I have worked on recently on the following areas:

COVID-19 Modeling: I entered into an agreement with the City of San Antonio and the South Texas Regional Advisory Council (STRAC) to model COVID-19 cases during the first year of the pandemic. The model was accurate. It had substantial media coverage.

Machine learning: We use machine learning on a regular basis, but we are also developing new algorithms that improve the performance of artificial neural networks as compared to the state of the art. Also, I have been involved in applications of machine learning to analysis of cardiac signals, text summarization, and student success at the university level.

Data harmonization: We are able to represent heterogeneous and complex data sets of arbitrary size with a reduced set of data primitives (well-defined mathematical objects that are the building blocks to represent reality). Our analysis pipelines only consume data primitives, and only produce data primitives. The ultimate goal is to design and implement an agent capable of automated knowledge discovery.

Adaptive learning: The training of interdisciplinary scientists poses tremendous challenges. This is particularly true when teams are composed of people with heterogeneous backgrounds. We have developed technology that minimizes the cognitive overhead to train individuals, and to integrate teams into a research project.

Multi-scale analysis of infectious disease: We study mechanisms that connect multiple scales, from milliseconds through evolutionary time, and from quantum interactions through continental dynamics of infection. This endeavor requires advances in multiple areas, as described below. We have been able to produce advances in every single aspect, and we are capable of integration of all these dimensions.

Epidemiology of asymptomatic carriers: We study the effect of asymptomatic hosts in the dynamics of malaria transmission. The results can be extrapolated to other diseases.

Dispersal of vectors: We study methods to model the dispersal of mosquitoes in a heterogeneous landscape dominated by species distribution, climate, and vegetation cover.

Physiology: We are able to identify when a host is infected before the onset of symptoms occurs. We accomplish this via high-frequency measures of accelerometers, blood pressure, ECG, and temperature.

Cellular models of immune interaction: We are using flow-cytometry data and cytokine information during a malaria infection to model cellular-level interactions between the adaptive and innate immune system, and healthy and infected red blood cells.

Multi-omic integration: We are able to analyze transcriptomic data, and integrate it with proteomics, metabolomics, immunomics, and other -omic technologies. Using this type of integration we have been able to determine factors that confer resilience against an infection.

Computational Drug Design: Given a gene regulatory network (that we can reconstruct from a time series of transcriptomic data), we can identify the most sensitive elements of the network, and target them with molecular docking studies against databases of drug-like molecules.