I worked at Orbital Insight building machine learning models and pipelines.
In the past, I did research at the University of Chicago in machine learning and statistics. In particular, I developed theoretical and applied statistics related to time series and the climate using the R software and Python.
In the past, I have developed algorithms for extraction of one or more signals embedded in multivariate time series. The concept of signal-to-noise is defined and optimized over a certain class of models. In this vein, I work closely within and around the domain known as linear dimensionality reduction, which includes methods like Principal Component Analysis and Multivariate Statistical Analysis.
I have also developed methods for extracting signals for incomplete multivariate time series, for example time series with different sampling intervals or missing data, a common feature of real life data sets. Here, I infer the missing data points while extracting a smooth underlying signal. This outperforms traditional algorithms for data imputation because we take advantage of the underlying time structure.