Edward Jay Wang, PhD

Researcher / Educator/ Entrepreneur

Associate Professor UCSD ECE & Design Lab

Jacobs Faculty Chair in Entrepreneurship

CEO & Founder Billion Labs Inc.

I am the Jacobs Faculty Chair in Entrepreneurship at UC San Diego in the Electrical and Computer Engineering department and the Design Lab as a jointly appointed Associate Professor with tenure. I am the PI of the UCSD Digital Health Technologies Lab. I am affiliated with the CSE department, Center for Wireless and Population Health Systems (CWPHS), Center for Wearable Systems (CWS), and serve as a Board of Director for the Center for Mental Health Technology (MHTech). I am also the Founder of two digital health companies born out of technologies developed in my academic laboratory: Billion Labs Inc. (NIH Funded) and Motion Minder Inc. 

My research establishes point-of-need medical technologies as a bioengineering+design research domain focused on creating medical-grade physiological measurements where health concerns arise and care decisions must be made, rather than where clinical infrastructure happens to exist. This framing shifts the design challenge from replicating clinical conditions elsewhere to understanding what measurements are needed, when they become critical, and what constraints shape how they can be obtained. Point-of-need sensing encompasses home monitoring during chronic disease management, community screening in underserved areas, perioperative care as patients transition between hospital and home, and maternal-child health support following discharge. In these contexts, measurements must accommodate diverse users with varying technical literacy, function across inconsistent environmental conditions, and operate without stable power, controlled settings, or trained personnel.

Point-of-need is fundamentally distinct from both clinical point-of-care testing and consumer health tracking. Point-of-care devices operate within healthcare facilities with trained personnel; consumer wellness devices prioritize trends over clinical accuracy. Point-of-need sensing requires clinical-grade rigor while functioning in uncontrolled environments with non-expert users, a design space demanding a new approach. This is not an engineering optimization problem but a design research challenge requiring understanding stakeholder needs beyond measurement specifications where we need to generate solutions within severe resource constraints and validate systems when embedded in complex social and environmental contexts.

I pursue this through three complementary approaches: (1) designing sensing artifacts that appropriate existing consumer technologies and create novel wearable form factors; (2) understanding how sensing technologies are adopted and adapted in real deployment contexts; and (3) developing methods, representations, and design principles that support robust point-of-need sensing across diverse populations. As Associate Professor at UC San Diego (joint ECE/Design Lab), I direct the Digital Health Technologies Lab with collaborations spanning cardiology, psychiatry, geriatrics, anesthesiology, and gastroenterology. My work has produced 39 publications receiving 9 paper awards and led to two companies translating research toward clinical adoption.

My work has been published in top tier venues such as Nature family journals (npj Digital Medicine, Scientific Reports), ACM (Ubicomp, IMWUT, CHI, UIST, ISWC), and IEEE (Pervasive Computing, EMBC), cumulating 9 paper awards for our contributions to the field of digital health. I have been awarded an NIH Trailblazer, Google Research Faculty Fellow, Don Norman Foundation Design Laureate, and an NSF Graduate Fellow. The work in my group is made possible by generous funding from the National Institute of Health, National Science Foundation, American College of Cardiology, and Google Research.

Contact me at: ejaywang {at} ucsd {dot} edu

Most current statements: Research | Teaching

Publications

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Research Themes

Bootstrapping The Next Billion Medical Devices

Today's medical devices are typically centralized in high-resource places like hospitals and cities, leaving a large portion of the world like low-income regions, rural areas, and chronic at home care scenarios under serviced. The proliferation of smartphones present an interesting opportunity to solve this issue of global access to medical screening and management. Smartphones have a variety of sensors built in to them, and by tapping into these sensors through software augmentations, I've worked on solutions to transform the billions of smartphone into a medical device, with a simple app download.

Widely Distributable Hypertension Screening: VibroBP & BPClip (NIH MassAITC Pilot Award) - In a series of explorations, we developed first an ultra-low cost blood pressure monitor (<$1) attachment to a smartphone. The system, entirely passive, repurposes the phone’s camera system as a pinhole camera that responds to pressure application. We further advanced this concept to avoid any additional attachment to the phone through a sensing method on the phone we call Vibrometric Force Estimation. Where by we leverage the natural dampening effect of a vibrating object (the phone) to sense the pressure applied (to the artery) as the finger presses against a vibrating phone. In this new method, we can simultaneously capture how hard the finger is pressing against the camera and when the pulse is cut off, the two necessary components to measuring blood pressure.  

