Seminar - Industrial Data Science, Machine-, Transfer- and Federated Learning

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Copyright Jana Kemnitz

The LIV.DAT Virtual Seminars are an opportunity for its staff and students to broaden their horizon in Data Science. This year we already had excellent talks that covered areas such as “Big-data for nano-electronics”, “AI in nuclear power generation applications” and “Cardiovascular risk prediction using big data” to name a few. A complete overview of all past and future seminars can be found here. The Series continues on 7 December 2020 where Dr Jana Kemnitz (Siemens) will talk about the potential and challenges of Machine Learning and Data Science in industry.

Digitization and the Internet of Things (IoT) are transforming complete industries and allow the collection of large amounts of data of various types as machine data and sensor data. Data Science and Machine Learning offer the potential to generate enormous value and a competitive advantage. Typical industrial use cases include soft sensors, machine failure detection, predictive maintenance, and product quality assessment. One major challenge in Industrial Data Science is the Scalability of Machine Learning tasks due to the non-unified data format, varying data distribution and missing ground truth. Two approaches have the potential to overcome this challenge: Transfer Learning applying gained knowledge presented in a trained Machine Learning Model to a novel, but similar task and Federated Learning enables privacy preserving training of a Machine Learning Model across multiple decentralized edge devices allowing the collaboration across different companies. This talk will also address the challenges in Industrial Data Science and Machine Learning projects, what skillset is needed in a team and how to make sure the right problem is being solved.



Dr Jana Kemnitz is a Senior Data Scientist and Machine Learning Expert at the Distributed-AI-Systems Research Group, Siemens. Previously she was based at the Paracelsus Medical University in Salzburg, Austria, and the ETH in Zurich, Switzerland, where she specialised in deep learning for medical image analysis. During this time she was also a lecturer in signal- and image processing at the University of Vienna, Austria, and worked as a machine learning specialist at Chondrometrics GmBH.

During her PhD she was awarded a Marie Curie Fellowship by the European Union, the DAdorW Future Prize by the German Academy of Osteological and Rheumatological Sciences, a visiting scholarship for the ETH in Zurich by the German Society for Biomechanics and the Paracelsus Science Prize by the Paracelsus Medical University. Between 2018-2019 she was the vice chair of the Austrian Chapter of the Marie Curie Alumni Association.

The seminar is open to staff, students and anyone else who is interested. Participation is free, but you need to register to attend this seminar. This is the final seminar of this calendar year and we will back with the next series early 2021.


For more information and how to register please visit the events webpage


Upcoming Seminar

Dr Jana Kemnitz

Senior Data Scientist, Distributed-AI-Systems Research Group Siemens

Seminar Title: “Industrial Data Science, Machine-, Transfer- and Federated Learning”

Monday 7 December 2020 at 14:00 (Europe/London)