Laura Niss

Laura Niss

Graduate Student Research Assistant

University of Michigan

Biography

Laura Niss is a fifth year graduate student research assistant in statistics at the University of Michigan. Her research involves applications of bandit algorithms in education and fairness considerations in bandit algorithms. This work is done in collaboration with her advisor Ambuj Tewari.

Interests

  • Bandit learning
  • Fairness in ML
  • ML applications in education

Education

  • PhD Statistics, 2021 (expected)

    University of Michigan

  • MA Statistics, 2018

    University of Michigan

  • BA Mathematics, 2015

    University of Colorado at Boulder

  • BA Psychology, 2012

    University of Colorado at Boulder

Publications

What You See May Not Be What You Get: UCB Bandit Algorithms Robust to epsilon-Contamination

Laura Niss, Ambuj Tewari
August 2020 Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)
PDF

Debiasing Representations by Removing Unwanted Variation Due to Protected Attributes

Amanda Bower, Laura Niss, Yuekai Sun, Alexander Vargo
July 2018 5th Workshop on Fairness, Accountability, and Transparency in Machine Learning
PDF

Fair Pipelines

Amanda Bower, Sarah N. Kitchen, Laura Niss, Martin J. Strauss, Alexander Vargas, Suresh Venkatasubramanian
July 2017 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning
PDF

Experience

 
 
 
 
 

Graduate Student Research Assistant

University of Michigan

Jan 2019 – Present Ann Arbor
 
 
 
 
 

Data Science Intern

Knewton (absorbed by Wiley)

May 2018 – Jul 2018 New York
 
 
 
 
 

Online (Coursera) Course Developer

University of Michigan

Feb 2018 – Dec 2018 Ann Arbor
 
 
 
 
 

Graduate Student Instructor

University of Michigan

Sep 2016 – Dec 2018 Ann Arbor

Contact

  • lniss@umich.edu