Publications [Google Scholar]

Utilizing a Novel Unified Healthcare Model to Compare Practice Patterns Between Telemedicine and In-Person Visits
Justin Gregory Norden’, Jonathan X Wang’, Sumbul A. Desai, Lauren Cheung.
Digital Health. August 2020.

ClinicNet: machine learning for personalized clinical order set recommendations
Jonathan X Wang’, Delaney K Sullivan’, Alex C Wells, Jonathan H Chen

Neural Networks for Clinical Order Decision Support
JX Wang, DK Sullivan, AJ Wells, AC Wells, JH Chen (equal contributors)
AMIA Summits on Translational Science Proceedings 2019, 315

Healthcare Service Utilization under a New Virtual Primary Care Delivery Model
Lauren Cheung, Tiffany I. Leung, Victoria Y. Ding, Jonathan X. Wang, Justin Norden, Manisha Desai, Robert A. Harrington, and Sumbul Desai
Telemedicine and e-Health, 2018

Non Peer-Reviewed:

The Role of Macrophages in Tumor Cell Recurrence Following Radiation Therapy
Wang, Jonathan X and Graves, Edward and Feldman, Marcus.
Stanford Digital Repository, 2019. (Stanford Undergraduate Honors Thesis)

DeepSign: Efficient Siamese Convolutional Neural Networks for Signature Verification
Jonathan X Wang’, Kevin Ko

DeepDoc: Natural Language Processing with Deep Neural Networks for the American Board of Internal Medicine Certification Exam
Jonathan X Wang’, Britni Chau’, Kinbert Chou’, Jonathan H Chen’

Deep Vein Thrombosis Screening with Three-Dimensional Deep Learning on Lower Extremity Computed Tomography Studies
Jonathan X Wang’, Brianna Kozemzak’, Anoop Manjunath’, Trevor Tsue’, Andre Souffrant, and Lawrence Hoffman

Machine Learning for Automated Classification of Patient Cases
Jonathan X Wang’, Cole Deisseroth’, James Bai’, Jonathan H Chen’

‘ = equal contributors


DeepSign: Efficient Siamese Convolutional Neural Networks for Signature Verification [Demo] [Paper] [Poster]

Jonathan X Wang, Kevin Ko (equal contributors)
We develop a deep learning algorithm that performs handwritten signature verification. We created our own SqueezeNet-inspired efficient siamese convolutional neural network architecture, DeepSign, that uses 65% fewer parameters than Google’s MobileNetv2 and 97% fewer parameters than the current state of the art, SigNet, while acheiving similar if not better performance. We test our models on both the CEDAR and BHSig260 datasets and demonstrate that our model outperforms both models in all evaluation metrics (accuracy: 0.85, precision: 0.76, recall: 0.84, AUROC: 0.93). This lightweight model is readily applicable to mobile devices for both online or offline signature verification.

DeepSign is integrated into a React web app so that viewers can demo and test their own signatures against the model.

DeepDoc: Natural Language Processing with Deep Neural Networks for the American Board of Internal Medicine Certification Exam [Paper] [Poster]

Jonathan X Wang, Britni Chau, Kinbert Chou, Jonathan H Chen (equal contributors)
We train a model to answer review questions for the American Board of Internal Medicine Certification Exam. We adapt approaches traditionally used for question answer tasks to our multiple choice exam, as well as experiment with the following enhancements: PubMed Embeddings, BiDAF, DrQA, SAR, GA, and RACE. Ultimately we find that GA models perform best (Accuracy: 0.38, AUROC: 0.64). Our work is an initial study towards the development of a intelligent medical QA system, demonstrating the capability of modern day machine learning to answer questions clinicians typically take many years to study for.

UVify: A simple, non-disruptive far UV-c light stethoscope sterilization device [Hong Kong Economic Journal] [SingTao Daily] [Money Talk HK Podcast (32:28-43:20)]

Jonathan X Wang, Mashiat Lamisa, Jasmine Poon, Xuelai Wei, Sik Kwan Chan (equal contributors)
As part of the Dreamcatchers MedTech Hackathon, we shadow physicians in the Hong Kong hospitals and use the BioDesign curriculum to search for problems in patient care. We discover a central issue where physicians were not consistently sterilizing their own stethoscopes, leading to hospital acquired infections. We develop a sterilization device using far UV-c light to automatically sterilize stethoscopes for physicians during their daily routine checkups.

