Research Experiences for Undergraduates

Research Experiences for Undergraduates (or REUs) are competitive summer research programs for undergraduates in the STEM majors. The programs are sponsored by the National Science Foundation, and are hosted in various universities.  https://www.nsf.gov/crssprgm/reu/  Some available opportunities are listed below along with additional research experiences that may be of interest for MCC students.

 

Rochester Institute of Technology 10 Week REU from May 26 - August 1

Students will be provided with a $6,000.00 stipend, free on-campus housing and meal plan, and a travel voucher to cover costs of traveling to Rochester.  More information can be found at http://astroreu.rit.edu.  Applications may be submitted online (priority deadline March 1st; rolling until filled).   


UML Summer Research Opportunities - Positions begin June 1st and will be remote requiring access to technology/internet.  All positions require MCC faculty recommendation.  


Structural Engineering

Structural Health Monitoring of Building and Bridge Models using Experimental Structural Analysis
Faculty: Tzuyang Yu
Nominations accepted from MCC STEM faculty

Structural engineering research group (SERG) in the Department of Civil and Environmental Engineering is looking for one or two REU students to work on the structural health monitoring (SHM) of buildings and bridges. The student(s) will learn how to carry out SHM research by conducting the following tasks:

1) Learn how to build building and bridge models.

2) Learn how to operate a uniaxial shake table.

3) Learn how to collect static response of building/bridge models using force sensors

4) Learn how to collect dynamic response of building/bridge models using a laser Doppler vibrometer (LDV).

5) Learn how to illustrate collected data using Matlab.

6) Learn how to analysis and summary the result.  


Software

Scalable system software
20 hours per week; 8-10 weeks
Faculty:  SeungWoo Son
Nominations accepted from MCC STEM faculty

My research group seeks well-prepared candidates for summer research experiences in scalable system software. The successful candidate will be performing research and development in data and storage systems for high-end computing systems (including large-scale systems and embedded systems). This work will include the development of lossy or lossless data compression algorithms and scalable machine learning and deep learning algorithms on a wide range of applications such as climate/weather modeling and simulation, high-performance computing, astrophysics, agriculture, etc.

Desired qualifications:

  • Programming experience in C, C++, and/or Python
  • Good communication skills both verbal and written
  • Experience in Keras, Tensorflow, Pytorch would be plus

Computer Science

Two computer science positions available
25 hours per week; 10 weeks
Faculty:  Holly Yanco
Nominations accepted from MCC STEM faculty

There are many opportunities in UMass Lowell's Human-Robot Interaction (HRI) Lab and New England Robotics Validation and Experimentation (NERVE) Center for students to work with the current undergraduate and graduate students in the lab on a wide range of projects. Opportunities range from building hardware to programming robots to designing interfaces for multi-touch computing to running user studies. The exact project will be determined based upon the applicant's interests and technical skills as well as the lab's needs.

10 weeks at 30 hours/week, likely starting the first week of June.
Students will learn ROS, Git and other tools.
Qualifications:  Successful completion of Data Structures and recommendation of MCC faculty


Civil/Environmental Engineering
Intelligent Transportation Systems (ITS) Research Assistantships (Two Openings)
Faculty:  Yuanchang Xie
Nominations accepted from MCC STEM faculty

 Position 1: the student is expected to develop a prototype deep learning system to count and track vehicles at intersections using videos. Traffic videos will be provided to the student.  Based on the result of the tracking component, the system should be able to count the number of left-turn, through, and right-turn vehicles for each approach of the intersection.  Additional features (e.g., vehicle type classification, counting pedestrians and cyclists) may also be added to the system if possible.  

 Position 2: the student will need to develop a vehicle re-identification system based on deep learning. Such a system can compare traffic videos taken at two or more locations and match vehicles that have passed by those locations.  The result can be used in many intelligent transportation systems applications, such as deriving traffic Origin-Destination matrix, helping traffic engineers estimate travel time and identify bottlenecks, and improving traffic signal control.  

 For both projects, students will need to

  • Conduct literature review
  • Modify existing open source programs or write completely new code
  • Test the developed system and compare it with previous studies
  • Document research findings

Desired qualifications

  • Good academic standing
  • Strong Python programming and deep neural networks background
  • Familiar with PyTorch and other machine learning libraries
  • Familiar with computer vision and image processing

         

 

Last Modified: 5/13/20