AI/ML Scientist — Center for Individualized Medicine, Mayo Clinic

Mayo Clinic - Biomedical Artificial General Intelligence Lab (BAGIL) >

I work on:

What I like to do: Analyzing data from all sorts of sources, and creating tools to support others' ability to do the same; Creating tools designed to enable the design of data-rich systems in novel interdisclipinary research situations. My focus and the focus that I encourage in others, is to use algorithms that can be clearly communicated and interpreted in the organizations that deploy them.
Computational Systems Biology

Artificial Intelligence

Machine Learning

Complex Adaptive Systems

Algebraic Topology

Data analysis tools

Bayesian Statistics

Numerical simulations

Visualization

Stochastic Grammars

Java

Algebraic Topology

Combinatorics

General-Purpose Modeling

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Simplicial Grammar (SG) is a novel computational abstraction that extends traditional grammars used in automata theory and linguistics. An SG is a Bayesian nonparametric modeling formalism that describes complex pattern classes and the process of their formation as a topological computation. This formalism is used to describe the explicit, hierarchical, and multi-dimensional structure of patterns inherent in sequence data.

Artificial General Intelligence Framework

Unsupervised Sequence Learning Algorithm

Hierarchical Generative Modeling

Bayesian Nonparametrics

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SYNACX is an unsupervised learning algorithm that extracts high-level, complex abstractions from time-series data through a hierarchical learning process.

Cell Biology

Predictive Analytics

Java

In-silico modeling

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CEP is a software package for modeling and analyzing cellular morphology data. It consists of a fast, parallelized Java library (which you can easily integrate into your own project), an R package providing a high-level interface to the library, and an easy-to-use web interface for interactive analysis and plotting.

CEP offers incremental modeling and visualization, Markov-Chain Monte Carlo and uncertainty estimation, cross-validation.