About

Researcher, educator and solver of computationally-intensive mathematical problems.

Currently, I lead AI Research at Redis.

Prior to this role I was a Research Lead at Sage AI working on domain-specific LLMs, a Senior Data Scientist at NerdWallet, a Senior Data Science Developer Advocate at Databricks and a Computational Scientist at Virginia Tech. My current research interests are Natural Language Understanding and building reliable products with Large Language Models.

From Product Specs to Execution: A Two-Agent Spec Pipeline

product-spec-agent → product-specs.md → technical-spec-agent → technical-specs.md → execution

Posted on August 8, 2026
A two-stage pipeline for going from a PM request to executable specs. The product-spec-agent interrogates the PM until the what and why are unambiguous; the technical-spec-agent reads the codebase and drafts the how for engineers to iterate on. The actual agent prompts and templates are below. Role-coded: white = people, blue = product stage, green = technical stage, gold = spec states, black = execution. .spec-figure { background: #f6f8fa; border: 1px solid #d0d7de; border-radius: 12px; box-shadow: 0 1px 3px rgba(25, 25, 25, 0. [Read More]
#AI Agents  #LLM  #Product Management  #Software Engineering 

Using Bayesian Hierarchical Models to Infer the Disease Parameters of COVID-19

Bayesian Modeling with PyMC3

Posted on June 6, 2021
In a previous post (https://lnkd.in/dZvmsRm), I looked at the available data for the infected cases in the United States as a time-series, modeling this as a compartmental probabilistic model and inferring the disease parameters such as R0 using Bayesian estimation. However, we can use the case counts from several countries and use Bayesian hierarchical models to extend this work and better estimate R0. In this post I illustrate how we can do exactly that using PyMC3. [Read More]
#Bayesian  #PyMC3  #COVID-19 

Bayesian Modeling of the Temporal Dynamics of COVID-19 using PyMC3

Data+AI Summit, Europe 2020

Posted on November 11, 2020
These are the slides for the talk given in the Data Science Lounge at the Data+AI Summit, 2020. Introduction This post is a demonstration of how to use PyMC3 to infer the disease parameters for COVID-19. PyMC3 is a probablistic programming framework that is used for Bayesian modeling. It accomplishes this through both Markov Chain Monte Carlo (MCMC) and Variational Inference methods. The work here looks at using the currently available data for the infected cases in the United States as a time-series and attempts to model this using a compartmental model. [Read More]
#COVID  #PyMC3 

Rclone for Data Transfer - Google Drive

Data backup

Posted on July 7, 2020
Rclone website is a tool for data transfer to and from a variety of sources including your local machine. A few commands for interacting with Google Drive and transferring data to and from a local machine is shown below. Use the following to setup your remote for Google gdrive rclone config To list remotes rclone listremotes remote_google: To list the directories in this drive rclone lsd remote_google: To list all the files [Read More]
#Rclone  #Data backup