|
Catherine Yu-Chi Chen Hi! I'm Catherine, a second year PhD in the Institute for Computational and Mathematical Engineering (ICME) at Stanford. I received my undergraduate degree in Data Science - a joint major between Computer Science and Statistics, at Columbia University in 2022. This is my website, feel free to browse around :) |
|
ResearchMy research interests lie in studying situations in which machine learning algorithms fail, including problems such as, uncertainty quantification, anomaly detection, distribution shifts, and out-of-distribution detection. |
|
Semi-supervised sequence modeling for improved behavioral segmentation
bioRXiv, 2021 Advised by Prof. Liam Paninski and Matthew Whitway @ the Zuckerman Institute bioRXiv / code We introduce a semi-supervised sequence model for behavioural segmentation that takes advantage of weak- and self-supervision. This approach displays superior performance when including a large number of unlabeled frames in the regime of sparse hand labels, and including a small number of hand labeled frames in an unsupervised setting. |
|||||
|
Community implications for gun violence prevention during co-occurring pandemics
PubMed, 2022 Advised by Prof. Shih-Fu Chang and Prof. Chris Thomas @ the DVMMLab PubMed We develop a natural language processing (NLP) and computer vision (CV) data science system that leverages Twitter data with multimodal machine learning to automatically identify tweets related to expression of loss and aggression. |
My Research Notes (On-Going) |
|
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
J. C. Snell, T. P. Zollo, Z. Deng, T. Pitassi, and R. Zemel, “Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions.” arXiv, Dec. 27, 2022. Accessed: Dec. 17, 2023. [Online]. Available: http://arxiv.org/abs/2212.13629 Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control A. N. Angelopoulos, S. Bates, E. J. Candès, M. I. Jordan, and L. Lei, “Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control.” arXiv, Sep. 29, 2022. Accessed: Dec. 18, 2023. [Online]. Available: http://arxiv.org/abs/2110.01052 Distribution-free, Risk-controlling Prediction Sets S. Bates, A. Angelopoulos, L. Lei, J. Malik, and M. Jordan, “Distribution-free, Risk-controlling Prediction Sets,” J. ACM, vol. 68, no. 6, pp. 1–34, Dec. 2021, doi: 10.1145/3478535. Conformal Risk Control A. N. Angelopoulos, S. Bates, A. Fisch, L. Lei, and T. Schuster, “Conformal Risk Control.” arXiv, Apr. 29, 2023. Accessed: Dec. 14, 2023. [Online]. Available: http://arxiv.org/abs/2208.02814 The Nonexistence of Certain Statistical Procedures in Nonparametric Problems R. R. Bahadur, L. J. Savage, "The Nonexistence of Certain Statistical Procedures in Nonparametric Problems." The Annals of Mathematical Statistics, 27 (4) 1115-1122 December, 1956. https://doi.org/10.1214/aoms/1177728077 Conformal Prediction A. N. Angelopoulos and S. Bates, “A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.” arXiv, Dec. 07, 2022. Accessed: Dec. 14, 2023. [Online]. Available: http://arxiv.org/abs/2107.07511 |
Teaching |
|
CME 102: Ordinary Differential Equations
Fall 2023 Analytical and numerical methods for solving ordinary differential equations arising in engineering applications. |
|||||
|
MATH 1102: Calculus II
Fall 2021 Methods of integration, applications of the integral, Taylors theorem, infinite series. |
Fun |
|
