[SNU AI Seminar] Toward Resource Efficient Learning and its Computer Vision Applications

Host: 
김건희 교수
Date: 
Thursday, November 24th 2022, 5:00pm - Thursday, November 24th 2022, 6:00pm
Location: 
43-1동 402호

Summary

Recent progress in machine learning has led to many advances in engineering and science fields, including computer vision and graphics. Most remarkable successes have been shown in supervised learning with deep neural networks. Despite these successes, as a consequence of using high-capacity models, we are struggling with a lack of high-quality annotations in supervised methods or limitations of hardware resources. Directly dealing with these bottlenecks is often very expensive or challenging. In this talk, I will present a few potential strategies by showing my past and recent research, which include leveraging synthetic data, injecting prior knowledge into the model, and leveraging worldwide user resources.

Speaker Bio

학력
2012 ~ 2017 KAIST 전기전자공학과 박사
2010 ~ 2012 KAIST 전기전자공학과 석사
2006 ~ 2010 광운대학교 컴퓨터공학과 학사

주요경력
2020 ~ 현재 POSTECH 전자전기공학과 조교수 (인공지능대학원 겸임)
2021 ~ 현재 POSCO-RIST 오픈랩 연구단장 (겸직)
2019 ~ 2020 Facebook AI Research 박사후과정
2017 ~ 2019 MIT CSAIL 박사후과정
2014 ~ 2015, 2016 Microsoft Research 연구인턴

상훈
2020 POSTECH 우수강의상 (총장상)
2015 Microsoft Research Asia Fellowship
2015 삼성 휴먼테크 논문상 금상
2015 KAIST 전기전자공학과 김충기 상 (최우수 연구성과상)
Qualcomm Innovation Awards 5회 수상