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个人简介
Matthew received his Ph.D. in Department of Computer Science at Stanford University in 2016 where he was advised by Professor Ron Fedkiw and supported by a National Defense Science and Engineering Graduate Fellowship. From 2012 to 2020, he worked as a Research and Development Engineer at Industrial Light & Magic focusing on facial animation and simulation. He has received screen credits for this work on "Kong: Skull Island", "Avengers: Endgame", and "The Irishman".
Prior to attending Stanford, Matthew received his B.S. in Engineering Physics and Computer Science from Cornell University in 2011. As an undergraduate, he worked on computational ab initio physics and electrochemistry as well as path planning for personal robotics. Matthew was also an intern at Morgan Stanley in 2010, and has fond memories of NYC which prompt him to visit as often as possible.
Outside of work, Matthew participates in CrossFit and Olympic weightlifting. He just completed his first triathlon and is looking forward to doing more endurance races in the future. To balance things out, Matthew enjoys trying new food establishments, especially bakeries and coffee shops.
Prior to attending Stanford, Matthew received his B.S. in Engineering Physics and Computer Science from Cornell University in 2011. As an undergraduate, he worked on computational ab initio physics and electrochemistry as well as path planning for personal robotics. Matthew was also an intern at Morgan Stanley in 2010, and has fond memories of NYC which prompt him to visit as often as possible.
Outside of work, Matthew participates in CrossFit and Olympic weightlifting. He just completed his first triathlon and is looking forward to doing more endurance races in the future. To balance things out, Matthew enjoys trying new food establishments, especially bakeries and coffee shops.
研究兴趣
论文共 13 篇作者统计合作学者相似作者
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Hyojoon Park,Sangeetha Grama Srinivasan,Matthew Cong,Doyub Kim, Byungsoo Kim, Jonathan Swartz,Ken Museth,Eftychios Sifakis
CoRR (2023)
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2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018): 10794-10803
ACM SIGGRAPH 2017 Talks (2017)
SCApp.175-183, (2015)
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