I'm a "full stack" data scientist focused on building practical machine learning solutions, experimenting with real-world datasets, and deploying models that create measurable impact. I have built and deployed projects using various tech stacks from databricks to cron jobs.
I am a generalist who has worked for both scrappy startups and multi-billion dollar companies, building out data science solutions for companies working in the physical sciences (batteries, polymers) and retail.
- Building out the Electrochemical-Impedance-Spectroscopy as a proper library and include curve-fitting to real world data
- Consulting with individuals and businesses on analytic projects
- Lindy Hop
Code for simulating EIS responses from equivalent circuits, targeting battery applications.
Future improvements I would like to make:
- Turn it into a proper library rather than demonstration code
- Add curve fitting for experimental data
- Add simulations for voltage profiles as a function of state of charge
Code for solving the Peng-Robinson Equation of State, which is unreasonably accurate for a model from the 1970s. It requires knowing the critical temperature and pressure for a component, as well as acentric factor. If these are not available, there are plural methods for estimating them including methods by Lydersen, Fedors, or Joback.