Morafah

Mahdi Morafah is a graduate student at the University of California, San Diego, specializing in Machine Learning and Data Science. He is actively involved in research on topics like Federated Learning, Decentralized Optimization, and Object Recognition in Autonomous Driving.

His expertise includes programming languages such as Python, C/C++, and MATLAB, along with popular libraries like TensorFlow, PyTorch, and scikit-learn. He also possesses strong mathematical acumen in areas like Statistics, Linear Algebra, Convex Optimization, and Data Analysis.

Mahdi demonstrates advanced skills in Machine Learning, working with algorithms like Linear Regression, Logistic Regression, and Support Vector Machines. He is also proficient in Deep Learning techniques, including Neural Networks, CNNs, and RNNs, with experience in areas like Transfer Learning and Hyperparameter Tuning.

Additionally, he excels in Object Recognition technologies like R-CNN, Fast R-CNN, and YOLO, as well as in Signal Processing, particularly in areas such as Compressed Sensing, DT Filters, and Wavelets.

With excellent communication abilities in English and Persian, Mahdi has a proven academic track record, achieving a 4/4 GPA at UCSD and securing the top rank during his Bachelor's in Electrical Engineering at Amirkabir University of Technology.

His professional experiences include roles as a Research Assistant at Qualcomm Institute, a former Teaching Assistant at UCSD, a Machine Learning Engineer at Tesla, and positions in various prestigious research institutions and organizations.