitalisierungskompetenzen. Rolle der Hochschulen.

Carl Hanser Verlag GmbH & Co. KG München

  • (2021)
Contribution
  • Horst Kunhardt

Home 4.0: With Sensor Data from Everyday Life to Health and Care Prognosis.

In: Digitalization in Healthcare. Implementing Innovation and Artificial Intelligence (Future of Business and Finance)

  • Eds.:
  • P. Plugmann
  • Patrick Glauner
  • G. Lerzynski

Springer Nature Switzerland AG [S.l.]

  • (2021)
Journal article
  • F. Ünal
  • A. Almalaq
  • S. Ekici
  • Patrick Glauner

Big Data-Driven Detection of False Data Injection Attacks in Smart Meters.

In: IEEE Access vol. 9 pg. 144313-144326

  • (2021)

DOI: 10.1109/ACCESS.2021.3122009

show abstract

Today’s energy resources are closer to consumers thanks to sustainable energy and advanced metering infrastructure (AMI), such as smart meters. Smart meters are controlled and manipulated through various interfaces in smart grids, such as cyber, physical and social interfaces. Recently, a large number of non-technical losses (NTLs) have been reported in smart grids worldwide. These are partially caused by false data injections (FDIs). Therefore, ensuring a secure communication medium and protected AMIs is critical to ensuring reliable power supply to consumers. In this paper, we propose a novel Big Data-driven solution that employs machine learning, deep learning and parallel computing techniques. We additionally obtained robust statistical features to detect the FDIs based cyber threats at the distribution level. The performance of the proposed model for NTL detection is investigated using private smart grid datasets in the Turkish distribution network for AMI-level cyber threats, and the results are compared to state-of-the-art machine learning algorithms used for NTL classification problems. Our approach shows promising results, as the accuracy, specificity, and precision metrics of most classifiers are above 90% and false positive rates vary between 0.005 to 0.027.
Contribution
  • Patrick Glauner

Innovation Management for Artificial Intelligence.

In: Creating Innovation Spaces: Impulses for Start-ups and Established Companies in Global Competition. (Management for Professionals) pg. 1-13

  • Eds.:
  • P. Plugmann
  • V. Nestle
  • Patrick Glauner

Springer International Publishing [S.l.]

  • (2021)
Contribution
  • Agnes Nocon

Evaluating the ethical aspects of online counselling.

In: Digitalization in Healthcare. Implementing Innovation and Artificial Intelligence (Future of Business and Finance)

  • Eds.:
  • P. Plugmann
  • Patrick Glauner
  • G. Lerzynski

Springer Nature Switzerland AG [S.l.]

  • (2021)
Contribution
  • Horst Kunhardt

Modern Home Care: A Glimpse into the Future of Patient-Centered Healthcare Systems.

In: The Future Circle of Healthcare: AI, 3D Printing, Longevity, Ethics, and Uncertainty Mitigation. (Future of Business and Finance) pg. 251-261

  • Eds.:
  • F. Thieringer
  • Patrick Glauner
  • P. Plugmann
  • S. Ehsani

Springer International Publishing Cham, Switzerland

  • (2022)
Contribution
  • Patrick Glauner

An Assessment of the AI Regulation Proposed by the European Commission.

In: The Future Circle of Healthcare: AI, 3D Printing, Longevity, Ethics, and Uncertainty Mitigation. (Future of Business and Finance)

  • Eds.:
  • F. Thieringer
  • Patrick Glauner
  • P. Plugmann
  • S. Ehsani

Springer International Publishing Cham, Switzerland

  • (2022)

show abstract

In April 2021, the European Commission published a proposed regulation on AI. It intends to create a uniform legal framework for AI within the European Union (EU). In this chapter, we analyze and assess the proposal. We show that the proposed regulation is actually not needed due to existing regulations. We also argue that the proposal clearly poses the risk of overregulation. As a consequence, this would make the use or development of AI applications in safety-critical application areas, such as in healthcare, almost impossible in the EU. This would also likely further strengthen Chinese and US corporations in their technology leadership. Our assessment is based on the oral evidence we gave in May 2021 to the joint session of the European Union affairs committees of the German federal parliament and the French National Assembly.
Contribution
  • Anna-Maria Kasparbauer
  • Veronika Reisner
  • C. Schenk
  • A. Glas
  • Helana Lutfi
  • Oscar Blanco
  • Thomas Spittler

Sensor Devices, the Source of Innovative Therapy and Prevention.

