I am a MSc student in Computer Science and Engineering at
the University of
California, Santa
Cruz. I am also a
researcher at Professor Jeffrey
Flanigan's lab, a place where I have enjoyed working for the last two and a half years. I will
apply for PhD in Fall 2022.

My research interests broadly lie in {deep learning} ∪ {computational
neuroscience}. My long-term research goal is to leverage knowledge from both machine learning and
the brain to build intelligent systems that can learn adaptively, continuously, and with
interpretability.

My current research focuses on fundamental problems in deep learning. I am particularly interested
in demystifying generalization properties and behaviors in large and small neural networks (e.g.,
double descent, expressive capacity of large networks, data and model scaling laws, learning noise,
lottery tickets, out-of-distribution generalization, neural tangent kernel, etc.). I am also working
with Professor Yang Liu on
unifying bias in data and machine.

Previously, I obtained my B.S. in Computer Science and Engineering at UC Santa Cruz.

My last name, Liu (刘 or 劉), is pronounced "Leo" but with shorter "[I]" sound.

We empirically estimate the power-law exponents of various model architectures and study how they
are altered by a wide range of training conditions for classification.

We present a dataset filtering approach that uses sets of classifiers, similar to ensembling, to
estimate noisy (or non-realizable) examples and exclude them so a faster sample complexity rate is
achievable in practice.

We show that adversarially training (Fast Gradient Sign Method and Projected Gradient Descent)
reduces the empirically sample complexity rate for MLP and a variety of CNN architectures on MNIST
and CIFAR-10.

Trained distilled RoBERTa model as a text classifier and a GPT-2 as a text generator trained using
proximal policy optimization synchronously to generate augmented text for text classification tasks.

Fine-tuned a GPT-2 model using all research paper titles and abstracts under cs.AI, cs.LG, cs.CL,
and cs.CV on arXiv. This project was the winner of the Generative Modeling Competition for the
course CSE142
Machine Learning in Spring 2020.

Fine-tuned a RoBERTa model on the IMDb dataset for sentiment analysis. This project was the winner
of the Sentiment Analysis Competition for the course CSE142
Machine Learning in Spring 2020.