  • 2019 Dreamcatchers MedTech Hackathon Champion

Deep Vein Thrombosis Screening with Three-Dimensional Deep Learning on Lower Extremity Computed Tomography Studies [Paper]

Jonathan X Wang*, Brianna Kozemzak*, Anoop Manjunath*, Trevor Tsue*, Andre Souffrant, and Lawrence Hoffman (*equal contributors)
Recent advances in deep neural networks (DNNs) allow us to leverage spatial dependencies between slices in imaging studies to identify false positives and ultimately deploy DNN systems that lighten physicians’ workloads while not exacerbating alarm fatigue. To train our DNN, we have acquired 119 lower-body CT imaging studies labeled by radiologists for DVT at the pixel level. Using these studies, we have developed a DNN-based CAD system that will (1) segment targeted deep veins in a CT slice, (2) classify whether a DVT is present within multiple slices given segmentations of deep veins, and (3) evaluate different deep learning approaches for handling 3D datasets for DVT detection. For segmentation, we use a 2D U-Net, 2D VGG encoder-decoder, and 3D U-Net and find that VGG performs best (Dice: 0.07, IoU: 0.48, AUROC: 0.78). For classification, we use a 2D ResNet, CNN-RNN, and 3D Inception model with and without segmentation masks. We find that our CNN-RNN without masks performs best in AUROC (Average Precision: 0.31, AUROC 0.64) and 3D Inception with masks performs best in average precision (Average Precision: 0.33, AUROC: 0.62). By developing more effective detection algorithms, we hope to ensure more frequent and accurate diagnosis of DVT, thereby reducing its high mortality rate.

Automated Electronic Calculator for Management of DKA/HHS [Poster]

Sara Choi, Madeline Grade, Jonathan X Wang, Lawrence Cai, Julie Chen
Surveyed 73 staff members to develop DKA software estimated to save $78,000 a year and reduce readmissions by 45%. Currently implemented in Stanford Hospital and integrated into the hospital’s health record system along with educational video.

  • Awarded best poster at Resident & Fellow Quality Improvement & Patient Safety Symposium

Machine Learning for Automated Classification of Patient Cases [Paper][Poster]

Jonathan X Wang, Cole Deisseroth, James Bai, Jonathan H Chen (equal contributors)
This is an initial study toward the development of an intelligent patient-allocation system to save medical personnel valuable time, and help patients find the care they need more efficiently by automatically categorizing cases into specific departments. We develop an algorithm which predicts the categories of patient cases from the American Board of Internal Medicine Examinations—a certification that all physicians must go through to practice general medicine. Our ontology breaks questions into their components (Case, AnswerChoice, Explanation). We then run an automatic concept extractor (ClinPhen) on the passage (description of the case) to compile a list of concepts (words, phenotypes, and phenotype closures). We then use a Naïve Bayes classifier to take the concepts and predict the category of the case. We have developed a classifier that predicts the category of a patient case correctly 80.5% of the time, and has over 80% precision and recall. Future work will include developing more-sophisticated techniques of leveraging up-to-date knowledge graphs, and building our own graphs to categorize these cases. Ultimately, this classifier should become applicable in clinical settings (and not just for medical board cases), and be able to accurately suggest a department to send a patient to.


shimmer – Co-Founder

Aug 2020 - Present
On a mission to make your mental health journey fun, accessible, and simple.

Bill and Melinda Gates Foundation – Artificial Intelligence Research Associate; Seattle, WA

May 2020 - Oct 2020
Identify, partner (including Google, Wadwani AI, Plug n Play), and form grants with emerging AI technologies to improve health care for all, especially in resource-limited settings. Teach on AI and lead landscaping study on building local innovation ecosystems in sub-saharan Africa. Advisor for $80M global health AI strategy. Hired via AgileOne.

UCSF School of Medicine - Writer; San Francisco, CA

Jan 2020 - Present
Write stories related to health and health inequities published on UCSF’s website.

AI4ALL - Lead TA; San Francisco, CA

Jan 2020 - Aug 2020
Serve as lead teaching assistant for project on fairness in AI for global health in group of motivated high school women.

Apple - Machine Learning Intern; Cupertino, CA

June 2018 – September 2018
Data science research intern for Apple’s Health team working on AI-assisted decision making and unsupervised learning problems.

Stanford School of Medicine, HealthRex (AI/Medicine) Lab – Artificial Intelligence Researcher; Stanford, CA

Jan 2018 – Present
Developing deep feedforward and recurrent neural network clinical order decision support system for 55 million clinical orders.