In: The Future Circle of Healthcare: AI, 3D Printing, Longevity, Ethics, and Uncertainty Mitigation. (Future of Business and Finance) pg. 207-226

  • Eds.:
  • F. Thieringer
  • Patrick Glauner
  • P. Plugmann
  • S. Ehsani

Springer International Publishing Cham, Switzerland

  • (2022)
Contribution
  • S. Ehsani
  • Patrick Glauner
  • P. Plugmann
  • F. Thieringer

Introduction: Trends, Puzzles and Hopes for the Future of Healthcare.

In: The Future Circle of Healthcare: AI, 3D Printing, Longevity, Ethics, and Uncertainty Mitigation. (Future of Business and Finance)

  • Eds.:
  • F. Thieringer
  • Patrick Glauner
  • P. Plugmann
  • S. Ehsani

Springer International Publishing Cham, Switzerland

  • (2022)

show abstract

This book is being published at a time when the collective attention of the world has been focused, for more than 2 years, on the coronavirus pandemic. The interrelatedness of various facets of biomedicine (whether scientific, societal, political, legal, or cultural) has been vividly illustrated to health practitioners, researchers, and the public at large—often on a very personal level. It is now manifestly obvious to many that planning for the future of clinical and experimental medicine is a must. Although the task of predicting the exact trajectory of any profession might be in vain, it is essential that one at least looks at past and current trends in order to envision future scenarios and plan for them.
Contribution
  • Thomas Spittler
  • Helana Lutfi

Innovations for Sustainable Healthcare.

In: The Future Circle of Healthcare: AI, 3D Printing, Longevity, Ethics, and Uncertainty Mitigation. (Future of Business and Finance) pg. 343-357

  • Eds.:
  • F. Thieringer
  • Patrick Glauner
  • P. Plugmann
  • S. Ehsani

Springer International Publishing Cham, Switzerland

  • (2022)
Contribution
  • Patrick Glauner

Künstliche Intelligenz im Gesundheitswesen: Grundlagen, Möglichkeiten und Herausforderungen.

In: Innovationen im Gesundheitswesen. Rechtliche und ökonomische Rahmenbedingungen und Potentiale pg. 143-160

  • Eds.:
  • R. Grinblat
  • D. Etterer
  • P. Plugmann

Springer Fachmedien Wiesbaden GmbH Wiesbaden

  • (2022)
Contribution
  • T. Jelinek
  • A. Bhave
  • N. Buchoud
  • M. Buehler
  • Patrick Glauner
  • O. Inderwildi
  • M. Kraft
  • C. Mok
  • K. Nuebel
  • M. Pathak
  • S. Some
  • A. Voss

Advancing AI for Climate Action: Global Collaboration on Intelligent Decarbonisation.

In: T20 Policy Brief. Task Force 4 Refuelling Growth: Clean Energy and Green Transitions.

  • Eds.:
  • Observer Research Foundation

Observer Research Foundation

  • (2023)

Contribution
  • Patrick Glauner

Lessons from Germany.

In: How to enable the employability of university graduates. (How to guides) pg. 295-303

  • Eds.:
  • K. Daniels
  • S. Hansen

Edward Elgar Publishing Cheltenham, UK; Northampton, MA

  • (2023)
Journal article
  • T. Jelinek
  • A. Bhave
  • N. Buchoud
  • M. Bühler
  • Patrick Glauner
  • O. Inderwildi
  • M. Kraft
  • C. Mok
  • K. Nübel
  • A. Voss

International Collaboration: Mainstreaming Artificial Intelligence and Cyberphysical Systems for Carbon Neutrality.

In: IEEE Transactions on Industrial Cyber-Physical Systems vol. 2 pg. 26-34

  • (2024)

DOI: 10.1109/TICPS.2024.3351624

show abstract

Cyberphysical systems together with Artificial Intelligence play vital roles in reducing, eliminating, and removing greenhouse gas emissions across sectors. Electrification with renewables introduces complexity in systems in the deployment, integration, and efficient orchestration of electrified economic systems. AI-driven cyberphysical systems are uniquely suited to tackle this complexity, potentially accelerating the transition towards a low-carbon economy. The objective of this policy brief is to advocate for the mainstreaming of AI-driven cyberphysical systems for climate change risk mitigation and adaptation. To effectively and more rapidly realize the Intelligent Decarbonation potential, the concept of AI-driven cyberphysical systems must be elevated to a global level of collaboration and coordination, fostering research and development, capacity building, as well as knowledge and technology transfer. Drawing on a multidisciplinary, international study about intelligent decarbonization use cases, this brief also highlights factors impeding the transition to carbon neutrality and risks associated with technology determinism. The importance of governance is emphasized to avoid unwanted path dependency and avert a technology-solutionist approach dominating climate policy that delivers limited results. Given only 12% of the Sustainable Development Goals have been realized, a condensed version of this policy brief was submitted to the India T20, a G20 engagement group, urging global collaboration to prioritize AI-driven CPSs.
Contribution
  • Patrick Glauner

§ 1 Technische Grundlagen von generativen KI-Modellen.

