Starting Fall 2019, I will be working as a PhD student under Marzyeh Ghassemi at the University of Toronto and Vector Institute. Among many exciting things, we'll be broadly looking at novel applications of Reinforcement Learning to assist clinical decision making.

Starting in the summer of 2020, I will be working at Google Brain in Montreal as a summer intern under the primary direction of Marlos Machado along with Marc Bellemare and Pablo Samuel Castro.

I work in the fields of reinforcement learning, machine learning, and causal inference. I have long been interested in decision making and the mechanisms by which humans summarize and reason about the world. In my work, I aim to develop models and algorithms that enable actors (whether human or not) to efficiently make decisions in the face of various forms of uncertainty.

Previously, I was employed at MIT Lincoln Laboratory and completed degrees at Harvard University (working with Finale Doshi-Velez) and Brigham Young University (working with Tadd Truscott).




Some of my work is available as preprints on arXiv.

Having lived in Sweden, I put together a brief guide to acquaint co-workers and colleagues with Stockholm. You can find the guide here


An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
We investigate several information encoding approaches to develop state representations of patient health from sequential data. We evaluate these representations utility for predicting the next physiological patient observation as well as the development of treatment policies.
Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Maryzeh Ghassemi
ML4H: Machine Learning for Health Workshop at NeurIPS

Multiple Sclerosis Severity Classification From Clinical Text
We present the first publicly available transformer model trained on real clinical data other than MIMIC, specifically finetuned for the support of Multiple Sclerosis prediciton and treatment based on clincal consult notes. The model can be found here
Alister D'Costa, Stefan Denkovski, Michal Malyska, Sae Young Moon, Brandon Rufino, Zhen Yang, Taylor W. Killian, Marzyeh Ghassemi
The 3rd Clinical Natural Language Processing Workshop

Counterfactual Transfer via Inductive Bias in Clinical Settings
By using counterfactual inference, we establish an approach to transfer learning within offline, off-policy Reinforcement Learning that provides improved policy performance in data-scarce target environments.
Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi
Inductive Biases, Invariances and Generalization in RL (BIG) ICML Workshop

Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning
We leverage a differentiable form of a decision tree for Reinforcement Learning which allows for online updates via SGD. From this decision tree, an interpretable policy is extracted. We analyze the optimization behavior of such classes of policies and demonstrate equitable or better performance over batch trained decision trees and similarly sized neural networks.
Andrew Silva, Taylor W. Killian, Ivan Rodriguez Jimenez, Sung-Hyun Son, Matthew Gombolay
The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)


Kernelized Capsule Networks
A hybrid Gaussian Process-Deep Neural Network approach, Kernelized Capsule Networks construct a GP kernel function from the feature representations of a Capsule Network. This combination provides a model robust to adversarial perturbations while also providing a mechanism to detect perturbed inputs.
Taylor W. Killian, Justin Goodwin, Olivia Brown, Sung-Hyun Son
1st Workshop on Understanding and Improving Generalization in Deep Learning


Direct Policy Transfer with Hidden Parameter Markov Decision Processes
An extension of the HiP-MDP framework presented in Killian and Daulton, et al (2017) wherein the latent parameters used to describe dynamical variations are included as input to a general policy trained from the optimal policies learned from past instances.
Jiayu Yao, Taylor W. Killian, George Konidaris, Finale Doshi-Velez
Lifelong Learning: A Reinforcement Learning Approach Workshop at FAIM 2018


Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
A reformulation of the HiP-MDP to admit more robust and efficient transfer learning when deployed in complex environments with highly nonlinear dynamics.
Taylor W. Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez
Neural Information Processing Systems, pp. 6245-6250, 2017

Paper Poster Code Slides Video (starts at 17:15)

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
An extended abstract of some preliminary transfer learning work. Submitted to the Student Abstract track of AAAI 2017.
Taylor W. Killian, George Konidaris, Finale Doshi-Velez
AAAI, pp.4949-4950. 2017


Rebound and jet formation of a fluid-filled sphere
Investigation how fluid filled spheres have little to no rebound when dropped.
Taylor W. Killian, Robert A. Klaus, and Tadd T. Truscott
Physics of Fluids, 24 122106. 2012.