Daniel Braun is an assistant professor at the Department of Industrial Engineering and Business Information Systems (IEBIS) at the University of Twente. Previously, he worked in the Department of Informatics at the Technical University of Munich, where he also received his PhD. Daniel holds a master's degree in Computing Science from the University of Aberdeen and a bachelor's in Computer Science from Saarland University.
His research focuses on the application of Natural Language Processing (NLP) and Artificial Intelligence (AI) in knowledge-intensive processes, with a focus on problems from the legal domain (Legal Tech). More broadly, he is also interested in the general application of NLP and Natural Language Generation (NLG) in the context of businesses and administration.
Organisations
Publications
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Research profiles
Supervision of Bachelor thesis projects (1st or 2nd Supervision):
- Franka Redeker, Increasing the predictability of the Radar Front-End Engineering department at Thales NL, IEM, 2024
- Ben Pikkemaat, Digital Transformation of Order Processing Baas Metaal B.V., IEM, 2024
- Dmytro Yevmenchuk, Self-Service Business Intelligence at Topicus: An Assessment of Employee-Related Enablers, IEM, 2024
- Yasin Fahmy, Student Perception on AI-Driven Assessment: Motivation, Engagement and Feedback Capabilities, BIT, 2024
- Elizaveta Stashevskaia, How do teachers envision AI grading for open-ended questions in universities?, CS, 2024
- Stefan Ilich, License-Aware Web Crawling, CS, 2024
- Felicia Burlacu, Patterns of success and failure: Analysing Large Language Models in Question Answering in Exam Contexts, CS, 2024
- Luc van Hienen, Building an effective warehouse dashboard: Improving operational insight through KPIS, IEM, 2024
- Alexandru Matcov, Explainable AI in Credit Risk Assessment for External Customers, BIT, 2024
- Mengmeng Li, The Dual Role of AI in Cybersecurity and Cybersafety: Insights from ChatGPT, BIT, 2024
- Kaleb Dan, The optimization of the requesting procedure, IEM, 2023
- Iris te Koppele, Dashboard on lead times, IEM, 2023
- Vitalii Fishchuk, Adversarial attacks on neural text detectors, BIT, 2023
- Vlad-Gabriel Stoian, Students' Trust in Automated Grading Through Explainable AI Visualizations, BIT, 2023
- Ruta Ergle, Enabling the eCMR signing process by implementing e-signature software for Vervo, IEM, 2023
- Jelmer Hofman, Transparency in AI-driven Grading Tools for Open-ended Questions in Higher Education, BIT, 2023
- Daniël van Horn, Process improvement using machine learning, IEM, 2022
- Hugo Vaatstra, Optimising the safety of crossing railroads to aim for a safer rail net, IEM, 2022
- Max Miedema, Creating insights, KPIs, visualizations and a dashboard of the operational residual streams’ performance regarding the circularity goals of Company X, IEM, 2022
- Nils Idema, Improving the utilisation of AI Ops analyses by improving the quality of incident logging data, IEM, 2022
- Ramish Bhutto, Automating Privacy Policy Extraction And Summarization, TCS, 2022
- Eva Stoica, A student’s take on challenges of AI-driven grading in higher education, BIT, 2022
- Kristians Balickis, Influence of human-in-the-loop on the acceptance of AI-driven evaluation of essay questions by students, IBA, 2022
- Anastasia Coviliac, Improvement of AI-assessment systems in grading open questions based on the teaching assistant’s view, IBA, 2022
- Henry Kurzhals, Challenges and approaches related to AI-driven grading of open exam questions in higher education : human in the loop, IBA, 2022
- Rozemarijn van de Leur, Challenges and approaches related to AI-driven grading in higher education: the procedural trust of students, IBA, 2022
- Alex Kijk in de Vegte, The impact of product Y of company X on corporate social responsibility, IEM, 2022
Supervision of Master thesis projects (1st or 2nd Supervision):
- Keara Schaaij, Exploring lexical alignment for understandable information from trusted healthcare chatbots, I-Tech, 2024
- Abel van Raalte, Assessing Fairness in Machine Learning: The Use of Soft Labels to Adress Annotator Bias in NLP, BIT, 2024
- Jin Xu, Leveraging Disagreement among Annotators for Text Classification, CS, 2024
- Zhenqi Zhao, Exploring Lexical Alignment Influences in Price Bargain Chatbot, BIT, 2024
- Robert Brouwer, Differentiating user groups within an educational dashboard using log data, BIT, 2023
- Simon Chamoun, Unleashing the potential of Business Intelligence & Analytics for SMEs in the Netherlands: A Comprehensive Analysis, BA, 2023
Affiliated study programs
Courses academic year 2024/2025
Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.
