About

Pulkit Agrawal is a Senior Machine Learning Engineer at Apple, with over 5 years of relevant experience in the field of ML and deep learning. His current role involves working on hardware aware training of deep learning models, which is crucial for improving the efficiency and effectiveness of machine learning algorithms. Prior to this, he worked on distributed training of machine learning models and data systems for machine learning. Pulkit's academic background includes a Master of Science (MS) in Electrical Engineering from Stanford University and a Bachelor of Technology (B.Tech) in Electrical Engineering (Honors) with a Minor in Computer Science from the Indian Institute of Technology, Bombay. During his time at Stanford, he worked as a Graduate Research Assistant in Prof. Andrew Ng's Stanford ML group, where he developed deep learning models for tracking and re-identifying people in retail-store videos to quantify customer engagement with products. He also worked as a Research Intern at the Massachusetts Institute of Technology in Prof. Anantha Chandrakasan's Energy Efficient Circuits and Systems group, where he designed a low power hardware accelerator for reconstructing light-fields from sparse samples. Pulkit's tech stack includes ML, Deep Learning, and Research Scientist, which are all essential skills for his current role at Apple. His past experiences at Stanford and MIT have also equipped him with a strong foundation in hardware design and development, which is crucial for his work on hardware aware training of deep learning models. Overall, Pulkit Agrawal is a highly skilled and experienced Senior Machine Learning Engineer with a strong academic background and a diverse range of experiences in the field. His expertise in ML, deep learning, and hardware design make him a valuable asset to any team working in this space.