Die folgende Liste zeigt die offenen Thesisthemen am Lehrstuhl für Wirtschhaftsinformatik und Systementwicklung.
Durch Klicken auf den Titel werden detailliertere Angaben zum Inhalt und Ansprechpartner angezeigt.
Beschreibung | Synthetic Media bezeichnet digitale Medieninhalte (Bilder, Video, Audio, Text etc.), die durch künstliche Intelligenz erzeugt, bearbeitet oder überhaupt erst möglich gemacht werden. Der Teilbereich "Synthetic Audio" umfasst die Generierung von Sprache (z.B. Imitation realer Sprecher), die Umwandlung von Text in Sprache, aber auch KI-generierte Musik und einzelne Klänge. Der Fokus der Thesis soll auf dem letztgenannten Bereich liegen und sowohl konzeptionell wie auch praktisch die Nutzung von KI zur Erzeugung von Klängen z.B. für Musikproduktion oder die Nutzung in Software betrachten. |
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Status | Frei |
Betreuer/-in | Frédéric Thiesse (E-Mail) |
Beschreibung | Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. Considerable work has been devoted to the interpretation of machine learning models. However, optimization solvers often represent inexplicable black boxes whose solutions are not accessible to and cannot be interacted with by the user. This lack of interpretability may hinder the adoption of data-driven solutions, as practitioners may not understand or trust the recommended decisions. In this thesis you will study the intersection between explainability and data-driven optimization. For this purpose, you will first conduct a structured literature review according to vom Brocke (2009). In doing so, you will identify a variety of approaches, review them with respect to their potentials and limitations, and establish a state-of-the-art. In the second part of your thesis you will illustrate the results of your research in a practical example. Develop a prototype of an explainable data-driven optimization approach based on a self-selected example. Conclude your work with possible research opportunities and address the limitations of your study. If you are interested in this topic: 1. First inquire if this topic is still available. 2. If the topic is available and it is assigned to you, you must prepare an "exposé". In this, you should summarize the results of your initial research, your resulting research question(s), and your plan for how you will answer those question(s). The exposé should also include a rough outline of the thesis you plan to write. 3. Please arrange an initial meeting only AFTER you have prepared the exposé. 4. The thesis will not be registered until the exposé has been prepared and approved. Please note that the thesis must be registered before the end of the semester in which you have been awarded a supervisory position and plan for buffer for several rounds of revisions of the exposé, if necessary. Literature: - Forel, A., Parmentier, A., & Vidal, T. (2023). Explainable Data-Driven Optimization: From Context to Decision and Back Again. arXiv preprint arXiv:2301.10074. - Sadana, U., Chenreddy, A., Delage, E., Forel, A., Frejinger, E., & Vidal, T. (2023). A Survey of Contextual Optimization Methods for Decision Making under Uncertainty. arXiv preprint arXiv:2306.10374. - Brocke, J. V., Simons, A., Niehaves, B., Niehaves, B., Reimer, K., Plattfaut, R., & Cleven, A. (2009). Reconstructing the giant: On the importance of rigour in documenting the literature search process. |
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Status | Vorgemerkt |
Betreuer/-in | Janine Rottmann (E-Mail) |
Beschreibung | The combination of predictive algorithms and data-driven decision techniques to solve decision problems in the presence of uncertainty has received increasing attention in the recent literature. However, optimization solvers are often unexplained black boxes whose solutions are not accessible to users. This lack of interpretability can hinder the adoption of data-driven solutions, as practitioners may not understand the solutions or trust the recommended decisions. In this thesis, you will review the current state of this fairly new field of research on the example of scheduling problems. Furthermore, your work will include a practical part in which you will apply the "Schedule Explainer" developed by Čyras et al. (2020). If you are interested in this topic: 1. First inquire if this topic is still available. 2. If the topic is available and it is assigned to you, you must prepare an "exposé". In this, you should summarize the results of your initial research, your resulting research question(s), and your plan for how you will answer those question(s). The exposé should also include a rough outline of the thesis you plan to write. 3. Please arrange an initial meeting only AFTER you have prepared the exposé. 4. The thesis will not be registered until the exposé has been prepared and approved. Please note that the thesis must be registered before the end of the semester in which you have been awarded a supervisory position and plan for buffer for several rounds of revisions of the exposé, if necessary. Related Work: - Čyras, K., Letsios, D., Misener, R., & Toni, F. (2019, July). Argumentation for explainable scheduling. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 2752-2759). - Čyras, K., Karamlou, A., Lee, M., Letsios, D., Misener, R., & Toni, F. (2020). AI-assisted Schedule Explainer for Nurse Rostering. In 19th International Conference on Autonomous Agents and MultiAgent Systems-Demo Track (pp. 2101-2103). Auckland: IFAAMAS. |
