FOUNDER & PRINCIPAL ANALYST
Hello! My name is Chen-hui Bergl. [Pronounced: ‘shen-way berg-ul’]
I graduated from The Wharton School with a dual concentration in Statistics and Operations. The intersection of my studies taught me the value of digging into data and how to operationally support data-driven decision making in organizations.
Since then, I’ve worked with companies in Retail, Tech, and the Public Sector to help them uncover insights from qualitative and quantitative data.
Why Fully? I started Fully because I saw so many teams struggling with data overload who just needed a bit of help. I named my LLC ‘Fully’ because I want my clients to feel fully supported in their data process and fully enabled to make actionable decisions from data!
Thank you for reading!
COllaborators
When the project calls for outside perspective, these are my close friends and colleagues that I highly recommend bringing in!
Michael Lavin
Michael started his career in healthcare SaaS managing $3 million in MRR as a Client Success Manager at Experity, where he built trusted advisor relationships with executive stakeholders and leveraged data analytics to optimize client financial performance.
Michael holds a Bachelor of Arts in Political Science with minors in Business and Entrepreneurship from the University of Florida, where he graduated Cum Laude.
His expertise spans SaaS business models, client success strategies, and data-driven decision making in healthcare and software.
Spencer Hong
Spencer is a PhD data scientist at Capital One and Co-founder of Yap Inc., specializing in large language models and causal inference.
His PhD from Northwestern University focused on document intelligence, developing multimodal foundational models for millions of privileged documents inside the government. Spencer also applied econometrics to portfolio analysis to advise federal agencies during his PhD.
Before Capital One, Spencer was the technical lead and interim CTO for Renota, a handwriting recognition AI edtech. He received a Bachelor's degree in chemical and biomolecular engineering from Cornell University.
Ryan Chen
Ryan is a machine learning researcher with over five years of experience working on statistics, AI, and machine learning with Fortune 500 companies. He is working on his PhD in Statistics at Northwestern University where his research encompasses deep learning, reinforcement learning, and language models.
His theoretical training and experience in applied mathematics and machine learning has given him a unique perspective on how to apply AI to solve real-world problems.
Ryan holds a Master’s degree in Statistics from Rutgers University - New Brunswick and a Bachelor’s degree in Economics from the University of Pennsylvania with a concentration in mathematics, statistics, and linguistics.
Want to collaborate with me?