Fall’25: CS 6301 Special Topics in CS: Machine Learning in Mobile Computing
Course Info
Instructor: Yi Ding
Office: ECSS 4.703
Office hours: by appointment
Email: yi.ding@utdallas.edu
Lecture: 4:00 pm - 5:15 pm, Monday/Wednesday
Location: CR 1.202
Course Description
Machine learning is transforming the way mobile and embedded systems perceive and interact with the world. Empowered by rich data from sensors embedded in our phones, wearables, vehicles, and infrastructure, mobile computing is becoming increasingly intelligent, context-aware, and human-centric.
In this course, we explore how sensing technologies, machine learning techniques, and mobile systems jointly enable applications in wireless sensing, multimodal fusion, on-device learning, and adaptive edge intelligence. Topics include RF-based sensing (e.g., Wi-Fi, Bluetooth, GPS, satellite), acoustic and visual sensing, inertial and environmental sensing, signal tokenization and feature modeling, mobile system optimization, privacy-preserving learning, and the use of foundation models in mobile and sensing scenarios.
Students are expected to:
(i) read and present research papers from top-tier conferences (e.g., MobiCom, SenSys, UbiComp, NeurIPS),
(ii) participate actively in in-class discussions and invited talks from academia and industry, and
(iii) design, implement, and present a final project that explores new ideas in mobile sensing and machine learning.
Course Learning Objectives
By the end of this course, you will be able to
- Understand the core principles of applying machine learning techniques to mobile and embedded systems, including sensing modalities, signal processing, and on-device learning.
- Explain state-of-the-art research and system designs in mobile sensing, multimodal data fusion, edge intelligence, and federated learning.
- Evaluate the trade-offs and constraints in mobile and resource-constrained environments (e.g., latency, energy, privacy), and how they affect the deployment of machine learning models.
- Design and propose intelligent mobile sensing systems that integrate machine learning, signal modeling, and system-level optimization.
- Implement prototypes or simulations using real-world or simulated sensor data, employing tools such as Python, PyTorch/TensorFlow, and edge deployment toolkits.
- Communicate technical insights effectively through paper presentations, invited talk discussions, and final project demos.
Required Texts
No books are required. All the materials will be online.
Course Schedule (Tentative)
W1: Course Introduction & Guidance on Paper Reading and Presentation (08/25, 08/27)
- Lecture: Course Introduction & Logistics
Topic 1: Sensing: Wi-Fi & Bluetooth (09/10, 09/15)
- Topic1-Paper1 Ding, Jian, et al. “Cost-effective soil carbon sensing with wi-fi and optical signals.” Proceedings of the 30th Annual International Conference on Mobile Computing and Networking. 2024.
- Topic1-Paper2 Li, Chenning, et al. “Wi-fi see it all: generative adversarial network-augmented versatile wi-fi imaging.” Proceedings of the 18th Conference on Embedded Networked Sensor Systems. 2020.
- Topic1-Paper3 Ni, Jiazhi, et al. “Experience: Pushing indoor localization from laboratory to the wild.” Proceedings of the 28th Annual International Conference on Mobile Computing And Networking. 2022.
- Topic1-Paper4 Li, Xin, et al. “Uwb-fi: Pushing wi-fi towards ultra-wideband for fine-granularity sensing.” Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services. 2024.
- Topic1-Paper5 Adib, Fadel, and Dina Katabi. “See through walls with WiFi!.” Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM. 2013.
- Topic1-Paper6 Wang, Yuxi, Kaishun Wu, and Lionel M. Ni. “Wifall: Device-free fall detection by wireless networks.” IEEE Transactions on Mobile Computing 16.2 (2016): 581-594.
Topic 2: Sensing: Motion & Environmental Sensors (IMU, Biochemical, etc.) (09/03, 09/10)
- Topic2-Paper1 Xu, Huatao, et al. “Practically adopting human activity recognition.” Proceedings of the 29th Annual International Conference on Mobile Computing and Networking. 2023.
- Topic2-Paper2 Brooks, Jas, and Pedro Lopes. “Smell & paste: Low-fidelity prototyping for olfactory experiences.” Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 2023.
- Topic2-Paper3 Yin, Xiangyu, et al. “PTEase: objective airway examination for pulmonary telemedicine using commodity smartphones.” Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services. 2023.
- Topic2-Paper4 Zhou, Pengfei, et al. “Iodetector: A generic service for indoor outdoor detection.” Proceedings of the 10th acm conference on embedded network sensor systems. 2012.
- Topic2-Paper5 Xie, Zhiqing, et al. “TransFloor: Transparent floor localization for crowdsourcing instant delivery.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6.4 (2023): 1-30.
Topic 3: Sensing: Acoustic & Vision (09/22)
- Topic3-Paper1 Liang, Xiaoxuan, et al. “Sondar: Size and shape measurements using acoustic imaging.” Proceedings of the Twenty-fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. 2024.
- Topic3-Paper2 Cao, Shirui, et al. “Powerphone: Unleashing the acoustic sensing capability of smartphones.” Proceedings of the 29th Annual International Conference on Mobile Computing and Networking. 2023.