Risk Prediction of Surgical Complication through Grip Strength (NIH Trailblazer) - Surgical complication represents one of the biggest factors impacting quality of care and is one of the major burdens on the healthcare system. The difficulty in increasing surgical success is not necessarily in improving the surgical procedures, but rather the perioperative care that surrounds the surgery: pre-habilitating the patient into fitness prior to surgery, improving patient recovery to discharge patients to recover comfortably at-home, detecting onsets of complications early to provide non-emergent treatment. The main objective of this project is to develop a hand grip strength (HGS) measurement solution based completely on a smartphone application that converts the phone’s vibration motor and sensor into a mobile dynamometer. Our scientific premise, demonstrated by a berth of clinical evidence, is that hand grip strength provides a biomarker of physical frailty that corresponds to general physiologic reserve and cardiopulmonary status, as well as systemic inflammation. To increase the scalability of HGS screening, we propose a smartphone assessment that patients, including older adults, can administer themselves at home that tracks changes in HGS during the preoperative period.

Supporting Tele-Lactation Consulting (Google Health Equity Research Award) - Breastfeeding benefits both the mother and infant and is a topic of attention in public health. After childbirth, untreated medical conditions or lack of support lead many mothers to discontinue breastfeeding. For instance, nipple damage and mastitis affect 80% and 20% of US mothers, respectively. Lactation consultants (LCs) help mothers with breastfeeding, providing in-person, remote, and hybrid lactation support. LCs guide, encourage, and find ways for mothers to have a better experience breastfeeding. Current telehealth services help mothers seek LCs for breastfeeding support, where images help them identify and address many issues. Due to the disproportional ratio of LCs and mothers in need, these professionals are often overloaded and burned out.

Ocular Biomarkers of Alzheimers (NIH R21) - Leveraging the IR facial recognition camera of smartphones, we developed a mobile Pupillometry solution to capture pupil Digital Biomarkers around digit span cognitive testing. Our approach, based on the scientific premise that pupillary responses recorded during cognitive tasks may provide a novel digital biomarker of early risk for cognitive decline. Pupil size during cognitive tasks (e.g., digit span recall) increases in response to increased demands, is inversely related to cognitive ability (individuals with lower ability show greater dilation/compensatory effort), and pupil size decreases and performance declines when task demands exceed abilities and compensatory capacity. Someone requiring more effort to achieve the same score as another person is likely to be closer to maximum compensatory capacity and, therefore, at higher risk for decline. We have found that individuals with mild cognitive impairment (MCI), who are at greater risk for AD, show greater dilation (effort) on the digit span task, and that greater dilation is associated with greater polygenic risk for AD and neuroimaging indicators of locus coeruleus (LC) dysfunction. 

Global Anemia Screening: HemaApp (NSF GRFP) - Noninvasive hemoglobin measurement using a smartphone camera and flash LED. The camera measures the change in spectral absorption distribution to determine the concentration of hemoglobin in the blood. The user places their finger over the camera and flash LED, allowing the camera to capture the blood movement in the finger. The blood movement caused by the heart beat provides a signal source for non-invasively extracting the absorption contribution by blood against the baseline absorption of skin, muscle, and bone. 

Through the development of this work, I've done multiple rounds of in-clinic studies with UW Medical Center, Seattle Children's Hospital, and is gearing up for new large scale and longitudinal studies with Harborview Medical Center and the Fred Hutch. Not only have I tested HemaApp in clinical settings, I've worked with NGOs in Peru to test the concept on ground zero. Community health workers used our app during an anemia screening campaign in the Amazonian jungle. 

Passive and continuous monitoring of health and activity

A new generation of medical devices will take the form of “everyday” things like clothing, glasses, watches, tattoos and maybe even tooth fillings. These devices will act more like the way medical devices are used in the hospital room, continuous and in the background, constantly providing insights, used in testing hypothesis about a person’s conditions; all of this being done everyday when the person may or may not be sick. We are interested in taking a data science approach to (1) develop new populational/physiological insights through examining large-scale wearable data and (2) design future wearable devices.

Uncovering Hidden Biases in Wearable Illness Detection: Wearable health devices have revolutionized our ability to continuously analyze human behavior and build longitudinal statistical models around illness by measuring physiological indicators like heart rate over several months of an individual's life. Shifts in these indicators have been correlated with the onset of illnesses such as COVID-19, leading to the development of Wearable-Based Illness Detection (W-BID) models that aim to detect the onset of illness. While W-BID models accurately detect illness, they often over-predict illness during healthy time periods due to variance caused by seemingly random human choices. However, it is because W-BID models treat each input window as independent and identically distributed samples that we are unable to account for the weekly structure of variance that causes false positives. Towards preventing this, we propose a system for identifying structural variance in wearable signals and measuring the effect they have on W-BID models. We demonstrate how a simple statistical model that does not account for weekly structure is strongly biased by weekly structure

Uncover Sleep Phenotypes in Natural Longitudinal Users: Consumer wearable devices that continuously measure physiological metrics hold promise as tools to understand our human biology through large scale population studies. In a study that examined 5 Million nights of sleep patterns of over 33k users of the Oura Ring device, we extracted sleep phenotypes that showed that sleep phenotypes are transient; one where the trajectory of sleep pattern shifts hold more information than being in any type of sleep pattern alone. 

