Cheng Yang

I am a PhD student at the Department of Information Science and Engineering (ISE) since June 2022, under the supervision of Prof. Tobias Oechtering and Prof. Saikat Chatterjee. I received my master's degree in Information and Network Engineering in 2021 from KTH, Sweden (thesis: Data-driven Dynamic Baseline Calibration Method for Gas Sensors, patent), and my bachelor's degree in Electronic Information Engineering in 2019 from Zhejiang University, China.

Research

My current research focus on gas sensor calibration and sensor fusion. In particular, I am deeply interested inunderstanding why and how sensors drift, as well as developing smart calibration algorithms using statistical tools.

My Publications:

• Cheng Yang, Gustav Bohlin, and Tobias J. Oechtering, “Stochastic Drift Modeling for NDIR Gas Sensors: Separating Environmental Effects and Instrumental Drift,” IEEE Transactions on Instrumentation and Measurement, vol. 75, pp. 1–15, Art. no. 1003315, 2026.
• Cheng Yang and Tobias J. Oechtering, “Dynamic Baseline Calibration of Low-Cost CO₂ Sensors via Spatio-temporal Fusion,” in 2026 29th International Conference on Information Fusion (FUSION), 2026. Accepted and presented
• Cheng Yang and Tobias J. Oechtering, “Mean-Reverting Stochastic Modeling of Instrumental Drift in NDIR CO₂ Sensors,” in 2025 IEEE SENSORS, pp. 1–4, 2025
• Cheng Yang, Saikat Chatterjee, and Tobias J. Oechtering, “Enhancing Network Calibration for Low-Cost Gas Sensor Networks Through Adaptive Similarity Search,” in ICASSP 2025—2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5, 2025.
• Cheng Yang, Gustav Bohlin, and Tobias Oechtering, “Environmental Variation or Instrumental Drift? A Probabilistic Approach to Gas Sensor Drift Modeling and Evaluation,” in 2024 IEEE SENSORS, pp. 1–4, 2024.

Teaching

Beyond my own research, I am actively involved in teaching and student supevision. I enjoy helping students develop rigorous theoretical understanding as well as and the confidence to tackle open-ended engineering problems.  I serve as the teaching assistant for EQ1220 Signal Theory, which covers stochastic process and its applications in estimation, filtering, signal sampling and reconstruction, etc.

I also supervise groups of students in the project course EQ2443 Project in Information Engineering. The topics in 2022 and 2023 were gas sensor network calibration and sensor fusion.

Master thesis projects that I have supervised:

Ci Song, thesis: Predictive Maintenance for Air-Conditioning Refrigeration and Heat Pump Systems: Data-driven Condition Monitoring and Fault Detection for Compressors

Jike Li, thesis: CO2 Sensor: Outlier Detection, Calibration and Prediction