Zhongjie Yu
Machine Learning Group, Computer Science Department, TU Darmstadt. Hochschulstrasse 1, Room S1|03 078, 64289 Darmstadt, Germany
+49-6151-16-21374 yu (at) cs (dot) tu-darmstadt (dot) de

Meetings by appointment

Mission. My research is currently centered around constructing probabilistic graphical models which illustrate the correlation from data. I worked on the InDaS project, which aims to develop a Machine Learning course for engineers. I also work on the MADESI and KompAKI projects, focusing on deep probabilistic models for time series as well as hybrid data.

Bio. I'm a PhD student of Machine Learning at the Computer Science Department of the TU Darmstadt University, Germany. After receiving my M.Sc. from Saarland University (Germany) in 2018, I joined the Machine Learning group at TU Darmstadt University.

2018 - now: Ph.D. student at the Machine Learning Lab, CS Department, TU Darmstadt, Germany
2015 - 2018: M.Sc. student of computer science at Saaland University, Germany
2012 - 2015: M.Sc. student of pattern recognition and intelligent systems at Shanghai Jiao Tong University, China
2008 - 2012: B.Sc. student of automation at Zhejiang University, China

Advised Theses.
2023. Differentiable Model Selection for Time Series Forecasting. M.Sc. thesis, F. Kalter. (so-supervised with Jonas Seng)
2022. Structure Learning of Probabilistic Circuits using Deep Random Projections. M.Sc. thesis, K. Nguyen (co-supervised with Arseny Skryagin)
2021. Leveraging Whittle Networks for Time Series Forecasting in the Spectral Domain. M.Sc. thesis, N. Thoma.
2021. Using virtual met masts based on reanalysis data and Machine Learning concepts to monitor and evaluate the measurement data and performance of wind turbines. M.Sc. thesis, J. Faulhaber. (co-supervised with Fraunhofer IEE)
2020. Sensor-based Internal Event Detection Using an IoT Device. B.Sc. thesis, C. Chu.
2019. Deep Demand Management. M.Sc. thesis, F. Otto.
2019. Root Cause Analysis of Production Line Data with Deep Tractable Probabilistic Graphical Models. M.Sc. thesis, N. Förster.
2019. Automatisiertes Machine Learning Tool zur Approximation des Betriebsverhaltens von Maschinen und Anlagen. B.Sc. thesis, T. Pechatschek.
2019. Monitoring of Forming Processes on the Basis of Time Series using Machine Learning Methods. M.Sc. thesis, H. Ali. (co-supervised with PTU, TU Darmstadt)


Publications can be found at DBLP