My name is Varun Viswanath, and I am a 5th year PhD student at the University of California, San Diego working in the DigiHealth lab lead by Edward Wang and in the Smarr Lab led by Benjamin Smarr.
My research focuses on how deep learning can transform ubiquitous and wearable healthcare. My research to date has focused on identifying health outcomes from wearable PPG and temperature data. My prior work focused on novel uses of smartphones with deep learning to predict medical outcomes.
Specifically, I’ve explored how wearable device data can predict the onset of illnesses like COVID-19 and how biases can challenge such illness detection tasks. I’ve recently been studying sleep phenotypes and diabetes in the context of wearable health data, as well as how large cohort wearable datasets (N>60,000) can be leveraged to improve health algorithms. I previously explored how smartphones can predict lung condition (smartphone spirometry) and blood oxygen percentage (smartphone oximetry). In the future, I hope to further explore how deep learning and other statistical methods can augment wearable and ubiquitous technology to improve peoples’ health and lifestyle.
I received my Bachelor’s in Computer Science from the Paul G. Allen School of Computer Science and Engineering at the University of Washington. As an undergraduate researcher, I was advised by Shwetak Patel for 3 years as part of the Ubicomp Lab. My senior research thesis was on “Using Confidence in Smartphone Spirometry”. I’ve also interned with the Uber Elevate (Summer 2017). I maintain a list of my publications under the Research tab.
CV / Resume, Google Scholar, Email ID: vkviswan@ucsd.edu
Updates
| Sept 2022: | Washington Post writes article Study undercuts premise for excluding women from medical research on our prior workVariability of temperature measurements recorded by a wearable device by biological sex |
| Sept 2022: | Published Variability of temperature measurements recorded by a wearable device by biological sex in the Biology of Sex Differences Journal with Lauryn Bruce. |
| Oct 2023: | Co-Chair of Student Volunteers at Ubicomp 2023 in Cancun, Mexico. |
| Mar 2023: | Network Award Winner at the Center for Circadian Biology Symposium 2023 in La Jolla, California. Presented poster RhyPredict: Detecting Periodic Biases in Wearable TimeSeries |
| Sept 2022: | Presented Detecting Periodic Biases in Wearable-Based Illness Detection Models at ICLR 2023, Time-Series Representation Learning for Health Workshop with Amit Klein. |
| Sept 2022: | Published Smartphone Camera Oximetry in an Induced Hypoxemia Study at npj digital medicine with Jason S. Hoffman. |
| May 2022: | Published Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study at Scientific Reports. |
| Mar 2022: | Published Stepping into the Next Decade of Ubiquitous and Pervasive Computing: Ubicomp & ISWC 2021 at IEEE Pervasive Computing. |
| Dec 2021: | Published Dynamical clustering of U.S. states reveals four distinct infection patterns that predict SARS-CoV-2 pandemic behavior on Arxiv with a group of Latin American students through the ENLACE program and Joseph Lane Natale. |
| Dec 2021: | Published TemPredict: A Big Data Analytical Platform for Scalable Exploration and Monitoring of Personalized Multimodal Data for COVID-19 at IEEE BigData '21. |
| Dec 2021: | Attened NeurIPS 2021 online! |
| Nov 2021: | Presented Tempredict DL: Using Deep Learning to Analyze Longitudinal High Granularity Signals at the UC San Diego Design Innovation Building Grand Opening. |
| Sept 2021: | Served as a Student Volunteers at Ubicomp 2021, fully online and happening in 3 different timezones. |
| Sept 2020: | Joined the Smarr Lab at UC San Diego led by Benjamin Smarr. |
| Sept 2020: | Served as a Student Volunteers at Ubicomp 2020, fully online. |
| Sept 2019: | Joined the Electrical and Computer Engineering Department at the University of California, San Diego, as part of the Machine Learning and Data Science track. Joined the DigiHealth Lab led by Edward Wang. |
| June 2019: | Graduated from the Paul G. Allen School of Computer Science and Engineering at the University of Washington with a B.S. in Computer Science and Engineering. |
| July 2018: | Presented Senior Research Thesis: Using Confidence in Smartphone Spirometry, advised by Shwetak Patel. |
| July 2018: | Presented SpiroConfidence: Determining the Validity of Smartphone Spirometry using Machine Learning at the **40th International Conference of the IEEE Engineering in Medicine and Biology Society** (EMBC'18) in Honolulu, Hawaii.</td> </tr> |