Professor
Jinhyun So
Electrical Engineering and Computer Science (EECS)
Daegu Gyeongbuk Institute of Science & Technology (DGIST)
Electrical Engineering and Computer Science (EECS)
Daegu Gyeongbuk Institute of Science & Technology (DGIST)
Dr. Jinhyun So is an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) and the Department of Interdisciplinary Studies of Atrificial Intelligence (AI) at Daegu Gyeongbuk Institute of Science & Technology (DGIST) in South Korea, working as a faculty member of DGIST Distributed AI Lab. His research interests lie broadly in distributed AI, trustworthy AI, federated and collaborative learning, privacy-preserving machine learning, and on-device learning.
He received my B.S and M.S degrees in Electrical Engineering from KAIST, and received Ph.D. degree in Electrical and Computer Engineering from USC under the supervision of Prof. Salman Avestimehr. He was a Ph.D Research Intern at Microsoft Research, Redmond in 2021, and a Staff Research Engineer at Samsung Cellular & Multimedia Labs, San Diego in 2022-2024.
For more details, please visit his personal website.
Education
2022
2012
2010
Ph.D. in Electrical and Computer Engineering (ECE)
vITAL Lab (Prof. Avestimehr), University of Southern California, USA
M.S. in Electrical Engineering (EE)
Digital Communication Lab (Prof. Yong. H. Lee), KAIST, South Korea
B.S. in Electrical Engineering (EE)
KAIST, South Korea
Professional Experience
2024-Now
2022-2024
2021
2013-2017
Assistant Professor
DGIST, Dept. of Electrical Engineering and Computer Science (EECS)
Staff Research Engineer
Samsung Cellular and Multimedia Lab, San Diego, USA
Ph.D. Research Intern
Microsoft Research, Redmond, USA
System Engineer
Modem Development Team, Samsung Electronics, Hawseong, South Korea
Academic Activites
Teaching
IC566c, Random Variables and Random Process, 2024 Spring
Technical Program Committee
International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with IJCAI (FL@FM-IJCAI'24).
International Workshop on Federated Foundation Models for the Web 2024 (FL@FM-TheWebConf'24)
Privacy Regulation and Protection in Machine Learning Workshop at ICLR 2024 (PRIVATE ML @ ICLR 2024)
Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2023 (FL-IJCAI'23)
ICML 2023 Workshop on Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities (FL-ICML'23)
Sixth Conference on Machine Learning and Systems (MLSys) 2023
International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022 (FL-NeurIPS'22)
ICML 2021 Workshop on Federated Learning for User Privacy and Data Confidentiality (FL-ICML'21)
AAAI 2022 Workshop on Trustable, Verifiable and Auditable Federated Learning (FL-AAAI'22)
ACL 2022 Workshop on Federated Learning for Natural Language Processing (FL4NLP-ACL'22)
Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI'22)
Reviewer (Journal/Conference)
Conference on Neural Information Processing Systems (NeurIPS)
International Conference on Learning Representations (ICLR)
International Conference on Machine Learning (ICML)
Conference on Machine Learning and Systems (MLSys)
AAAI Conference on Artificial Intelligence (AAAI)
Journal of Machine Learning Research (JMLR)
IEEE Journal of Selected Areas in Communications (JSAC)
IEEE Transaction on Information Theory (TIT)
IEEE Transactions on Information Forensics and Security (TIFS)
IEEE Internet of Things Journal
IEEE Communication Magazine
IEEE Transactions on Dependable and Secure Computing (TDSC)
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
IEEE International Symposium on Information Theory (ISIT)
IEEE Information Theory Workshop (ITW)
Awards
Travel Grant, MLSYS 2023 Conference
Best Paper Award, NeurIPS SpicyFL Workshop 2020
Annenberg Fellowship, USC, 2017-2022
Graduate Student Fellowship, KAIST, 2011-2012
KOSAF Fellowship, Korea Student Aid Foundation (KOSAF), 2004-2008
Samsung SDI Fellowship, Samsung SDI, 2003-2008