Malte Prieß

Kiel, Schleswig-Holstein

Hi there, my name is Malte Prieß and I am an enthusiastic professor and scientist in the field of cloud technologies, programming, software engineering and KI/ML, data science, working as a professor for cloud technologies and applied computer science at the HAW Kiel, Kiel University of Applied Sciences, Faculty of Computer Science and Electrical Engineering.

Before taking over the professorship for cloud technologies at FH Kiel on October 1, 2024, I was dean and professor of applied computer science at the DHSH University of Applied Sciences for eight years. I gained extensive experience in research and teaching as well as in business. After studying computer engineering and physics at the Berliner Hochschule für Technik and Leibniz University Hanover, I completed my doctorate in the junior research group Algorithmic Optimal Control at the Department of Computer Science at Kiel University.

In my professional career, I have worked as a research assistant at Helmholtz Centre for Ocean Research Kiel (GEOMAR) and at the Engineering Optimization & Modeling Center (EOMC), Department of Engineering at Reykjavik University as well as an IT developer and consultant at d-fine GmbH in various national and international projects. My research adresses the interface of cloud computing, artificial intelligence and modern software engineering. The focus is on innovative approaches to scaling, automation and optimisation of complex systems.

I am excited about the opportunity to drive innovative projects and research projects at Kiel UAS and to inspire students for current technological topics. I see my new position as an opportunity to make optimal use of my extensive expertise in teaching and research and to further expand it in the great environment at Kiel UAS.

Apart from work, I am crazy for many kinds of sports, climbing, hiking, mountain biking, kitesurfing, snowboarding and skiing (especially touring ski) and finally wing and pump foiling. I started the latter in 2018 and this is, among being in the mountains, my absolute favorite sport. Its fast, challenging, feels like flying and you can go out at and explore so many different places not possible using usual surfing setups. I am father of two lovely and crazy little boys.

news

Jan 26, 2026	The Seventeenth International Conference on Cloud Computing, GRIDs, and Virtualization CLOUD COMPUTING 2026: Joint publication submitted: B. Puhani, K. Brehmer und M. Prieß, “A Cloud-Native Architecture for Human-in-Control LLM-Assisted OpenSearch in Investigative Settings”
Jan 4, 2026	The Seventeenth International Conference on Cloud Computing, GRIDs, and Virtualization CLOUD COMPUTING 2026: Joint publication submitted: M. G. Zizler, C. P. Neumann und M. Prieß, “Towards Global Multi-Cloud Strategies: Insights into AWS and Alibaba Cloud Synergy” Part of Steering Committee: https://www.iaria.org/conferences2026/CLOUDCOMPUTING26.html

selected publications & proposals

CLOUD_LLM-IR
submitted
Jan 26
A Cloud-Native Architecture for Human-in-Control LLM-Assisted OpenSearch in Investigative Settings (submitted)

Puhani, B., Brehmer, K., and Prieß, M.

2026

Abs Bib

Complex criminal investigations are often hindered by the volume of unstructured evidence and the semantic gap between natural language investigative intent and technical search logic. To address this challenge, we present a design and feasibility study of a cloud-native microservices architecture tailored to private cloud deployments. The proposed system integrates Large Language Models into a “Human-in-Control” workflow that translates natural language queries into syntactically valid OpenSearch Domain-Specific Language expressions. We describe the implementation of a hybrid retrieval strategy within OpenSearch that combines BM25-based lexical search with nested semantic vector embeddings. The paper focuses on system design and preliminary functional validation, establishing an architectural baseline for empirical evaluation. Technical feasibility is demonstrated through a functional prototype, and a rigorous evaluation methodology is outlined using the Enron email dataset as a structural proxy for restricted investigative corpora.
@conference{Puhani2026, author = {Puhani, B. and Brehmer, K. and Prieß, M.}, booktitle = {Cloud Computing 2026, The Seventeenth International Conference on Cloud Computing, GRIDs, and Virtualization}, title = {{A} {C}loud-{N}ative {A}rchitecture for {H}uman-in-{C}ontrol {LLM}-{A}ssisted {O}pen{S}earch in {I}nvestigative {S}ettings (submitted)}, year = {2026}, publisher = {IARIA}, keywords = {Information Retrieval, Semantic Search, Investigative Settings, Enron Dataset} }
CLOUD_Mul-Cl
submitted
Jan 26
Towards Global Multi-Cloud Strategies: Insights into AWS and Alibaba Cloud Synergy (submitted)

Zizler, M. G., Neumann, C. P., and Prieß, M.

2026

Abs Bib

Multi-cloud strategies are increasingly adopted by modern enterprises to improve agility and resilience and to reduce vendor lock-in. Integrating workloads across providers such as AWS and Alibaba Cloud remains challenging due to interoperability and migration issues. This paper presents a comparative analysis of AWS and Alibaba Cloud focusing on architectural, ability and migration issues. This paper presents a comparative analysis of AWS and Alibaba Cloud focusing on architectural, service, and policy differences affecting workload migration. Using both provider-native and open source Infrastructure-as-Code tools, we conduct an exploratory case study about migration of IoT workloads. The results highlight key technical trade-offs and best practices for secure multi-cloud deployments, offering guidance for organizations pursuing AWS and Alibaba Cloud interoperability.
@conference{Zizler2026, author = {Zizler, M.~G. and Neumann, C.~P. and Prieß, M.}, booktitle = {Cloud Computing 2026, The Seventeenth International Conference on Cloud Computing, GRIDs, and Virtualization}, title = {{T}owards {G}lobal {M}ulti-{C}loud {S}trategies: {I}nsights into {AWS} and {A}libaba {C}loud {S}ynergy (submitted)}, year = {2026}, publisher = {IARIA}, keywords = {Cloud Computing, Multi-Cloud, AWS, Alibaba Cloud, Infrastructure-as-Code}, }
AI_Vuln-Ass
Graph of Effort: Quantifying Risk of AI Usage for Vulnerability Assessment

Mehra, A., Aßmuth, A., and Prieß, M.

2025

Abs Bib Link

With AI-based software becoming widely available, the risk of exploiting its capabilities, such as high automation and complex pattern recognition, could significantly increase. An AI used offensively to attack non-AI assets is referred to as offensive AI. Current research explores how offensive AI can be utilized and how its usage can be classified. Additionally, methods for threat modeling are being developed for AI-based assets within organizations. However, there are gaps that need to be addressed. Firstly, there is a need to quantify the factors contributing to the AI threat. Secondly, there is a requirement to create threat models that analyze the risk of being attacked by AI for vulnerability assessment across all assets of an organization. This is particularly crucial and challenging in cloud environments, where sophisticated infrastructure and access control landscapes are prevalent. The ability to quantify and further analyze the threat posed by offensive AI enables analysts to rank vulnerabilities and prioritize the implementation of proactive countermeasures. To address these gaps, this paper introduces the Graph of Effort, an intuitive, flexible, and effective threat modeling method for analyzing the effort required to use offensive AI for vulnerability exploitation by an adversary. While the threat model is functional and provides valuable support, its design choices need further empirical validation in future work.
@conference{Mehra2025a, author = {Mehra, A. and Aßmuth, A. and Prieß, M.}, booktitle = {Cloud Computing 2025, The Sixteenth International Conference on Cloud Computing, GRIDs, and Virtualization 2025}, title = {{G}raph of {E}ffort: {Q}uantifying {R}isk of {AI} {U}sage for {V}ulnerability {A}ssessment}, year = {2025}, publisher = {IARIA}, copyright = {arXiv.org perpetual, non-exclusive license}, doi = {10.48550/ARXIV.2503.16392}, keywords = {Cryptography and Security (cs.CR), Artificial Intelligence (cs.AI), Distributed, Parallel, and Cluster Computing (cs.DC), FOS: Computer and information sciences, FOS: Computer and information sciences}, }

AI_DL-Metrics

Improving Applicability of Deep Learning based Token Classification models during Training

Mehra, A., Prieß, M., and Himstedt, M.

2025

Bib Link

@misc{Mehra2025b,
  author = {Mehra, A. and Prieß, M. and Himstedt, M.},
  title = {Improving Applicability of Deep Learning based Token Classification models during Training},
  year = {2025},
  copyright = {arXiv.org perpetual, non-exclusive license},
  doi = {10.48550/ARXIV.2504.01028},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},
  publisher = {arXiv},
}

AI_Ethics
Ethische Rahmenbedingungen beim Einsatz von KI im Projektgeschäft – Gefahren und Chancen der KI

Prechel, K., and Prieß, M.

2024

Abs Bib Link

In diesem Kapitel wird der ethische Rahmen beim Einsatz von Künstlicher Intelligenz (KI) im Projektgeschäft beleuchtet. Mit dem Fortschritt der KI ergeben sich sowohl Chancen als auch Gefahren im Projektmanagement. Während KI die Effizienz und Präzision von Projekten steigern kann, birgt sie auch ethische Herausforderungen, insbesondere in Bezug auf Fairness, Datenschutz und Transparenz. Kerstin Prechel und Malte Prieß diskutieren die Relevanz ethischer Überlegungen im Projektmanagement und stellen Fragen zur Integration von KI in ethisch verantwortungsvolle Weise. Das Kapitel betont die Bedeutung von Transparenz, Verantwortung und Verifizierung im Umgang mit KI und bietet einen Einblick in die ethischen Aspekte des Einsatzes von KI im Projektgeschäft.
@inbook{Prechel2024, author = {Prechel, K. and Prie{\ss}, M.}, editor = {Bernert, C. and Scheurer, S. and Wehnes, H.}, pages = {47--55}, publisher = {UVK Verlag}, title = {{E}thische {R}ahmenbedingungen beim {E}insatz von {KI} im {P}rojektgeschäft -- {G}efahren und {C}hancen der {KI}}, year = {2024}, keywords = {Künstliche Intelligenz, Projektmanagement, Ethik, Datenschutz, Transparenz, Verantwortung, Verifizierung, Bias, Trainingsdaten, Entscheidungsfindung, Fairness}, isbn = {978-3-381-11132-9}, series = {KI in der Projektwirtschaft, Gesellschaft für Projektmanagement (GPM)}, }

AI_Edu

KI lehren, KI lernen an der DHSH – Potentiale für die Bildungslandschaft

Prieß, M., and Prechel, K.

2023

Bib Link

@incollection{Priess2023,
  author = {Prie{\ss}, M. and Prechel, K.},
  booktitle = {INFORMATIK 2023 -- Designing Futures: Zukünfte gestalten},
  publisher = {Gesellschaft für Informatik e.V.},
  title = {{K}I lehren, {K}I lernen an der {DHSH} -- {P}otentiale für die {B}ildungslandschaft},
  year = {2023},
  address = {Bonn},
  isbn = {978-3-88579-731-9},
  pages = {457--460},
  doi = {10.18420/inf2023_56},
  pissn = {1617-5468},
}

KI_Campus
Fellowship proposal "‘KI lehren, KI lernen an der DHSH"’, KI-Campus (Stifterverband für die Deutsche Wissenschaft e.V.) / accepted

Prieß, M.

2022

Abs Bib

Mit dem Fellow-Projekt möchte ich Lernressourcen und didaktische Konzepte zum Themenkomplex KI an der Duale Hochschule Schleswig-Holstein (DHSH) ausbauen und zur Digitalisierung an der DHSH beitragen, indem Lehrende den Umgang mit digitalen Lehr-Formaten und Studierende unterstützt durch KI-Campus Ressourcen KI-Einsatzkontexte erlernen. Hierzu beabsichtige ich, im Rahmen eines Moduls zum Thema Data Science und Maschinelles Lernen Ressourcen des KI-Campus im Sinne eines Blended-Learnings zu nutzen. Grundlegende Verfahren des Maschinellen Lernens, eine Einführung in künstliche neuronale Netze sowie einen Ausblick auf den Bereich Deep Learning sollen abgedeckt werden. Weiterhin möchte ich für eine gesellschaftliche Folgenabwägung und kritische Reflektion der eingesetzten Technologien über entsprechende Kurse sensibilisieren.
@article{Priess2022, author = {Prie{\ss}, M.}, title = {Fellowship proposal "`KI lehren, KI lernen an der DHSH"', KI-Campus (Stifterverband für die Deutsche Wissenschaft e.V.) / accepted}, year = {2022}, cite-key = {prpr2022a}, note = {accepted}, }
AI_Doc-Search
Evaluierung des Dense Passage Retrievals zur Dokumentensuche in Behörden im Vergleich zu BM25

Mehra, A., Prieß, M., and Peters, T. V.

2022

Abs Bib Link

Die Suche nach Dokumenten ist eine der Aufgaben, die Maschinen am häufigsten ausführen. Täglich genutzte Suchmaschinen suchen nach Dokumenten, um das Informationsbedürfnis eines Anfragenden zu erfüllen. Aktuelle Forschungsergebnisse zeigen, dass der Bewertungsansatz des Dense Passage Retrieval (DPR) – welcher die zu suchenden Dokumente mittels tiefer neuronaler Netze in Dense-Vektoren umwandelt – in englischer ebenso wie in deutscher Sprache bessere Ergebnisse liefert als aktuell implementierte Algorithmen wie der BM25-Algorithmus. Die Dokumentensuche in deutschen Behörden stellt eine besondere Aufgabe dar, da in diesen komplexe behördenspezifische Fachausdrücke verwendet werden. Im Rahmen dieser Arbeit wurde für die Dokumentensuche zu Corona-Regelungen in der öffentlichen Verwaltung ein DPR mit einem öffentlichen deutschsprachigen Datensatz trainiert und mit dem BM25-Algorithmus von Elasticsearch anhand der Relevanz als Gütemaß verglichen. Die Ergebnisse zeigen, dass das DPR mehr Dokumente mit gesuchten Informationen findet, Synonyme in Suchanfragen berücksichtigen kann und damit das Potenzial zur Verbesserung der Dokumentensuche in Behördenanwendungen hat.
@inbook{Mehra2022a, author = {Mehra, A. and Prie{\ss}, M. and Peters, T. V.}, pages = {353--362}, title = {{E}valuierung des {D}ense {P}assage {R}etrievals zur {D}okumentensuche in {B}ehörden im {V}ergleich zu {BM}25}, year = {2022}, volume = {AKWI-Tagungsband zur 35. AKWI-Jahrestagung}, cite-key = {mehra2022}, doi = {https://doi.org/10.30844/AKWI_2022_26}, journal = {Monographien}, keywords = {Dense Passage Retrieval, BM25, Natürliche Sprachverarbeitung, Künstliche Intelligenz, Neuronale Netze, Information Retrieval, Dokumentensuche}, }
SBO_OneShot
Surrogate-Based and One-Shot Optimization Methods for PDE-Constrained Problems with an Application in Climate Models

Slawig, T., Prieß, M., and Kratzenstein, C.

In Solving Computationally Expensive Engineering Problems 2014

Abs Bib Link

We discuss PDE-constrained optimization problems with iterative state solvers. As typical and challenging example, we present an application in climate research, namely a parameter optimization problem for a marine ecosystem model. Therein, a periodic state is obtained via a slowly convergent fixed-point type iteration. We recall the algorithm that results from a direct or black-box optimization of such kind of problems, and discuss ways to obtain derivative information to use in gradient-based methods. Then we describe two optimization approaches, the One-shot and the Surrogate-based Optimization method. Both methods aim to reduce the high computational effort caused by the slow state iteration. The idea of the One-shot approach is to construct a combined iteration for state, adjoint and parameters, thus avoiding expensive forward and reverse computations of a standard adjoint method. In the Surrogate-based Optimization method, the original model is replaced by a surrogate which is here based on a truncated iteration with fewer steps. We compare both approaches, provide implementation details for the presented application, and give some numerical results.
@inproceedings{Slawig2014a, author = {Slawig, T. and Prie{\ss}, M. and Kratzenstein, C.}, booktitle = {Solving Computationally Expensive Engineering Problems}, title = {Surrogate-Based and One-Shot Optimization Methods for PDE-Constrained Problems with an Application in Climate Models}, year = {2014}, editor = {Koziel, S. and Leifsson, L. and Yang, X.S.}, pages = {1--24}, publisher = {Springer}, series = {Springer Proceedings in Mathematics \& Statistics}, volume = {97}, keywords = {optimization, climate model, marine ecosystem model, one-shot method, surrogate-based optimization}, }
SBO
Accelerated Parameter Identification in a 3D Marine Ecosystem Model Using Surrogate-Based Optimization

Prieß, M., Piwonski, J., Koziel, S., Oschlies, A., and Slawig, T.

