Q&A with Dana Yang
Get to know more about one of ILR’s two new faculty members
Dana Yang will join the Department of Statistics and Data Sciences in December after completing a Simons-Berkeley fellowship where she will participate in the Computational Complexity of Statistical Inference program at UC Berkley. Prior to that, she had a stint as a postdoctoral associate at Duke University’s Fuqua School of Business. Yang obtained a B.S. in mathematics from Tsinghua University and earned an M.A. and a Ph.D. in statistics from Yale University.
What is your research about?
I work in the broad field of high-dimensional statistics and machine learning, mostly focusing on the theoretical aspects. Some specific topics I work on include high-dimensional linear models and large-scale network analysis. I also work on ethics and safety in machine learning.
How did you become interested in your field?
I guess I am drawn to the mathematical elegance of theoretical research. I also have my amazing advisers and collaborators to thank for introducing me to new research areas.
What impact do you hope your research will have?
If in 30 years some graduate students in statistics still read my proofs and find my work helpful to their own research, then I will be very happy.
What attracted you to the ILR School?
The endless opportunities to collaborate with the brilliant scholars at the ILR School with whom I share common research interests. I am also excited to explore new research areas with my new colleagues.
What are you most excited for about your time at ILR?
Continuing the answer above, I am definitely most excited about collaborating with my new colleagues in the school. Collaborative work and brainstorming have always been my favorite part of research. I am also looking forward to mentoring the talented students at Cornell.
Cornell’s “Any Person, Any Study” ethos – how will you be part of that?
“Any Study” — my advisers have always encouraged me to explore any research topic I am interested in. I hope to be as supportive to my students as my advisers have been to me, and allow the students freedom in pursuing any research direction they are passionate about.
“Any Person” — I have dedicated part of my research to fair selection in machine learning, such as in the college admission process. If the students are interested, I plan to involve them in this line of research to promote a more inclusive academic environment.
If you could share one piece of advice with your students, what would it be?
Never stop having fun in you work.
Favorite piece of advice from a mentor or inspiring figure in your life?
From my adviser David Pollard: “In every paper you write, make sure there is at least one clever new idea.”