As a graduate of UC Berkeley's School of Information Master of Information and Data Science (MIDS), I am currently applying my skills at USAA as a Data Scientist.
I am a solutions-oriented Data Scientist with a strong foundation in machine learning, artificial intelligence, deep learning, generative models, and their applications. Over the past 20 years, I have delivered actionable insights across academic, government, and Fortune 500 organizations, leveraging my expertise in data storytelling, statistics, visualization, design, and development.
I am passionate about building practical, impactful solutions that incorporate advanced techniques such as generative AI, prompt engineering, computer vision, graph databases, and geospatial analytics. My ability to see the big picture, manage details, and drive projects to completion makes me an invaluable team player.
I excel at learning new technologies quickly and conducting in-depth research, while continuously expanding my knowledge in areas like deep learning, multi modal AI, and security techniques. Whether working independently or collaborating across teams, I thrive on solving complex problems, developing reusable code, mentoring others, and ensuring ethical and human-centered approaches in AI applications.
I am eager to contribute my skills and collaborate with a team to develop innovative solutions that make a meaningful impact.
uConserve Home Energy Dashboard - MIDS Capstone project to drive global awareness and conservation of the world's natural resources through compelling analytics, insightful visualizations, and targeted actionable conservation recommendations.
Large-scale Machine Learning and Statistical Analysis of Dark Matter Halos Using Apache Spark - created a pipeline to preprocess and apply statistical and machine learning analysis to 2 terabytes of cosmological data.
Guiding the Future of the Internet of Things - As systems of systems are linked together by sensors, we are faced with a new set of ethical and legal questions.
Spritz Speed Reading Field Experiment - designed and implemented a field experiment from scratch.
Forecasting Use of a City Bikeshare System - iPython notebook from our kaggle competition to predict demand for Capital Bikeshare (ranked 389th/1866 - top 21% - as of January 5, 2015).
My Best Hospital - using public data to find your best hospital based on your criteria.
Correlating Stock Price Shifts with Predictions from Twitter - using information retrieval (IR) search mechanisms and sentiment analysis to search for correlations.
Using Data to Identify Venture Capital Appropriate Companies before VCs - ideas for early identification of high-growth potential companies.
The Ford Motor Company and Autonomous Car Adoption - How does Ford build on the research it has already done, combine the existing pieces in a cost-effective manner to help promote acceptance of autonomous vehicles with consumers, legislators, and insurance companies?
My kaggle competition work
MY Background
News and Publications
Women in Tech Career Panel at UC Berkeley MIDS Program - April 12, 2018.
Lisa Kirch on Data Science & Smart Homes - dataleaders.io, November 19, 2015.
UC Berkeley Graduate Students Develop Hospital-Ranking App - The Daily Californian, September 2, 2014.
The Community Reinvestment Act and the Profitability of Mortgage-Oriented Banks - statistical research analysis for Glenn Canner and Wayne Passmore, 1997.
COMPANIES I Have Worked for
Logo credits: kaggle, datascience@berkeley (UC Berkeley School of Information), George Washington University, Stockton University, Drew University, dataleaders.io, USAA, Citi, BusinessWire, Charles Schwab, Providian Financial, Cornerstone Research, and the Board of Governors of the Federal Reserve System.