This is Dr. Matt Thatcher, Professor and Chair of the Department of Computer Science and Engineering. I want to make you aware of an excellent opportunity available to you – the Bachelor’s/Masters Accelerated Program. This is a combined B.S./M.S degree program available to undergraduate students in Computer Science and Engineering (CSE) with 90 or more hours earned toward their baccalaureate degrees. Students accepted into this program must have a minimum overall GPA of 3.40 and at least 3.40 in the course work taken in CSE. Up to 12 credit hours at the 500 level or above may be applied toward both the B.S. and M.S. degree requirements; this means that with one additional year of study you will leave USC with both a B.S. and an M.S. degree!
The approval of the student's advisor and the graduate director is required. You must have approval before enrolling in the 500 level or above courses in order for them to apply toward this accelerated degree program. So, make sure to contact the graduate director (via this link) soon to learn more about this opportunity; when you complete the contact form at this link please select Graduate admissions or questions as the Category. I hope everyone has a wonderful Fall Break! Sincerely, Matt E. Thatcher, Ph.D. Professor and Chair
Dr. Jianjun Hu from the National Science Foundation (NSF) for his project titled "Collaborative Research: Integrating Physics and Generative Machine Learning Models for Inverse Materials Design".
Dr. Jason Bakos has received and NSF grant award for his project "A Unified Approach for Scheduling Computer Vision Dataflow Graphs".
Please congratulate Austin Downey (PI) and Jason Bakos (Co-PI) for receiving a collaborative NSF grant. This is a collaborative grant with Iowa State University and UofSC is the lead institution. Austin Downey (PI) is from Mechanical Engineering and Jason Bakos from Computer Science and Engineering. The project is titled "A Programming Model and Platform Architecture for Real-time Machine Learning for Sub-second Systems". This project develops and evaluates novel frameworks for achieving real-time machine learning; that is, for a given target application that is producing a lot of data, how to process that data to concurrently prediction what comes next while learning from the past data at the same pace of the target application.
We are happy to report that Dr. Jianjun Hu, along with collaborators Qi Wang (PI), Sophya Garashchuk, Linda Shimizu, and Chuanbing Tang, have received a research award from the Department of energy for their project titled "Data-science enabled investigation of the mechanisms for multiscale ion transport in functional electrolytes and for the radical generation in crystalline assemblies".