About

Ambuj Arora is an Applied Scientist at Amazon with a passion for training machines to be smarter than himself. With 4.61 years of relevant experience, Ambuj has a wealth of knowledge in the field of machine learning and deep learning. He is currently pursuing an MS in Computer Science from the University of Utah while working as a Machine Learning Researcher at the Indian Institute of Technology in Delhi. Ambuj's portfolio can be found at http://ambujarora.com/, and it showcases his impressive work in the field. One of his notable collaborations was with AIIMS, Delhi, India, where he helped develop a mobile platform for the diagnosis of cervical cancer. Using ResNet18 in Keras, Ambuj classified cervical images into severely infected, infected, and normal, helping around 60% of cervical cancer patients in Orissa, India. In his past roles, Ambuj has worked at companies such as Algoscale and Intel, where he developed an Auto-Deep Learning pipeline that allowed non-technical users to train machine learning models using Keras. He also built an automated Cryptocurrency Trading Bot on Bittrex exchange with a 10% profit on each transaction based on historical features and financial indicators using XGBoost in Python. At Intel, Ambuj optimized Facebook's RetinaNet model with 2.1x improvement over native TensorFlow version using parallel programming in both C++ and Python. He also worked on improving training performance of Google's TensorFlow for Intel scalable CPUs using MKL library on models like ResNet50, RetinaNet, MaskRCNN, Transformer, and BERT-Large. Ambuj's education history includes a Bachelor of Technology in Electronics and Communications Engineering from Maharaja Agrasen Institute of Technology and a Master of Science in Computer Science from the University of Utah, which he is expected to complete in 2021. Ambuj's tech stack includes expertise in Research Scientist, Software Engineer, Computer Vision, Deep Learning, TensorFlow, BERT, and Python. Despite his impressive accomplishments, Ambuj remains humble, acknowledging that he fails continuously in his pursuit of training machines to be smarter than him.