About me

2017 – Present Stanford / Center for Biomedical Informatics Research Biostatistician, Khatri Lab; data science approaches to health and disease.
2016 – 2017 Udacity Software engineer, platform team.
2011 – 2015 Harvard / Joint AB/SM (PBK) Math and computer science.

Since August 2017, I’ve been a researcher in translational bioinformatics at the Khatri lab in Stanford’s Center for Biomedical Informatics Research. My research focuses on analyzing molecular and clinical data to develop clinically useful tools for disease (examples).

I’m excited by the impact I could have with this approach. For example, I’m pursuing a highly sensitive, specific gene signature for diagnosing tuberculosis by adapting the multi-cohort analysis approach used by the Khatri lab to discover its best-in-class 3-gene signature. If successful, “[a] rapid and widely available diagnostic for tuberculosis (TB) with ≥85% sensitivity for smear-positive and smear-negative cases, and 97% specificity, could save ∼400,000 lives annually.” This approach could generalize to disease prevention and pandemic preparedness.

Outside of bioinformatics, I remain curious about applications of data science, as well as public policy, to critical challenges like climate change.

Contact

Email: abliu at stanford dot edu

More about me

As a high school junior, I was lucky enough to spend my summer trying science research for the first time. Everyday, I would get up around 8:30am, stuff my backpack with my black Dell laptop, bike for 20 minutes along Palo Alto’s scenic backroads to Stanford’s Medical School Office Building and arrive, sweating, at my mentor Purvesh’s cubicle in the Butte lab around 9am. My summer project was to figure out which biological pathways were significant in organ transplant rejection, and to do this, I wrote an algorithm to analyze gene expression data from biopsies from transplant patients. I encountered new surprises almost everyday as I tested code, ran computational experiments and checked results with Purvesh and the immunology literature. Things rarely worked as expected, but that’s what captured my interest more than school: the sheer unpredictability.

While I was mainly interested in science research because it tested me, others were trying to impress upon me another purpose: research could help solve the world’s challenges. At the closing gala of the Intel Science Talent Search finals, Intel CEO Paul Otellini told me and 39 other self-contented finalists, “The creativity and leadership of these 40 Intel Science Talent Search mathematicians and scientists hold tremendous potential to move our country forward. They are already addressing real-world problems like cancer treatment, disease prevention and national security.” But during my press interviews, while I acknowledged that science research should focus on solving global challenges, it never occurred to me that I personally wanted to help solve them.

Harvard changed that. I took a global health class with Professors Paul Farmer, Arthur Kleinman, Anne Becker, and Salmaan Keshavjee, and for the first time, I felt deeply disturbed about the horrors of infectious disease, malnutrition and mental health epidemics in faraway India, Africa and Eastern Europe. I had read about these issues before, but there was something different about being in the room with four people who lived and breathed these issues, dedicating their lives to helping the world’s most disadvantaged people fight for their own survival. Many lectures, I got distracted by the email and Facebook on my laptop, but every so often, Professor Kleinman or another professor recounted an experience with such emotion that I could not help but put down my computer, and vicariously experience the suffering they had witnessed in the sick, their frustration at the world’s neglect, and their ambivalence as they realized that they personally had to work on these problems. After many of these lectures, having re-experienced these professors’ stories in my own emotions, there was no other way to feel but that I personally had to do the same. I had to use my strengths—in science research, math and computer science—whether for global health or another world challenge. For a few years afterwards, I explored different ways to do this, through internships and jobs in Silicon Valley, education and venture capital.

My translational bioinformatics research today is my best current answer to that original question: how do I use my strengths to do the most good I can?

If you’re interested in how my work has evolved on this question, check out Resume and Research. If you’re interested in how my ideas have evolved, check out Blog and Reading, and follow me below to stay up to date.