Continuous BP Sensing: Glabella is a device that integrates multiple optical sensors into a pair of glasses to measure blood pressure continuously using PTT. The PTT is measured using optical sensors placed strategically along the frame of a pair of glasses. When the user wears the glasses, the optical sensor captures the arrival time of the pulse at different parts of the facial arteries. Such a solution can be integrated into any head-mounted wearable device. 

Low Power Activity Tracking: MagnifiSense  is a wrist-worn magnetic sensing technique to capture what a person is doing by detecting the electronic environment they are current in the presence of. Most electronics have a unique electromagnetic radiation due to the internal electronics such as motors, power switches, processors, heating elements, etc. By knowing what someone is using, for example turned on the stove, it is possible to infer someone is cooking. If someone is driving vs riding a bus vs biking (the lack of EMI), the EMI footprint is different, providing information about commute patterns. The EMI based classification of activity patterns provides a rich set of information while maintaining a fairly low power requirement as compared to vision based systems, and only requires a wrist-worn device versus needing to instrument the environment. 

Through-body Device Charging: CASPER is a wearable device charging solution that uses the body as a conductive element for a 13.56MHz AC capacitive charger to charge devices worn on the user’s body. The purpose of CASPER is to allow charging of wearables without requiring frequent removal or human intervention. CASPER is a capacitive charging system that can be embedded in beds, seats, and other common furniture so that serendipitous daily contacts can facilitate opportunistic AC power circuits through the body to trickle charge body-worn devices. Using our charging system, we designed a wound monitoring gauze pad that charges each night in bed and monitors the patient’s wound with optical pH sensing and capacitive wetness sensing to indicate whether it is time to replace the gauze pad. This e-bandage design can recharge each night whenever the patient is in bed without ever needing to be removed.

Teaching & Course Development

I have had the opportunity to work with students from all levels through the courses that I develop. My teaching philosophy is that the best way to learn is to build it, break it, and have fun with it. Leave room for creativity and definitely challenge the students enough so that they crash and burn a few times. The classes I design motivate students to explore the world of embedded systems, signal processing, and interaction by directly using these concepts in projects that are designed to provide the students with the skills and understanding to be able to take on real world problems beyond the course. 

Mobile Health Device Design

Graduate level class introducing students to the vast array of solutions being targeted in the mHealth domain through a set of curated survey readings. The student will also get hands-on experience learning how to prototype both hardware (Arduino) and software (Python with Scipy) of a few example mHealth devices and standard evaluation techniques used in research for analyzing the performance of the system. Finally, as important as it is to learn how to build and test is the practice of ideation. In a student group, the student will engage in the proposal, design, build, and testing of their own mHealth system. Students should be proficient in programming and basic knowledge of data processing.

Introduction to Engineering Design

In a course focused on designing useful and meaningful solutions, this undergraduate level hands-on course teaches students how to work with a customer to build a tangible solution. Students are paired with fellow student customer from the class. Rather than making something that suits their own interests, students learn, often for the first time, how to work with a customer. To understand their needs, work with them to iterate on potential solutions, and ultimately build and test their solution with the customer. In this class, students learn the basics of embedded device programming, Rapid 3D prototyping, Edge Computing, and signal processing. But in the lens where they acquire new skills needed to achieve the needs of the customer. On top of technical skills, students learn how to apply design methods and analysis such as the Kano Method to assess customer needs, and design with Affordance in mind.

Biosignals Processing

The Biosignals Processing Lab is a laboratory course that introduces students to the biophysics of biosignals, the acquisition/processing of biosignals, and applications of bio signals. The course uses a mix of conceptual readings on physiology and lab experiments. Students learn about the eye (EOG), muscle (EMG), heart (ECG), and the brain (EEG), each designed to teach the student a different concept of signal processing. In each of the experiments, the students are introduced to the physiological basis for each signal in their pre-labs and during their in-lab sessions, students were presented with a series of questions such as "how do the placement of electrodes affect the signal quality" and "how does the frequency content of the EMG change with respect to fatigue." The students were not given the experimental protocol, but instead were to draft their own experimental procedure in order to answer these questions.