I’m a fourth year PhD student at School of Electrical and Computer Engineering, Cornell University, advised by Prof. Edwin C. Kan (expected graduation: May 2023).

I am looking for a technical position in the field of Digital Healthcare or Human-Computer Interface. LinkedIn

Download my CV here.

Research Area: biosensing, smart wearable sensor, machine learning in healthcare and human computer interface

My present research interests focus on non-invasive sensing of physiological signals including respiration and muscle motion for biomedical applications and human computer interaction (HCI).

Respiration Monitoring: I have research experience in diagnosis and prognosis of respiratory and pulmonary diseases, including dyspnea, COPD, COVID-19, and sleep disorders. Non-invasive sensing of physiological signals including respiratory rate, respiratory volume, and heart rate with high user comfort is of great significance for health diagnostic systems. My research seeks non-contact and continuous measuring system utilizing RF sensors that can be invisible to the user and greatly enhance comfort and convenience to facilitate many healthcare applications.

Muscle Tracking: I proposed a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable and touchless, capturing both superficial and deep muscle groups. Our system can be applied for accurate hand gesture recognition, eye movement detection, and leg muscle monitoring. RMG has promising future applications in HCI, including VR, smart devices, robotic arm and prosthesis control;biomedical applications in kinesiology, physiotherapy, muscle rehabilitation, and diagnosis of neuromuscular disorders.

Algorithm Development:I have research experience in digital signal processing, machine learning algorithms, deep neural networks including convolutional neural network(CNN) and Vision-based Transformer.

Research Experience

• Adandunt experience with: EMG, EOG for muscle monitroing, ECG, PPG for heart rate and blood oxygen; respiratory chest belt; PSG for sleep monitoring; accelerometers, gyroscope for motion detection.

• Understanding for human physioloy, including skeletal muscle and cardiopulmonary system.

• Multiple Experiences with design of human studies to test the feasibility and performance of health sensing functions and features.

• Apply signal processing tools to extract features from physiological time-series data and use machine learning or deep learning algorithm for data analysis.

Research interest

• Non-invasive sensing of physiological signals including respiration, heartbeat, and muscle motion.

• Diagnosis and prognosis of respiratory disorders, including dyspnea, COPD, COVID-19, and sleep apnea/disorders.

• Muscle monitoring system for hand gesture recognition, motion detection, biometrics, and muscle fatigue.

• Machine learning, deep neural network, digital signal processing, and feature extraction.