REU Site for Machine Learning in Natural Language Processing and Computer Vision

The University of Colorado at Colorado Springs (UCCS) has had an REU (Research Experiece for Undergraduates) program funded by the National Science Foundation (NSF) for the past several years. The past two programs lasted six years, from 2009-2011, and 2014-16. The current program is starting from the summer of 2017.

UCCS is a predominantly undergraduate institution with selected graduate programs. As of Fall 2016, UCCS enrolled 12,017 students of which 10,188 were undergraduates. UCCS has been ranked as a top Western university every year since 2002 by US News and World Report. UCCS also has the reputation of being a leading institution in enrolling women students in STEM (Science, Technology, Engineering and Mathematics) majors. The PhD program in Computer Science is relatively new, being about 10 years old, with about 100 students.

Colorado Springs is a beautiful city, only miles from the famed Pikes Peak sitting atop the Front Range of the Rocky Mountains. There are many things to do outdoors as well as it has a decent city life. It's a city with about 500,000 people in the metropolitan area.

The objective of the REU site is to expose bright and motivated undergraduates who want to pursue advanced careers in Computer Science related fields to work on hands-on research projects in theory and practical applications of Machine Learning. Students will work with several professors who specialize in specific research areas, but each with a great deal of interest in machine learning.


Important Dates

The deadline for submitting a completed application has been extended to April 8 from the original March 13th. This is because of late formal notification from NSF regarding our grant. We will make offers soon after the deadline passes.


$500/week during the summer for full-time research.

Interns are expected to be full-time. Interns are expected to put in at least 8 hours of work per day. Research should be your focus during the summer. Since we are paying for a lot more than 8 hours of work, interns are expected to focus on doing well on their projects, whatever it takes.

Additional Benefits

We will pay each student $150/week for food. Food Service on campus during the summer is limited, but there are many eating establishments within 15 minutes of walking. Dorms on campus are new and have excellent views of the Rocky Mountains. In our previous, our students stayed in Alpine Village. We will provide the basics: linens, some pots and pans and utensils. We will make arrangements for your travel, although the amount may be limited. Local students may recieve a housing allowance also.

Project Ideas

We have many interesting projects in the application of machine learning in natural language processing and computer vision. You can also look at the final papers written by the prior REU students: 2010, 2011, 2014, 2015 and 2016. Once you have been selected for a position, we will be in touch with you so that you have a project topic before you arrive. Each students will work on an individual project although there will be other projects with similar topics. Individual work will be emphasized although collaboration will be encouraged wherever possible. For more details on project ideas, look at the faculty Web pages. This are link to our prior REU programs: 2009-2011 and 2014-16.

Faculty Web Pages

Please look at the Web sites of Professors Kalita, Boult, and Ventura if you would like to know more about the professors involved in this REU program.

How to Apply

Please send a cover letter, an (un)official transcript, current vita containing pertinent course work, related experience, and professional and academic goals, a phone number for interview to Dr. Jugal Kalita. Please make a single PDF file containing all the documents before attaching it to your email.

Please request two individuals who know you as professor or supervisor to send letters of reference (which can be an email) to

Since successful research involves copious writing, please include at least one technical paper that you have written. This can be paper for a class.

Links to Prior Years

2016, 2015, 2014, 2011, 2010 and 2009.