Dr. Beat Tödtli

Beat Tödtli is a researcher at the the Institute for Information and Process Management and a teacher in the field of data science. He holds a PhD in theoretical particle physics and worked in industry, developing algorithms to detect fraudulent bank notes from sensory data. He is actively engaged in promoting understanding and communication of science and matters around artificial intelligence and its explainability.

Area of Expertise

artificial intelligence, machine learning, natural language processing, Deep Learning, Statistics, Data Science, Big Data, Data Mining, Computer Vision, sensor data analysis, statistical image processing, recommender systems

Education

phd in Quantum Chromodynamics, MSc in theoretical particle physic

Professional Experience

Engineer in sensor development, sensor data analysis for banknote processing devices

Teaching Experience

Teaching a universities of applied sciences in Machine Learning, Physics, Data Science, Statistics, Data Mining and Datenmodellierung.
Engaging in outreach activities to promote data mining knowledge in university and high school curricula

Projects

projects in digital health such (Recommender systems for health care), natural language processing (reducing CO2-Emissions, land use planning etc.), information retrieval (leveraging legal documents to help in abuse cases), Explainability in Machine Learning

Memberships

Swiss alliance of data intensive services, digital health subgroup

Editorials and Reviewing

publications and review activities in applied machine learning and quantum computing, e.g. for patterns, KSEM or IEEE-ITST

Peer-Reviewed Journal Articles and Conference Proceedings

• RISS, U., TÖDTLI, B., WOLF, P. (2023). Privacy Intermediaries: A Business Model Perspective. 24th International CINet Conference.
• JUNGINGER, S., TÖDTLI, B., ULMER, T. (2023). Giving Form to the Invisible: Can we make in-home network data traffic tangible to users? DeSForM 2023.
• TÖDTLI, B. (2022). From Words to Sound: Neural Audio Synthesis of Guitar Sounds with Timbral Descriptors. 3rd Conference on AI Music Creativity, AIMC (pp. 11). Proceedings of the 3rd Conference on AI Music Creativity, AIMC. https://doi.org/10.5281/zenodo.7088416
• MEISSNER, J., MINDER, B., KLOTZ, U., TODISCO, A., MURRI, M., JUNGINGER, S., ... ULMER, T. (2022). Voice Assistant Use: Challenges for the Home Office Work Context. European Academy of Management Conference (EURAM), "Leading Digital Transformation", Zurich / Winterthur (15-17 June 2022)..
• TÖDTLI, B., MAURUS KÜHNE, M. (2020). Combining Universal Adversarial Perturbations. In D. Trabold, P. Welke, N. Piatowski (Eds.), Proceedings of the LWDA 2020 Workshops: KDML, FGWM, FGWI-BIA, and FGDB (pp. 35-46).
• REIMER, U., MAIER, E., TÖDTLI, B. (2020). Going beyond Explainability in Medical AI Systems. Proc. Modellierung 2020 Short Papers, Workshop Papers, and Tools & Demo Papers. CEUR-WS.org/Vol-2542 (pp. 185-191).
• REIMER, U., TÖDTLI, B., MAIER, E. (2020). How to Induce Trust in Medical AI Systems. Advances in Conceptual Modeling: ER 2020 Workshops CMAI. Lecture Notes in Computer Science (LNCS).

Professional Journals and Newspaper

• TÖDTLI, B., KÜNZI, U.-M. (2019, August). Vertrauen oder Angst vor Fakes. kmuRUNDSCHAU, 2019 (3). Muttenz.

Teaching related publications

• TÖDTLI, B., WULLSCHLEGER, N. (2024). Open Data Dokument-Tagging Fallstudie für den Data Science Unterricht. Open Education Platform for Management Schools. https://doi.org/10.25938/oepms.340

Presentations

• TÖDTLI, B., KRAFT, M., ZIEGLER, M., BINDER, P. (2023). Cyberbullying Detection using Machine Learning: Insights from an Applied Research Project. "EXPLORING CRITICAL CHALLENGES FOR THE CHANGING NATURE OF WORK".
• MÜLLER, S., TÖDTLI, B., VETSCH, J., RICKENMANN, M., HAUG, S., BALDAUF, M., FRÖHLICH, P. (2022). Designing Experts' Interactions with a Semi-Automated Document Tagging System. AutomationXP22: Engaging with Automation, Workshop at CHI'22.
• TÖDTLI, B. (2017). Die Grenzen von Deep Learning. Asut.