Wei Gao

I am an Associate Professor in the Department of Electrical and Computer Engineering and an affiliated faculty member of the McGowan Institute of Regenerative Medicine at University of Pittsburgh. I direct the Pitt Intelligent System Laboratory (ISL).

My research focuses on the design, deployment, analysis and measurement of on-device AI architectures and algorithms on mobile, embedded and networked systems. I have strong interests in unveiling analytical principles underneath practical AI deployment problems, and designing systems based on these principles. The developed AI and system solutions are widely applied to various application scenarios, including Internet of Things, edge computing and smart health. My research areas mainly include the following:

Note: I am currently looking for strong and self-motivated students with an interest in the above areas. If you are interested in my research and working with me, please send me email with your CV and transcripts. You can find more details about my research here, and a list of my recent publications here.

 

Professional Services

 

Research Highlights

September 2023: Two preprints of our recent works on on-device AI have been made publicly available on arXiv. The first work, Towards Green AI in Fine-Tuning Large Language Models via Adaptive Backpropagation, extends our prior work of ElasticTrainer (published at ACM MobiSys 2023) to Large Language Models (LLMs) and facilitates computationally efficient LLM fine-tuning towards green AI. It can reduce the fine-tuning FLOPs by extra 30% compared to existing techniques such as LoRA, without noticeable accuracy loss. With the same amount of FLOPs reduction, it can provide 4% model accuracy improvement compared to LoRA. The second work, Tackling the Unlimited Staleness in Federated Learning with Intertwined Data and Device Heterogeneities, targets a FL problem motivated by practical system settings, where data samples in certain classes or with particular features may only be produced from some slow clients. Our work leverages gradient inversion to move the staleness of model updates from these slow clients, and can improve the trained model accuracy by 20% and speed up the training progress by 35%, compared to existing techniques in asynchronous FL. The source codes have been publicly available at the Pitt ISL webpage.
April 2023: Our paper, ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic Tensor Selection, has been accepted for publication at the 2023 ACM Conference on Mobile Systems, Applications, and Services (MobiSys). This paper presents the first on-device AI technique that achieves full elasticity of on-device training on resource-constrained mobile and embedded devices. By leveraging the principle of eXplainable AI (XAI) and evaluating the importance of different tensors in training, we allow fully flexible adaptation of the trainable neural network portion at runtime, according to the current training needs and online data patterns, to minimize the training cost without accuracy loss. Check our paper and source codes for more details.
April 2023: Our paper, PTEase: Objective Airway Examination for Pulmonary Telemedicine using Commodity Smartphones, has been accepted for publication at the 2023 ACM Conference on Mobile Systems, Applications, and Services (MobiSys). This is the first mobile health system that turns a commodity smartphone into a fully functional pulmonary examination device to measure the internal physiological conditions of human airways, such as airway caliber, obstruction and possible inflammation. Information about these airway conditions could provide vital clues for precise and objective pulmonary disease evaluation. Check our paper for more details.
Oct 2022: Our paper, AiFi: AI-Enabled WiFi Interference Cancellation with Commodity PHY-Layer Information, has been accepted for publication at the 2022 ACM Conference on Embedded Networked Sensor Systems (SenSys). This is the first work that applies on-device AI techniques to interference cancellation in WiFi networks and enables generalizable interference cancellation on commodity WiFi devices without any extra RF hardware. By using neural network models to mimic WiFi network's PHY-layer operation, AiFi can be generally applied to different types of interference signals ranging from concurrent WiFi transmissions, ZigBee/Bluetooth to wireless baby monitors or even microwave oven, and improves the MAC-layer frame reception rate by 18x. Check our paper for more details. mcc
Aug 2022: Our paper, Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AI, has been accepted for publication at the 2022 ACM Int'l Conference on Mobile Computing and Networking (MobiCom). This is the first work that achieves real-time inference (<20ms) of mainstream neural network models (e.g., ImageNet) on extremely weak MCUs (e.g., STM32 series with <1MB of memory), without impairing the inference accuracy. The usage of eXplainable AI (XAI) techniques allows >6x improvement of feature compressibility during offloading and >8x reduction of the local device's resource consumption. Check our paper and source codes for more details.
May 2022: Our paper, TransFi: Emulating Custom Wireless Physical Layer from Commodity WiFi, has been accepted for publication at the 2022 ACM Int'l Conference on Mobile Systems, Applications and Services (MobiSys). This is the first work that realizes fine-grained signal emulation and allows commodity WiFi devices to emulate custom wireless physical layer, including but not limited to, custom PHY-layer preambles and new ways of agile spectrum usage. It could also improve the performance of cross-technology communication and many other wireless applications by up to 50x, enabling high-speed data communication on par with commodity WiFi! Watch the teaser video for details.
   
January 2022: Our paper, RAScatter: Achieving Energy-Efficient Backscatter Readers via AI-Assisted Power Adaptation, has been accepted for publication at the 2022 ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI).
 
January 2022: Our paper, FaceListener: Recognizing Human Facial Expressions via Acoustic Sensing on Commodity Smartphones, has been accepted for publication at the 2022 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).
   

November 2021: Our paper, Eavesdropping User Credentials via GPU Side Channels on Smartphones, has been accepted for publication at the 2022 ACM Int'l Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). This is one of the few works that demonstrate critical security vulnerabilities of mainstream GPUs (QualComm Adreno GPU on Snapdragon SoCs) on smartphones, which allow an unprivileged attacker to eavesdrop the user's sensitive credentials such as app username and password. This attack has been acknowledged by Google and has been incorporated by Google in its future Android security updates. Watch our demo video below for details.
   

August 2020: Our paper, SpiroSonic: Monitoring Human Lung Function via Acoustic Sensing on Commodity Smartphones, has been accepted for publication at the 2020 International Conference on Mobile Computing and Networking (MobiCom). This is the first work that allows commodity smartphones to be used as a portable spirometer and provide accuracy lung function test results on par with clinical-grade spirometers. This is a collaborative work with the Children's Hospital of Pittsburgh, and could also potentially contribute to in-home evaluation of COVID-19 risks by allowing convenient out-of-clinic lung function evaluation. Watch our presentation video for details.
 
May 2020: We have been exploiting the power of modern mobile computing technologies to fight against COVID-19. Our new project of using commodity smartphones for early-stage COVID-19 diagnosis has been funded by NSF, and was reported by several news media internationally. [WGN TV, Daily Mail, News Medical, Medical Express, Pittsburgh Post-Gazette]

More research highlights...