Growing up in Claremont, California, Sharon Chiang had many interests. She was interested in hiking and liked rocks, which led her to thinking about becoming a geologist. She also liked math, enjoyed finding patterns in data, and thought about becoming a statistician. And, she saw the relationship that her father, a physician, had with his patients, and thought about becoming a physician.
In deciding where to go to college, Sharon was accepted into a unique eight-year program at Rice University. This program combines a four-year bachelor’s degree with four years of medical school at Baylor College of Medicine. Students spend their first four years as undergrads at Rice, where they are encouraged to pursue broad interests. They then go on to medical school.
Sharon followed this encouragement and took classes outside of the traditional pre-med track, in areas such as economics and statistics. Through her interest in microeconomics, she had an internship as a case manager at a homeless shelter. She designed a program to model homelessness and assess the factors to predict why some people are able to exit homelessness while others are not.
Her interest in statistics led Sharon to take an introductory stats course followed by classes in data mining, machine learning, and Bayesian modeling. She liked statistics so much that this became her major.
As was her plan, upon completing her undergraduate studies, Sharon proceeded on to medical school. In her first year of medical school, while shadowing a physician, she saw a young woman with epilepsy have an unexpected seizure. This experience heightened Sharon’s interest in specializing in neurology and also spurred her curiosity to use data and statistical models to better understand and predict seizures.
After Sharon’s second year of medical school, she decided to embark on an unconventional path. She took a break from medical school to get her PhD in statistics, as she wanted to strengthen her skills in developing statistical models and conducting research. She then returned to medical school to complete her MD, as she could not see herself not being a physician.
The result: a unique combination of an MD and a PhD in statistics. This path was encouraged by mentors who advised Sharon, “You can do both.”
Sharon is currently in the middle of her residency in neurology at the University of California, San Francisco. Ideally, after she completes her residency she will spend approximately 10% of her time as a clinician, treating patients, and 80-90% of her time conducting research.
“I think seeing patients every day and helping them get better is rewarding. . . . Then, being able to model those phenomena that we see clinically and translate that into improvements in patient outcomes, I think is probably one of the most rewarding things.”
Her research to date has focused on the unpredictability of seizures among patients with epilepsy. By gathering real-time data using biosensors, Sharon is working to develop models that can predict seizures, assist patients, and produce a measurable improvement in patients’ quality of life.
While her path is somewhat unconventional, as the amount of technology and data explodes, Sharon believes there will be more physician scientists who combine the clinical knowledge of a physician with the skills of a statistician, computer scientist, or biomedical informaticist.
She advises today’s students to accumulate as many skills as possible in areas such as computer science, statistics, information technology, and biomedical informatics. Also, get involved with a project and start working with data. These skills and experiences will create amazing career opportunities.
“My goal is to develop a career as a physician scientist. I would like to continue statistical modeling and continue in the field of biomedical informatics. I want to continue with the application of statistics to address issues that people with epilepsy and other neurological disorders face.”
To date, Sharon’s focus has largely been on neurology, and specifically on epilepsy. But she sees the skills she has gained as generalizable in harnessing the massive amounts of data being generated from different sources to improve human health. She sees biomedical informatics as essential to that process.