NYU
The Center for Health Data Science at NYU offers a diverse array of opportunities for students to engage in hand-on learning experiences in the field of Health Data Science. Our programs are designed to provide practical skills, foster academic growth, and prepare students for successful careers in this rapidly evolving field.
Internships
We support internships with our faculty and other researchers at the School of Global Public Health, allowing students to contribute to data science projects that address critical public health priorities. These internships provide valuable real-world experience and the opportunity to apply classroom knowledge to practical challenges.
Learning Opportunities
Our center offers unique courses and workshops that cover cutting-edge topics in health data science. These educational experiences are designed to keep students at the forefront of technological advancements and methodological innovations in the field.
Networking and Mentorship
We facilitate connections between students, faculty, industry professionals, and peers to foster meaningful relationships and provide guidance for academic and career growth, Our industry thesis opportunity, for example, allows students to work on real-world projects while receiving mentorship from both academic and industry experts.
We have been delighted to receive so many inquiries from external students to our Health Data Science Center. Please direct your correspondence to healthdatasci@nyu.edu. Due to the substantial volume of incoming communications, we regret that we cannot guarantee a personalized response to every email received.
What Participants Loved
The practical demonstrations stood out as the most valuable segment, with 66.7% of attendees highlighting them as their top takeaway. Another 16.7% found the overview of NYU’s Computing Resources particularly useful, while the same percentage praised Professor Rumi Chunara’s research talk on LLMs/VLMs, which provided compelling insights into real-world AI applications in public health.
Many also appreciated the hands-on HPC walkthroughs, though some noted: “I wish I had discovered these resources earlier in my research!”
Suggestions for Future Workshops
To build on this success, attendees shared excellent ideas for improvement, including:
More project-based examples in demos to illustrate real-world use cases.
Clearer instructions for uploading and managing data in R Studio (both on HPC and locally).
Additional interactive exercises applying foundation models to public health challenges.
Looking Ahead
The April 23rd workshop effectively bridged theory and practice, introducing students to GenAI and HPC’s potential while gathering feedback to enhance future sessions. We’re already working on integrating these suggestions to make the next workshop even more engaging and practical.
A huge thank you to all who participated—we can’t wait to see you at the next one!
The workshop provided an overview of existing climatic data and its application in analyzing the impact of environmental exposures on population health. It was divided into two parts. The first part focused on key concepts in geospatial analysis, major sources of climatic data, and the challenges of using such data in population health research. In the second part, participants had the opportunity to gain hands-on experience working with open-access meteorological data using the statistical software R.
