Ching-Yun (Irene) Ko @ MIT
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Ph.D. Student, |
About Me
I am a 3rd year PhD student at MIT EECS working with Prof. Luca Daniel. My current research interests include representation learning, physics-informed inverse problem solving in medical imaging, robustness and fairness analysis of models, and broadening the use of tensor decomposition techniques in memory/computationally-intensive applications. I am also interested in system control and identification. Visitors are more than welcome to reach out to me if you are interested in either of these topics! I was with IBM Thomas J. Watson Research as a research summer intern in 2021 working on efficient representations for reinforcement learning.
Prior to joining MIT, I have been with the Department of Electrical and Electronic Engineering at the University of Hong Kong for my M.Phil. title. During the two years as a master student, I grasped my first opportunity to do research in tensor decomposition arithmetics and system identification with Prof. Ngai Wong and Prof. Kim Batselier. Previously, I obtained my B.Sc. degree from the School of Mathematics and Statistics, Wuhan University, where I spent four years doing nothing related to research but only drama, basketball, and maths.
Publications
Conference Papers
Jeet Mohapatra*, Ching-Yun Ko*, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel, “Hidden Cost of Randomized Smoothing,” in Proc. of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS’21). [PDF]
Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel, “Higher-Order Certification For Randomized Smoothing,” in Proc. of the 34th Conference on Neural Information Processing Systems (NeurIPS’20). (Spotlight)
Rui Lin, Ching-Yun Ko, Zhuolun He, Cong Chen, Yuan Cheng, Hao Yu, Graziano Chesi, Ngai Wong, “HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression,” in Proc. of the 15th International Conference on Solid-State & Integrated Circuit Technology (ICSICT’20). [PDF]
Zhaoyang Lyu*, Ching-Yun Ko*,Zhifeng Kong, Ngai Wong, Dahua Lin, Luca Daniel, “Fastened CROWN: Tightened Neural Network Robustness Certificates,” in Proc. of the 34th AAAI Conference on Artificial Intelligence (AAAI’20). [PDF] [Code]
Kim Batselier, Ching-Yun Ko, Ngai Wong, “Extended Kalman Filtering with Low-Rank Tensor Networks for MIMO Volterra System Identiļ¬cation,” in Proc. of the 58th Conference on Decision and Control (CDC’19). (Invited Paper). [PDF] [Code]
Ching-Yun Ko, Rui Lin, Shu Li, Ngai Wong, “MiSC: Mixed Strategies Crowdsourcing,” in Proc. of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). [PDF]
Cong Chen, Kim Batselier, Ching-Yun Ko, Ngai Wong, “A support tensor train machine,” in Proc. of the 28th International Joint Conference on Neural Networks (IJCNN’19).
Cong Chen, Kim Batselier, Ching-Yun Ko, Ngai Wong, “Matrix product operator restricted Boltzmann machines,” in Proc. International Joint Conference on Neural Networks (IJCNN’19).
Ching-Yun Ko*, Zhaoyang Lyu*, Tsui-Wei Weng, Luca Daniel, Ngai Wong, Dahua Lin, “POPCORN: Certifying robustness of recurrent neural networks,” in Proc. of the 36th International Conference on Machine Learning (ICML’19). [PDF] [Code]
Yuke Zhang, Ching-Yun Ko, Cong Chen, Kim Batselier, Ngai Wong, “Sparse Tensor Network System Identification for Nonlinear Circuit Macromodeling,” in Proc. of the 14th International Conference on Solid-State and Integrated Circuit Technology (ICSICT’18). (Invited Paper) [PDF]
Kim Batselier, Ching-Yun Ko, Anh-Huy Phan, Andrzej Cichocki, Ngai Wong, “Multilinear state space system identification with matrix product operators,” in Proc. of the 18th IFAC Symposium on System Identification (Sysid’18). [PDF]
Journal Articles
Ching-Yun Ko, Kim Batselier, Luca Daniel, Wenjian Yu, Ngai Wong, “Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains,” IEEE Transactions on Image Processing, vol. 29, pp. 6918-6931, 2020. [PDF] [Code]
Ching-Yun Ko, Cong Chen, Zhuolun He, Yuke Zhang, Kim Batselier, Ngai Wong, “Deep Model Compression and Inference Speedup of Sum-Product Networks on Tensor Trains,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 2665-2671, 2020. [PDF] [Code]
Kim Batselier, Ching-Yun Ko, Ngai Wong, “Tensor network subspace identification of polynomial state space models,” Automatica, vol. 95, pp. 87-196, 2018. [PDF] [Code]
Technical Reports
Ching-Yun Ko, Jeet Mohapatra, Pin-Yu Chen, Sijia Liu, Luca Daniel, Tsui-Wei Weng, Table Salt Inspired Contrastive Learning (this is an alias!), Under Reivew.
Professional Activities
Duties
Journal reviewer of Neurocomputing, IEEE Transactions on Image Processing, IEEE Transactions on Signal Processing, IEEE Transactions on Cybernetics, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Optimization Methods and Software.
Conference reviewer of ICLR’22, NeurIPS’21, AISTATS’21, NeurIPS’20, ICML’20, CDC’19.
Program committee member of New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership
Talks
An invited lightning talk in IJCAI 2019 workshop “Humanizing AI”, Aug. 2019.
An invited talk on “Tensor Decomposition in Machine Learning Applications” at Hangzhou Dianzi University, Dec. 2018.
Teaching
MATH1853 Linear Algebra, Probability and Statistics (undergraduate), Fall 2018.
WHU Elite Program, Spring 2017, Fall 2016.
More About Me
I was (and am still!) a fan of basketball and drama during my undergrad fun time. Specially, I was the president of Shuying drama club in my sophomore year and the captain of women's basketball team of the school of Mathematics and Statistics in my junior year. I also enjoy travelling and hiking, especially hiking when travelling. I went on a trip for half month in the summer of 2017 and completed a 35-km hike with all the laugages. Later that year, I challanged myself with a 50-km hike (Hong Kong Trail) and won a place with my teammates. Research (My laziness) kept me occupied since then so no more fun stories (for now). XD
(Updated in 2021 oct) I found life itself could be extremely hard when one is too conscious or has too much compassion. It could also be hard when you are aware of the misalignment between others’ values and yours. While denying is easy, with consciousness and compassion, you are encouraged to rather live with their existence. Then one day, you realize that getting something that only involves yourself done is relatively easy (like your own research), but encouraging changes that involve other minds is almost impossible. So you stay in your comfort zone, you become a loner. But no, don't be a loner. No pressure, but let's confront it sometime together