Hyokun Yun Machine Learning Scientist

profile

Introduction

I am Hyokun Yun (윤효근), a Machine Learning Scientist at Amazon.

Until Spring 2014, I was a Ph.D student in the Department of Statistics at Purdue University. I worked with Prof. S.V.N. Vishwanathan on efficient optimization algorithms for large-scale machine learning, and statistical modeling of graphs.

Before joining Purdue, I was an associate researcher in Cyram(c) and developed graph analysis/visualization algorithms for network analysis software products including NetMiner and NetMetrica.

I am also a proud alumnus of POSTECH (Pohang University of Science and TECHnology). I hope to see more international presence of POSTECH alumni; please feel free to contact me if I could be of any help.

Contact

E-mail: yunhyoku -at- amazon -dot- com.

Education

  • Ph.D. in Statistics, Purdue University, USA, Spring 2014
  • M.S. in Statistics, Purdue University, USA, Spring 2013
  • B.S. in Industrial Engineering and Mathematics (Summa Cum Laude), POSTECH, Korea, Spring 2009

Employment

  • Machine Learning Scientist, Amazon LLC (current)
  • Machine Learning Intern, Amazon LLC, Summer 2013
  • Research Intern, Microsoft Research India, Fall 2012
  • Predictive Analytics Intern, Blizzard Entertainment, Summer 2012
  • Internship Researcher, Max Planck Institute Tuebingen, Summer 2011
  • Associate Researcher, Cyram(c), 2006 - 2008
  • Internship Consultant, Accenture Company, Summer 2005

Publications

Thesis

  • Doubly Separable Models and Distributed Parameter Estimation [Paper] [Slides]

Papers

  • Robustness to Capitalization Errors in Named Entity Recognition (Sravan Bodapati, Hyokun Yun, Yaser Al-Onaizan), W-NUT 2019 (EMNLP 2019 Workshop), [Paper]
  • Scaling Multinomial Logistic Regression via Hybrid Parallelism (Parameswaran Raman, Sriram Srinivasan, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S.V.N. Vishwanathan), KDD 2019, [Paper]
  • Deep Active Learning for Named Entity Recognition (Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar), ICLR 2018 [Paper]
  • Distributed Stochastic Optimization of the Regularized Risk (Shin Matsushima, Hyokun Yun, Xinhua Zhang, S.V.N. Vishwanathan), ECML PKDD 2017 [Paper]
  • WordRank: Learning Word Embeddings via Robust Ranking (Shihao Ji, Hyokun Yun, Pinar Yanardag, Shin Matsushima, S. V. N. Vishwanathan), EMNLP 2016 [Paper]
  • Nomadic Computing for Big Data Analytics (Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S.V.N. Vishwanathan, Inderjit S. Dhillon), IEEE Computer 2016 [Paper]
  • A Scalable Asynchronous Distributed Algorithm for Topic Modeling (Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S.V.N. Vishwanathan, Inderjit S. Dhillon), WWW 2015 [Paper]
  • Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data (Hyokun Yun, Parameswaran Raman, S.V.N. Vishwanathan), NIPS 2014 [Paper] [Code] [Poster]
  • NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion (Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S.V.N. Vishwanathan, Inderjit S. Dhillon), VLDB 2014 [Paper] [Code]
  • How to Filter out Random Clickers in a Crowdsourcing-Based Study? (Sung-Hee Kim, Hyokun Yun and Ji Soo Yi), BELIV Workshop, VisWeek 2012 [Paper]
  • Efficiently Sampling Multiplicative Attribute Graphs Using a Ball-Dropping Process (Hyokun Yun and S.V.N. Vishwanathan), Technical Paper, 2012 [Paper] [Slides]
  • Quilting Stochastic Kronecker Graphs to Generate Multiplicative Attribute Graphs (Hyokun Yun and S.V.N. Vishwanathan), AIStats, 2012 [Paper] [Slides] [Poster] [Code]

Book Chapters

  • Valid Prior-Free Probabilistic Inference and its Applications in Medical Statistics (Duncan Emily Leaf, Hyokun Yun and Chuanhai Liu) [Paper]

Software

  • NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion [Download]