Education
My academic journey started at the University of California, Berkeley, where I studied Statistics and
Biochemistry. I performed undergraduate research on the mechanism of DNA replication in Prof. John Kuriyan’s
lab with Dr. Brian Kelch. I mastered the foundation of biochemical and
biophysical methods for experimental design, project management and data collection; additionally, I applied
computational biology methods to organize, process, analyze, and interpret the data to
model complex biological systems.
To further my passion for unraveling biological complexities, I pursued the intersection of
biology and statistics at Stanford University, where I completed a PhD in Structural Biology (Biophysics). Under
the
guidance of renowned advisors like Prof. Wing Wong, Prof. Garry Nolan, and Prof. Michael Levitt, I completed
my doctoral research on cellular states and biological systems. My work shed new light on dynamical gene
networks, contributed to our understanding of the biochemical and genetic factors in bone development and
aging, and pioneered new algorithms for high-dimensional single-cell datasets with statistical emphasis on
data batch effects and cross-sample mapping of cell populations.
In my postdoctoral work at the Stanford Blood Center with Prof. Ed Engleman, I developed and applied
computational techniques on high-dimensional tissue images to understand the side effects of immunotherapy
strategies.
Professional History
To expand my knowledge as a statistician, I decided to move into the tech industry to learn how to
solve business problems at scale by launching reliable machine learning capabilities. I joined Uber
Technologies, a fast-moving leading startup at the time, to learn high-quality,
industry-standard data science and engineering best practices from some of the most experienced
professionals in the industry.
At Uber, I optimized processes behind various business products as part
of a small and agile data science team. I developed and built Uber’s first statistical data anomaly detection
service to scale the company’s data quality monitoring. In addition, I developed Uber’s data-driven business
outage detection and mitigation framework. I also worked with the internal hardware and infrastructure teams to
model the company’s computing needs, validate hardware purchasing plans with scenario forecasting,
and support the transition of data storage from on-premise data centers to the cloud.
In all, these infrastructure projects improved the company’s cost efficiency by >$50 million.
After building Uber’s data quality monitoring service, I wanted to develop the same product for all
companies. No software company at the time offered an enterprise tool for data monitoring–most companies
relied on building data monitoring in-house, which is very expensive. I joined Bigeye Data as the third
employee and first data scientist to achieve the vision of a data observability enterprise software for all.
I developed and built machine learning and AI automation in the Bigeye software.
Bigeye is used by organizations in many industries, and past and present users come from many companies
including Zoom, Instacart, Confluent, and the intelligence community in a partnership with In-Q-Tel.
I will continue to use my deep expertise in statistics and the sciences to contribute to groundbreaking
advancements. I am committed to making a significant impact in information technology, the biosciences, and
policy through data-driven innovations.
