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Phd Student
GAAN Fellow
(787) 361-6493
Anibal is a new PhD student supported by GAAN since January 2019. Dr. Weichao Wang is his PhD advisor, and Dr. Marlon Mejias is his mentor. He worked as a Teaching Assistant in his Teaching Mentor class "OS & Networking" in Spring and Fall 2019. His tasks as Teaching Assistant included creating and presenting PollEverywhere quizzes for each class, attending each class, and reviewing exam material. All students supported by GAANN are required to take and successfully complete ITSC 8665 [Teaching Seminar] class offered every semester, of which he has already taken 3, and will take again in Spring 2021.
In Spring’20, Anibal worked as an instructor for the online section of ITSC3146 (Introduction to Operating Systems and Networking). He worked directly with students through Canvas, providing material and feedback to them. The class forum was managed using Piazza inside of Canvas. In both semesters he completed the teaching seminar ITSC 8665. Also, he attended two seminars offered by the Center for Teaching and Learning at UNC-Charlotte.
Anibal taught the online, synchronous section of ITSC3146 (Introduction to Operating Systems and Networking), in Fall'20. In this section, he had lectures twice a week using Zoom. He worked with Canvas and Piazza (as well as PollEverywhere) similar to the Spring'20 semester, except that the lectures provided an environment to interact directly with the students, answer any questions, and explain concepts in more depth. Like the Spring'20 semester, Anibal attended two more seminars offered by the Center for Teaching and Learning at UNC-Charlotte.
Anibal’s research is focused on “data analysis for user safety in intelligent transportation systems”. He has made solid progress by reading and discussing many papers in related areas. He passed his QE exam in September 2019. After that, he has worked on programming an App that can collect and analyze the data from bicycle users. Features for collecting GPS, audio, and magnetic field data from the smartphone are being finalized for the App. Progress has also been made in recent semesters on creating a Machine Learning classifier to identify the presence of cars using the recording of car sounds, such as the noise of the tires, engine, or car horn. The goal is to detect the mutual impacts between the automobiles and the bike riders and the safety of the road. The information will be valuable for the future planning of the city.
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