Stanford Healthcare Consulting Group – Consultant; Stanford, CA

March 2016 – January 2018
Youngest member across the graduate, law, and medical school, clients include Directors of Digital Health and Strategic Initiatives. Researched patient perspectives on OR communication through 50 patient interviews and developed pop-up course curriculum. Worked with 3 different specialty clinics and developed patient flow map to ease implementation of quality metrics at Stanford. Performed competitive industry analysis of local hospitals and startups for development of Lucile Packard’s digital health program

Stanford School of Medicine, Graves Lab – Research Assistant; Stanford, CA

Jan 2016 – May 2019
Researching the role of macrophages in circulating tumor cell recruitment following irradiation in vivo and in vitro. Awarded $13,000 in grants through Stanford’s Major Grant and selection as Bio-X Fellow

Mobineo – Software Engineer; Minneapolis, MN

July 2015 – June 2016
Coded user interface for app to alleviate land entitlement corruption using Android Studio, Java, and Google API. Wrote articles regarding land entitlement, HIV/AIDS, and political issues in Africa

MIT Computational Immunology Lab – Data Science Researcher, Cambridge, MA

Jun 2014 – Aug 2014 Utilized ACCENSE medical software and clustering algorithms to discover novel subpopulations of CD8+ T-Cells patients. Studied the functional and phenotypic features of antigen-specific immune cells that mediate effective viral control


MD++ - Director of West Coast Operations [Launch Post]

April 2019 - Present
MD++ aims to empower the next generation of over 250 physician-innovators who will improve healthcare delivery through the intersection of technology, business, and life sciences. The Director of West Coast Operations is responsible for managing and expanding west coast membership.

Data Science Interest Group, Health Technology Interest Group- Coordinator; UCSF

Jan 2020 - Jan 2021

Admissions Advisory Cabinet - Media Coordinator; UCSF

Nov 2019 - Nov 2020

XP Health – Co-founder; Stanford, CA [Press]

Jan 2018 – Jan 2019
110 recorded research interviews, Global health Innovation Challenge Finalist, provisional patent, IRB Approval
4M seed round

Stanford Undergraduate Hospice and Palliative Care – Founder and President; Stanford, CA [Service Story]

Jan 2017 – June 2018
Founded Stanford hospice organization focused on bringing awareness and hands-on exposure towards end-of-life care. Part of the 2017 Public Service Leadership Program hosted by the Hass Center, awarded iThrive mental wellness grant.

ImpactMed – Founder and President; Stanford, CA

Jan 2016 – Jun 2019
Teach a class at Stanford and curating data of over 600 neuro-health companies. Lead 20 MD, MBA, PhD, and undergraduate students from Stanford partnered with serial entrepreneur Dr. Michael McCullough

Golden Gate Science Olympiad – Founder and Executive Director; Stanford, CA [Scientific American]

Aug 2015 – January 2018
Organized 60 Science Olympiad teams from across the country to compete in the first University sponsored west coast tournament. Registered over 800 of the nation’s brightest high school scholars and raised over $19,000 in sponsorship

API Health Awareness Month – Founder; Stanford, CA

March 2016 – February 2018
Founded inaugural API Health Awareness month, collaborating with over 10 student orgs and events to raise money for charity. Host annual bone marrow drives and fundraisers to raise awareness and money for blood disease.

Health++ and TreeHacks Health – HR Director and founding organizer; Stanford, CA [Press]

Nov 2015 – March 2017
Part of founding team of 15 to develop first inaugural health hackathon, health++, with 300 entrepreneurs, designers, health professionals, and engineers. TreeHacks brings over 700-800 undergraduate and raised over $300,000, over 30 percent of hacks were health-related.


Wang J, Lamisa M, Poon J, Wei X. UVify: A simple, non-disruptive far UV-c light stethoscope sterilization device. 2019 Dreamcatchers MedTech Hackathon. 7/19. [Hong Kong Economic Journal] [SingTao Daily] [Money Talk HK Podcast (32:28-43:20)]

Delaney S, Wang J…Chen J. Neural Networks for Clinical Order Decision Support. 2019 AMIA Informatics Summit.3/19.

Wang J, Kaufman G, Ramos A. Deep Neural Networks and Cluster Analysis for Patient Phenotyping. Apple. 8/18

Wang J, et al. AI-enabled Mobile Optometry. Bay area global health innovation challenge. 5/18.

Sole J, Wang J…Girod S. OR Teams and Communication. Anesthesia Grand Rounds. 8/16.

Wang J, Yonghun K, Periyakoil VJ. Empathy and Patient Care Improvement for Asian Americans. Palliative Medicine, Hospice, and End of Life Care. 12/16.

