About Me

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Hi, I'm Fan Luo! 👋

Welcome to my Learning Space! I'm passionate about creating, learning, and sharing knowledge with others.

What I Do

I hold a PhD in Computer Science, and worked at Amazon as an Applied Scientist for more than 2 years. My work focuses on machine learning, language understanding, and recommender systems — exploring how intelligent systems can enhance digital experiences.
I also share insights and reflections on these topics through my personal blog.

My Publications

  1. Fan Luo Towards the Advancement of Open-Domain Textual Question Answering Methods. PhD Dissertation Published in ProQuest, 2022
  1. Fan Luo, Mihai Surdeanu. A STEP towards Interpretable Multi-Hop Reasoning: Bridge Phrase Identification and Query Expansion. Accepted by 13th International Conference on Language Resources and Evaluation
  1. Fan Luo, Ajay Nagesh, Rebecca Sharp, Mihai Surdeanu. Semi-Supervised Teacher-Student Architecture for Relation Extraction. Accepted by NAACL 2019 Workshop on Structured Prediction for NLP.
  1. Fan Luo, Mihai Surdeanu. Perturbation-based Active Learning for Question Answering. Accepted by Widening Natural Language Processing 2023.
  1. Fan Luo, Marco A. Valenzuela-Escarcega, Gustave Hahn-Powell, Mihai Surdeanu. Scientific Discovery as Link Prediction in Influence and Citation Graphs. Accepted by NAACL 2018 Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs-12, 2018.
  1. Fan Luo, Mihai Surdeanu. Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval. Accepted by NAACL-HLT SRW 2022
  1. Rebecca Sharp, Adarsh Pyarelal, Fan Luo, Mihai Surdeanu et al. Eidos, INDRA, & Delphi: from free text to executable causal models. Accepted by North American Chapter of the Association for Computational Linguistics, 2019.
  1. Haonan Wang, Fan Luo, Mohamed Ibrahim, Onur Kayiran, Adwait Jog. Efficient and Fair Multi-programming in GPUs via Effective Bandwidth Management. Accepted by High Performance Computer Architecture (HPCA), 2018 IEEE International Symposium on. IEEE, 2018.

My Journey

My academic path has not been linear, yet it has deeply rooted in computer science. Since my undergraduate studies, I’ve explored a wide range of areas—from computer vision and GPU job scheduling optimization to game development, machine learning, natural language processing and conversational AI. In industry, I’ve worked on AI-powered products, gaining hands-on experience with the machine learning ecosystem — understanding how research ideas evolve into production pipelines and are ultimately deployed to serve real customers.

Selected Coursework

Over the years, I’ve taken a diverse set of courses that deepened my expertise across core areas of computer science, machine learning, and systems:
Machine Learning & Natural Language Processing
  • Text Retrieval and Web Search (Fall 2020)
  • Algorithms for NLP (Fall 2018)
  • Neural Networks (Spring 2018)
  • Statistical Natural Language Processing (Fall 2017)
Systems & Security
  • Advanced System & Network Security (Fall 2015)
  • Advanced Operating Systems (Fall 2014)
  • Advanced Computer Networking (Fall 2014)
  • Computer Architecture (Fall 2015)
  • GPU Architectures (Spring 2016)
Algorithms, Theory & Data
  • Analysis of Algorithms (Fall 2014)
  • Theory of Computation (Spring 2015)
  • Data Analysis and Simulation (Spring 2015)
Human–Computer Interaction & Graphics
  • Game Development (Fall 2019)
  • Virtual Reality (Spring 2019)
  • Sensors & Ubiquitous Computing (Spring 2015)
 

Let's Connect!

I'm always eager to meet new people and collaborate on interesting projects. Feel free to CONTACT ME if you'd like to chat about technology, books, or anything creative!