In: Rechtshandbuch ChatGPT. KI-basierte Modelle in der Praxis pg. 21-42

  • Eds.:
  • M. Ebers
  • B. Quarch

Nomos Verlagsgesellschaft mbH & Co. KG Baden-Baden

  • (2024)
UnpublishedWork
  • J. Zhou
  • Patrick Glauner
  • D. Kaminskiy

IFF Global Artificial Intelligence Competitiveness Index Report - Part 1: Analyzing AI Competitiveness From the Enterprise Perspective.

International Finance Forum

  • 2024 (2024)

Journal article
  • Patrick Glauner

Technical foundations of generative AI models.

In: Legal Tech - Zeitschrift für die digitale Anwendung pg. 24-34

Nomos

  • (2024)

show abstract

In recent years, generative AI has gained prominence for its ability to create realistic and creative content, from text and images to music and beyond. This paper serves as a comprehensive guide to the technical foundations underpinning the fascinating field of generative AI models. Understanding the core principles and techniques behind these models is essential for both practitioners, lawyers, judges, policy makers, and enthusiasts seeking to harness their potential.
Contribution
  • Richard Latzel
  • Patrick Glauner

Artificial Intelligence in Sport Scientific Creation and Writing Process.

In: Artificial Intelligence in Sports, Movement, and Health. pg. 15-29

  • Eds.:
  • F. Gassmann
  • C. Dirndorf
  • M. Fröhlich
  • E. Bartaguiz

Imprint Springer, Springer Nature Switzerland Cham

  • (2024)
Contribution
  • Patrick Glauner

KI: Auswirkungen auf Altersvorsorge und Vermögensbildung.

In: Staatliche Förderung der Altersvorsorge und Vermögensbildung. (ESV-Digital) pg. 1-5

  • Eds.:
  • H.-G. Horlemann
  • A. Briese

Erich Schmidt Verlag Berlin

  • (2024)

core competencies

  • AI and Machine Learning
  • Big Data, Computer Vision and Natural Language Processing
  • Commercialization of AI and innovation management
  • Legal, economic and political issues
  • Quantum Computing
  • Industry knowledge: agriculture, automotive, construction, consulting, defense, education, energy, finance, healthcare, insurance, intelligence, IT, logistics, management, materials science, mechanical engineering, police, power, semiconductors, transportation, and others

More information: www.glauner.info

Forschungs- und Lehrgebiete

Courses:

  • AI Project
  • Algorithms and Data Structures
  • Big Data
  • Computer Vision
  • Innovation Management for Artificial Intelligence
  • Quantum Computing

More information: www.glauner.info/teaching

Vita

Special achievements:

  • Advised the parliaments of France, Germany, and Luxembourg as an expert witness
  • Ranked by CDO Magazine and Global AI Hub among the worldwide academic data leaders
  • Panelist at AI House Davos during the 2024 Annual Meeting of the World Economic Forum
  • Speaker at the 2024 Annual Meeting of the International Finance Forum
  • Hosted the 2024 CERN Spring Campus at Deggendorf Institute of Technology

Positions:

  • Since 2020: Full Professor of Artificial Intelligence, Deggendorf Institute of Technology
  • 2022 - 2023: Ramon O'Callaghan Professor of Technology Management and Innovation, Woxsen University
  • 2019 - 2020: Head of Data Academy, Alexander Thamm GmbH
  • 2018 - 2019: Innovation Manager for Artificial Intelligence, Krones Group
  • 2018: Visiting Researcher, Université du Québec à Montréal (UQAM)
  • 2015 - 2018: PhD Candidate, University of Luxembourg
  • 2012 - 2014: Fellow, European Organization for Nuclear Research (CERN)

Degrees:

  • 2019: PhD in Computer Science, University of Luxembourg
  • 2018: MBA, Quantic School of Business and Technology
  • 2015: MSc in Machine Learning, Imperial College London
  • 2012: BSc in Computer Science, Karlsruhe University of Applied Sciences

Scholarship:

  • German National Academic Foundation