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Status | Frei |
Betreuer/-in | Janine Rottmann (E-Mail) |
Beschreibung | Despite the promising potential of AI in healthcare, there are also concerns about the reliability of these approaches. To ensure the safety and reliability of predictions, it is critical to assess the uncertainty of AI systems' predictions. Techniques such as Bayesian methods and fuzzy systems provide uncertainty estimates and help understand the uncertainty or variability associated with predictions. In your thesis you will study these methods in more detail. If you are interested in this topic: 1. First inquire if this topic is still available. 2. If the topic is available and it is assigned to you, you must prepare an "exposé". In this, you should summarize the results of your initial research, your resulting research question(s), and your plan for how you will answer those question(s). The exposé should also include a rough outline of the thesis you plan to write. 3. Please arrange an initial meeting only AFTER you have prepared the exposé. 4. The thesis will not be registered until the exposé has been prepared and approved. Please note that the thesis must be registered before the end of the semester in which you have been awarded a supervisory position and plan for buffer for several rounds of revisions of the exposé, if necessary. Literature: - Seoni, S., Jahmunah, V., Salvi, M., Barua, P. D., Molinari, F., & Acharya, U. R. (2023). Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023). Computers in Biology and Medicine, 107441. - Psaros, A. F., Meng, X., Zou, Z., Guo, L., & Karniadakis, G. E. (2023). Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons. Journal of Computational Physics, 477, 111902. - Begoli, E., Bhattacharya, T., & Kusnezov, D. (2019). The need for uncertainty quantification in machine-assisted medical decision making. Nature Machine Intelligence, 1(1), 20-23. |
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Status | Vorgemerkt |
Betreuer/-in | Janine Rottmann (E-Mail) |
Beschreibung | Existing studies on the combination of predictive and prescriptive analytics take predictions as fixed and then make choices based on fixed predictions, for example, predictions are parameters in an optimization model. Recent studies call for a fusion of predictive modeling and prescriptive analysis. The growing interest in embedding predictive models in MIPs has led to the development of toolboxes such as JANOS (Bergman et al., 2022), OMLT (Ceccon et al., 2022) and gurobi-machinelearning. First, provide a comprehensive overview of integrated predictive and prescriptive analyses in the current literature and research existing toolboxes. Subsequently, your task is to analyze the functionalities and limitations of the identified toolboxes. You should then evaluate the efficiency of these toolboxes on the example of an optimization problem of your choice (you can refer to the examples given in the toolbox documentations). Conclude your work with a summary and discuss any limitations and open research questions. Knowledge in Python is a Plus! If you are interested in this topic: 1. First inquire if this topic is still available. 2. If the topic is available and it is assigned to you, you must prepare an "exposé". In this, you should summarize the results of your initial research, your resulting research question(s), and your plan for how you will answer those question(s). The exposé should also include a rough outline of the thesis you plan to write. 3. Please arrange an initial meeting only AFTER you have prepared the exposé. 4. The thesis will not be registered until the exposé has been prepared and approved. Please note that the thesis must be registered before the end of the semester in which you have been awarded a supervisory position and plan for buffer for several rounds of revisions of the exposé, if necessary. Literature and Libraries: - Bergman, D., Huang, T., Brooks, P., Lodi, A., & Raghunathan, A. U. (2022). Janos: an integrated predictive and prescriptive modeling framework. INFORMS Journal on Computing, 34(2), 807-816. - Ceccon, F., Jalving, J., Haddad, J., Thebelt, A., Tsay, C., Laird, C. D., & Misener, R. (2022). OMLT: Optimization & machine learning toolkit. Journal of Machine Learning Research, 23(349), 1-8. - https://github.com/Gurobi/gurobi-machinelearning |
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Status | Frei |
Betreuer/-in | Janine Rottmann (E-Mail) |
Beschreibung | Real world data has errors, biases and missingness. However, most ML is done on sanitized data, not real-world data. That includes health care applications. Your task is to explore the potential of Large Language Models (LLMs) to create more fair and reality-centric health records. |
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Status | Vorgemerkt |
Betreuer/-in | Janine Rottmann (E-Mail) |
