Prof. Sunghun Kim (NAVER Corp., South Korea / Hong Kong University of Science and Technology, Hong Kong)

“Managing Deep Learning Debt @Naver/Line”

Short bio

Sunghun Kim is an Associate Professor of Computer Science at the Hong Kong University of Science and Technology, and currently leading the Naver Clova AI team. He got his BS in Electrical Engineering at Daegu University, Korea in 1996. He completed his Ph.D. in the Computer Science Department at the University of California, Santa Cruz in 2006. He was a postdoctoral associate at Massachusetts Institute of Technology and a member of the Program Analysis Group. He was a Chief Technical Officer (CTO), and led a 25-person team at the Nara Vision Co. Ltd, a leading Internet software company in Korea for six years. His core research area is Software Engineering, focusing on software evolution, program analysis, and empirical studies. He publishes his work on top venues such as TSE, ICSE, FSE, AAAI, SOSP, and ISSTA. He received the Most Influential Paper Award at ICSME2018 (from his ICSME 2008 paper). He is a four-time winner of the ACM SIGSOFT Distinguished Paper Award (ICSE 2007, ASE 2012, ICSE 2013 and ISSTA 2014). He served on a variety of program committees including ICSE, FSE, and ASE. He was a program co-chair of MSR 2013 and 2014. He is the general chair of MSR 2020. He is an associate editor of IEEE Transactions on Software Engineering and Empirical Software Engineering. His online deep learning course, https://www.youtube.com/user/hunkims has more than 4M views and 30K subscribers. Further information is available at http://www.cse.ust.hk/~hunkim

Deep learning has shown amazing results in both academic and industry, yielding many practical services and products such as machine translation, personal assistant, image retrieval, video recommendation and photo enhancement. However, in industry, it is common to observe legacy products with deep learning debt – the tremendous cost in converting traditional machine learning systems to deep learning. In order to utilize deep learning and boost the performance of existing products, it is necessary to examine our systems and repay any deep learning dept. This talk will describe my experience at Naver/LINE on managing deep learning dept, including challenges and their potential solutions.