Tumlumbe Juliana Chengula a distinguished academic and researcher in the field of Computer Vision. He possesses proficiency in several programming languages, with a focus on Python. His expertise extends to utilizing various tools such as Tableau, QGIS, PyTorch, and Tensorflow, showcasing a well-rounded skill set in data science and machine learning. Additionally, he has earned certifications in Data Science Tools, SQL for Data Science, and Machine Learning with Python, all from IBM. Furthermore, he has completed the “Using Python for Research” certification from Harvard University, underscoring his commitment to continuous learning and staying at the forefront of relevant technologies in the field. These skills and honors collectively highlight his comprehensive knowledge and dedication to the dynamic and evolving realm of data science.
Eduvation
His master’s studies at Amirkabir University of Technology (AUT) in Tehran, Iran, from September 2018 to October 2021, he specialized in Electrical Engineering with a focus on Control. During this period, he maintained a GPA of 3.5/4, and his final project earned a perfect score of 4/4. Prior to his master’s degree, he completed his Bachelor’s in Power Electrical Engineering at Yazd University, Iran, from September 2014 to August 2018, achieving a GPA of 3.1/4.
Employment Experience
As a Graduate Research Assistant at South Carolina State University since August 2022, she has been actively engaged in the collection, recording, and analysis of transportation data, utilizing proficient tools such as Python, Tableau, PowerBI, and QGIS. Her research focus involves the application of cutting-edge technologies, including Machine Learning, Deep Learning, and Artificial
Intelligence, to address challenges within the transportation industry.
Over the course of her tenure, she has showcased her contributions by delivering six impactful presentations on her research in Machine Learning and Artificial Intelligence at seven distinguished transportation conferences. Furthermore, her commitment to scholarly dissemination is evident through the submission and acceptance of two peer-reviewed articles, which are slated for presentation at the prestigious 2024 Annual Transportation Research Board conference. These accomplishments underscore her dedication to advancing knowledge and providing innovative solutions to enhance the efficiency and effectiveness of the
transportation sector.
Research Project Highlights
She has made notable contributions to the field of
transportation through her research endeavors, addressing critical issues with cutting-edge technologies. One of her significant projects involves enhancing road safety through Ensemble Learning, specifically in detecting driver anomalies using vehicle inbuilt cameras. In another study, she employed Topic Modeling and Categorical Correlations to unveil patterns associated with autonomous vehicle disengagements, shedding light on crucial aspects of
autonomous driving systems.
Furthermore, she delved into the realm of quantum computing to improve classification performance in traffic sign recognition, utilizing an optimized hybrid classical-quantum approach. Additionally, her research extends to the realm of sustainable urban mobility, where she has applied Explainable Artificial Intelligence to predict bike-sharing station capacity. These diverse projects showcase her proficiency in utilizing advanced technologies and methodologies to address multifaceted challenges within the transportation sector.
Publication
Improving road safety with ensemble learning: Detecting driver anomalies using vehicle inbuilt cameras
Machine Learning with Applications
2023-12 | Journal article
CONTRIBUTORS: Tumlumbe Juliana Chengula; Judith Mwakalonge; Gurcan Comert; Saidi Siuhi