Research

Published research across AI/ML, mobile sensing, and on-device inference — the depth that informs the agent platform I am building today. All papers appear in CORE A* venues.

9Total
3Journals
5Conferences
1Patents

Journal Articles

J.1
UbiComp 2025A*

AccCall: Enhancing Real-time Phone Call Quality with Smartphone Built-in Accelerometer

Xi Zhang, et al.

AccCall leverages the smartphone's built-in accelerometer to monitor and enhance real-time phone call quality. By detecting device orientation, user motion, and acoustic coupling conditions, the system dynamically adjusts audio processing parameters to improve speech clarity and reduce noise during calls without additional hardware.

J.2
IEEE TMCA*

MDLdroidLite: A Release-and-Inhibit Control for Resource-Efficient Deep Neural Networks on Mobile Devices

Xi Zhang, et al.

MDLdroidLite proposes a release-and-inhibit control mechanism that dynamically allocates computational resources for deep neural networks on mobile devices. The system selectively activates and inhibits neural network layers based on real-time resource availability, achieving significant energy savings while maintaining inference accuracy.

J.3
IEEE ToNA*

MDLdroid: A ChainSGD-Reduce Approach to Mobile Deep Learning for Smartphones

Xi Zhang, et al.

MDLdroid presents ChainSGD-Reduce, a distributed deep learning framework that enables efficient on-device training across a chain of smartphones. The approach reduces communication overhead and memory requirements by propagating gradient updates sequentially through the device chain, enabling collaborative model training without a central server.

Conference Papers

C.1
INFOCOM 2026A*first author

LargeCall: Large-Model-Assisted Phone Call Enhancement Using Smartphone's Built-in Accelerometer

Xi Zhang, et al.

LargeCall integrates large language models with a smartphone's built-in accelerometer to enhance phone call quality in real time. By using the LLM as a reasoning layer over accelerometer-derived signals, the system adapts speech enhancement to call conditions on-device, lifting intelligibility without additional hardware.

C.2
MobiCom 2023A*first author

mmFER: Millimetre-wave Radar based Facial Expression Recognition

Xi Zhang, et al.

mmFER presents the first system to perform facial expression recognition using millimetre-wave radar, enabling privacy-preserving, contact-free emotion sensing. The system captures subtle facial muscle movements from reflected mmWave signals and classifies them into seven universal expression categories with high accuracy, even through occlusions.

C.3
SenSys 2022A*

mmBP: Contact-free mmWave Radar based Blood Pressure Measurement

Xi Zhang, et al.

mmBP introduces a non-contact blood pressure measurement system using commodity mmWave radar. By capturing pulse wave velocity and arterial stiffness indicators from radar reflections on the wrist area, mmBP estimates systolic and diastolic blood pressure without any physical contact, opening new possibilities for continuous health monitoring.

C.4
SenSys 2020A*

MDLdroidLite: A Release-and-Inhibit Control for Resource-Efficient Deep Neural Networks on Mobile Devices

Xi Zhang, et al.

Conference version of MDLdroidLite, presenting the release-and-inhibit control mechanism for resource-efficient deep neural network execution on mobile devices.

C.5
IPSN 2020A*

MDLdroid: A ChainSGD-Reduce Approach to Mobile Deep Learning for Smartphones

Xi Zhang, et al.

Conference version of MDLdroid, introducing the ChainSGD-Reduce approach for distributed mobile deep learning across a chain of smartphones.

Patents

P.1
Patent No.2023902311patent

Facial Expression Sensing

Xi Zhang, et al.

Patent covering methods and systems for non-contact facial expression sensing using millimetre-wave radar technology.