I am currently a fifth year PhD student at Stanford in the Department of Statistics working with Jonathan Taylor. Prior to Stanford, I was an undergraduate student at MIT, majoring in mathematics.
My research primarily focuses of developing selective inference methods, enabling valid confirmation for data-dependent hypotheses. Developing computational tools for randomized selective inference has lead me to investigate Markov chain Monte Carlo sampling techniques, so I have done some work in this topic as well.
Jelena Markovic, Kevin Fry, and Keli Liu, Jonathan Taylor, Rob Tibshirani, Relevant one-step selective inference, 2018. [manuscript available upon request]
Jonathan Taylor, Jelena Markovic, Jeremy Taylor, Inference after black box selection, 2018. [manuscript available upon request]
Keli Liu, Jelena Markovic, Robert Tibshirani, More powerful post-selection inference, with application to the Lasso, 2018. [arXiv]
Nan Bi, Jelena Markovic, Lucy Xia, Jonathan Taylor, Inferactive data analysis, 2017. [arXiv]
Jelena Markovic, Jonathan Taylor, Lucy Xia, Unifying approach to selective inference with applications to cross-validation, 2017. [arXiv]
Snigdha Panigrahi, Jelena Markovic, Jonathan Taylor, An MCMC-free approach to post-selective inference, 2017. [arXiv]
Jelena Markovic, Jonathan Taylor, Bootstrap inference after using multiple queries for model selection, 2016. [arXiv]
Xiaoying Tian, Snigdha Panigrahi, Jelena Markovic, Nan Bi, Jonathan Taylor, Selective sampling after solving a convex problem, 2016. [arXiv]
Jelena Markovic, Amir Sepehri, Bouncy hybrid sampler as a unifying device, 2018. [arXiv]
Jelena Markovic, Amir Sepehri, Non-reversible, tuning- and rejection-free Markov chain Monte Carlo via iterated random functions, 2017. [arXiv]