Undergraduate Funded Research Opportunities


Data Science REU at WPI

Program Overview

The Data Science Program at WPI, Worcester, MA, sponsored by the National Science Foundation, offers a 10-week, all expenses paid Research Experience for undergraduate students starting Summer 2016. The students, working with WPI faculty and peer students, learn state-of-the-art data science techniques and technologies and apply them to research focused on societal challenges in 'smart and connected' communities. The research topics include healthcare, sustainability, security, and mobility; all interlinked concerns of critical national importance. Students present the results of their research at an Open Poster Session during the final week of the program, and, where appropriate, at scholarly venues and conferences.

 Data Science Faculty Prof. Carolina Ruiz, Mohamed Eltabakh, and Elke Rundensteiner 
with Data Science REU 2016 Cohort Students
Data Science REU 2016 Cohort Students


Student Stipends, Travel, Housing, and Meals

  • $5000.00 stipend over the 10 week program ($500/week)
  • LEED Gold–certificated on-campus residence housing, paid for directly by the program
  • Weekly meal allowance
  • Up to $600.00 in travel expenses


The REU Program includes weekly group meetings, information exchange, training sessions, technical presentations about data science research, information sessions for professional development, and progress reports by participants. Social activities are organized and planned by the students to take in the history, culture and great places to visit in the greater New England area.

Revere Beach Sand Sculpting Festival 2016
From left: Moustapha Tiam, Puja Trivedi, Vimig Socrates, Rebekah Eversole, Lauren Sedita, and Eric Salina
at the Revere Beach Sand Sculpting Festival 2016

Program Requirements

Accepted participants work a full 40 hour work week toward completing a research project. This research is then displayed at a Poster Presentation Symposium at the completion of the program with WPI faculty, and guests and fellow students in attendance.

Research Project Areas

Students participating in the program have the opportunity to work on a research project related to some of the areas listed below. During the application process, students indicate their preferred areas of interest. In the application, students may also describe their research interests in more detail, along with any prior experiences. Application areas relate to smart and connected communities including sustainability, health, security, mobility, economic prosperity, social good, and more. Technical areas include data analytics, data mining, machine learning, statistical learning, data bases, visualization, big data infrastructures, cloud computing, and mobile computing.

Amber Wallace with fellow 2016 DS REU Cohort Students

Eligibility and Requirements

NSF requires that all student participants are United States citizens or permanent residents; no exceptions. We prefer sophomores and juniors who have a strong interest in research. Courses taken in programming, computer science, linear algebra, and/or statistics are a plus. We especially encourage female and underrepresented students in STEM fields. We also encourage students with prior community college experience who have transferred to a 4-year institution.

Application Process

Students complete the Online Student Application, and attach their informal transcript. Students ask two references to submit their brief recommendations via the Submit Faculty Recommendation. These recommenders are preferably faculty members at the student's home institution or another university, who know the student well. All application materials, including letters, must be submitted by the application deadline.

During their REU site, students not only work on their projects, but also attend symposia in subjects related to job searches, applying to graduate school, writing a resume, dress for success and ever popular Interview skills. They also go on informative and enlightening field trips to industry partners who are heavily involved in data science; Google, MathWorks and others.

REU 2017

                                                                                                    REU PYRAMID SCHEMES!

DS - REU - 2017
A Pile of REU Students


The REU students work hard and play hard, as can be seen by the pyramid scheme they configured in the lab with Ph.D. mentor Ermal Toto.
In jumbled order on the bottom row are a Ph.D. mentor, Luke Pelegrin, Cole Polychronis and Sean Tucci.
Next up is Christian Huacon with Tran Khang and Kate Finnerty.
Next to the top...we have Noori Apoorva and Jennifer Ha supporting our little Courtney Burns as the crown on the castle!
Sarah Brownell is our left spotter with Ph.D. mentor Ermal Toto spotting on the right.

Industry Visits

Each year the WPI Data Science REU students visit Industry partners to experience the world beyond academic walls. We sincerely appreciate MathWorks in Natick, MA and Google in Cambridge, MA, National Grid in Waltham, MA and many others, who open their doors to these bright young students.

REU 2018

This year to date, the students have visited MathWorks and Google. Both of these amazing corporate offices inspire the students to work toward further education so they too can experience working in such vibrant communities.


REU at MathWorks
REU at MathWorks, MA

and who doesn't love GOOGLE!



reu at Google










Move-In Date: Tuesday, May 29, 2018
Program Begins: Wednesday, May 30, 2018
Program Ends: Friday, August 3, 2018
Move-Out Date: Saturday, August 4, 2018

Email: wpi.ds.reu@gmail.com

Data Science Program, REU
Worcester Polytechnic Institute, Fuller Labs 243, 
100 Institute Road, Worcester, MA 01609

Data Science REU 2017

Sean Tocci and Jennifer Ha collaborating on research in the Data Science Innovation Lab at WPI

Learn how ten undergraduates from colleges and universities across the country are spending the summer at WPI working alongside data scientists, conducting groundbreaking research that could someday impact critical facets of society from healthcare to mobility.

REU KIDS 2016Media1

Data Science REU 2017 Student Team Presents Their Research at the Council of Undergraduate Research REU Conference


Cole Polychronis and Apoorva Nori
Cole and Nori

During the final days of the Data Science REU Program, the student research teams prepare posters of their work and compete in a poster competition symposium at WPI. The winning Team/Poster is nominated for admission to the CUR Symposium where they show their poster and are student representatives of WPI Data Science REU.

This year, we are proud to sponsor the winning REU 2017 Team of Apoorva Nori and Cole Polychronis, who presented their winning poster at the CUR Symposium on October 22 and 23.  Apoorva Nori, NYU and Cole Polychronis, Westminster College, UT,  titled their work, "Turning Design Artifacts into Assets: Capturing and Analyzing User Interaction in  Web-based Data." 

This was CUR's 7th Research Experience for Undergraduates Symposium. It features student posters, a graduate school recruitment fair, and for faculty and students, a professional development workshop.  This year's CUR was held at the Westin Alexandria in Alexandria, Virginia, near the site of the new National Science Foundation.


Kate Finnerty Presents at WISH-2017

Kate Finnerty at the podium
Kate Finnerty at WISH-2017

Kate Finnerty, an undergrad from Amherst College in Massachusetts, was nominated to present her research poster at this year's workshop on Interactive Systems in Healthcare (WISH), in conjunction with the American Medical Informatics Association (AMIA) 2017 Annual Symposium, November 4 - 8 in Washington, D.C. Katherine worked with fellow REU student Khang Tran who was unable to make the trip to Washington.

Collaborating on the research were DS faculty Professor Carolina Ruiz, as well as Bengisu Tulu, Qiaoyu Liao, Jessica Oleski, and Sherry Pagoto.

Abstract: As obesity becomes more widespread, weight loss has become a major focus of mobile health. Katherine Finnerty’s project investigates how to best address limited data with analysis techniques such as text mining. Future work will include developing a single captology framework that works in the context of mobile health.

Kate gave an excellent presentation, one that is considered far above the ability of an undergraduate student.

Congratulations Kate!