Beschreibung | Zum Schutz der menschlichen Gesundheit und Sicherheit im Umgang mit potenziell gefährlichen Chemikalien am Arbeitsplatz oder im Alltag werden sogenannte Sicherheitsdatenblätter mit ausgestellt. Diese geben Auskünfte über etwa die Zusammensetzung oder die Auswirkung auf den Menschen. Diese Sicherheitsdatenblätter werden allerdings nicht immer zuverlässig und konsistent erstellt, d.h. es ist eine Konsistenzprüfung notwendig. Die GeSi Software GmbH bietet eine webbasierte, halbautomatische Lösung für einen solchen Konsistenzcheck an. Ein wichtiger Schritt für die Konsistenzprüfung ist es, zunächst die Informationen aus einem Sicherheitsdatenblatt (gegeben als PDF-Dokument) zu extrahieren, um anschließend die Konsistenzprüfung durchführen zu können. Auch wenn die Sicherheitsdatenblätter partiell standardisiert sind, gibt es Abschnitte darin, die eine sehr große Varianz aufweisen und daher eine vollautomatische Extraktion anspruchsvoll werden lassen. In letzter Zeit haben die Methoden der künstlichen Intelligenz (KI) das Potenzial im Bereich der Textverarbeitung gezeigt und können möglicherweise ein wertvolles Werkzeug für diese Aufgabe sein. Daher liegt der Fokus dieser Masterarbeit auf der Untersuchung und Evaluation des Verbesserungspotenzials durch Erweiterung der bisherigen (regelbasierten) Softwarelösung durch KI. Ihre Aufgabe wäre es, die Anwendbarkeit und Fähigkeiten von großen Sprachmodellen auf diesen Task zu evaluieren. Dazu sind Experimente auf Basis von Few-Shot bzw. Many-Shot angedacht. Dieses Thema wird vom Lehrstuhl von Prof. Thiesse in Zusammenarbeit mit der GeSi Software GmbH betreut. Bei Interesse wenden Sie sich an Prof. Thiesse oder Shreeraj Joglekar. Die genauen Forschungsfragen werden in Absprache mit der GeSi Software GmbH konkretisiert sowie weitere Details zum Thema (verfügbare Daten, Erwartungen, usw.) werden beim ersten Besprechungstermin diskutiert. Bearbeitungssprache: Deutsch oder Englisch. Hinweis: Für diese Masterarbeit bereitgestellte Sicherheitsdatenblätter (Trainings /Testdaten) sind jedoch in deutscher Sprache ausgestellt und der Fokus muss darauf gerichtet sein. |
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Status | Frei |
Betreuer/-in | Shreeraj Joglekar (E-Mail) |
Beschreibung | Although machine learning (ML) research has reached a level of maturity where applications can be implemented in real-world business applications, many ML projects never leave the pilot stage. Furthermore, core ML research focuses on optimizing model development rather than exploring the difficulties of model deployment. That is why most ML lifecycle frameworks (e.g., CRISP-DM, TDSP, etc.) barely deal with model deployment, even though the added value of ML projects is achieved in this stage in practice. In order to improve the adoption of ML applications in industrial companies, it is necessary to understand the challenges that companies face when trying to implement ML into their processes. Therefore, the aim of this thesis is to thoroughly investigate a real-world case (i.e. an industrial company – note: contact will be made by supervisor) and identify how companies operationalize ML projects and which key pain points they face when adopting ML applications. The study will explore challenges related to data integration, scalability, privacy, model maintenance, and adaptation in industrial settings. Moreover, the thesis should establish a new ML lifecycle framework based on the findings from the case. In your thesis you will follow a single case study approach. This methodology examines several sources of information, such as secondary material provided by the company as well as semi-structured interviews with different stakeholders throughout the company’s organizational structure. If you are interested in this topic, please follow the following steps: 1. Ask whether this topic is still available. 2. If the topic is available and it is assigned to you, you must prepare an "exposé". In this, you should summarize the results of your initial research, your resulting research question(s), and your plan for how you will answer those question(s). The exposé should also include a rough outline of the thesis you plan to write. 3. Please arrange an initial meeting only AFTER you have prepared the exposé. 4. The thesis will not be registered until the exposé has been prepared and approved. Please note that the thesis must be registered before the end of the semester in which you have been awarded a supervisory position and plan for buffer for several rounds of revisions of the exposé, if necessary. Literature: Paleyes, Andrei; Urma, Raoul-Gabriel; Lawrence, Neil D. (2023): Challenges in Deploying Machine Learning: A Survey of Case Studies. In: ACM Comput. Surv. 55 (6). Baier, L., Jöhren, F., & and Seebacher, S. (2019). CHALLENGES IN THE DEPLOYMENT AND OPERATION OF MACHINE LEARNING IN PRACTICE. In Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden, June 8-14, 2019. AIS. Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software Engineering for Machine Learning: A Case Study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (pp. 291–300). IEEE |
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Status | Frei |
Betreuer/-in | Manuel Zall (E-Mail) |
