• Environmental Data Science
• Chemical Exposure and Risk Assessment
• Machine Learning for Ecotoxicology
• Large-scale Monitoring Data Analysis

• Postdoctoral Researcher (2026 - present); RPTU Kaiserslautern-Landau
• Doctoral Researcher (2021 - 2025); Karlsruhe Institute of Technology (KIT)
• M.Sc. Informatics (2018 - 2021); Karlsruhe Institute of Technology (KIT)
• B.Eng. Computer Science and Technology (2012 - 2016); Harbin Institute of Technology

• AI4ChemRisk: Artificial Intelligence for Chemical Risk Assessment (Carl-Zeiss-Stiftung Project, 2026 - present)
• HEPTA: Helmholtz European Partnership for Technological Advancement (Helmholtz Association Project, 2021 - 2025) concluded

• Li, C.; Riedel, T.; Beigl, M., CESI: Sparse Input Spatial Interpolation for Heterogeneous and Noisy Hybrid Wireless Sensor Networks. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track – ECML PKDD 20252026, 16022, 164–179.
• Li, C.; Riedel, T.; Beigl, M., Feature Deviation Embedding Improves Graph Structure Learning for Spatial Interpolation. Proceedings of the 2025 SIAM International Conference on Data Mining (SDM)2025, 356–365.
• Li, C.; Riedel, T.; Beigl, M., Isolating Latent Context Information Enhances Graph Structure Learning for Spatial Interpolation. Advances in Knowledge Discovery and Data Mining – PAKDD 20252025, 15871, 366–377.

■ Ioannidis, G.; Bouloti, N.; Tremper, P.; Li, C.; Boikos, C.; Rapkos, N.; Riedel, T.; Dal Maso, M.; Ntziachristos, L. (2026) Development and Implementation of SOMA: A Secondary Organic Module for Aerosol Integration in High-Resolution Air Quality Simulations. ATMOSPHERIC POLLUTION RESEARCH, 17 (8), 103096.

■ Li, C.; Riedel, T.; Beigl, M. (2026) CESI: Sparse Input Spatial Interpolation for Heterogeneous and Noisy Hybrid Wireless Sensor Networks. In: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track – ECML PKDD 2025, 164–179.

■ Li, C.; Riedel, T.; Beigl, M. (2025) Isolating Latent Context Information Enhances Graph Structure Learning for Spatial Interpolation. In: Advances in Knowledge Discovery and Data Mining – PAKDD 2025, Part II, 366–377.

■ Li, C.; Riedel, T.; Beigl, M. (2025) Feature Deviation Embedding Improves Graph Structure Learning for Spatial Interpolation. In: Proceedings of the 2025 SIAM International Conference on Data Mining (SDM), 356–365.

■ Ioannidis, G.; Tremper, P.; Li, C.; Riedel, T.; Rapkos, N.; Boikos, C.; Ntziachristos, L. (2024) Integrating Cost-Effective Measurements and CFD Modeling for Accurate Air Quality Assessment. ATMOSPHERE, 15 (9), 1056.

■ Ioannidis, G.; Li, C.; Tremper, P.; Riedel, T.; Ntziachristos, L. (2024) Application of CFD Modelling for Pollutant Dispersion at an Urban Traffic Hotspot. ATMOSPHERE, 15 (1), 113.

■ Huang, Y.; Li, C.; Lu, H.; Riedel, T.; Beigl, M. (2023) State Graph Based Explanation Approach for Black-Box Time Series Model. In: Explainable Artificial Intelligence – First World Conference, xAI 2023, Proceedings, Part III, 153–164.

■ Li, C.; Riedel, T.; Beigl, M. (2022) Neural Kernel Network Deep Kernel Learning for Predicting Particulate Matter from Heterogeneous Sensors with Uncertainty. In: Information Integration and Web Intelligence – 24th International Conference, iiWAS 2022, 252–266.

■ Li, C.; Budde, M.; Tremper, P.; Schäfer, K.; Riesterer, J.; Redelstein, J.; Petersen, E.; Khedr, M.; Liu, X.; Köpke, M.; Hussain, S.; Ernst, F.; Kowalski, M.; Pesch, M.; Werhahn, J.; Hank, M.; Philipp, A.; Cyrys, J.; Schnelle-Kreis, J.; Grimm, H.; Ziegler, V.; Peters, A.; Emeis, S.; Riedel, T.; Beigl, M. (2022) SmartAQnet 2020: A New Open Urban Air Quality Dataset from Heterogeneous PM Sensors. PROSCIENCE, 8.

https://www.researchgate.net/profile/Chaofan-Li-13