Centers and Research
The PathCheck Foundation is committed to exploring and participating in the transformation of public health by combining innovative research with technological solutions that are capable of responding to modern health crises.
PathCheck's Centers and Institutes
ITGH
Institute for Technology and Global Health
The Institute for Technology and Global Health is PathCheck Foundation's hub for research, innovation, and development.
Its mission is research with ripples. It aims to explore new frontiers in public health technology and study its socio-economic impact through a robust research agenda at the intersection of public health, technology and governance. Learn more
DICE
Data Informatics Center for Epidemiology
The Data Informatics Center of Epidemiology is a branch of the PathCheck Foundation that aims to consolidate and analyze information surrounding diseases.
Our mission is to provide a comprehensive look at the health issues we are facing today. We aim to employ data informatics methods to do in-depth research and explore new avenues to stay on the forefront of health technologies. Learn more
Our Research
Papers
Privacy
- Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic
- Verifiable Proof of Health using Public Key Cryptography
- PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic
Digital Contact Tracing
- Comparing manual contact tracing and digital contact advice
- Proximity Sensing: Modeling and Understanding Noisy RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracing
- Proximity Interference with Wifi-Colocation during the COVID-19 Pandemic
- Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geo-spatial Technologies
- COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers
- Contact Tracing: Holistic Solution beyond Bluetooth
- Contact Tracing to Manage COVID19 Spread – Balancing Personal Privacy and Public Health
- The Architecture of Trust in Contact Tracing
- Adding Location and Global Context to the Google/Apple Exposure Notification Bluetooth API
Equitable Vaccine Distribution and Coordination
- Mobile Apps Prioritizing Privacy, Efficiency and Equity: A Decentralized Approach to COVID-19 Vaccination Coordination
- Challenges of Equitable Vaccine Distribution in the COVID-19 Pandemic
- Vaccination Worldwide: Strategies, Distribution and Challenges
- The Public Health Impact of Delaying a Second Dose of the BNT162b2 or mRNA-1273 COVID-19 Vaccine
Vaccine Credentials
- MIT SafePaths Card (MiSaCa): Augmenting Paper Based Vaccination Cards with Printed Codes
- Paper card-based vs application-based vaccine credentials: a comparison
COVID-19 Testing
- Clinical landscape of covid-19 testing: Difficult choices
- COVID-19 Tests Gone Rogue: Privacy, Efficacy, Mismanagement and Misunderstandings
- Digital Landscape of COVID-19 Testing: Challenges and Opportunities
Pandemic Prediction
- COVID-driven Risk Profile
- Can self-reported symptoms predict daily COVID-19 cases?
- COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms
- Analysis of Tata-1mg data for Covid-19 2nd wave prediction in India
- Estimating Active Cases of COVID-19
Split Learning and Federated Learning
- Distributed learning of deep neural network over multiple agents, Accepted in Journal of Network and Computer Applications 116
- DISCO: Dynamic and Invariant Sensitive Channel Obfuscation, Accepted to CVPR 2021
- FedML: A Research Library and Benchmark for Federated Machine Learning, (Baidu Best Paper Award at NeurIPS-SpicyFL 2020)
- NoPeek: Information leakage reduction to share activations in distributed deep learning
- Split learning for health: Distributed deep learning without sharing raw patient data, Accepted to ICLR 2019 Workshop on AI for social good
- Detailed comparison of communication efficiency of split learning and federated learning
- ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
- Split Learning for collaborative deep learning in healthcare
Differential Privacy