Xinkun Nie

Ph.D. Student, Department of Computer Science
Email: xinkun 'at' stanford 'dot' edu

I am a third-year Ph.D. student in the Department of Computer Science at Stanford University, advised by Stefan Wager. I am fascinated by how we can effectively learn from data to answer questions that arise in the social and medical sciences and to improve the way our society makes decisions. I am excited about building reliable, fair, and efficient statistical frameworks that are accessible to practitioners and policymakers. I am particularly interested in causal inference.

I gratefully acknowledge support from a Stanford Data Science Scholar Award, a Stanford School of Engineering Fellowship, and Stanford Human-Centered AI. Prior to Stanford, I obtained my B.S. in Electrical Engineering and Computer Science at MIT in 2016. As an undergraduate, I was very curious about how we could build smart robots, and had the fortune of doing robotics research in the Learning and Intelligent Systems Group supervised by Leslie Pack Kaelbling and Tomás Lozano-Pérez. I have also spent time in the Machine Learning team at Hunch (acquired by eBay), and in the Platform Infrastructure division at Akamai Technologies.

If you are interested in my work or would like to chat about technical interests we might share, potential collaboration, diversity, etc., feel free to get in touch!

Publications

Robust Nonparametric Difference-in-Differences Estimation
Chen Lu, Xinkun Nie, Stefan Wager
[arxiv]

Learning When-to-Treat Policies
Xinkun Nie, Emma Brunskill, Stefan Wager
[arxiv][slides (ACIC 2019)]

Quasi-Oracle Estimation of Heterogeneous Treatment Effects
Xinkun Nie, Stefan Wager
Thomas R. Ten Have Memorial Award
(awarded at the 2018 Atlantic Causal Inference Conference (ACIC) for "exceptionally creative or skillful research in causal inference")
[arxiv][software][poster]

Why Adaptively Collected Data Have Negative Bias and How to Correct for It
Xinkun Nie, Xiaoying Tian, Jonathan Taylor, James Zou
Best Paper Award at ICML Workshop in Picky Learners: Choosing Alternative Ways to Process Data, 2017.
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
[arxiv][software][poster]

Searching for Physical Objects in Partially Known Environments
Xinkun Nie, Lawson L.S. Wong, Leslie Pack Kaelbling.
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2016.
[paper]

Teaching

CS 234: Reinforcement Learning, Teaching Assistant, Winter 2017-2018.

ECON 293/MGTECON 634: Machine Learning and Causal Inference, Teaching Assistant, Spring 2018-2019.