Deep Learning

Autoencoder-Based Causal Mediation Analysis for High-Dimensional Time Series: Application to Postprandial Glycemic Response in Type 1 Diabetes

Developing autoencoder-based methods to understand how meal carbohydrate intake affects post-meal glucose trajectories through insulin bolusing behavior in Type 1 Diabetes …

Spencer Hilligoss

• Jan 1, 2026 • 1 min read

Deep Learning

Predicting the Dow Jones Industrial Average with Sentiment-Enhanced LSTM Models

Project demonstrating the efficacy of LSTM models in enhancing prediction of stock indexes such as the DJIA. Tools Used: Python, R

Dec 1, 2025 • 1 min read

Causal Inference

Time-Varying Effects of Meals and Insulin on Postprandial Glucose Response Using Autoencoder-Based Causal Representations

Presented research on autoencoder-based causal mediation methods for analyzing postprandial glucose response in Type 1 Diabetes.

Spencer Hilligoss

• Nov 6, 2025 • 1 min read

Teaching

Guest Lecturer: STATS 295 - Special Topics in Machine Learning

Guest lecturer for STATS 295, a graduate-level special topics course in machine learning.

Spencer Hilligoss

• Jan 15, 2025 • 1 min read

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Deep Learning

Autoencoder-Based Causal Mediation Analysis for High-Dimensional Time Series: Application to Postprandial Glycemic Response in Type 1 Diabetes

Predicting the Dow Jones Industrial Average with Sentiment-Enhanced LSTM Models

Time-Varying Effects of Meals and Insulin on Postprandial Glucose Response Using Autoencoder-Based Causal Representations

Guest Lecturer: STATS 295 - Special Topics in Machine Learning