| Ashutosh Saxena | |
| Nationality: | American |
| Work Institution: | Co-founder and CEO of Caspar.AI |
| Field: | Artificial intelligence (machine learning) |
| Occupation: | CEO, Caspar.AI |
| Thesis Year: | 2009 |
| Doctoral Advisor: | Andrew Ng |
Ashutosh Saxena is an Indian-American computer scientist, researcher, and entrepreneur known for his contributions to the field of artificial intelligence and large-scale robot learning. His interests include building enterprise AI agents and embodied AI. Saxena is the co-founder and CEO of Caspar.AI, where generative AI parses data from ambient 3D radar sensors to predict 20+ health & wellness markers for pro-active patient care. Prior to Caspar.AI, Ashutosh co-founded Cognical Katapult (NSDQ: KPLT), which provides a no credit required alternative to traditional financing for online and omni-channel retail. Before Katapult, Saxena was an assistant professor in the Computer Science Department and faculty director of the RoboBrain Project (a large-scale AI model for robotics) at Cornell University.[1]
In 2009, with artificial intelligence pioneer Andrew Ng as his advisor, Saxena received both his M.S. and Ph.D. in computer science with an emphasis on artificial intelligence from Stanford University.[2] Saxena received his bachelor's degree in electrical engineering from the Indian Institute of Technology, Kanpur in 2004.
Saxena was the chief scientist of New York-based Holopad, where he worked with Steven Spielberg's team to create walkthroughs and 3D experiences for his movie TinTin.[3] His past experiences include building acoustic AI models at Bose Corporation. Once Ashutosh completed his undergraduate degree, he became a researcher at the Commonwealth Scientific and Industrial Research Organization, where he developed AI models for medical devices.
Before Caspar, Saxena pursued other entrepreneurial ventures, such as ZunaVision, an artificial intelligence startup he co-founded with Andrew Ng that uses AI to embed advertising space within videos. Ashutosh served as the CTO of ZunaVision from 2008 to 2010.[4] After ZunaVision, Saxena co-founded Cognical Katapult, which provided financing solutions to nonprime and underbanked consumers powered by artificial intelligence. From 2014 to 2016, Saxena served as the faculty director of the RoboBrain project, which was a joint venture that he started between Stanford University, Cornell University, Brown University, and the University of California, Berkeley that made a knowledge engine for robots.[5] [6]
Saxena co-founded Brain of Things in 2015 with David Cheriton, who serves as chief scientist, and was listed as the fastest growing private company reaching an annual recurring revenue of $8 million in three years. It has been widely covered in several outlets including Forbes Japan, and MIT Technology Review.[7]
Saxena's work on deep learning won test of time award in 2023 by Robotics Science and Systems.[8] Ashutosh has been recognized for his work by receiving the Alfred P. Sloan Fellow in 2011, Google Faculty Research Award in 2012,[9] Microsoft Faculty Fellowship in 2012, NSF Career award in 2013,[10] One of the Eight Innovators to Watch by the Smithsonian Institution[11] in 2015, and received TR35 Innovator Award by MIT Technology Review[12] in 2018. He was named by San Francisco Business Times as a 40 under 40 young business leader.[13]
Saxena has authored over 100 published papers in the areas of large-scale robot learning and artificial intelligence, with 20,000+ citations. His work in the fields of computer vision and deep learning have been featured in press releases and academic journal reviews.
Ashutosh's early work includes the Stanford Artificial Intelligence Robot (STAIR),[14] an AI models that enables to perform tasks such as unload items from a dishwasher, which was covered on the front-page of New York Times. His work on Make3D, was the first work that estimated 3D depth from a single still image.[15] At Cornell University, Ashutosh led the Robot Learning Lab, which used a machine learning approach to train robots to perform tasks in human environments such as generalizing manipulation in 3D point-clouds where robots learn to transfer manipulation trajectories to novel objects utilizing a large sample of demonstrations from crowdsourcing.