Research Experience for Undergraduates in Computer and Data Science
The Research Experience for Undergraduates (REU) in Computer and Data Science is a 10-week summer undergraduate research program in the general area of Computer and Data Science.
REU participants are assigned to a Rice faculty mentor and work closely with a Rice graduate student or Postdoctoral researcher to perform cutting-edge research in Computer Systems or Data Science. In the summer of 2023, we partnered with REUs at Texas A&M University in College Station and Prairie View A&M University. The summer starts with a joint kick-off program and ends with a shared research symposium. There are workshops on conducting research, reading papers, preparing scientific posters, as well as research, career, and technical talks.
What Is Data Science?
Data science is an interdisciplinary field of study, encompassing sub-areas of computer science, statistics, electrical engineering, and applied mathematics. It is the science of extracting actionable knowledge from large and complex data repositories, where “complex” may refer to the modality of the data (images, time series, text, as well as traditional tabular data) or other facets of the data in question (data can be complex because they are geographically distributed, or characterized by the ubiquity of missing or inaccurate values). Data Science has quickly become a critical enabling capability in many different fields: science, healthcare, energy, manufacturing, finance, and many others. Data can be used to train algorithms that are more accurate than experienced doctors in recognizing early-state tumors. It can also be used to predict when it is time to do preventative maintenance on a multi-million dollar machine, before a catastrophic failure. Data can be used to detect fraudulent activity in credit card transactions. Furthermore, it is becoming a core enabler for detecting and preventing cyberattacks.
Research in Data Science could include designing and implementing new machine learning algorithms, applying machine learning algorithms to solve specific problems, or developing methods to manage huge data sets.
What are systems projects?
Systems projects could include designing and implementing new networking algorithms, new computer architectures, or software systems.
- Majors in computer science, data science, statistics, applied mathematics, electrical engineering, or applied/pure mathematics
- Some knowledge of computer programming in a language such as Python or Java is required. Python is strongly preferred
- International students must be enrolled at a US university
- All students must be continuing their undergraduate studies at a US institution the semester following the program
- Students must be at least 18 years old by the time the program starts
- Students must have the ability to come to the Rice campus
- Applicants must be available during full-time business hours and possibly occasional weekends if required by the mentor
- Students must plan on being at Rice University for the entire duration of the program
Why come to Rice?
Boasting a 300-acre tree-lined campus in Houston, Rice University is ranked among the nation’s top 15 universities by U.S. News & World Report. Rice has a 6-to-1 undergraduate student-to-faculty ratio. Rice students and faculty discover, create and innovate, and solve real-world problems that make a measurable global impact.
- Application Deadline: TBD
- Start Date: TBD
- End Date: TBD
How to Apply
The program is not currently accepting applications, but check back for future updates.
Recommendations from professors and/or mentors who can vouch for the applicant's intellectual strength and personality are required.
In your application materials, please outline:
- Research interests: Clearly describe your interests and motivations in computer and/or data science research and any specific areas in which you are most interested
- Readiness: State what you would like to gain from the program
- Contribution: Describe the unique perspectives you bring to computer or data science research and how you would contribute to a diverse, equitable, inclusive and impactful community