I am a Research Scientist in Artificial Intelligence working within the DistributedAI group at Samsung AI Centre in Cambridge, UK. My research focuses on efficient inference and fine-tuning of large language models and stable diffusion models in resource constrained environment. In the past years, I have worked on a number of different machine learning paradigms including meta-learning, few-shot learning, and federated learning, and applications of these in different domains such as vision recognition and wireless mobile communications.

Prior to Samsung AI, I obtained my PhD on communications and deep learning from the School of Informatics, University of Edinburgh, in 2019. I was advised by Prof Paul Patras. During my study, I visited INRIA Lyon in France to work on reinforcement learning for mobility control of fleet of drone base stations in emergency networking, and I interned with the 5G research group at Samsung Research Institute UK (SRUK).

News

📝 Nov. 2025: Our paper on compressing state-of-the-art stable diffusion models titled HierarchicalPrune: Position-Aware Compression for Large-Scale Diffusion Models has been accepted by AAAI’26. Link to preprint.
👩🏻‍🏫 Oct. 2025: Presenting our work Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference at EMNLP’25 in Suzhou. Links to preprint, demo and code.
🏆 Aug. 2025: Our position paper on The Future of Consumer Edge-AI Computing has been selected as the Runner-Up for the Best Paper Award 2024 by IEEE Pervasive Computing. Link to paper.
👩🏻‍🏫 Feb. 2025: I gave a guest lecture for the Federated Learnling, Theory and Practice course at Department of Computer Science and Technology, University of Cambridge. I talked about our recent works around enabling FL on commercial edge devices.
👩🏻‍💻 Sep. 2024: Served as a Technical Program Committee member for Design, Automation and Test in Europe Conference (DATE) 2025 Topic A6 Applications of Artificial Intelligence.
🏆 Sep. 2024: Our paper TinyTrain won the Silver Samsung Best Paper Award 🎉.
👩🏻‍🏫 Jul. 2024: Attended ICML 2024 in Vienna, Austria to present our paper titled TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge.
📝 Dec. 2023: Our paper on Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation by Royson Lee, Rui Li, Stylianos Venieris, Timothy Hospedales, Ferenc Huszár, and Nicholas Lane was accepted at IEEE/CVF WACV 2024. Pre-print.
📝 Oct. 2021: Our paper titled A Channel Coding Benchmark for Meta-Learning is accepted at NeurIPS’21 Benchmarks Track.
📝 Sep. 2021: Work with Yu-Lin Wei and Prof Romit Roy Choudhury of UIUC on Infering Human’s Facing Direction from Voice Signals is online.
👩🏻‍🏫 Feb. 2021: Presented our recent work on A Channel Coding Benchmark for Meta-Learning at AAAI Workshop on Meta-Learning. Check out the paper, slides and poster.
👩🏻‍💻 Dec. 2020: Co-organised the 1st DistributedML workshop co-located with ACM CoNEXT.
📝 May 2020: Co-authored a short position paper on on-device ASR for ACM GetMobile.
🎓 May 2019: Finishing my PhD and joining Samsung AI to work on AI for communications. New jorney begins 🛣️.
🏆 Nov. 2018: Honoured to win the Best Student Paper at MLN’18 in Paris!
🏆 Jul. 2018: Incredibly honoured to receive the Brendan Murphy Memorial Prize at the 30th Next Generation Networking, Multi-Service Networks workshop (Coseners).