Ocean Modelling 2013

Abs Bib Link

We present the application of the Surrogate-based Optimization (SBO) method on a parameter identification problem for a 3-D biogeochemical model. SBO is a method for acceleration of optimization processes when the underlying model itself is of very high computational complexity. In these cases, coupled simulation runs require large amounts of computer time, where optimization runs may become unfeasible even with high-performance hardware. As a consequence, the key idea of SBO is to replace the original and computationally expensive (high-fidelity) model by a so-called surrogate, which is created from a less accurate but computationally cheaper (low-fidelity) model and a suitable correction approach to increase its accuracy. To date, the SBO approach has been widely and successfully used in engineering applications and also for parameter identification in a 1-D marine ecosystem model of NPZD type. In this paper, we apply the approach onto a two-component biogeochemical model. The model is spun-up into a steady seasonal cycle via the Transport Matrix Approach. The low-fidelity model we use consists of a reduced number of spin-up iterations (several decades instead of millennia used for the original model). A multiplicative correction operator is further exploited to extrapolate the rather inaccurate low-fidelity model onto the original one. This corrected model builds our surrogate. We validate this SBO method by twin-experiments that use synthetic observations generated by the original model. We motivate our choice of the low-fidelity model and the multiplicative correction and discuss the computational advantage of SBO in comparison to an expensive parameter optimization in the context of the high-fidelity model. The proposed SBO technique is shown to yield a solution close to the target at a significant gain of computational efficiency. Without further regularization techniques, the method is able to identify most model parameters. The method is simple to implement and presents a promising and pragmatic tool to calibrate biogeochemical models in a global three-dimensional setting.
@article{Priess2013a, author = {Prie{\ss}, M. and Piwonski, J. and Koziel, S. and Oschlies, A. and Slawig, T.}, journal = {Ocean Modelling}, title = {Accelerated Parameter Identification in a 3D Marine Ecosystem Model Using Surrogate-Based Optimization}, year = {2013}, pages = {22–36}, volume = {68}, keywords = {marine biogeochemical model, parameter identification, model calibration, surrogate-based optimization, rsponse correction, low-fidelity model}, }
GravWave
Subtraction-Noise Projection in Gravitational-Wave Detector Networks

Harms, J., Mahrdt, C., Otto, C., Prieß, M., and Danzmann, K.

Physical Review D 2008

Abs Bib Link

In this paper, we present a successful implementation of a subtraction-noise projection method into a simple, simulated data analysis pipeline of a gravitational-wave search. We investigate the problem to reveal a weak stochastic background signal which is covered by a strong foreground of compact-binary coalescences. The foreground, which is estimated by matched filters, has to be subtracted from the data. Even an optimal analysis of foreground signals will leave subtraction noise due to estimation errors of template parameters which may corrupt the measurement of the background signal. The subtraction noise can be removed by a noise projection. We apply our analysis pipeline to the proposed future-generation space-borne Big Bang Observer mission which seeks for a stochastic background of primordial gravitational waves in the frequency range ∼0.1 Hz-1 Hz covered by a foreground of black-hole and neutron-star binaries. Our analysis is based on a simulation code which provides a dynamical model of a time-delay interferometer network. It generates the data as time series and incorporates the analysis pipeline together with the noise projection. Our results confirm previous ad hoc predictions which say that the Big Bang Observer will be sensitive to backgrounds with fractional energy densities below Ω=1E-16.
@article{Harms2008, author = {Harms, J. and Mahrdt, C. and Otto, C. and Prie{\ss}, M. and Danzmann, K.}, journal = {Physical Review D}, title = {Subtraction-Noise Projection in Gravitational-Wave Detector Networks}, year = {2008}, number = {12}, volume = {77}, }