Machine learning prediction modeling

I work in an emerging and competitive area that involves developing novel machine learning methodology for clustered and longitudinal data, which arise when outcomes are correlated within a group or collected repeatedly throughout a study. I am proud to be among the first to publish these powerful and practical models that address challenges that arise with correlated data. Motivated by the longitudinal nature of many cohort studies aimed to assess outcomes over time, I invented a decision tree method called Binary Mixed Model (BiMM) tree and random forest method called BiMM forest that can handle clustered and longitudinal data. Additional work involved incorporating variable selection within the BiMM forest method. This is important because it facilitates removal of superfluous variables in the model, thereby reducing the burden of data collection and improving ease of prediction due to model parsimony. This work resulted in a solo-authored publication in a top bioinformatics journal , as well as a paper that provides practical guidance about choosing optimal random forest variable selection methods for different types of datasets. The latter paper has been cited over 1600 times and is one of the top cited papers in the journal Expert Systems with Applications. 

Collaborative research

Aside from methodology development and practical recommendations for modeling, I enjoy collaborating with scientists and researchers in various medical fields. My role as the lead statistician on a research team involves designing studies, conducting analysis, interpreting results, and writing the statistical methods section for the publications. Early in my career, many of my collaborations focused on acute liver failure and acute liver injury, a rare condition characterized by encephalopathy and severe coagulopathy. I served as lead statistician for studies that evaluated biomarkers: fatty acid binding proteins and regeneration linked microRNA, assessed clinical outcomes over time, and tested a new therapy: molecular adsorbent recirculating system. Aside from collaborations in acute liver failure, studies to which I have contributed include developing a machine learning model for classifying types of mitochondria, analyzing use of contact lenses after surgery, assessing food insecurity in obese individuals, evaluating a structured rounding program at a large healthcare center, and analyzing the association between C-reactive protein, Lipoprotein(a) and cardiovascular disease.

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