Education

• 04/2024 – 05/2026, Technical University of Munich (Germany), M.Sc. in Mathematics.
• 11/2020 – 10/2023, Technical University of Munich (Germany), M.Sc. in Electrical and Computer Engineering.
• 09/2016 – 07/2020, Beijing Institute of Technology (China), B.Eng. in Electronic Information Engineering.

Organizations & Scholarships

• 10/2024 – Present, Konrad Zuse School of Excellence in Reliable AI (relAI) — M.Sc. Student Fellow (Mathematical Foundations).

Research & Teaching Experience

• 10/2025 – 05/2026, Student Assistant (Computational Biology), Helmholtz Munich & University Clinic Munich (LMU).
  Built deep learning pipelines to predict cell types and healthy/pathological states from DAPI-stained images.

• 10/2025 – 05/2026, Master’s Thesis (Biomathematics), Helmholtz Munich & TUM.
  Extending prior-fitted networks with Riemannian Neural Processes to enable Riemannian predictives, quantify the sim-to-real gap, and validate on real-world datasets.

• 04/2025 – 07/2025, Teaching Assistant (Statistics), TUM, Dept. of Mathematical Statistics.
  Introduction to Data Science and Statistical Thinking (probability, statistical principles, and data exploration in R).

• 10/2024 – 02/2025, Data Innovation Lab, Munich Data Science Institute (MDSI) & Helmholtz Munich.
  Developed graph- and sequence-based deep learning models for RNA–small molecule interaction prediction.

• 04/2022 – 10/2023, Working Student & Master’s Thesis (Robotics), Machine Learning Research Lab, Volkswagen, Munich.
  Integrated model-based RL with a spatial world model for QCar navigation; designed an information-theoretic exploration framework in a deep variational Bayesian state-space model for real-time navigation in dense 3D environments.

• 04/2021 – 10/2021, Research Intern, fortiss GmbH, Munich.
  Implemented a sampling-free Laplace Approximation for Bayesian neural networks and evaluated diagonal Hessian approximations for autonomous-driving object detection.

• 03/2019 – 06/2020, Bachelor’s Thesis, Computer Vision Lab, BIT, Beijing.
  Built a C++ defect-detection system for steel plates; implemented contour-based matching with Hausdorff distance.

Research Topics

Bayesian networks, neural processes, causal inference, graphical models, computational statistics.

Contact

Email: ziqingzhao.23@gmail.com