This team project aims to investigate different approaches to presenting individuals with daily suggestions for meeting their nutritional goals. Specifically, we are interested in developing new mechanisms for choosing suggestions that satisfy an individual’s’ preferences and habits (e.g., similar to an individual’s previous meals, based on the analysis of textual meal descriptions), comparing effectiveness of meal recommendations that are expressed as text versus those expressed as images of meals, and developing ways to balance an individual’s preferences with exposing them to new meal ideas.

We are looking for a student in data science to assist with development of computational methods needed to enable generation of personalized meal suggestions. These methods may include different approaches to calculating text similarity (e.g., clustering of meals based on inclusion of different foods in the descriptions), using word embeddings, and others. The student will work with the dataset of meals collected in other prior studies, labeled by registered dieticians. Our current plan is to primarily focus on similarity in text descriptions, rather than meal images; however, we are happy to explore a combination of these two modalities.

This project is an opportunity to collaborate with an interdisciplinary research team at the Department of Biomedical Informatics at CUIMC.

This project is eligible for a matching fund stipend from the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Lena Mamykina
  • Center/Lab: Department of Biomedical Informatics
  • Location: PH-20

Project Timeline

  • Earliest starting date: 3/1/2022
  • End date: 8/30/2022
  • Number of hours per week of research expected during Spring/Summer 2022: ~10
  • Number of hours per week of research expected during Summer 2022: ~10-15

Candidate requirements

  • Skill sets: Knowledge and experience with basic natural language processing and data science methods such as text similarity, word embeddings, clustering, etc.
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes