Research Interests

Combining Data Science, Systems Science, Computational Science and Applied Math to improve decision making in health and healthcare

• My research is focused on providing cross-linked system simulation, mobile data collection, and theory-informed machine learning/artificial intelligence tools, and Applied Category Theory to inform decision making in health. Such tools can, for example, aid public health decision makers in putting into place cost-effective preventive policies, design more effective screening or treatment strategies for an illness, help support epidemiological models that learn from incoming evidence and are kept perpetually up-to-date with the latest evidence so as to provide for more reliable policy planning. These methodologies can further enable insight into the causes underlying changes in the number of cases of a disease reported, and react more quickly to an outbreak of infectious disease when it occurs.

• Tools of choice include supplementing system science dynamic models (particularly Agent-Based models, System Dynamics, and discrete event simulation) with particle filtering and particle MCMC with such system science models, systems for visualizing state space reconstruction and for Convergent Cross Mapping (CCM), existing and novel machine learning and dynamic modeling toolsets, GPU-based computational statistics algorithms (PMCMC and Particle Filtering) and CM, and a growing amount of FPGA-based performance acceleration of key algorithms. Of late, I have a particularly strong focus on leveraging understanding from Applied Category Theory -- an area I find to offer compelling alignment with the systems science perspective, synergies with systems science techniques, the requisite power to deliver compelling insight, and an outstanding foundation for more powerful tools for study of complex systems. I am particularly committed to using Applied Category Theory as the basis for use of higher-level functional programming and metalinguistic abstraction to enhance the clarity, transparency, concision, modularity, flexibility and power of languages for characterizing dynamic models.

• All such tools are applied within the health sphere, as this is our elected point of focus, inspiration and dedication. We further make extensive investment in machine learning to sharpen and broaden the health surveillance, including using our Ethica epidemiological smartphone and wearable-based data collection system, time series of search volumes, time series of machine-learning-classifed Twitter messages, web-scraped data, and other mechanisms. For example, as part of our strategy of using twitter for health surveillance in Canada, we have amassed more than 200M tweets. The types of data we obtain through surveillance data are classified using machine learning tools (including more traditional tools through to deep learning) to flag tweets of relevance and inform our particle-filtered and PMCMC-regrounded models.

Tags: Nate Osgood, Mental health