About Me
Dr. Yoshitomo Matsubara is an Applied Scientist at Amazon Alexa AI. He completed the Ph.D. program in Computer Science at University of California, Irvine (UCI) and worked on deep learning for resource-constrained edge computing systems with Profs. Marco Levorato, Stephan Mandt, and Sameer Singh. Before UCI, he obtained his Master and Bachelor degrees in University of Hyogo and National Institute of Technology, Akashi College (Previously, Akashi National College of Technology), Japan, respectively.His main research interests are in machine learning, natural language processing, computer vision, information retrieval, and symbolic regression. For deep learning, his main interests are in knowledge distillation and supervised compression.
Latest News
March 7, 2023
I renovated my personal website.
November 29, 2022
I attended NeurIPS 2022 at New Orleans, LA, USA.
November 1, 2022
I was selected as an Outstanding Reviewer of NeurIPS 2022 Datasets & Benchmarks track.
November 1, 2022
I was selected as a Top Reviewer of NeurIPS 2022 (main track).
October 20, 2022
Our paper was accepted to NeurIPS 2022 AI for Science: Progress and Promises.
October 6, 2022
Our paper was accepted to EMNLP 2022 as a long paper (Findings).
July 10, 2022
I attended NAACL 2022 at Seattle, WA, USA.
April 21, 2022
I was selected as a Highlighted Reviewer of ICLR 2022.
March 28, 2022
I joined Amazon Alexa AI as an applied scientist.
March 23, 2022
I was selected as a Top Contributor of Papers with Code.
March 18, 2022
I completed my Ph.D. program at University of California, Irvine and obtained Ph.D. in Computer Science.
March 14, 2022
Our paper was accepted to ACM Computing Surveys (CSUR).
March 5, 2022
Our paper was accepted to IEEE WoWMoM 2022.
February 14, 2022
I successfully defended my Ph.D. thesis "Towards Split Computing: Supervised Compression for Resource-Constrained Edge Computing Systems".
January 13, 2022
I gave a tutorial "Reliable Real-Time Distributed AI for Mobile Autonomous Systems" with Prof. Marco Levorato at ICOIN 2022.
January 4, 2022
I attended WACV 2022 at Waikoloa, HI, USA.
January 3, 2022
This quarter, I am working as a graduate student researcher.
I renovated my personal website.
November 29, 2022
I attended NeurIPS 2022 at New Orleans, LA, USA.
November 1, 2022
I was selected as an Outstanding Reviewer of NeurIPS 2022 Datasets & Benchmarks track.
November 1, 2022
I was selected as a Top Reviewer of NeurIPS 2022 (main track).
October 20, 2022
Our paper was accepted to NeurIPS 2022 AI for Science: Progress and Promises.
October 6, 2022
Our paper was accepted to EMNLP 2022 as a long paper (Findings).
July 10, 2022
I attended NAACL 2022 at Seattle, WA, USA.
April 21, 2022
I was selected as a Highlighted Reviewer of ICLR 2022.
March 28, 2022
I joined Amazon Alexa AI as an applied scientist.
March 23, 2022
I was selected as a Top Contributor of Papers with Code.
March 18, 2022
I completed my Ph.D. program at University of California, Irvine and obtained Ph.D. in Computer Science.
March 14, 2022
Our paper was accepted to ACM Computing Surveys (CSUR).
March 5, 2022
Our paper was accepted to IEEE WoWMoM 2022.
February 14, 2022
I successfully defended my Ph.D. thesis "Towards Split Computing: Supervised Compression for Resource-Constrained Edge Computing Systems".
January 13, 2022
I gave a tutorial "Reliable Real-Time Distributed AI for Mobile Autonomous Systems" with Prof. Marco Levorato at ICOIN 2022.
January 4, 2022
I attended WACV 2022 at Waikoloa, HI, USA.
January 3, 2022
This quarter, I am working as a graduate student researcher.
My Recent Projects
Knowledge Distillation
Model compression and beyond.
Open Source Software
I develop and open-source (mostly ML) frameworks that boost my research and I hope would be useful for the research community too.
Information Retrieval / Question Answering
Natural language processing for efficient IR and QA systems
Supervised Compression for Split Computing
Weak mobile device, limited communication capacity, strong edge server. How can we achieve efficient inference?
Symbolic Regression for Scientific Discovery
Can we (re)discover hidden laws from observed data?
Peer Review Systems
What are the problems in the current scientific peer review systems? How can we imporve the systems?