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

• TÖDTLI, B., MEISSNER, J., MINDER, B., KLOTZ, U., TODISCO, A., MURRI, M., ... ULMER, T. (2022). Voice Assistant Use: Challenges for the Home Office Work Context. Euram conference..
• 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., TÖDTLI, B., MAIER, E. (2020). How to Induce Trust in Medical AI Systems. Lecture Notes in Computer Science (LNCS).
• 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).
• TÖDTLI, B. (2016). Continuous-time quantum walks on directed bipartite graphs., 94(5), pp. 9.

Professional Journals and Newspaper

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

Presentations

• 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.