Tim Schopf

Department of Computer Science
Chair of Software Engineering for Business Information Systems (sebis)    

Technische Universität München 
Boltzmannstraße 3
85748 Garching bei München, Germany

Room FMI 01.12.057 

Office hours: by appointment 

Student offers:

• At the moment, unfortunately, I do not have additional capacity for thesis supervision.

Open HiWi Position

• Currently, I have no open Hiwi positions

As one of the organizers Tim Schopf is responsible for hackaTUM, the official annual TUM hackathon hosted by the Department of Informatics since 2016. Over the last years, it has become one of the biggest hackathons in the EU with over 1700 applications and 900 participants in the last event. 

The next edition of hackaTUM will take place in 2024!
If you are interested in sponsoring the event, contact hackatum.in@tum.de. Further information can be found at https://hack.tum.de

Natural Language Processing Knowledge Graph (NLP-KG)

The NLP knowledge graph is an approach to tackle the information overload challenge of researchers. It aims to help researchers in obtaining an overview of NLP-related topics and find relevant papers more efficiently. More here...

CreateData4AI (CD4AI)

Estimated zettabytes of data are generated every day, with about 80% of this data being unannotated, unstructured text. An as-of-yet unsolved problem with this type of data is how to make it useful for AI applications. Manual annotation of the data can be very precise and incorporate domain-specific knowledge, but it is costly, inefficient, and not scalable. The so-called "80/20 rule" refers to the fact that data scientists often spend up to 80% of their time sorting, cleaning, and otherwise preparing datasets. This project aims to develop a novel hybrid framework that helps domain experts annotate text using Natural Language Processing algorithms, reducing the process to a fraction of the time. More here...

Technology Scouting as a Service (TSaaS)

Due to the quantity and complexity of data (Big Data), the classical information gathering of engineers, for the search for technologies to solve problems in mechanical and plant engineering, no longer meets the requirements of a modern information society. This project aims to research AI-based processes that offer engineers a completely new possibility to find tailor-made solutions for their problems. For this purpose, natural language processing algorithms will be developed that semantically analyze large amounts of text, extract essential information and transfer it into an information model (knowledge graph) in order to capture and link problems and technologies. By means of matching, solutions for a wide range of problems can then be generated by engineers at the push of a button. More here...

Knowledge4Retail (K4R)

The sebis chair is part of the project "Knowledge4Retail", which is a winning project of the "Künstliche Intelligenz als Treiber für volkswirtschaftlich relevante Ökosysteme" programme and thus funded by the Bundesministerium für Wirtschaft und Energie (BMWi). The goal of Knowledge4Retail is to advance the digitization of retail by developing a data platform that enables the combination of online and stationary retail, serves strategic marketing and makes digital solutions available for individual customer service using a semantic digital twin. More here...

Research Institution Knowledge Graph (RIKG)

Given that scientific knowledge usually is available in large quantities as unstructured texts, it is very difficult for researchers to obtain an overview of research fields or scientific domains. Similarly, it is difficult for researchers to gain insight into topics being researched at research institutions. Information about conducted research often only exists in unstructured texts on homepages or intranet pages. In addition, the websites are usually designed according to organizational structures of research institutions rather than a logical structure based on research areas. Therefore, a research area based navigation through the instutution websites is hardly possible. This makes exploration and navigation of topics being researched in an institution very difficult for external as well as for internal users. Structuring the scientific knowledge of research institutions and linking semantically related scientific domains offers researchers the potential for enhanced exploration of research areas. More here ...