Professor Xihong Lin Reflects on NAS Election and Career Highlights
On a spring afternoon in the Program in Quantitative Genomics Seminar at the T.H. Chan School of Public Health, Professor Xihong Lin felt a buzz in her pocket. Silencing the pesky phone, she was surprised to see a text saying that she was now a member of the National Academy of Sciences. The reality of this only set in when the official announcement was released at 3:00 pm and phone calls and emails started rolling in – work would have to be on pause for the day.
Professor Lin was elected as one of 120 new members of the National Academy of Sciences this year in recognition of her continuing achievements in original research focusing on developing and applying scalable statistical and machine learning methods for big genetics and health data (read more about her award in the May 2nd Press Release of the National Academy of Sciences). Her research helps identify potential drug targets and precision intervention and treatment strategies. In a conversation with Dr. Lin, she shared her reaction and reflected on her sources of inspiration, highlights from her career and work, and advice for young researchers seeking to make an impact. (Note: This interview has been revised and condensed and contains some relevant content from a previous interview).
1. What has this honor from the National Academy of Sciences meant to you?
Lin: Receiving a congratulatory text about being elected to the NAS at a seminar was a genuinely special surprise! I am deeply honored and humbled to receive this prestigious recognition. I would like to thank the Academy and its members for this incredible honor. My heartfelt gratitude also goes out to the many collaborators, students, and postdocs whose contributions over the years have been instrumental. Their talent and creativity have been a constant source of inspiration to me. This achievement reflects what we have accomplished collectively, rather than only my individual efforts.
2. What is most rewarding about your work? Which role models inspired you to become a statistician?
Lin: The most fulfilling aspect of my work is the collaboration with many colleagues, students, and postdocs over years to contribute to scientific research by aiming to benefit society, improve lives, and pave the way for a better tomorrow.
When I was young, I was inspired by my grandparents, who were faculty members in preventative medicine in the US. They dedicated their careers to studying and developing a vaccine for schistosomiasis, a parasitic disease that affected many areas of the world, especially before 1960. During graduate school, my dissertation advisor, Professor Norman Breslow, the former Chair of the Biostatistics Department at the University of Washington [Professor Breslow passed away in 2015], had a substantial impact on my development as a biostatistician. I learned a lot from Norm, not only through his remarkable statistical sense, high scientific standards, and commitment to solving pressing health problems using statistics, but also through his dedication to building a strong scientific community. In the 1970s, he was the leading statistician of the National Wilms Tumor Study, which helped improve the two-year survival rate of pediatric kidney cancer from 80% to 100%. Inspired by Norm, I find fulfillment in statistical methodological research driven by real-world problems and its applications in health science.
3. What have you learned from the challenges that you faced in your career?
Lin: I would say embracing risks, even if it means experiencing a touch of embarrassment, and stepping beyond one's comfort zone can pave the way for significant professional growth and new opportunities. When I joined the faculty at Harvard in 2005, I made the decision to transition into a new field where I had little prior expertise: the realm of statistical genetics and genomics [‘field of biology focused on studying all of the DNA of an organism’, according to Genomics (genome.gov)]. I started collaborating with Dr. David Christiani, Professor of Environmental Genetics, in the Department of Environmental Health. When I first attended his lab meetings, everything was jargon to me! To become more familiar with the field and its terms, I audited a first-year, graduate molecular biology course (I was the only full professor in the course!) during my sabbatical in 2008. While I felt like I was starting from square one and was embarrassed to ask basic questions, these efforts ultimately played a crucial role in repositioning me and opening a new door of opportunities. I began working on developing methods for analyzing rare variants in candidate gene sequencing studies well before there was sufficient whole genome sequencing data [‘a process to determine all of approximately 3 billion nucleotides of an individual’s DNA sequence’, according to NIH’s National Cancer Institute]. However, in time our intuition that the area of whole genome sequencing studies would grow proved to be correct; our methods are now being widely used by the research community to analyze large amounts of whole genome sequencing data and identify genetic variants that may cause diseases. Taking a risk to embark on a journey in a field in its infancy can yield significant long-term rewards. Having such courage is not an easy decision.
