I build data science tools to address challenges in medicine and clinical care. Specifically, I apply signal processing, image processing and machine learning techniques, including deep convolutional and recurrent neural networks and natural language processing, to aid diagnosis, prognosis and treatment of patients with acute and chronic conditions. In addition, I conduct research on novel approaches to represent clinical data and combine supervised and unsupervised methods to improve model performance and reduce the labeling burden. Another active area of my research is design, implementation and utilization of novel wearable devices for non-invasive patient monitoring in hospital and at home. This includes integration of the information that is measured by wearables with the data available in the electronic health records, including medical codes, waveforms and images, among others. Another area of my research involves linear, non-linear and discrete optimization and queuing theory to build new solutions for healthcare logistic planning, including stochastic approximation methods to model complex systems such as dispatch policies for emergency systems with multi-server dispatches, variable server load, multiple priority levels, etc.
My main research interest is in the old-fashioned goal of Artificial Intelligence (AI), that of building autonomous agents that can learn to be broadly competent in complex, dynamic, and uncertain environments. The field of reinforcement learning (RL) has focused on this goal and accordingly my deepest contributions are in RL.
A very recent effort combines Deep Learning and Reinforcement Learning.
From time to time, I take seriously the challenge of building agents that can interact with other agents and even humans in both artificial and natural environments. This has led to research in:
Over the past few years, I have begun to focus on Healthcare as an application area.