I am Ian, a third-year PhD student in the Department of Computer Science and Technology at the University of Cambridge, where I am supervised by Professor Cecilia Mascolo. I am a member of Hughes Hall, Cambridge and a Cambridge Trust Scholar. My research is supported by the Doris Zimmern Charitable Foundation, Cambrdige Trust and Nokia Corporation.
I am interested in deep learning applications, especially on mobile systems. I am currently working on building scalable human activity recognition systems using semi-supervised learning techniques, which include contrastive learning, self-supervised learning and self-training.
During my masters studies, I worked on collaborative activity recognition systems which leverage signals from multiple devices for better recognition efficiency and performance, under the supervision of Dr Robert Harle. In the summer of 2018, I worked at the Bioinformatics Algorithms and Core Technology Research Laboratory of the University of Hong Kong, on the development of an accurate germline small variant calling system powered by deep neural networks. I received my bachelor degree in Computer Science from the University of Hong Kong in 2018.
June 2022 - I am starting a research internship at Nokia Bell Labs (Cambridge), conducting research in machine learning on heterogenous edge devices.
May 2022 - Source code for "Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering" has been released on GitHub.
May 2022 - Paper "Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering" has been accepted for presentation in ICML 2022 and is now available on arXiv.
May 2022 - Presented "Improving Feature Generalizability with Multitask Learning in Class Incremental Learning" at ICASSP 2022. Recorded talk available here.
March 2022 - Paper "ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition" has been published in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).
March 2022 - A set of notes on Lambda calculus "How to Use the Y Combinator" has been uploaded to this website, as complementary materials for the course Computation Theory (2021-2022).
June 2021 - Source code for "SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data" has been released on GitHub.
April 2021 - Paper "SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data" has been published in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering
Ekdeep Singh Lubana, Chi Ian Tang, Fahim Kawsar, Robert P. Dick, Akhil Mathur
In ICML 2022 (International Conference on Machine Learning)
Improving Feature Generalizability with Multitask Learning in Class Incremental Learning
Dong Ma*, Chi Ian Tang*, Cecilia Mascolo
*Ordered alphabetically, equal contribution
In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition
Yash Jain*, Chi Ian Tang*, Chulhong Min, Fahim Kawsar, Akhil Mathur
*Ordered alphabetically, equal contribution
In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). Volume 6 Issue 1, Article 17 (March 2022).
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes
Kevalee Shah, Dimitris Spathis, Chi Ian Tang, Cecilia Mascolo
In Machine Learning for Health (ML4H) 2021
Group Supervised Learning: Extending Self-Supervised Learning to Multi-Device Settings
Yash Jain*, Chi Ian Tang*, Chulhong Min, Fahim Kawsar, Akhil Mathur
*Equal Contribution
In ICML 2021 Workshop: Self-Supervised Learning for Reasoning and Perception
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data
Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, Soren Brage, Nick Wareham, Cecilia Mascolo.
In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). Volume 5 Issue 1, Article 36 (March 2021).
Exploring Contrastive Learning in Human Activity Recognition for Healthcare
Chi Ian Tang, Dimitris Spathis, Ignacio Perez Pozuelo, Cecilia Mascolo.
In ML for Mobile Health Workshop at NeurIPS. 2020.
Exploring the limit of using a deep neural network on pileup data for germline variant calling
Ruibang Luo, Chak-Lim Wong, Yat-Sing Wong, Chi-Ian Tang, Chi-Man Liu, Henry CM Leung, Tak-Wah Lam.
In Nature Machine Intelligence. 2020.
I have supervised students and demonstrated for the following courses at the University of Cambridge:
Teaching Materials
© 2020-2022 Chi Ian Tang. All rights reserved.