Poster Sessions

Wang J, Ko K. DeepSign: Efficient Siamese Convolutional Neural Networks for Signature Verification. Convolutional Neural Networks for Visual Recognition. 6/19. [Demo] [Paper] [Poster]

Wang J, Chau O, Chou K. DeepDoc: Natural Language Processing with Deep Neural Networks for the American Board of Internal Medicine Certification Exam. Stanford Natural Language Processing with Deep Learning Poster Session. 3/19.[Paper] [Poster]

Wang J, Deisseroth C, Bai J, Chen J. Machine Learning for Automated Classification of Patient Cases. 3/19 [Paper][Poster]

Wang J…Chen J. Automated Clinical Decision Support and Patient Progression Prediction through Deep Neural Networks. Stanford Deep Learning Poster Session. 3/18.

Wang J…Graves E. Effects of Tumor Irradiation on Circulating Macrophage Localization. Stanford Bio-X Symposium. 8/17.

Choi S, Grade M, Wang J, Chen J. Automated Electronic Calculator for Management of DKA/HHS. Stanford Resident & Fellow Quality Improvement & Patient Safety Symposium. 5/17.


Men’s Mental Health and Wellness Support Group Coordinator

SVDP-SF Homeless Shelter
Dec 2019 - Present

Hospice Patient Care Volunteer

Vitas Hospice
Jan 2016 – May 2019

Test Writer and Event Supervisor

Science Olympiad
Sep 2015 - Present

Emergency Medical Technician

Stanford Emergency Medical Services
Sep 2015 – Sep 2018

Hospice Patient Care Volunteer

Interim HealthCare of the Twin Cities
July 2015 – Sep 2015

Standardized Patient Actor

University of Minnesota Medical School
July 2014 – Sep 2014

Guest Experience Volunteer

Regions Hospital
June 2014 – Sep 2014

Honors and Awards

DreamCatchers Medtech Hackathon Champion, University of Hong Kong and Hong Kong Science and Technology Parks Corporation - July 2019

Gates-Cambridge Scholar Elect, Gates-Cambridge Trust - Jan 2019 [Press]

Awarded to 1% of applicants; for pursuance of PhD in Computer Science at Cambridge University through NIH Oxford Cambridge MD-PhD Scholars

J.E. Wallace Sterling Award for Scholastic Achievement, Dean of H&S - Jan 2019

Awarded to top 25 students of each year’s graduating senior class who majored in a Humanities and Sciences department or program.

Public Service Honor Society, Stanford Hass Center for Public Service - Nov 2018

Small Grant, Stanford Undergraduate Advising and Research - Nov 2018

$1000 to develop neural network recommender system for clinical order decision support.

Phi Beta Kappa - May 2018

1 of 32 juniors elected for the Phi Beta Kappa nomination at Stanford

Bay Area Global Health Innovation Challenge Finalist - May 2018

13 out of 80 different finalist teams from affiliated research institutions internationally. Organizer: Dr. Michelle Barry.

President’s Award for Excellence in the Freshman Year, Stanford University Office of the President - October 2017

Honors the top 3% of undergraduates at Stanford University

Top Presentation, Stanford Resident & Fellow Quality Improvement & Patient Safety Symposium - May 2017

Awarded for presentation of poster, “Automated Electronic Calculator for Management of DKA/HHS”

Academic Excellence, Lambda Phi Epsilon - May 2017

Awarded to single individual across national fraternity for high academic caliber, successful bone marrow drive campaigns, and development of API Health Awareness Month.

Stanford Bio-X Undergraduate Research Fellowship, Stanford Bio-X - January 2017

Awarded $7000 for project to understand macrophage localization in irradiated tumor mouse model.

Major Grant, Stanford Undergraduate Advising and Research - April 2016

Awarded $6400 for grant to study chemotaxis of irradiated macrophage (Raw 264.7) and breast cancer (4T1) cell lines.

2nd Place Individual, Minnesota Mathematics League Championship - May 2015

Otto Bremer Entrepreneur of the Year, Otto Bremer Bank - April 2015 [Article]

Selected out of all students in Upper Midwest for leadership and entrepreneurial work. Featured in the 2015 JA Hall of Fame, JAUM mission report, “Stories of Success”, and JA USA blog

2nd Place National Tournament, Science Olympiad - May 2014

AIME Qualifier, Mathematics Association of America - 2013, 2015

Post-Secondary Enrollment Option, University of Minnesota Twin Cities - 2013-2015

Took college courses (Organic Chemistry, Multivariable Calculus, Linear Algebra w Diff Eqs, Data Structures and Algorithms) at the University of Minnesota as a Junior and Senior in high school.