- “Energy-Adaptive Checkpoint-Free Intermittent Inference for Low Power Energy Harvesting Systems” and “Energy-efficient Persistently Secure Block-based Differential Checkpointing for Energy Harvesting Devices”, accepted to ISQED’25.
- “Intermittent OTA Code Update Framework for Tiny Energy Harvesting Devices”, accepted to TCAD.
- “Energy-aware Incremental OTA Update for Flash-based Batteryless IoT Devices”, accepted to ISVLSI’24.
- “Autotile: Autonomous Task-tiling for Deep Inference on Battery-less Embedded System”, accepted to GLSVLSI’24.
- “Construction Worker Ergonomic Assessment via LSTM-Based Multi-Task Learning Framework”, accepted to CRC’22.
- “An Intermittent OTA Approach to Update the DL Weights on Energy Harvesting Devices”, accepted to ISQED’22.
- “Memory-aware Efficient Deep Learning Mechanism for IoT Devices”, accepted to ASAP’21.
Awards:
- July 2024: NSF ISVLSI Student Travel Grant Award and Graduate School Professional Development Travel Award.
- March 2022: Graduate School Professional Development Travel Award.
Research Experience
Ph.D. candidate in the Department of Computer Science at The University of Texas at San Antonio, focusing on over-the-air (OTA) updates and Tiny Machine Learning (TinyML) since 2020.
Education
Ph.D. Candidate in Computer Science at The University of Texas at San Antonio since 2020; M.S. in Computer Engineering from The University of Texas at San Antonio in 2017.
Background
Research interests include over-the-air (OTA) updates and Tiny Machine Learning (TinyML) in energy harvesting Internet of Things (IoT) devices. Prior to starting doctoral studies in 2020, worked as a Software Development Engineer for three years.