Research
Colloquium Series
The Department of Computer Science benefits greatly from researchers who take the time to come to UNCG and talk about their research with our faculty and students. If you are a researcher and are interested in visiting our campus and speaking about your work, please contact the department and ask to be put in contact with our colloquium coordinator.
Special note for Fall 2020 – Fall 2021: in wake of COVID-19, we plan to host Zoom-based virtual colloquium. Please contact us if you are interested in giving a talk to your research without the hassle of travel.
| COLLOQUIA ANNOUNCEMENTS | |
| Dr. Dong Dai, UNC Charlotte, October 27, 2021, Zoom, 10:00am – 11:00am |
Reinforcement Learning on Batch Job Scheduling |
| Dr. Megan Squire, Elon University, April 9, 2021, Zoom, 1:00pm – 2:00pm |
Using Data Science to Understand Extremist Group Financing |
| Dr. Joao Ricardo Vissoci, Duke University, November 15, 2019, Room 136, Petty Building 2:00pm – 3:00pm |
Data Science, Machine Learning, and Mixed Reality for Global Health Challenges |
| Dr. Minwoo “Jake” Lee UNC Charlotte Oct 9, 2019 Room 142, Stone Building 11:30pm – 12:30pm |
Opening the Black Box for Reinforcement Learning |
| Dr. Mohammad Shahzad NC State University Feb 22, 2019 Room 150, Petty Building 2:00pm – 3:00pm |
Contact-less Human Sensing using Ubiquitous Modalities |
| Dr. Nancy Green UNC Greensboro Feb 19, 2019 Room 150, Petty Building 12:30pm – 1:30pm |
Argument Mining Scientific Arguments: A Knowledge-Based Approach |
| Dr. Sayed Mostafa NC A&T University Nov 16, 2018 Room 223, Petty Building 2:00pm – 3:00pm |
Computing Happiness from Textual Data Using Text Mining and Machine Learning Techniques |
| Dr. Jag Sankar NC A&T University Oct 26, 2018 Room 313, Petty Building 2:00pm – 3:00pm |
Mg Materials Innovation – Bio Implants to Light Weighting for Greensboro’s Industrial Revolution |
| Dr. Siobahn Day UNC Greensboro Sept 21, 2018 Room 224, Petty Building 2:00pm – 3:00pm |
A Natural Language Processing and Machine-Learning Based Approach to Authorship Attribution of Tweets |
