Shayan Aziznejad

Shayan Aziznejad has a diverse work experience in the field of machine learning and data science. Shayan is currently a Machine Learning Certification Researcher at Daedalean AI. Prior to this, they worked as a Research Assistant at EPFL, where they developed a theoretical framework for optimization problems in data science. Shayan utilized this framework to create numerical schemes for machine learning and signal reconstruction. Some of their notable contributions include a regression method using sparse kernels, a Pytorch module for training activation functions of deep neural networks, and a direct approach for learning input-output mapping of ReLU neural networks. Shayan also had previous experience as a Summer Research Intern at EPFL.

Shayan Aziznejad started their education journey in 2012 at Sharif University of Technology, where they pursued a Bachelor of Science degree in Electrical and Electronics Engineering. Simultaneously, they also studied Mathematics as part of their Bachelor of Science degree program at the same university. Shayan completed both programs in 2017.

Afterward, Shayan Aziznejad continued their academic pursuits and joined EPFL (École polytechnique fédérale de Lausanne) in 2017. Shayan enrolled in the Doctor of Philosophy (PhD) program in Electrical and Electronics Engineering and is expected to complete their degree in 2022.

In addition to their formal education, Shayan Aziznejad has obtained several certifications. In August 2022, they received certifications in Problem Solving, Python, and SQL from HackerRank.