MaryLena Bleile

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Recognition:

  1. NSF/Harshbarger Student Travel Award (2022). Southern Regional Council on Statistics.  "Statistical and Machine Learning Methods for Adaptive Radiotherapy Treatment Scheduling." 

  2. Dean's Award (2022). Moody School of Graduate Studies, Research and Innovation Week. "Optimizing the Combination of Radiotherapy with Immunotherapy Using Deep Reinforcement Learning."

  3. Poster Presentation Award (2020 & 2021). Conference of Texas Statisticians.

  4. Ph.D Fellowship [Funded by Varian] (2021-present). UT Southwestern Medical Artificial Intelligence and Automation lab.

  5. Department Award for Academic Excellence (2019). Department of Statistical Science, Southern Methodist University.                                                Special thanks to Grant Sanderson, aka 3Blue1Brown, for the creation of this gif!

  6. Summer Institute Scholarship (2020). University of Washington Biostatstics

First Author Works:

Statistical and Machine Learning Methods for Adaptive Radiotherapy.  

In this project, we developed and tested a generalized method for determining personalized adaptive radiotherapy schedules for use in the context of combination treatment with immunotherapy. We describe this method in the context of a tumor xenograft experiment: One observes a series of pre-treatment tumor volumes, applies an initial dose at a fixed date, then waits a few more days to see what happens. Using this information along with some predictive model, one can then forecast future growth under a variety of potential radiotherapy schedules and select the schedule corresponding to the optimal outcome. We test three versions of our method which use three different models for prediction: A fully nonlinear Bayesian Mixed model, a mixed effects spline model, and a neural network. Simulation results suggest that the mixed effects spline model provides the optimal balance between robustness and efficiency.

Optimizing the Combination of Radiotherapy and Immunotherapy Using Deep Reinforcement Learning. 

This work focused on developing a Reinforcement Learning framework for maximizing the synergy between radiotherapy and immunotherapy. We train and test our agent in a virtual environment governed by a difference-equation dynamical systems model which encodes a recent radiobiological hypothesis about T cell sensitivity, and show that the actions selected by our agent can outperform the application of the best-performing action observed in a real study. We also perform a virtual adaptive clinical trial, and investigate the potential of bridging the Sim2Real gap by calibrating our agent's predictions on a real dataset.

Imputation of Truncated Tumor Growth Data Using Bayesian Mixed Modelling.  

In tumor xenograft experiments, experimental animals are often lost due to death or forced sacrifice (if the tumor becomes too painful) before the planned end of the experiment. This phenomenon, similar to dropout in human trials, poses potential issues for visualization and analysis. We propose here an adaptation of tools from the missing data literature - specifically multiple imputation  - to figure out what would have  happened, i.e. how the tumor would have grown, had the animal not perished. We accomplish this using Bayesian Mixed Modelling.

A Bayesian Analysis of Heavy Metal Subgenre Prevalence in Northern Europe and the West.  

Heavy metal ethnography and historiography has been extensively explored from a qualitative perspective. However, quantitative methods of analysis have not been developed. We conduct a Bayesian geographical analysis of heavy metal subgenres, investigating the relative prevalence of each subgenre in nations (for 86 countries in northern Europe and the West), and the overall popularity, according to the selected countries. Data from two different websites, MetalStorm and Encyclopaedia Metallum, were harvested via web ‘scraping’ and used for analysis. Results for Norway and Sweden in particular clearly agree with the qualitative historical documentation, while Germany surprisingly favoured black metal above Gothic.

  

Collaborative Works:

Diversity, Equity, and Inclusion (Organization, Speaking, & Writing):

  @ML_does_ML

  marylenableile@.comMy personal symbol: a swirly line attached to a straight one with three dots above it

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