Jing Liu

What I do: As the MIDAS Executive Director, I oversee the operations of the institute and the staff team. I also design programs and projects to enable groundbreaking research ideas, build interdisciplinary teams, develop research resources, and strengthen research skills, all for the purpose of maximizing the scientific impact of data science and AI. In addition, I build partnerships with academia, industry, government and community organizations, both for research collaboration and for maximizing the societal impact of data science and AI. Working with colleagues at data science institutes at other universities, I support the Data for Social Good effort (academic research in support of data-driven policy making), seek ways to develop career paths for staff scientists in academia, and improve data science infrastructure and collaboration for crisis response.

My research activity includes examining the data science research infrastructure and data science reproducibility, and using data science to support public policy. A couple of recent projects are: data analytics to support Detroit’s digital inclusion initiative, and examining biases in face recognition algorithms and in museum art collections. 

Recent fun projects: I lead MIDAS effort to enable reproducible data science and AI research and open science, through gathering best practices and tools from researchers and developing central resources and training. Researchers are familiar with the concepts of open science and reproducible research, but they often lack resources, time, and even skills to turn such concepts into action. To me, this is a challenging but important problem to solve.

Seeking the silver lining of the Zoom culture during the COVID pandemic, I worked with six other data science institutes at leading universities to offer a Data Science Coast-to-Coast (DS C2C) seminar series, bringing together faculty and postdoc researchers at these universities for collaboration. 

Who I am: I am a scientist by training. I received my PhD degree from the California Institute of Technology, where I studied how animal development is shaped by genetic factors and the molecular signaling pathways, with Dr. Paul Sternberg as my mentor. Caltech was a special place for me, where I had endless discussions with my fellow students and professors – often into the wee hours of the morning – about all kinds of exciting (and sometimes “crazy”) science ideas and let our imaginations take flight. I did my postdoc training at Stanford University with Dr. Bill Newsome, using electrophysiology and psychophysics to study the neural mechanism of visual motion perception and cognition. Through my research, I became fluent in statistics and coding, and started exploring machine learning (very new to visual neuroscience at the time). After a short stint as a faculty member at the University of Michigan, I moved to research administration because what I truly enjoy is to see “the big picture” and to contribute to science in a broad way.

Why I’m passionate about my work: Humans have used data for thousands of years, but the unprecedented amount of data that we have now and the sophisticated ways with which we examine data expose us to such rich possibilities. I am enjoying my work everyday because I’m helping to unleash the potential of data science and AI to advance science. Data and AI are also becoming an integral part of policy making across all types of organizations and will shape human society in profound ways. As a scientist, I feel the excitement and a sense of duty to help ensure that data and AI are a positive force in our society, not a new way to cause harm.

Fun facts: In addition to science, I am also passionate about reading and music. I also write and translate about science and education, and have received multiple awards including China Book Award and China National Library Book Award. My favorite piece of volunteer work was that I built a math club and taught for 8 years at Martin Luther King Jr. Elementary School (Ann Arbor), where I got to show the kids how much fun math can be and how amazing they can be at math.