From Natural Hazards to Global Health and Sustainability, and Finding a Community

Beginnings and a Career-Changing Paper

Haojie Wang's academic journey began with a Bachelor's degree in Civil Engineering from China University of Geosciences, followed by a PhD in Civil Engineering from the Hong Kong University of Science and Technology. Initially, Haojie was on the path to becoming a traditional engineering geologist. However, during the second year of his PhD, a pivotal moment occurred. His advisor, Professor Limin Zhang, introduced him to a conference paper exploring unsupervised machine learning in landslide feature classification. It was a novel approach at that time, as machine learning had yet to be widely applied to understanding landslides.

"After I read that paper, I thought about the many cool scientific topics we could tackle," Haojie recalls. This moment marked the beginning of his journey into machine learning and its application to landslide research.

Upon completing his Ph.D., Haojie realized that he had developed a second identity: a data scientist with a strong foundation in geotechnics. Reflecting on the rapid evolution of the field, Haojie notes, "Years ago, no one talked about using machine learning and similar tools in this context. Traditional methods like conducting experiments and using numerical models to simulate physical processes were the norm. But today, data science is transforming the landscape of geotechnical research. The field is growing so quickly that if you don’t keep up, you risk falling behind."

Transitioning from Geotechnics to Sustainability and Global Health

Haojie’s doctoral research, Machine Learning-Powered Natural Terrain Landslide Identification and Susceptibility Assessment, focused on integrating machine learning with satellite imagery and geospatial big data to identify and forecast landslides. Multiple publications that arose from his doctoral research are recognized as highly cited papers by Clarivate. His thesis work not only advanced the field of landslide research but also allowed him to integrate his knowledge of data science and remote sensing. As he delved deeper into this field, Haojie recognized that his skillset could be applied to address more global sustainability challenges.

"New research areas mean new challenges and the opportunity to embrace new possibilities," says Haojie. "And new possibilities inspire me to stay passionate about research."

His curiosity led him to another exciting research project, this time under the guidance of Pascal Geldsetzer, Assistant Professor of Medicine. Professor Geldsetzer was seeking a data scientist with expertise in remote sensing to monitor global health indicators from space. The challenge was irresistible to Haojie.

"Finding new ways to monitor global health is truly exciting, and the project is highly interdisciplinary,” says Haojie. “I am also keen to understand the role of climate change and natural hazards in shaping today’s global health landscape," Haojie explains. With guidance from esteemed mentors such as Professors Pascal Geldsetzer, David Lobell, Marshall Burke, Stefano Ermon, Eran Bendavid, Carlos Guestrin, and Gary Darmstadt, Haojie found his intellectual homes at the Stanford School of Medicine and Stanford Data Science.

The Vision Behind Haojie Wang’s Postdoctoral Research Project

Haojie’s postdoctoral research focuses on the development of new earth observation approaches for global population health monitoring. Traditional household surveys rely on door-to-door data collection, which can only cover a small fraction of the country and is conducted at best every few years. It is time-consuming, expensive, and often logistically challenging in many parts of the world. Policymakers often have no choice but to make decisions based on extrapolated health indicators from old household surveys.

Haojie is pioneering a new approach to overcome these limitations. He is leveraging machine learning, satellite imagery—which provides continuous coverage for all countries—and publicly available geotagged big data to predict health indicators. If successful, this method could offer worldwide up-to-date health indicators more quickly than ever before, enabling governments and decision-makers to track population health, allocate medical resources more effectively, and inform healthcare policy. The project is currently in its early stages, with the development of a preliminary model underway.

The Role of Data Science in Global Health Research

Haojie’s postdoctoral work is grounded in data science, focusing on population health through a remote sensing lens. His project involves fusing and analyzing data sourced from various satellite imagery, such as Landsat, alongside other geospatial data and health records. Predictive analytics play a critical role in this research, offering new insights into health trends on a global scale.

Finding a Community at Stanford Data Science

Haojie was introduced to the Stanford Data Science Fellow Program by Professor Pascal Geldsetzer, who believed Haojie would be an ideal fit. The interdisciplinary nature of the research conducted at Stanford Data Science appealed to Haojie, who had struggled to find a community that aligned with his diverse research interests at conventional universities.

"What’s unique about Stanford Data Science is its commitment to interdisciplinary research," Haojie says. "As an interdisciplinary scientist, I often felt isolated in traditional academic environments. But here, I found a community of fellows and scholars—a huge family! It’s incredibly gratifying to know that other data scientists are also pursuing interdisciplinary research. I’m not alone on this path."

Advice for Aspiring Data Scientists

Haojie was one of the technical mentors of the Data Science for Social Good (DSSG) program in 2023, where he mentored three undergraduate DSSG fellows on the project Maternal and Child Health - A Satellite’s Perspective throughout the summer. “It was a really enjoyable and inspiring summer working with aspiring researchers. DSSG sets a solid platform to connect with young minds—I was continually impressed by their enthusiasm and how they brought fresh perspectives to our project,” Haojie gushes.

Haojie encourages aspiring data scientists to find new ways to approach problems and to view the world through a data-driven lens. "Be Brave to explore new areas. Data science allows us to tackle problems we never imagined we could address. That’s the unique charm of the field," he admits.

Ambitions, Dreams, and Hobbies

This fall, Haojie plans to apply for faculty positions in global sustainability. His goal is to continue his work at the intersection of population health, climate change, and natural hazards, using his skill set to address pressing questions and make a meaningful impact. Outside of his research, Haojie enjoys cooking, traveling, and immersing himself in nature. As an engineering geologist at heart, he finds peace and inspiration in the natural world and loves going on road trips and camping, where he can combine his passion for nature with his love of good food.