Haoyan (Ryan) Luo
MRes, Machine Learning, Imperial College London / AI Associate, J.P.Morgan
h.luo23 [AT] imperial.ac.uk
Bio
I am an MRes (Master of Research) student in Machine Learning at Imperial College London, supervised by Prof. Lucia Specia. I obtained my B.Eng. degree in Computer Science from The Chinese University of Hong Kong, Shenzhen. Currently, I am working as an AI & Data Science Associate at J.P. Morgan, where I design and implement LLM-based solutions for impactful, real-world applications in finance. Previously, I interned at Microsoft Research Asia and Shanghai AI Lab. I was also a Big Data Engineer Intern at Tencent and a Software Engineer Intern at Seasun Entertainment.
I am actively looking for PhD opportunities or research positions starting in 2025. If you think my background and research interests align with your group, please feel free to reach out!
My research interests lie primarily in machine learning and natural language processing, with a focus on:
- Explainable and interpretable machine learning models: developing a deeper understanding of large-scale language systems.
- Applied machine learning: building fast and automated machine learning systems.
During my undergraduate studies, I served as a student coordinator at Shaw College and was a host in the Communication and Public Relations Office. I also led TEDxCUHKSZ as president (check out our annual events!), promoting our slogan: "ideas worth spreading" :p In my free time, I'm a passionate piano and cello enthusiast. I also enjoy playing football, basketball, and tennis. Go Warriors and KTBFFH💙
Recent News
September 2024: Joined J.P. Morgan as an AI Associate (Research Scientist). September 2024: Completed MRes (Master of Research) in AI & Machine Learning at Imperial College with Distinction. August 2024: Completed MRes thesis titled: "Explainable and Controllable Language Models: Interpretability and Its Applications", awarded with Distinction. August 2024: MoD paper, a new PEFT framework using mixture-of-depths ensembles, available on arXiv. June 2024: Cambridge PhD Scholarship awarded. June 2024: Survey paper on explainability and its applications for large language models is available on arXiv.Publications
Explainability in Large Language Models: Pathways to Refinement and Alignment PDF Poster
Haoyan Luo
MRes Thesis
🔎 From Understanding to Utilization: A Survey on Explainability for Large Language Models PDF
Haoyan Luo, Lucia Specia
arXiv
Explainability in Large Language Models: Pathways to Refinement and Alignment PDF Poster
Haoyan Luo
MRes Thesis
🔎 From Understanding to Utilization: A Survey on Explainability for Large Language Models PDF
Haoyan Luo, Lucia Specia
arXiv
OpenFE: Automated Feature Generation With Expert-level Performance
PDF
Code
Tianping Zhang, Zheyu Zhang, Haoyan Luo, Fengyuan Liu, Wei Cao, Jian Li
Fortieth International Conference on Machine Learning (ICML 2023)
ActiveAD: Enhancing Anomaly Detection in Tabular Data through Active Learning Strategies PDF Code
Haoyan Luo, Xiaofan Gui, Wei Cao, Jiang Bian
arXiv
🗺️ Steering the Networked Temporal Point Processes via Controlling the Network Graph PDF Code
Haoyan Luo, Shuang Li
Open-source Project
🛠️ Deep-Learning-Lookup: Deep Learning Algorithms and Utils in Python Code
Haoyan Luo
Open-source Project
Misc.
- Reviewer for EMNLP'24, ICLR'24
- Teaching Assistant for CSC3170 Database System and CSC4020 Machine Learning at CUHK(SZ)
- President of TEDxCUHK(SZ)