Sebastian Erhardt, M.Sc.
Doctoral Student and Junior Research Fellow
Innovation and Entrepreneurship Research
+49 89 24246-589
sebastian.erhardt(at)ip.mpg.de
Areas of Interest
Data Science, Artificial Intelligence, Machine Learning, Natural Language Processing, Economics of Innovation
Academic Résumé
Since 2020
Junior Research Fellow and Doctoral Candidate, Max Planck Institute for Innovation and Competition (Innovation and Entrepreneurship Research)
Since 2020
Postgraduate Studies in Business Research (MBR) at Munich School of Management, LMU Munich
02/2020 - 07/2020
Visiting Blockchain Researcher, Liphardt Lab ‒ Stanford Distributed Trust Initiative, Leland Stanford Junior University
10/2019 - 05/2021
Master of Science (M.Sc.) in Computer Science, Ludwig-Maximilians-Universität (LMU) Munich
08/2018 - 07/2020
Honours Degree in Technology Management, Center for Digital Technology and Management (CDTM), LMU Munich and Technical University of Munich (TUM)
10/2017 - 04/2019
Master of Science (M.Sc.) in Information Systems, Technical University of Munich (TUM)
10/2014 - 03/2017
Bachelor of Science (B.Sc.) in Information Systems, Technical University of Munich (TUM)
Work Experience
12/2005 - today
Self-Employed Developer/Consultant ‒ Freelance work for small, medium and large companies and for the Federal Government, Germany
08/2020 - 10/2020
Developer/Product Manager ‒ Tech4Germany, Federal Ministry of the Interior, Federal Ministry of Finance and German Federal Centre for Information Technology, Berlin
02/2016 - 03/2017
Working Student, Deloitte Digital, Munich
09/2015 - 02/2016
Working Student, Deloitte Consulting, Munich
Honors, Scholarships, Academic Prizes
Since 2019
Fellow of the Tech4Germany Program of the Federal Government of Germany, an initiative under the patronage of Prof. Dr. Helge Braun - Federal Minister & Chief of Staff of the Federal Government of Germany
2018 - 2019
Think Digital Scholarship of the Internet Business Cluster (IBC) e.V.
Since 2018
Member of the Elite Network of Bavaria, an initiative of the Free State of Bavaria to promote young scientists
2016 - 2018
Manage&More Scholarship of the UnternehmerTUM
Publications
Discussion Papers
(2024). PaECTER: Patent-level Representation Learning using Citation-informed Transformers, arXiv preprint 2402.19411. DOI
- PaECTER is a publicly available, open-source document-level encoder specific for patents. We fine-tune BERT for Patents with examiner-added citation information to generate numerical representations for patent documents. PaECTER performs better in similarity tasks than current state-of-the-art models used in the patent domain. More specifically, our model outperforms the next-best patent specific pre-trained language model (BERT for Patents) on our patent citation prediction test dataset on two different rank evaluation metrics. PaECTER predicts at least one most similar patent at a rank of 1.32 on average when compared against 25 irrelevant patents. Numerical representations generated by PaECTER from patent text can be used for downstream tasks such as classification, tracing knowledge flows, or semantic similarity search. Semantic similarity search is especially relevant in the context of prior art search for both inventors and patent examiners. PaECTER is available on Hugging Face.
(2022). Logic Mill - A Knowledge Navigation System, arXiv preprint 2301.00200.
- Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.
- https://doi.org/10.48550/arXiv.2301.00200
Presentations
07.12.2023
Logic Mill - A Knowledge Navigation System
2nd CESifo / ifo Junior Workshop on Big Data
Location: Munich
02.12.2023
Logic Mill - A Knowledge Navigation System
Innovation Information Initiative Technical Working Group Meeting - National Bureau of Economic Research
Location: Cambridge, MA, US
09.06.2022
Logic Mill
Munich Summer Institute
Location: Munich
08.04.2022
Tracing the Flow of Knowledge from Science to Technology Using Deep Learning
European Patent Office ARP Program
Location: Munich
04.11.2020
Introduction to Git & Github
Max Planck Institute for Innovation and Competition
Location: online