PhD Candidate, Stanford Computer Science
Stanford Artificial Intelligence Laboratory (SAIL), Stanford NLP Group
lxuechen [at] cs [dot] stanford [dot] edu
I'm a PhD candidate at Stanford CS. I go by Chen, the second segment of my first name.
I research statistical machine learning, deep learning, and natural language processing. I work on developing methods to make machine learning trustworthy and enable safe general intelligence. I enjoy working on topics that have a technical piece where progress would have tangible positive societal impact. Here are some topics of recent interest:
Memorization, Generalization, Influence, & Privacy: Large models can memorize training data. This poses privacy risks and raises emergent sociotechnical questions (e.g., on copyright and intellectual property). I am interested in understanding the memorization phenomenon and building tools to mitigate undesirable consequences of this. Here is a recent statement I wrote on privacy and security in machine learning.
Learning From Human Feedback: Human feedback has become a primary driver for recent successes in AI. But collecting and training on such data can be costly and cumbersome. Some of the questions I'm recently interested in are: How can we efficiently elicit high-quality feedback? How can we augment the feedback data when they come limited in quantity? How should we aggregate this feedback signal without marginalizing the minority voices and views?
Red Teaming & Auditing: Despite the rapid progress in capability research, machine learning models still show systematic flaws. I am interested in building automated tools to aid humans in discovering and fixing these flaws.