Short CV

Tianheng Ling, M.Sc., is a research associate and PhD candidate in the Department of Intelligent Embedded Systems, Faculty of Computer Science at the University of Duisburg-Essen, since November 2022. She holds a bachelor degree in Information Management and Information Systems, and a master degree in Applied Cognitive and Media Science with a specialization in Artificial Intelligence. Her master thesis dealt with quantized neural networks for the prediction of time-series data.

She is currently involved in the RIWWER smart city project, which aims to reduce the environmental impact of untreated wastewater during heavy rainfall. In this context, she is responsible for optimizing and compressing Deep Learning models for edge deployment.

Research

Her research focuses on energy-efficient time-series analysis on resource-constrained hardware. She works on quantization and hardware-aware deployment of deep neural networks (especially Transformers) on embedded FPGAs. Her work covers forecasting, classification, and anomaly detection, with comparative studies involving LSTM and 1D-CNN models. Key methods include multi-objective optimization and mixed-precision quantization to balance accuracy, latency, and energy use, supported by an automated deployment framework across different FPGA platforms.