Chi Ian Tang

AI/ML Researcher in Mobile Systems
Nokia Bell Labs

Nokia Bell Labs

I am a Research Scientist in AI/ML in Mobile Systems at Nokia Bell Labs Cambridge and a former PhD student at the Mobile Systems Research Lab, University of Cambridge. My work focuses on developing data-efficient and scalable machine learning algorithms, particularly for mobile systems. I specialize in developing robust AI models that can handle complex, real-world scenarios by leveraging data-efficient approaches, including semi-supervised and self-supervised learning.

My key research areas include:

  • Scalable and Data-efficient Machine Learning for Human Activity Recognition: I develop novel training algorithms that reduce data dependence while ensuring robust recognition in mobile applications. My work leverages semi-supervised and self-supervised learning techniques for human activity recognition, utilizing methods such as multimodal learning [TSALM @ NeurIPS 2024], contrastive learning [HCRL @ AAAI 2024, ML4MH @ NeurIPS 2020], self-training [IWMUT 2021], and multi-device collaboration [IWMUT 2022].
  • Overcoming Catastrophic Forgetting in Continual Learning: I investigate strategies to address the challenges of catastrophic forgetting in continual learning, where models must learn from evolving data streams without losing previously acquired knowledge. My work focuses on approaches that balance stability and plasticity, ensuring models can generalize effectively across new tasks while retaining performance on past tasks [WACV 2024, ICASSP 2022].
  • Federated Learning for Scalable Learning Algorithms: I explore decentralized machine learning paradigms that prioritize data privacy and enable collaborative learning across distributed devices [ICML 2022].
  • AI for Health-related Applications: I apply machine learning techniques to health-related challenges, leveraging AI to enhance the accuracy and scalability of health monitoring applications [ML4H 2021, Nat. Mach. Intell. 2020].

I am passionate about leveraging AI to advance human-centric applications, from healthcare to mobile systems. By developing robust and scalable solutions, I aim to contribute to the future of ubiquitous computing, creating technologies that seamlessly integrate into and enhance our daily lives.

CV

News

May 2025 - Call for participation! I will be running the GenAI4HS (Generative AI and Foundation Models for Human Sensing Workshop) at UbiComp 2025 in Espoo, Finland (on 12, 13 October 2025). Preliminary and early works are welcome! Click here to learn more!

Works/Publications

2025

Past, Present, and Future of Sensor Based Human Activity Recognition using Wearables: A Surveying Tutorial on a Still Challenging Task
Haresamudram, Harish, Chi Ian Tang, Sungho Suh, Paul Lukowicz, Thomas Ploetz
To Appear In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).

arXiv

BioQ: Towards Context-Aware Multi-Device Collaboration with Bio-cues
Orzikulova, Adiba, Diana Vasile, Chi Ian Tang, Fahim Kawsar, Sung-Ju Lee, Chulhong Min
In SenSys 2025 (ACM Conference on Embedded Networked Sensor Systems)

ACM SenSys GitHub

PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervision
Arnav Das, Chi Ian Tang, Fahim Kawsar, Mohammad Malekzadeh
In ICASSP 2025 Lecture (Oral Presentation) 🏆
Also in NeurIPS 2024 Workshop: Time Series in the Age of Large Models (TSALM)

IEEE ICASSP arXiv GitHub Model Poster

2024

Solving the Sensor-Based Activity Recognition Problem (SOAR): Self-Supervised, Multi-Modal Recognition of Activities from Wearable Sensors
Harish Haresamudram, Chi Ian Tang, Sungho Suh, Paul Lukowicz, Thomas Ploetz
In UbiComp 2024 Companion

ACM UbiComp Proposal Website

Balancing Continual Learning and Fine-tuning for Human Activity Recognition
Chi Ian Tang, Lorena Qendro, Dimitris Spathis, Fahim Kawsar, Akhil Mathur, Cecilia Mascolo
In AAAI 2024 Workshop: Human-Centric Representation Learning (HCRL)

arXiv Poster

Kaizen: Practical Self-Supervised Continual Learning With Continual Fine-Tuning
Chi Ian Tang, Lorena Qendro, Dimitris Spathis, Fahim Kawsar, Cecilia Mascolo, Akhil Mathur
In WACV 2024 (IEEE/CVF Winter Conference on Applications of Computer Vision)

IEEE WACV WACV Open Access arXiv GitHub Presentation Poster

2023

Self-supervised Learning for Data-efficient Human Activity Recognition
Chi Ian Tang
PhD Thesis

Open Access @ University of Cambridge

Solving the Sensor-based Activity Recognition Problem (SOAR): Self-supervised, Multi-modal Recognition of Activities from Wearable Sensors
Harish Haresamudram, Chi Ian Tang, Sungho Suh, Paul Lukowicz, Thomas Ploetz
In UbiComp/ISWC 2023 Adjunct

ACM UbiComp Proposal Website

2022

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)

PMLR arXiv GitHub Presentation & Slides

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

IEEE ICASSP arXiv Presentation

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).

ACM IMWUT arXiv https://github.com/akhilmathurs/collossl

2021

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

arXiv Poster

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

Paper Poster

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).

ACM IMWUT GitHub Presentation (Short) Presentation (Full)

2020

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.

arXiv GitHub Poster

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.

Nat. Mach. Intell. GitHub

Academic Service

I have taken up roles for the following:

Invited Talks

Mentoring and Teaching

Mentoring

I truly enjoy and always learn a lot in mentoring PhD students. Here are some research projects I have had the pleasure of supervising:

  • Arnav Das (University of Washington): Multimodal learning for mobile sensing

Lectures/Tutorials

I gave a lecture/tutorial on Machine Learning and Features of Health Data in the Mobile Health course (Master's level) at the Department of Computer Science and Technology, University of Cambridge. Slides are available Slides (2023)

Supervisions

I have supervised students and demonstrated for the following courses at the University of Cambridge:

Teaching Materials

© 2020-2025 Chi Ian Tang. All rights reserved.