About

Sara Bahramian is an analytical research scientist with a strong background in statistical inference, machine learning, computer vision, and signal/image processing. She has hands-on experience with computer vision applications and machine learning using large datasets. Sara is currently working as a Machine Learning Engineer at Apple, where she is responsible for developing and implementing machine learning algorithms to improve the performance of Apple's products. Prior to joining Apple, Sara worked at Wolfram as a Researcher and Research Intern. During her time at Wolfram, she implemented and improved widely used image quality assessment measures using machine learning methods, namely SVM and neural networks. She also used cross-validation methods on a set of 1,700 distorted images to tune learning parameters. Sara's doctoral research at the University of Illinois focused on signal processing and medical imaging. She developed new algorithms for medical image reconstruction and analysis, and her work has been published in several high-impact journals. Sara has a strong programming background and is proficient in Python, C++, and MATLAB. She also has experience working with ResNet, neural networks, and SVM. Sara's technical skills and extensive experience make her a valuable asset to any team working on machine learning, computer vision, and image processing projects. In total, Sara has 11.54 years of relevant experience in the field of research and machine learning. She holds a Doctor of Philosophy (PhD) in Signal Processing and Medical Imaging from the University of Illinois, a Master of Science (MS) in Electrical and Electronics Engineering from Sharif University of Technology, and a Bachelor of Science (BS) in Electrical and Computer Engineering from the University of Tehran.