I am a thrid-year PhD student in the School of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST), where I am advised by Jinwoo Shin. I recieved an M.S. in Electrical engineering and a B.S. in Electrical Engineering and Mathematics (minored) from KAIST.
My research interests focus on approximate algorithm design and analysis for large-scale machine learning and its applications. Here is my CV (Last update is October 2019).
Contact
Email : insu.han at kaist.ac.kr
Phone : +82 42 350 7632
Research experiences
[Summer 2019] Research intern at Google New York City with Jennifer Gillenwater
[Spring 2018] Visting student at Tel Aviv University with Haim Avron
Awards
I am a recipient of Microsoft Research Asia Fellowship 2019.
Publications
MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search
[paper]
Insu Han and Jennifer Gillenwater
Will be appeared in International Conference on Artificial Intelligence and Statistics (AISTATS) 2020
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
[paper]
Insu Han, Haim Avron and Jinwoo Shin
Submitted
Stochastic Chebyshev Gradient Descent for Spectral Optimization
[paper][poster][video]
Insu Han, Haim Avron and Jinwoo Shin
Neural Information Processing Systems (NeurIPS) 2018, Spotlight Presentation (168/4856=3.5%)
Faster Greedy MAP Inference for Determinantal Point Processes
[paper][code][video]
Insu Han, Prabhanjan Kambadur, Kyoungsoo Park and Jinwoo Shin
International Conference on Machine Learning (ICML) 2017
Approximating Spectral Sums of Large-scale Matrices using Stochastic Chebyshev Approximations
[paper]
Insu Han, Dmitry Malioutov, Haim Avron and Jinwoo Shin
SIAM Journal on Scientific Computing (SISC) 2017
Large-scale Log-determinant Computation through Stochastic Chebyshev Expansions
[paper][code][video]
Insu Han, Dmitry Malioutov, and Jinwoo Shin
International Conference on Machine Learning (ICML) 2015