JUMP Undergraduate Research Initiative

The Joint University Microelectronics Program (JUMP) Undergraduate Research Initiative (URI) is an academic year long paid program for UVA CS/EE/CPE majors that both exposes undergraduates to career opportunities associated with JUMP sponsors, and illustrates the benefits of obtaining an advanced degree in a technical area related to microelectronics and computer science. It combines a structured undergraduate research experience with PhD student and faculty mentorship.

Undergraduate research is something every student should consider.

Research experience helps expose students to the cutting edge of their field and can help make courses more interesting. Furthermore, this can help improve their competitive edge for full-time jobs, internships, or grad school-- and help to decide whether grad school is a good fit. Research experiences often lead to publications and chances to attend a conference in the research topic area. These research experiences can also give more direct interaction with faculty and help obtain strong letters of reference. 

The URI program also seeks to help the CS and ECE departments expand representation and build a more diverse group of undergraduate students engaged in research. 


  • Computer Science majors → visit research.cs.virginia.edu to see potential projects and advisors.
  • Electrical & Computer Engineering majors → contact the ECE Chair, Scott Acton, if you’re interested in finding ECE faculty advisors.
  • Reach out to potential research advisors to find the best fit for your goals.
  • Request letter/s of recommendation from faculty (which will be required with the application).

Application Process

If you are interested in applying for the Fall 2022 semester, you can fill out the application form

Browse more postings at the Undergraduate Research Posting Board


Past project presentation titles/topics from URI students included:

  • NIST Cognitive Assistant for EMS
  • Defending Against Persona Abuse Attacks
  • Smith-Waterman Optimizations with CUDA
  • Processing-In-Memory Optimizations for Low-Powered Embedded Systems
  • A Weighted Model for Rank Aggregation
  • Data Modeling for Three-Dimensional Hemispherical Neuromorphic Sensors
  • Spatio-Temporal Pattern Recognition of Bio-Physical Signals
  • Wireless AXI Bus to enable Chip-to-Chip communication between FPGAs
  • Computational Methods for the Construction of Optimal Error Correcting Codes
  • Minimum Spanning Tree Using Coupled Oscillator Networks
  • Inferring the Control Structure of Cyber-Physical Systems via Program Analysis Techniques