Beschreibung | Deep learning (DL), which includes construction and training of various neural network architectures, has been remarkably effective in image processing tasks in recent years. One such task includes image classification. Yet, training deep learning systems from scratch is computationally very demanding and frequently not applicable in data-limited areas. To address these challenges, transfer learning techniques have emerged, which entail applying and fine-tuning deep learning architectures that have been pre-trained on a source dataset to intended target dataset(s). According to recent research, transfer learning has proven to be effective on a number of image classification tasks, allowing for a reduction in computation requirements and an improvement in generalizability. Nonetheless, the impact of various source datasets on the model performance with respect to the target task has not received enough attention. In other words, it remains unclear whether and how the model performance (on target data) differs based on a choice of pre-training datasets. This thesis, thus, aims to empirically investigate how the choice of source dataset influences model performance on target dataset. To address this, you will examine pre-trained network architectures and conduct various computational experiments to figure out the most useful source dataset(s) for an image classification task. This mainly involves the assessment of model accuracy and test-set generalizability. The experiments may involve domain specific as well as non-domain specific source datasets. An exemplary target dataset is EuroSAT (land use / land cover data) – encompassing a variety of satellite images divided into 10 classes including forest, agricultural, industrial, among several others. If you are interested in this topic, please directly contact the associated supervisor to schedule the initial discussion. Note: The EuroSAT dataset represents a well-suited exemplary image classification dataset for this thesis. However, you are welcome to select the image classification dataset of your choice and/or domain. |
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Status | Frei |
Betreuer/-in | Shreeraj Joglekar (E-Mail) |
Beschreibung | Over recent years, Machine Learning (ML) has been demonstrating the effectiveness in tackling various tasks across an array of disciplines. However, a successful deployment of real-world ML applications involves many processes, besides building the most suited models. These usually involve components, such as data collection and management, model development and training, model hosting and monitoring, and prediction/inference service. Based on the challenges concerning one or more of these components, some valuable ML models may not be adopted into real-world systems at all. In this thesis, you will explore the requirements and difficulties with ML implementation that are unique to the manufacturing industry. The aim here is to generate insights which should assist comprehensive of existing difficulties in ML deployment w.r.t. the manufacturing applications. You are welcome to address a specific sub-sector, process or application area within manufacturing. This investigation should be conducted via a systematic literature survey or a standard literature review. If you are interested in pursuing this topic, please contact the associated supervisor. Related literature: Paleyes, Andrei; Urma, Raoul-Gabriel; Lawrence, Neil D. (2023): Challenges in Deploying Machine Learning: A Survey of Case Studies. In: ACM Comput. Surv. 55 (6). |
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Status | Frei |
Betreuer/-in | Shreeraj Joglekar (E-Mail) |
Beschreibung | Students are also welcome to submit ideas or proposals for their own thesis topics. We will be glad to hear about your own topic suggestions and also encourage them. Own topics will be supervised as long as they fall within the general scope of our chair along with the following areas: Areas with technical focus - 1. Applied machine learning: application areas can be within engineering, marketing analytics, finance, and agriculture 2. Uncertainty Quantification (UQ) and analysis in machine learning systems 3. Explainable AI. Areas with focus on economics and business - 1. Information and platform economics 2. Digital transformation along with its economic value 3. Data-driven business models. If you would like to discuss own proposals, please contact Shreeraj Joglekar. |
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Status | Frei |
Betreuer/-in | Shreeraj Joglekar (E-Mail) |
Beschreibung | Students are also welcome to submit ideas or proposals for their own thesis topics. We will be glad to hear about your own topic suggestions and also encourage them. Own topics will be supervised as long as they fall within the general scope of our chair and the corresponding research interests of the research assistant. In my case (Manuel Zall), topics will be considered that fall into the following categories/fields: Machine learning applications in manufacturing, digital transformation in manufacturing, transfer learning, behavioral research in the intersection of AI/ML and management. If you are interested in discussing your own proposal in these areas, please contact Manuel Zall. |
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Status | Frei |
Betreuer/-in | Manuel Zall (E-Mail) |