Himank Yadav

I am a graduate student in Computer Science at Cornell University broadly working in the area of machine learning and algorithms. More specifically, I am interested in problems around counterfactual policy learning and evaluation in addition to thinking about questions on fairness, interpretability and transparency in such settings. I am extremely fortunate to be advised by Thorsten Joachims.

I graduated from Texas A&M University in May 2018 with a B.S. in Computer Science (summa cum laude), completing an ACE Scholars degree with an honors thesis supervised by Dr. Jennifer Welch.

In the past, I’ve been an intern at Facebook, an intern at Nextdoor, and an intern at Apple. During undergrad, I also helped run TAMUhack.

Publications

• Fair Learning-to-Rank from Implicit Feedback [arXiv]
  Himank Yadav*, Zhengxiao Du*, Thorsten Joachims
  Under Review as a Conference Submission. 2019.

• Detecting Failures in an Asynchronous System That Never Stops Changing [thesis]
  Himank Yadav
  Dissertation. Texas A&M University. 2018.

* Equal contribution

Technical Reports

• Wide Baseline Matching [report]
  Qianqian Wang, Himank Yadav, Wenqi Xian

• Counters: Identifying and Summarizing Opposing Media Articles [report]
  Himank Yadav, Katherine Van Koevering

• Applicability of Language Models to Fact Checking [report]
  George Karagiannis, FlorianSuri-Payer, Himank Yadav

Teaching

• CS 4780/5780: Machine Learning [website]
  (Cross-listed as a graduate course)
  Head TA, Fall 2019
  Prof. Thorsten Joachims, Cornell University

• CS 2110: Object Oriented Programming and Data Structures [website]
  Head TA, Fall 2018
  Prof. David Gries, Cornell University