I anticipate that the reader will find my mathematical understanding, computational ability, translational acumen, and a research­oriented, collaborative attitude which will provide insight and acuity to your institution. In particular, my capacity to integrate practical experience and theoretical knowledge with domain-specific information yields highly comprehensible deliverables. I have embedded a short clip below to illustrate these traits in a formal setting.

A small clip of motivating normalization procedures (JSM 2020).

My formal training includes degrees in Biostatistics (Sc.M. Johns Hopkins University, 2021) and Applied Mathematics (B.A. New College of Florida, 2016). These pursuits represent intentional enrichment of my theoretical toolset; a necessary complement to my curiosity for the natural and digital worlds. In practice, these tools (mathematical modeling, statistical inference, machine learning, method development) have found application in biological (genetics, genomics, immunology, systems modeling), population (GIS, epidemiology, environmental/public health, ecology), and material science (statistical mechanics, chemical physics) sciences. Moreover, I possess aesthetic and inter-personal sensibilities which greatly facilitate my ability to extract and convey core concepts to non-technical audiences, that is, visually and thematically cohesive figures and animations. See below for an original design which demonstrates my capacity to graphically represent complex, multiphasic processes.

A schematic deliverable (original design). (A-B) Diagrammatic representation of data-generating mechanism. (C-D) Illustration of proposed solution (signal deconvolution).

The aforementioned capacities apply to technical deliverables, where my forte is the novel integration of multiple disciplines to overcome theoretical and practical challenges. In this sense, the majority of my work has required interface of the historical body of work in the topic (prior knowledge) with technical and theoretical expertise to produce novel, efficient, and insightful solutions.

A technical deliverable (original design). (A) Illustration of data modalities and (B) their expected (theoretical) behaviors in terms of a novel performance metric. (C-D) Simulated and real data demonstrating artifacts (observation magnitude) may induce (false) bi-modality downstream. (E) Comparison of relevant methods for addressing said artifacts.

You will find my adaptive body of experience, adaptability, as well as rigorous theoretical and practical backgrounds make me a compelling candidate to propel your organization through any data-driven problems. contact me (mouseover)