Experience

• 

  University of wisconsin-milwaukee

  Jun 2017 - Aug 2017

  Research intern

  • Explored the use of capacitance deionization to remove heavy metals from water in areas such as Flint, MI.• Coordinated and analyzed daily tests to optimize process parameters.• Designed and 3D-printed new experimental components using SolidWorks.

• 

  Colorado school of mines

  May 2019 - May 2021

  • Independently taught several lectures and review sessions to 70+ students.• Led an international learning program with a university in Uzbekistan with over 100 participants. Students met virtually to learn about the culture, college life, and problem solving approaches of the other college.• Held weekly office hours with regular attendance of 10-20 students. • Researched the use of ammonia production and decomposition for use in hydrogen fuel cells.• Organized and performed experimentation on catalysts, membranes and absorbents to improve process• Utilized MATLAB and COMSOL software to model the process and find areas of improvement.

  • Thermodynamics Teaching Assistant

    Aug 2019 - May 2021

  • Undergraduate Researcher

    May 2019 - Mar 2020
• 

  Marathon petroleum corporation

  Jun 2020 - Aug 2020

  Technical services intern

  • Analyzed crude feedstock effects on delayed coker production. Involved implementation of neural networks and other algorithms in Python to evaluate shot coke formation and fouling issues within the cooker.• Conducted a refinery-wide cooling water survey and recommended corrections for problematic heat exchangers.

• 

  Colorado school of mines

  May 2022 - Dec 2022

  Lead teaching assistant (csci 470 machine learning)

  • Led a group of five TAs for a Machine Learning course• Created new projects about the usage of neural networks and transfer learning using Keras/TensorFlow

• 

  Emd electronics

  May 2022 - now

  Led development of evaluation techniques for accuracy and truthfulness of Large Language Models • Drove a directed research project in collaboration with the University of Toronto to predict organometallic thermal stability using ML and DFT techniques• Developed and pitched 3 year R&D digitization strategy• Scouted key academic and industry partners for a high-temperature/high-pressure distillation process for metal halides • Built and applied active learning/bayesian optimzation framework to optimize Design of Experiment for a new product development. Reduced the number of physical experiments needed by ~40%• Implemented new regression and validation models to predict spin-on dielectric thickness. Improved predictive power by 37%

  • LLM Data Scientist | GoGlobal Associate

    Apr 2024 - now

  • Data Scientist | GoGlobal Associate

    Jun 2023 - Apr 2024

  • Data Science Intern

    May 2022 - Jun 2023