Research positions

Are you passionate about using computer science to make the world a better place? Do you have strong algorithmic or programming skills? Then we want you! We have positions available from the undergraduate to the postdoctoral levels.

Mentors

Cynthia Brandt, Professor, Yale School of Medicine and Director, Yale Center for Medical Informatics
Research interests: Design and development of informatics tools for clinical and health services research

Ashok Srinivasan, William Nystul Eminent Scholar Chair and Professor, Computer Science, University of West Florida
Research interests: High performance computing, simulations and data analytics for public health policy

Karen Wang, Assistant Professor, Yale School of Medicine and Director, Population Health Equity, Yale Center for Medical Informatics
Research interests: Addressing the health of marginalized communities through the use of health data and data on the social and structural determinants of health, such as race, ethnicity, and residential address

Project topic

Using Emerging Data Sources to Identify Social and Structural Determinants of Health in Underserved Populations

What will we do? The goal of this project is to use new data streams, such as location based services data (LBS) and social media data, to identify social and structural determinants of health (SSDH) that impact (i) health risks and (ii) the effectiveness of mitigation steps in underserved, at-risk populations. These SSDH include both policies that directly influence the drivers of infection risk and the feedback loop arising from the response of the target population to events and policies.

Why do we wish to do this work? Underserved populations, such as racial and ethnic minorities, are particularly vulnerable to a variety of chronic diseases and poor health outcomes as a result of their SSDH, such as living in food deserts. Furthermore, they provide many of the essential services needed to sustain social distancing by the rest of the population, such as working in food and sanitation services. A methodology to identify impactful SSDH will provide scientific insight and can guide mitigation strategies. Conventional data sources are inadequate to yield effective models for this purpose.

How will we accomplish our goal? We will develop models that augment conventional data sources with new data streams from social media, etc. Our methodology will extract meaningful signals from noisy and sparse data by enriching and aggregating them effectively. A variety of algorithmic approaches are possible, using multilayer networks, deep learning, etc. The details can be tuned to your expertise.

What will you learn?

  1. You will obtain expertise in translating real-world public health problems into computer science problems and identifying a combination of techniques that can solve these problems.
  2. You will learn high performance computing techniques to deal with large data streams using high-end supercomputers.
  3. You will obtain interdisciplinary training to work with researchers in multiple fields, communicate with decision makers, perform outreach to the public through media outlets, supervise students, and write grants.
  4. By the end of this project, you would have the skills to forge new research directions on leveraging emerging data sources to address problems in public health. It ought to place you in a competitive position for jobs in academia and industry.

What are we looking for?

You do not need any background in health applications. We do need those with strong algorithmic or programming skills. Algorithmic skills could be in various fields, such as machine learning, network theory, or graph algorithms, while programming skills would typically be in C/C++ or Python. Familiarity with numerical methods is desirable for graduate or postdoctoral level positions. We are also open to more theoretical areas, as long as you are open to learning new techniques and applying them to the solution of public health problems. At the end of the training period, we would like you to be in a position to use computer science techniques to bring about transformative changes in public health research. Therefore, we would like someone passionate about addressing the application domain problems rather than just publishing computer science papers.

Related work

VIPRA is a multi-university interdisciplinary project that started out with the goal of mitigating infection spread risk in air travel. Our work has been cited in congressional testimony and reported in over 300 news outlets around the world. We have extended it to other directions, such as addressing COVID-19 risk in crowded locations. We perform regular outreach to decision-makers to try to bring about a real-world impact.

Contacts

Please contact Karen Wang or Cynthia Brandt for Yale positions and Ashok Srinivasan for UWF positions.