Hello! My name is Minghao Qiu (邱明昊). I am a postdoctoral fellow in planetary health at Stanford’s Department of Earth System Science and Center for Innovation in Global Health. At Stanford, I am primamrily advised by Marshall Burke as a part of the ECHO (Environmental Change and Human Outcomes) Lab.

NEWS: I will join Stony Brook University (SUNY) as an assistant professor in Summer 2024. My appointment is jointly between School of Marine and Atmospheric Sciences and Program in Public Health. I am very excited to start the next chapter in this amazingly unique role to work with fellow atmospheric scientists and explore the implications of my work to improve human health!

I am looking for 1-2 PhD students to join my group at Stony Brook in Fall 2024. More info at Opportunity

I received my PhD degree from MIT’s Institute for Data, Systems, and Society on September 2021, advised by Noelle Selin. I also worked closely with my committee members: Valerie Karplus, Cory Zigler and Colette Heald. I am a Martin Family Fellow of Sustainability and a recipient of fellowship from the Young Scientist Summer Program at IIASA. I received bachelor degrees in environmental sciences and economics from Peking University in Beijing.

My broad research interest is in environmental and energy policies with a global focus on issues involving air pollution, climate change, and energy transitions. Specifically, I study the interplay between air pollution and climate change, two critical environmental challenges for humanity in the coming decades. My research uses causal inference, machine learning, atmospheric chemistry modeling, remote sensing to study the sustainability challenges at the intersection of energy, pollution and climate using real-world data. My PhD research has spanned the electricity, industrial, and transportation sectors in China, US, and EU. From a methodology perspective, I am interested in developing data-driven approaches with insights from complex atmospheric chemistry models (such as GEOS-Chem) and understanding the uncertainty and limitations of econometric and statistical models in such application.