About

Apaar Sadhwani is a highly experienced Machine Learning Researcher and Engineer with a passion for solving interdisciplinary problems. With 13 years of experience across various fields, including Computer Vision, Quantitative Finance, Healthcare, and Operations, Apaar has a proven track record in product-driven research. His work has been covered by several media outlets, highlighting his innovative approach to solving complex challenges. Apaar's educational background is impressive, with a PhD in Applied Mathematics and Machine Learning from Stanford, a Masters in Quantitative Finance and Operation Research from Stanford, and a B.Tech in Mechanical Engineering from IIT Delhi. This extensive education has given him a deep understanding of various technical fields and has helped him develop a unique interdisciplinary approach to problem-solving. Currently, Apaar is working as a Software Engineer and Technical Lead for Health AI at Google. He is responsible for leading multiple ML projects, including developing computer vision models for disease diagnosis, biomarker prediction, and survival prediction on ultra-large medical images. Additionally, he conducts statistical analyses and designs studies to evaluate their efficacy. Prior to his current role, Apaar worked as a Research Software Engineer at Google Brain, where he conducted deep learning research in various areas, including reinforcement learning under adversarial settings and for routing, speech-to-speech translation, and computer vision for medical applications. He also worked as a Summer Intern at McKinsey, where he worked with the Operations Practice group on supply chain and inventory optimization. Apaar's technical skills include AI, Research Scientist, Senior Software Engineer, Software Engineer, ML, Computer Vision, Reinforcement Learning, and Deep Learning. With 7.69 years of relevant experience, Apaar is a highly skilled and experienced professional with a passion for solving complex problems.