Courses academic year 2023/2024
Current projects
Finished projects
Software Aided Analysis of Terms of Services (SaToS)
The phrase "I have read and understood the terms of service" is often referred to as the biggest lie on the internet. Every time we buy something online, we are confronted with Terms of Services (ToS). However, only a few people actually read these terms, before accepting them, often to their disadvantage. General terms and conditions included in standard form contracts are of significant economic value, as most companies use these terms when entering into contractual relationships with their customers. The interdisciplinary computer and legal science research project SaToS (Software Aided Analysis of ToS) from the chair of Software Engineering for Business Information Systems (sebis) at TU Munich aims to automatically identify Terms of Services and summarise them with regard to their lawfulness and customer friendliness, in a simplified language. In this way, SaToS aims to empower customers to make educated decisions about where to buy or not within seconds, directly addressing the imbalance of powers and fostering the constitutional principle of legal clarity.
Meta Model based Natural Language Generation for Automatic Abstractive Text Summarization (A-SUM)
More than 5000 years after its invention, written language is still the most important medium to document and communicate knowledge. While the production of texts is simplified and accelerated by word processing software, template systems and other technologies, the consumption of texts is still a comparably little by technology supported process. The current state-of-the-art in automatic text summarization are mostly so called extractive methods, which extract the n most important sentences of a text by the means of metrics like TF/IDF. These summaries consist of mostly incoherent sentences. The goal of the BMBF founded Software Campus Project "Meta Model based Natural Language Generation for Automatic Abstractive Text Summarization" (A-SUM) is the creation of coherent, personalized automatic abstractive summaries. Instead of just extracting sentences, A-SUM aims to extract facts from texts and store them in an intermediate, meta model based, structured format. Based on user preferences and contextual information, this information can be personalized. By applying Natural Language Generation techniques, we aim to transform the structured representation of information back to a coherent text, which gives the recipient a quick and tailored overview of the content of the original text.
Vertical Social Software (VSS)
Contextual and Social Computing are converging. Three major trends are impacting software business and engineering: The rise of the verticals In the slipstream of various successful, large horizontal (i.e. generic) social networks such as Facebook, a plethora of niche vertical social websites and software has emerged to provide tailored services and value for niches and vertical domains (e.g. PatientsLikeMe, Stackoverflow or LinkedIn). The number and diversity of devices is growing Nowadays, end-user software is running on ever more types of connected devices from smartphones to smartwatches, augmented/virtual reality headsets etc. Platform business models and technology profoundly impact(ed) the economics of software development The Appeconomy enables third-party developers to target a world-wide audience at much lower costs than few years ago. The "app" construct popularized the idea of having a flexible set of small loosely coupled software modules for specific use-cases tailored to the user's evolving needs.
Technology Scouting as a Service (TSaaS)
The goal is to create a new process for innovation in industrial companies using a Software as a Service platform based on AI algorithms. This enables companies to identify their own customized technology gamechanger faster, cheaper and more efficiently and at the same time to build up internal technology know-how. This offers especially small and medium sized companies without their own research or innovation department an opportunity to keep up with large corporations and to compete. In this way, the project specifically strengthens small and medium-sized enterprises, the backbone of industry, and helps German companies to continue to expand their top position as world market leaders. The innovation of the project and central aspect is the research of a cognitive system in the form of Natural Language Processing (NLP) algorithms linked to an information model consisting of a knowledge graph. This system enables engineers to find technological solutions for domain-specific problems with just a single mouse click. The novelty consists of two core components: Development of an NLP system for the semantic analysis of texts on technologies and problems of mechanical and plant engineering. Development of a knowledge graph in structure and content, which describes and intelligently links the multitude of problems and technologies in mechanical and plant engineering.
AI-Supported Legal Review of Terms and Conditions to Strengthen Consumer Protection (AGB-Check)
The advancing digitization is increasingly influencing everyday work in so-called “knowledge-intensive” professions. Software that supports experts in dealing with legal texts and questions is often summarized under the term "Legal Tech". In theory, it can help make legal expertise available more quickly and cheaply, and hence making it more accessible, especially for consumers. In practice, however, the necessary technologies are often only available to commercial actors. The goal of AGB-Check is the development of innovative technologies that support legal knowledge workers in their efforts to represent the interests of consumer in the digital age better and more effectively. Using the example of a specific application - automated legal reviews of terms and conditions - we want to research technologies that: Support experts in dealing with German legal texts by automatically analyzing them semantically and classify them regarding their legality Enables geographically and organizationally separated experts to participate together in the learning and development process of legal expert systems Allow to combine machine learning and rule-based techniques to process German legal texts to in hybrid systems
In the press
Address
University of Twente
Ravelijn (building no. 10), room 3333
Hallenweg 17
7522 NH Enschede
Netherlands
University of Twente
Ravelijn 3333
P.O. Box 217
7500 AE Enschede
Netherlands
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