Uzma Nawaz holds a Master of Science (M.S.) degree in Computer Engineering from the National University of Sciences and Technology (NUST), Islamabad, Pakistan, which she completed in February 2024 with a CGPA of 3.3/4.0. Her masterās thesis focused on āA Novel Approach to Calculate Dominance-based Rough Sets Approximations for Dynamic Datasets,ā under the supervision of Dr. Usman Qamar and Dr. Summair Raza. Her academic work concentrated on Explainable Artificial Intelligence (XAI), decision support systems, data analysis, knowledge discovery, and pattern recognition. Uzma earned her Bachelor of Science (B.S.) degree in Computer Engineering from HITEC University, Taxila, in July 2020, graduating with a CGPA of 3.2/4.0. Her final year project, funded by IGNITE Pakistan, involved developing a machine learning-based clinical decision support system for early diagnosis using physiological data.
š©āš« Experience
Uzma gained valuable practical experience through internships at leading organizations. At IEngineering Corporation, Islamabad, she worked from September 2019 to February 2020 on the āAmerican Traffic Controlā project, performing detailed image annotations to support machine learning applications in computer vision. In JulyāAugust 2019, she interned at Scientific and Engineering Services, Atomic Energy, Islamabad, where she developed a real-time web application for water-level monitoring using load cells and integrated machine learning for predictive analysis. These roles helped her apply theoretical concepts in real-world industrial environments and enhanced her skills in data preprocessing, model integration, and system development.
š Skills
Uzma possesses strong programming capabilities in Python, MATLAB, C++, JavaScript, and SQL. She is experienced with web development frameworks such as Django and Flask, and skilled in using Git for version control. She also has hands-on experience in database management systems like MySQL and SQL, and is proficient in VBA macro programming. Her expertise extends to simulation tools such as MATLAB and practical implementation on embedded platforms including Raspberry Pi, Arduino, FPGA, Node MCU, Jetson Nano/Xavier, and ESP Wi-Fi modules.
š°ļø Research Focus
Uzmaās research interests span a broad range of topics in Artificial Intelligence and data science. Her primary focus lies in deep learning, machine learning, computer vision, and explainable AI. She has worked extensively on medical imaging, EEG signal classification, emotion recognition, and decision support systems. Her published work includes high-impact articles in Q1 journals such as IEEE Access and Engineering Applications of Artificial Intelligence. Her ongoing research involves dynamic data modeling, dominance-based rough set theory, and deep neural networks for clinical and agricultural applications.
š Awards and Honors
Uzma Nawaz has been consistently recognized for her academic and extracurricular contributions. She was awarded IGNITE funding for her final year project in 2020, acknowledging the practical significance and innovation in her work. She also served as an ambassador at the EME NUST Olympiad in 2019 and took on leadership roles as an organizer and technical team member at HITEC Olympiad and the Scientific Society. Her efforts were acknowledged through appreciation awards in various capacities during 2017.
šPublicationĀ