RR/ID Data Science/Tech Interns: AI for Science Discovery and Knowledge Evaluation/Management
Stefano M. Bertozzi, Professor
Public Health
Open. Apprentices needed for the fall semester. Enter your application online beginning August 21st. The deadline to apply is Monday, August 31st, 4pm.
In addition to applying through the URAP portal, please fill out this interest form as soon as possible if you are interested in joining us: https://forms.gle/1jsvCGKgj3rb6KZx8
Potential projects for AY 2026-27
· We are interested in direct application of both home grown and other tools in building out (fine tuning/tweaking) our current RR/ID Preprint Navigator tool for different users communities. We would like to adapt our current Preprint Navigator into a tool tailored for specific users/teams so we can filter the growing volume of scientific literature both by what is relevant for the user (e.g., immunization, climate, etc) and then to prioritize the feed based on select criteria (e.g. novel, important, with relevance for adjacent fields).
· While the above is relatively simple given our existing tools, a more advanced area of investigation is better understanding how existing AI tools and methods can assess new research novelty and impact (in infectious disease preprints initially). How might we use existing tools and work with tech partners to help elucidate and prioritize which preprints should be reviewed; and of those preprints, which might make the most impact upon policy/program implementation? How well does AI/machine peer review meaningfully support scientific discovery? [and how can we tie that into EdTech]
· We are also interested in exploring how to better support peer reviewers (those evaluating the science and writing reviews for feedback to both researchers and others) as they vet important infectious disease research. Many AI tools now exist for the support of writing peer reviews and providing automated peer review. We are interested in exploring and experimenting to understand which tools are most useful, and how we can leverage the best-in-class to support a more trustworthy, transparent and impactful peer review system as scientific discovery moves at warp speed.
· We also see this work opening a natural EdTech opportunity: adapting the Preprint Navigator, novelty/impact assessment, and peer-review support tools above with things we have already built for training (e.g., via Playlab) — helping integrate internal AI experimentation into hands-on learning experiences that reach and inspire students around the world as future peer reviewers and open science practitioners.
This is a long-term, mentored project and you will be matched with a more defined scope of work depending on our needs and your interest, within the context of work above. We’re looking for student apprentices to start with us starting fall semester (or earlier, if available), with strong potential to continue into spring and beyond, should the work and mission be interesting to you.
Role: What you’ll do:
· Research emerging research-intelligence platforms that classify papers by conceptual role (paradigm-reinforcing, anomaly-exposing, framework-synthesizing) at the moment of submission, rather than relying on retrospective citation metrics
· Investigate how these tools’ novelty/impact signals could help RR/ID triage incoming infectious disease preprints and route high-priority submissions to reviewers faster
· Design and run small experiments comparing different platforms/tools to test where time and cost savings in peer review are real versus overstated
· Contribute findings and prototypes toward the RR/ID Preprint Navigator tool
· Explore the broader question of how LLMs can support faster, more equitable dissemination of scientific knowledge
· Evaluate vendor claims critically, distinguishing substantiated capabilities from marketing hype
Qualifications: Skills you should have or want to develop:
· Interest in LLMs, AI-driven research evaluation, and scholarly publishing infrastructure
· Comfort with data analysis and structured experiment design
· Interested in evaluation of frontier AI tools and platforms
· Familiarity with (or willingness to learn) in general preprint servers, peer review workflows, and open science publishing
· Strong written communication for synthesizing findings into actionable recommendations
· Basic technical/data skills (e.g., Python, spreadsheets) a plus for prototyping and experiment tracking
· Willingness to problem solve with high levels of self motivation
· Ability/desire to see tasks through to the end
· A desire to be part of a team that is building something new and exciting
· Responsive. Our team is responsive to emails and each other; we honor meetings and transparent communication
Commitment & logistics:
· Fall semester apprenticeship (can start earlier), with the opportunity to extend into spring for continued mentorship and deeper involvement in the project’s next phase
· 6–8 hours per week required
· Option to enroll in at least 2 units (of URAP or independent study)
· Students will undergo around 5 weeks of initial training/learning about RR\ID
*Paid positions may be available after an initial semester of working with RR\ID
To apply, please fill out this short interest form: https://forms.gle/1jsvCGKgj3rb6KZx8.
You should also apply through the URAP program portal should you be seeking academic credit and want to go through those programs.
Day-to-day supervisor for this project: Hildy Fong Baker , Staff Researcher
Hours: 6-8 hrs
Off-Campus Research Site: The work will be largely remote but there will be some workshops in-person, including a required in person weekly workshop (5 Weeks) at the beginning of the semester.
Related website: https://rrid.softr.app/
Related website: https://rrid.softr.app/