4. Describe a project that has been important to you and your career.
Lin: During the spring of 2020, I basically put aside all the other ongoing work to focus on how to help the world respond to COVID-19. I co-led two papers in JAMA and Nature with a former postdoc of mine, Chaolong Wang, and his colleagues at Huazhong University of Science and Technology in Wuhan on analyzing the Wuhan COVID-19 epidemic data. I had not worked on epidemic modeling before, and it was a steep learning curve. However, when a pressing global public health crisis emerged, impacting countless lives, we rose to the occasion, learning and growing together. Chaolong and I shared with Nature the story behind this collaboration in the article “Learning from the Wuhan COVID-19 Data on the Fly.” The article describes how we analyzed on-the-ground data about the spread of the virus in the early phase of the pandemic to provide recommendations for public health intervention strategies, such as mask wearing, quarantine, and isolation, before the vaccine was available. Our paper garnered global attention and helped many countries develop their public health responses. I was interviewed by many news outlets, invited to be on the Massachusetts COVID-19 Task Force, and testified in front of the UK Parliament’s science and technology committee. My goal in undertaking this public outreach was to swiftly disseminate the most up-to-date knowledge to the public and policymakers across cities, states, and countries worldwide to help control outbreaks and save lives.
To support communicating about COVID-19 with the public, a student and postdoc in my lab took the lead in launching a real-time dashboard for calculating the Rt values [the rate at which one person with COVID-19 spreads the virus to other people], for different countries, states, and cities. The Rt value needed to be less than one to meet the criteria for having the spread of the virus under control in a region. Two additional postdocs in my lab took the lead on developing transmission dynamic models for analyzing more complex US COVID-19 data, and the work was published in a JASA discussion paper.
In the initial stages of the pandemic, I was deeply moved by the willingness of so many individuals to step up and lend a hand without seeking individual credit. This demonstrated an incredible sense of community, rather than competition, and was truly heartwarming to witness amidst such a challenging time.
5. Describe a project that you are currently working on and would like to highlight.
Lin: I am working on a project related to federated and distributed learning. Individual-level data in many healthcare systems cannot be shared due to concerns about patient privacy and federal regulations. The goal of federated learning is to develop machine learning methods using a common analysis protocol in individual healthcare systems, and then efficiently combine the site-specific results through a very small number of communications. We would like the results obtained from federated learning (with no sharing of individual-level data across different healthcare systems) to be similar to the results obtained using pooled data, while still ensuring efficient and scalable computation. The field of privacy-preserved data science is rapidly evolving and presents a lot of opportunity and promise.
6. What advice would you give to young statisticians who are hoping to one day have a similar type of impact in their careers?
Lin: I would tell younger researchers to identify important and promising areas at an early point in your career and to build a reputation by pinpointing a research niche with the potential for making a significant impact. It is often easier to build a reputation by getting involved in a new, understudied (but significant) area from the outset. It is beneficial to maintain an open mind, develop independence, follow your curiosity, and demonstrate a readiness to learn on the go and from setbacks. Also, having a clear sense of a purpose and a focus will provide a compass to navigate negative feedback and distractions. Keep believing in yourself and stay positive and forward-looking! I found that having this kind of perspective was helpful both when I started working on statistical genetics and on epidemic modeling for the first time.
Stats: Whether taking a first-year grad course in molecular biology as a full professor or modeling infectious diseases for the first time, Professor Lin has demonstrated a zest for jumping into the unknown. By choosing to take these risks and pursue fruitful collaborations, Prof. Lin has had a profound impact on research related to statistical science and public health. A clear example of her impact was when she worked with collaborators to model the spread of COVID-19 and suggest prevention strategies. At the beginning of our conversation, Prof. Lin was not sure of the specific reason for why she was elected to the Academy, but we think that her work speaks for itself.