Alumni Staff
Chaofan Li
Karlsruhe Institute of Technology (KIT)
Institute of Telematics / TECO
Vincenz-Prießnitz-Straße 1
76131 Karlsruhe, Germany
Building 07.07
Projects
Publications
2026
Development and implementation of SOMA: A secondary organic module for aerosol integration in high-resolution air quality simulations
Ioannidis, G.; Bouloti, N.; Tremper, P.; Li, C.; Boikos, C.; Rapkos, N.; Riedel, T.; Dal Maso, M.; Ntziachristos, L.
2026. Atmospheric Pollution Research, 17 (8), Art.Nr: 103096. doi:10.1016/j.apr.2026.103096
Ioannidis, G.; Bouloti, N.; Tremper, P.; Li, C.; Boikos, C.; Rapkos, N.; Riedel, T.; Dal Maso, M.; Ntziachristos, L.
2026. Atmospheric Pollution Research, 17 (8), Art.Nr: 103096. doi:10.1016/j.apr.2026.103096
2025
Evaluating the Spatial Coverage of Air Quality Monitoring Stations Using Computational Fluid Dynamics
Ioannidis, G.; Tremper, P.; Li, C.; Riedel, T.; Rapkos, N.; Boikos, C.; Ntziachristos, L.
2025. Atmosphere, 16 (3), 326. doi:10.3390/atmos16030326
Ioannidis, G.; Tremper, P.; Li, C.; Riedel, T.; Rapkos, N.; Boikos, C.; Ntziachristos, L.
2025. Atmosphere, 16 (3), 326. doi:10.3390/atmos16030326
Isolating Latent Context Information Enhances Graph Structure Learning for Spatial Interpolation
Li, C.; Riedel, T.; Beigl, M.
2025. Advances in Knowledge Discovery and Data Mining. Part II. Ed.: X. Wu, 366 – 377, Springer Nature Singapore. doi:10.1007/978-981-96-8173-0_29
Li, C.; Riedel, T.; Beigl, M.
2025. Advances in Knowledge Discovery and Data Mining. Part II. Ed.: X. Wu, 366 – 377, Springer Nature Singapore. doi:10.1007/978-981-96-8173-0_29
Feature Deviation Embedding Improves Graph Structure Learning for Spatial Interpolation
Li, C.; Riedel, T.; Beigl, M.
2025. Proceedings of the 2025 SIAM International Conference on Data Mining (SDM), 356 – 365, Society for Industrial and Applied Mathematics Publications (SIAM). doi:10.1137/1.9781611978520.38
Li, C.; Riedel, T.; Beigl, M.
2025. Proceedings of the 2025 SIAM International Conference on Data Mining (SDM), 356 – 365, Society for Industrial and Applied Mathematics Publications (SIAM). doi:10.1137/1.9781611978520.38
2024
Integrating Cost-Effective Measurements and CFD Modeling for Accurate Air Quality Assessment
Ioannidis, G.; Tremper, P.; Li, C.; Riedel, T.; Rapkos, N.; Boikos, C.; Ntziachristos, L.
2024. Atmosphere, 15 (9), 1056. doi:10.3390/atmos15091056
Ioannidis, G.; Tremper, P.; Li, C.; Riedel, T.; Rapkos, N.; Boikos, C.; Ntziachristos, L.
2024. Atmosphere, 15 (9), 1056. doi:10.3390/atmos15091056
Proseminar Mobile Computing SS 2023 : Mobile und Verteilte Systeme Ubiquitous Computing. Teil XX
Studt, A.; Li, C.; Zhao, H.; Beigl, M.
2024. (P. Tremper, Ed.), Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000170114
Studt, A.; Li, C.; Zhao, H.; Beigl, M.
2024. (P. Tremper, Ed.), Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000170114
Application of CFD Modelling for Pollutant Dispersion at an Urban Traffic Hotspot
Ioannidis, G.; Li, C.; Tremper, P.; Riedel, T.; Ntziachristos, L.
2024. Atmosphere, 15 (1), Art.-Nr.: 113. doi:10.3390/atmos15010113
Ioannidis, G.; Li, C.; Tremper, P.; Riedel, T.; Ntziachristos, L.
2024. Atmosphere, 15 (1), Art.-Nr.: 113. doi:10.3390/atmos15010113
2023
State Graph Based Explanation Approach for Black-Box Time Series Model
Huang, Y.; Li, C.; Lu, H.; Riedel, T.; Beigl, M.
2023. Explainable Artificial Intelligence – First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part III, Ed.: L. Longo, 153 – 164, Springer Nature Switzerland. doi:10.1007/978-3-031-44070-0_8
Huang, Y.; Li, C.; Lu, H.; Riedel, T.; Beigl, M.
2023. Explainable Artificial Intelligence – First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part III, Ed.: L. Longo, 153 – 164, Springer Nature Switzerland. doi:10.1007/978-3-031-44070-0_8
2022
SmartAQnet 2020: A New Open Urban Air Quality Dataset from Heterogeneous PM Sensors
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. ProScience, 8. doi:10.14644/dust2021.001
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. ProScience, 8. doi:10.14644/dust2021.001
Neural Kernel Network Deep Kernel Learning for Predicting Particulate Matter from Heterogeneous Sensors with Uncertainty
Li, C.; Riedel, T.; Beigl, M.
2022. Information Integration and Web Intelligence – 24th International Conference, iiWAS 2022, Virtual Event, November 28–30, 2022, Proceedings. Ed.: E. Pardede, 252–266, Springer Nature Switzerland. doi:10.1007/978-3-031-21047-1_22
Li, C.; Riedel, T.; Beigl, M.
2022. Information Integration and Web Intelligence – 24th International Conference, iiWAS 2022, Virtual Event, November 28–30, 2022, Proceedings. Ed.: E. Pardede, 252–266, Springer Nature Switzerland. doi:10.1007/978-3-031-21047-1_22