Tetiana Parshakova
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2019
Bio
I am a Ph.D. candidate in Computational Mathematics at Stanford, working with Prof. Stephen Boyd.
My primary research objective is to develop efficient algorithms for computational problems using techniques from optimization, discrete mathematics, and statistics. In particular, my research interests include large-scale and distributed convex optimization, network science, learning and inference for network data, numerical and randomized linear algebra, low rank and structured optimization, and machine learning.
Prior to my Ph.D., I received a Bachelor’s in Industrial Design and a Master’s in Electrical Engineering at KAIST.
I am Ukrainian.
Honors & Awards
-
Oliger Memorial Fellowship, Stanford ICME (2019-2022)
-
Qualcomm-KAIST Innovation Awards, Qualcomm (2016, 2018)
-
SIGGRAPH 2016 Emerging Technologies DC EXPO Special Prize, SIGGRAPH (2016)
-
International Scholarship, Korea Advanced Institute of Science and Technology (2012-2016, 2017-2019)
Education & Certifications
-
MS, Korea Advanced Institute of Science and Technology, Electrical Engineering (2019)
-
BS, Korea Advanced Institute of Science and Technology, Industrial Design (2017)
Work Experience
-
ML Researcher, NAVER Labs Europe (3/2019 - 7/2019)
Location
Meylan, France
-
ML Research Intern, Apple Inc. (6/2020 - 9/2020)
Accelerating the training of Neural Networks using Hessian-vector products
Location
Cupertino, CA, USA
All Publications
-
Implementation of an oracle-structured bundle method for distributed optimization
OPTIMIZATION AND ENGINEERING
2023
View details for DOI 10.1007/s11081-023-09859-z
View details for Web of Science ID 001109181100001