Soon-to-be PVAMU graduate sets stage for career as software engineer
For many undergraduate and graduate students alike, the current COVID-19 pandemic has put an extra challenge in their studies. But one Prairie View A&M University student hasn’t let it delay his path.
Uboho Victor, a native of Akwa-Ibom State, Nigeria, is set to graduate with a Master of Computer Science (MCS) degree from PVAMU. He earned a Bachelor of Technology degree from Federal University of Technology Owerri (Nigeria).
During his time at PVAMU, Victor was a member of the Nigerian Students Association, Honor Society and National Society of Black Engineers (PVAMU Chapter). He was also selected as the Most Outstanding Computer Science Graduate Student for 2020 in the Roy G Perry College of Engineering, and he came in second place in the poster competition at the Mission-Critical Big Data Analytics Workshop in May 2019.
“PVAMU is unique in that it provides an environment that allows students to collaborate more effectively in academics, research and other extra-curricular activities,” he added. “PVAMU also provides timely and relevant information about career opportunities in such a manner that the students are better positioned to take advantage of these opportunities. I’ve also enjoyed the inclusive culture found in all aspects of the school.”
Victor completed an internship at Dell Technologies in Austin during the summer of 2019.
“My internship with Dell Technologies has played a major role in helping me decide a focus in my career,” he said. “The project I was assigned gave me the opportunity to get hands-on knowledge on developing applications that provided solutions to support business processes. It also gave me a better understanding of how innovative business ideas can evolve into global products.”
Career-wise, Victor aspires to lead organizations focused on developing and providing technology-based solutions that support businesses and organizations.
He has a job secured as a software engineer working on client technologies at Dell Technologies, an opportunity he learned about during the NSBE (National Society of Black Engineers) Convention in March of 2019.
Victor has also published research about the application of artificial intelligence (AI) applications in detecting fake news on social media using Twitter and the creation of a system that allows machines to learn from data in special situations where there is limited existing knowledge about the data. He has also submitted research papers to the Institute of Electrical and Electronics Engineers (IEEE) Transactions on Computational Social Systems, IEEE Transactions on Biomedical Engineering, and the American Journal of Computer Science and Technology.
Victor is also currently working on using his proposed system in providing some insights into tackling the current pandemic caused by COVID-19.
“The goal is to implement deep learning models such as Convolutional Neural Networks (CNN) and Residual Neural Networks (ResNet) to accurately diagnose COVID-19 with chest X-ray images,” said Victor. “This will support and facilitate the testing and early detection and treatment of coronavirus patients.”
Victor chose to attend PVAMU because he was excited about the research opportunities available in the field of artificial intelligence. He was also inspired by the progress PVAMU has made as an HBCU over the years, as well as the impact its students have made on the corporate world.
“PVAMU has helped me in achieving my career goals by giving me the research opportunity I have always desired,” Victor said. “I had the opportunity to undertake research in the field of artificial intelligence through the Center of excellence in Research and Education for big military Data InTelligence (CREDIT), under the supervision of Dr. Lijun Qian, and this has propelled me into choosing a career in the AI field. My advice to current students is to stay committed to achieving their goals and make every experience they face an opportunity to learn and develop themselves.”
Links to Victor’s articles:
Two-path Deep Semi-supervised Learning for Timely Fake News Detection: https://arxiv.org/abs/2002.00763
Deep Two-path Semi-supervised Learning for Fake News Detection: https://arxiv.org/abs/1906.05659
Effective COVID-19 Screening using Chest Radiography Images via Deep Learning: http://credit.pvamu.edu/XrayV1.pdf
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By Emilia Benton