Najiya Omar , PhD, P.Eng

Green Power Labs Inc - Optimized the data downloads by writing a code utilizing “GOES-2-Go” and s3fs python libraries to access the NOAA’s GOES-16 geostationary satellite dataset that is stored as netCDF files in Amazon Web Services (AWS)/ S3 Bucket. - Automated downloaded GOES-16 satellites images from different channels of the Advanced Baseline Imagery (ABI) instrument with different temporal and spatial resolutions, and map projections. - utilized the GOES-16 cloud products including Cloud Optical Depth , Clear Sky Mask , Cloud-top products such as Cloud-Top Height, Cloud-Top Pressure , Cloud-Top Temperature, Cloud Top Phase, and Cloud Particle Size for proposing Could Index (CI) dataset. - Applied a map projection that positioned all the extracted variables from the ABI cloud products to the same projected grids. - Selected appropriate thresholds and computing measurements for CI and prepared the obtained CI measurements to be used by the CI predictive algorithm. - Employed the PVlib python package for calculating cloud irradiance components.

Provided hands-on instruction and support to undergraduate students in Electric Circuits and Computer Programming courses. I demonstrated laboratory instruments and helped students understand theoretical concepts and safe laboratory practices. I also marked and corrected assignments and lab reports, troubleshooting programming code to identify and resolve problems. Additionally, I supervised a large group of students and provided constructive feedback, as well as invigilated and marked final tests and provided detailed reports for grading and record-keeping purposes.

Developed and executed experimental designs in Proteus DS simulation using Matlab, resulting in improved data accuracy and efficiency. Additionally, I conducted complex analyses of Remotely Operated Vehicle (ROV) data to determine safe marine operation speed ranges and ROV thruster capacity requirements using advanced graphing techniques and data visualization tools. I also collaborated with team members to improve tool functionality, designed and maintained research databases and information systems, and contributed to the publication of research findings in academic journals and conferences.

Collaborated with a multidisciplinary team to successfully deliver government-funded energy projects with a budget of approximately $500,000. I leveraged sophisticated Artificial Neural Network (ANN) applications to accurately predict power consumption and applied Machine Learning (ML) algorithms to classify datasets for pattern recognition tasks. I also managed the financial plan for each project, optimizing resource allocation and budgeting to meet project goals, while attending regular meetings to provide up-to-date insights and outcomes.

• Best Oral Presentation in IEEE-CEEPE (2021) • FGS Allocation – Scholarship (2019, 2021) • Bruce and Dorothy Rosetti Engineering Research (2020) • Engineering Excellence Award Scholars (2019, 2020)

• Faculty Poster Award Second Prize, ECE Graduate Conference (2016) • FGS Allocation – Scholarship (2015)