I am a first year PhD student at IDS Lab, DGIST, South Korea. My research interests are in the application of deep learning models on low-powered edge devices. Currently, I am working on neural architecture search to discover novel computationally efficient Transformer based architectures that function well on edge devices.
I completed my MSc Data Science at the University of Bath, United Kingdom. My research project involved a novel approach in applying model compression methods to a state-of-the-art salient object detection models for the use in video compression. The project successfully applied filter pruning to Cascaded Partial Decoder for Fast and Accurate Salient Object Detection (Wu et al., CVPR 2019) with the result of running the compressed model on a mobile device.
Outside of academia, I am interested in Korean Language, Culture and Food. I spent nine months from 2018 to 2019 studying Korean at Seoul National University Language Institute. Currently, I am living and studying in Daegu, South Korea.
M. Bodenham, J. Kung, "On the Extensive Exploration of Transformer-based Language Models for Memory and Latency Bounded Edge Devices", Workshop on 2nd ROAD4NN, IEEE/ACM Design Automation Conference (DAC), December 2021.