Brief CV


Thanks for the great journey at Harvard that provides me opportunities to work with and learn from world leading experts.

I am grateful for the NSF CAREER Award that strongly supports the UbiEi Lab to make AI more effective, efficient, and holographic.

I am thankful for the opportunity to launch a startup now, and honored to commercialize the world 1st 4G system serving 1 billion people globally when in industry.

Many appreciations to all current and upcoming sponsors, mentors, collaborators, and colleagues for the invaluable help, supports and opportunities!!

Professional Preparation


It was a great journey at Harvard and Mass General Hospital from 2017 to 18, in collaboration with and learning from Dr. Julian M. Goldman (Medical Director, Biomedical Engineering Division @ MGH/Harvard Medical School; NSF CISE Advisory Committee; Federal FCC mHealth Co-Chair), Dr. Anthony Rosenzweig (Chief, Cardiology Division @ MGH/Harvard Medical School), Dr. Antonis Armoundas (Harvard/MIT) and CVRC, among many.

It was really interesting to combine my research and industrial experience with biomedical practices. By leveraging AI, Mobile Health, and Wearable Sensing, multiple system prototypes have been researched, designed and developed for precision medicine big data purposes.


With a memorable six-year journey in the world leading ICT company, and opportunities to lead excellent teams to R&D multiple commercial products used worldwide by about a billion people, I made a decision to further develop my career in academia.

What attracted me most was – the field of pervasive intelligence and big data was emerging but still in its infancy, which ignited my passion to think about new possibilities and ways to convert new ideas to transformative technologies.

Working with Dr. Dian Zhou (NSF Presidential Award; Panelist, World Economy Forum in Davos; IEEE CAS Society Darlingtong Award) at UT Dallas from 2013 to 17, I had researched, built and validated the world first AI-enabled ear-worn blood pressure monitor for precision hypertension big data (Featured Article on IEEE Access Journal, and patent pending), among many.


With great curiosity on how and why things work, during my MS/BS from 2000 to 07, I had not only taken diverse courses in CS, CE, EE and others, but also done my thesis, under the supervision of Dr. Hongcai Wu (Vice President, Xi’an Jiaotong-Liverpool University; Dean, School of Electronic and Information Engineering, Xi’an Jiaotong University).

Director, UbiEi Lab

Effective AI <= Learning Capability of Brain

Learning Capability, as indicated by how effective the brain abstracts and conceptualizes knowledge, is key to measure how intelligent Deep Learning is. Through diving into deep learning theories and principles, my lab tackles challenges in transferable knowledge learning, deep reinforcement learning, and multimodal big data learning.

One demo system selected below (News) is the ‘eyeSay’ prototype, which, empowered by Deep and Transfer learning of eye biodynamics, can allow people to speak and interact with the world. In addition to demonstration on helping ALS patients to ‘talk’ with their eyes, diverse new possibilities include voice-free communication, attention tracking, cognitive measurement, driver state monitoring, human-computer interactions, and virtual or augmented reality.

Efficient AI <= Learning Productivity of Brain

Learning Productivity, as indicated by how efficient the brain processes information, is crucial to measure how concise Deep Learning is. Google AlphoGo Zero takes 10 trillion operations/s in contract to 50/s on human, indicating the efficiency disparity, and inspiring our lab to innovate concise learning, pertinence learning, and real-time edge-deployable AI.

A demo system selected below is the edge-deployable efficient ‘RP-KDL’ algorithm for precision cardiac health, which, enabled by Robust-Preservation Knowledge Distillation Learning, yields a light-weight model for ECG analysis with 45x parameter reduction. The ultra-high-efficiency is achieved by distilling knowledge of a heavy teacher model’s soft target distribution to a student model, while enhancing its robustness through learning under adversarial perturbation.

Holographic AI <= Perceptual Learning of Brain

Perceptual learning, as indicated by how comprehensively the brain perceives the world, is essential to reflect the extent to which the system can sense necessary, or even holographic information for deep learning. To achieve novel perception systems, my lab systematically builds sensors, sensor boards, embedded systems, and wearable/IoT monitors.

A demo perception system selected below is ‘FlexBio’, which is a flexible, multi-channel sensor board for bio-potential sensing purpose. This small flex patch we made, can provide comprehensive spatial perception of target biomedical dynamics, or even holographic dynamics after easily boosting # channels. Applications we are investigating include but not limited to, perceiving dynamics from brain, heart, and muscular subsystems, for big data deep learning.



I am very thankful for and enjoying the opportunity to transform research into products. I am now launching a startup on mobile health big data applications, the foundation of which is laid on validated AI Algorithm, Edge Deployment, and Health Sensor technologies.

Same time, we are working on the FDA process to meet regulations and safety requirements. With strong support from Digital Health Center at FDA, we are confidently advancing the process forward.

Also, we have patents pending, and with continued transformative research, new patents are under preparation and/or to be filed.

World 1st 4G System

I was honored in industry (2007-2013) to work with hundreds of talents, lead excellent teams, spent several years, R&Ded multiple versions, and finally commercialized the world 1st 4G system. It is one of the most successful and impressive product, used by about 1 billion people worldwide, among many like world 1st 4G/3G/2G multi-mode system, etc.

With this system, the global 4G market share of the company boosted to top 1 and reached 39% in just 2 years (2013), followed by hitting the top 1 market share of the whole equipment market in another half a year.

The opportunity to work at a world leading ICT company rewarded me with systematic commercialization experience, including global market analysis, system solution determination, algorithm/hardware/software co-design, customer relationship management, project management, and team management.

I am honored to receive Gold Medal Team Award, Outstanding Individual Contributor Award, Excellent Employee Award, and Team Collaboration Award.


I, as a senior system architect in addition to the team and project management roles, also have seven international patents commercialized in industry. Thanks to the co-inventors in my team and collaborators in international research centers in US and Europe.

Sponsors, Collaborators & Services

I am very grateful for the valuable support from sponsors. I am very excited to receive the NSF Career Award and I am committed to leading UbiEi to make AI more effective, efficient, and holographic.

I am also appreciated for the sponsorships from and/or collaborations with Google, Intel, Amazon, American Heart Association, Harvard, MIT, WUSTL, UNIMI, Purdue, IU, among many.

Last but not least, I thank for the opportunities to server the communities as NSF Panelist, IEEE DACI2021 Workshop Chair, IEEE paiIoT2021 Workshop Chair, IEEE AE, and so on.

Selected are listed below.

Again, thanks for all these and upcoming valuable supports and opportunities!!