Graduate Student @ UCSD CSEMy research broadly focuses on bridging embedded and mobile systems with emerging technologies like artificial intelligence, emphasizing system efficiency, sustainability, and user accessibility. Mobile and embedded AI systems and applications often face challenges in limited computing resources and demands in long-term sustainable operations. I aim to develop innovative solutions that address real-world challenges while optimizing resource consumption and enhancing usability for diverse applications.
Meanwhile, I am also a lifelong HCI (aka. Human-Cat Interaction) researcher. My research primarily focuses on how to efficiently communicate and interact with Miss Hope, an adopted American Shorthair raised in the Midwest. My current projects—How to Prevent Your Cat from Tampering with Jumper Wires and How to Evict Your Feline Overlord from Your Keyboard and Desk—are graciously funded by our first electric officer and wiring manager. They ensure that my circuits (and sanity) remain intact while Miss Hope continues to offer new challenges in the ever-evolving field of HCI.
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University of Michigan, Ann ArborElectrical Engineering and Computer Science Department
Incoming Ph.D. Student in Computer Science and EngineeringStarting Sep. 2025 -
University of California, San DiegoDepartment of Computer Science and Engineering
M.S. Student in Computer ScienceSep. 2023 - Jun. 2025 -
University of North Carolina at Chapel HillB.A. in Computer ScienceAug. 2019 - Dec. 2022
Selected Publications (view all )
Offload Rethinking by Cloud Assistance for Efficient Environmental Sound Recognition on LPWANs
Le Zhang, Quanling Zhao, Run Wang, Shirley Bian, Onat Gungor, Flavio Ponzina, Tajana Rosing
Accepted by the ACM Conference on Embedded Networked Sensor Systems (SenSys) 2025
We present ORCA, a resource-efficient, cloud-assisted environmental sound recognition system designed for batteryless devices using Low-Power Wide-Area Networks (LPWANs). ORCA addresses the accuracy limitations of on-device methods and reduces communication costs of cloud-offloading strategies, enabling ultra-low-power, wide-area audio sensing applications.
Offload Rethinking by Cloud Assistance for Efficient Environmental Sound Recognition on LPWANs
Le Zhang, Quanling Zhao, Run Wang, Shirley Bian, Onat Gungor, Flavio Ponzina, Tajana Rosing
Accepted by the ACM Conference on Embedded Networked Sensor Systems (SenSys) 2025
We present ORCA, a resource-efficient, cloud-assisted environmental sound recognition system designed for batteryless devices using Low-Power Wide-Area Networks (LPWANs). ORCA addresses the accuracy limitations of on-device methods and reduces communication costs of cloud-offloading strategies, enabling ultra-low-power, wide-area audio sensing applications.
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning
Le Zhang, Onat Gungor, Flavio Ponzina, Tajana Rosing
Asia and South Pacific Design Automation Conference (ASPDAC) 2025
We propose E-QUARTIC, an energy-efficient edge ensembling framework that constructs CNN ensembles optimized for AI-based embedded systems, achieving higher accuracy than single-instance CNNs and existing edge solutions without additional memory overhead. Leveraging its multi-CNN architecture, E-QUARTIC also implements an energy-aware model selection strategy tailored for energy-harvesting AI environments.
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning
Le Zhang, Onat Gungor, Flavio Ponzina, Tajana Rosing
Asia and South Pacific Design Automation Conference (ASPDAC) 2025
We propose E-QUARTIC, an energy-efficient edge ensembling framework that constructs CNN ensembles optimized for AI-based embedded systems, achieving higher accuracy than single-instance CNNs and existing edge solutions without additional memory overhead. Leveraging its multi-CNN architecture, E-QUARTIC also implements an energy-aware model selection strategy tailored for energy-harvesting AI environments.
Efficient Multitask Learning on Resource-constrained Systems
Yubo Luo, Le Zhang, Zhenyu Wang, Shahriar Nirjon
International Conference on Embedded Wireless Systems and Networks (EWSN) 2024
We introduce Antler, a multitask inference system that constructs a compact task graph by exploiting task affinities, optimizing the execution order to significantly reduce end-to-end inference time and energy usage. Antler leverages overlaps and dependencies among tasks to avoid redundant computations while maintaining accuracy comparable to state-of-the-art approaches.
Efficient Multitask Learning on Resource-constrained Systems
Yubo Luo, Le Zhang, Zhenyu Wang, Shahriar Nirjon
International Conference on Embedded Wireless Systems and Networks (EWSN) 2024
We introduce Antler, a multitask inference system that constructs a compact task graph by exploiting task affinities, optimizing the execution order to significantly reduce end-to-end inference time and energy usage. Antler leverages overlaps and dependencies among tasks to avoid redundant computations while maintaining accuracy comparable to state-of-the-art approaches.
Demo Abstract: Capuchin: A Neural Network Model Generator for 16-bit Microcontrollers
Le Zhang, Yubo Luo, Shahriar Nirjon
ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) 2022
We present a neural network model generator that automatically transfers parameters from pre-trained DNN or CNN models to 16-bit microcontrollers and implements on-device inference. This tool bridges the gap in efficiency and usability between development tools for 16-bit and 32-bit microcontrollers, significantly simplifying deep learning deployment on ultra-low-power devices.
Demo Abstract: Capuchin: A Neural Network Model Generator for 16-bit Microcontrollers
Le Zhang, Yubo Luo, Shahriar Nirjon
ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) 2022
We present a neural network model generator that automatically transfers parameters from pre-trained DNN or CNN models to 16-bit microcontrollers and implements on-device inference. This tool bridges the gap in efficiency and usability between development tools for 16-bit and 32-bit microcontrollers, significantly simplifying deep learning deployment on ultra-low-power devices.