Owen Tucker

ML Researcher & Computer Science Graduate Student

Developing machine learning solutions for healthcare applications, with expertise in reinforcement learning for glucose control and spatial computing for epidemiological modeling.

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Computer Science MS Student

Emory University

Emory AI.Health Lab
RL for Glucose Control

Spatial Computing Lab
Phylogeographic Inference

Featured Publications

Peer-reviewed research in machine learning applications for healthcare and spatial computing

Spatial Inference on Phylogenetic Trees

3rd Place Winner

Owen Tucker, Andreas Züfle

ACM SIGSPATIAL 2025 Student Research Competition (SRC)

Given COVID-19 transmission locations from phlyogenetic trees, how can we impute missing cases and infer their most likely geographic location? We solved this problem using human mobility data and the viterbi algorithm. Won 3rd place in the competitive ACM SIGSPATIAL Student Research Competition.

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Novel multimodal mechanical stimulation is superior to TENS to treat and prevent chronic low back pain: a randomized controlled trial

Amy Lynn Baxter, Jena L. Etnoyer-Slaski, Owen Tucker, Jessica Allia Rice Williams, Kevin Swartout, Lindsey L. Cohen, M. Louise Lawson

Frontiers in Pain Research, 2025

Developed a random forest classification model to predict functional recovery trajectories in lower back pain patients. Analyzed multi-modal patient data including pain assessments, physical function scores, and clinical measurements. The model demonstrated strong predictive capability for identifying patients at risk of deterioration, with applications in personalized pain management interventions.

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Current Research

Active projects across machine learning for healthcare and spatial computing

RL for Blood Glucose Control

Emory AI Lab, RL in healthcare

Developing reinforcement learning algorithms for automated blood glucose management in Type 1 Diabetes patients. Working on the RL4BG framework to integrate exercise parameters and improve multi-patient generalization.

Phylogeographic Inference

Spatial Computing Lab

Comparing belief propagation algorithms against traditional tools like TreeTime and PastML for predicting geographic locations in epidemiological phylogenetic trees. Focus on reconstructing transmission trees from phylogenetic data.