About Me

Current Focus

• Expanding my ML engineering capabilities through MLOps Zoomcamp to learn MLE tools and robust engineering practices.
• Reviewing for AWS SAA certification to learn cloud infrastructure for deploying + scaling my models. Exam scheduled for end of June ✨
• Prepping for RenderATL 2025 and ATL Tech Week for my first tech conference. I’m terrified so excited!

I’m a data scientist with a unique background bridging chemical engineering, research operations, and machine learning. My career path been powered by my passion for solving ambiguous problems through both experimental methods and data-driven approaches.

Background & Expertise

Since starting and completing my B.S. in Chemical Engineering with a Computer Science minor from the University of Pittsburgh in 2021, I’ve built expertise in multiple domains:

Research Foundation: Conducted award-winning research in electrochemical systems (Liu Lab - Georgia Tech), nanocatalyst development (Veser Lab - Pitt), and drug delivery systems (Little Lab - Pitt), and expanded to coatings formulation and color science in industry (PPG).

Data Science Transition: Leveraged my love of automated experiment databases to move into data analytics, where I served as a “automation engineer” of sorts on R&D teams. I completed a data engineering cohort to learn about data infrastructure formally and proceeded to complete ML projects to internally streamline workflows.

Technical Versatility: Developed data engineering, data science, and ML skills including Python, SQL, AWS, and Tensorflow through project-based learning while maintaining domain expertise in research methodology, material characterization, and formulation science. With both wet and dry lab skills, I now maintain learning in the data space to fill in knowledge gaps I encounter.

Unique Value

My greatest strength lies in making connections between technical domains to operate more efficiently, whether translating scientific requirements into data features or communicating the importance of data infrastructure to business stakeholders eager to leverage “AI/ML”. This interdisciplinary fluidity has been my foundation across research and industry projects. As I learn more about data infrastructure and best practices, the more I’m able to understand as a chemical engineer who studied systems modeling and engineering.