I am a computer scientist, currently pursuing a doctorate degree from the Computer Science Department at Duke University. My academic advisors are Ron Parr and Cynthia Rudin. My research interests span across reasoning, reinforcement learning, interpretability and perception.

Before joining Duke I worked for two years in Augmented Reality team at Samsung Research and Development Institute Ukraine. I received my M.S. and B.S. in Applied Mathematics from the Taras Shevchenko National University of Kyiv, Department of Computer Science and Cybernetics.

Publications

• On the Existence of Simpler Machine Learning Models
  ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2022
  Lesia Semenova, Cynthia Rudin, and Ronald Parr
  (bib) (video)
• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
  Statistics Surveys, 2022
  Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong
  (bib)
• Multitask Learning for Citation Purpose Classification
  Second Workshop on Scholarly Document Processing, NAACL, 2021
  Alex Oesterling, Angikar Ghosal, Haoyang Yu, Rui Xin, Yasa Baig, Lesia Semenova, Cynthia Rudin
  Won third place in the 3C Shared Task Competition. One of four oral presentations.
  (bib)

Teaching Assistant

• CS474, Data Science Competition, Spring 2022, Duke University
• CS474, Data Science Competition, Spring 2021, Duke University
• CS571, Probabilistic Machine Learning, Spring 2018, Duke University
• CS101, Introduction to Computer Science, Spring 2017, Duke University