Maja Rudolph
Solving challenging machine learning research questions derived from industrial AI
"All models are wrong, but some are useful." - George Box
Engineering devices typically rely on sensors to measure necessary inputs. Machine-learning methods trained on sensor data can increase the utility of such devices by improving their efficiency or enabling exciting new features. However, modeling sensor data presents a number of challenges: real-world data often exhibits multimodal dynamics; some sensors are usually sampled at irregular time intervals; and most industrial datasets are unlabeled. Learning from sensor data requires methods that are flexible enough to capture the underlying complexity while handling various types of noise and uncertainty. For this reason, the main technical theme in my research is how to best combine deep learning with probabilistic modeling.
Curriculum vitae
- Ph.D. Computer Science, Columbia University, Advisor: David Blei
- M.Sc. Electrical Engineering, Columbia University
- B.Sc. Mathematics, MIT
Selected publications
Qiu et al. (2021)
- C. Qiu, T. Pfrommer, M. Kloft, S. Mandt, and M. Rudolph
- International Conference on Machine Learning
Schneider et al. (2021)
- T. Schneider, C. Qiu, and M. Rudolph
- ICML Workshop on Self-Supervised Learning
Schirmer et al. (2021)
- M. Schirmer, M. Eltayeb, and M. Rudolph
- ML4ITS Workshop
McClelland et al. (2020)
- J. L. McClelland, F. Hill, M. Rudolph, J. Baldridge, and H. Schütze
- Proceedings of the National Academy of Sciences
Interview with Maja Rudolph
Senior research scientist in deep probabilistic modeling
Please tell us what fascinates you most about research.
My favorite aspect about research is that you have an opportunity to explore and learn new things every day.
What makes research done at Bosch so special?
Bosch Research is a special place, as experts from many different engineering domains collaborate with us at the Bosch Center for AI to develop innovative AI solutions for many different application areas. I am really fascinated by my colleagues' wealth of engineering knowledge and am working on different ways to integrate their expertise into machine learning algorithms. We call this integrated approach hybrid modeling and to push this topic, I co-founded the hybrid modeling expert focus group.
What research topics are you currently working on at Bosch?
My research lies in the intersection of Bayesian machine learning and deep learning. Bayesian modeling helps communicate modeling choices and reason about uncertainty while neural networks provide the flexibility to model complex interactions in the data.
What are the biggest scientific challenges in your field of research?
One challenge in modeling sensor data from industrial applications is that it is often impractical to label the data. If we want to learn to detect anomalies in an assembly line's sensor data, for example, there are usually no labels. There is an area in machine learning called self-supervised learning where a model is trained on tasks that do not require labels. These methods are particularly well explored for computer vision, but we have been working to extend self-supervised anomaly detection to domains beyond images, including time series and graph data.
How do the results of your research become part of solutions "Invented for life"?
We specifically work on machine-learning research questions derived from real-world engineering problems: for example, how to model driving behavior, how to forecast a device's operating conditions, or how to find anomalies in an assembly line's sensor data. Our machine learning solutions can make engineering systems safer and more efficient.
Get in touch with me
Maja Rudolph
Senior research scientist in deep probabilistic modeling