Pervasive Autonomous Networked Systems Lab
Make the world a smarter place
The number of everyday smart devices is projected to grow to billions in the coming decade. Our vision and mission are to make these intelligent networked devices more pervasive/ubiquitous and autonomous. Our research focuses on acquiring, analyzing, and augmenting ambient information with limited/less resources.
[Pervasive Perception] To effectively measure and monitor people's physical and behavioral health, continual and user-imperceptible sensing is essential. We focus on contact-based sensing by converting everyday objects into sensors while preserving their regular usability (Surface as Sensors).
Vibration of Everything
Structural Intelligence for Physical Signal Augmentation
[Autonomous Learning Adaptation] Due to the complexity of the physical world, data acquisition quality and sensing data distributions can change significantly under different sensing conditions, resulting in inaccurate detection and inference. We focus on autonomous assessing, configuring, and adapting heterogeneous sensing systems via cross-modal association using people as the shared context.
Cross-Modal Signal Association
Collaborative Continual Learning for IoT
[Networked Spatial Intelligence] Data from multimodal networked devices provides complementary information. By exploring the complementary and correlation of multimodal sensor data with different spatial/mobile characteristics (static, semi-mobile, mobile), we establish efficient inference/model transfer schemes guided by the modality's spatial characteristics.
AIoT to Mitigate Socioeconomic Bias in Air Quality Monitoring
Data-Driven Spatial Sensing Quality Assessment
UC Merced is located between Silicon Valley and the beautiful Yosemite National Park
#18 in Mobile Computing and #63 in general by CSRankings
#60 in U.S. News College Ranking
I'm looking for highly motivated PhD students.
Please contact me via email (span24@ucmerced.edu)
Sponsors
Alumni/Student Spin-off
Contact
Email: span24@ucmerced.edu
Github: www.github.com/PANSLAB