Google Research

Reza Ghanadan


I joined Google AI in 2017 and am currently leading the Cloud AI research and strategic technology programs. I received my Ph.D. in Electrical Engineering from University of Maryland College Park. I also hold an Executive MBA from NYU, MS in Electrical Engineering, and two B.S. degrees in EE and in Physics (both Summa Cum Laude). Prior to joining Google, I held several management and technical leadership positions in high-tech research organizations in DARPA, Boeing Research & Technology, BAE Systems, and AT&T Bell Laboratories. At DARPA, I created and led several large-scale multidisciplinary teams with research and development programs in intelligent systems and devices, machine learning, and data science. I directed execution of 5+ complex and multidisciplinary AI/ML and data science initiatives in excess of $120M+ funding with teams of 200+ scientists and engineers, including Fundamental Limits of Learning (FUN LOL) program, Simplifying Complexity in Scientific Discovery (SIMPLEX), Mathematics of Sensing, Exploration and Exploitation; GRAPHS; Compressive Sensing; and Collaborating with Machines. Through these programs, we demonstrated several foundational applications of AI and ML in a number of complex domains including video intelligence, robotics, IoT, neuroscience, personalized medicine, knowledge extraction, finance, material science, 3D printing, human-machine interaction, social-cognitive systems, and anthropology. In 2015 our research impact showing novel AI techniques demonstrating “machines that learn tasks from watching YouTube videos” ranked in the Top 10 most popular DARPA programs based on nearly 20 million website visits. Prior to DARPA, I was a Boeing Technical Fellow, where I led a team of 50+ software and systems engineers, and successfully delivered a large-scale distributed mobile ad-hoc networked system of robots, UAV’s, and mobile sensors. I received Boeing Technology Innovation Award for inventing an efficient heterogeneous network access design optimized for large-scale distributed mobile network applications. At BAE systems, I was an Engineering Fellow and Technical Director. I managed a research organization focused on algorithms and information networks, and received BAE’s Gold Chairman’s Award for Innovation for inventing and demonstrating an adaptive mobile networking protocol optimized for real-time adaptation to traffic and high dynamic variations in network topology. I was also a founding team member of Flarion Technologies, later acquired by Qualcomm for $800M, and a member of the technical staff at AT&T/Lucent Bell Laboratories, where I was a group leader for research focused on efficient optimization and adaptive algorithms. I have 18 patents awarded and 36 peer-reviewed publications. Most recently I was an invited speaker at the workshop on Explainable AI at the Pacific Symposium for Biocomputing, 2018. My research areas of interest are: Intelligent Systems and Devices, Social-Cognitive Systems, Data Science, and applications of these in science, engineering, and a wide range of products. Most recently, I have been investigating methods for creating robust and reliable AI systems for real-world applications, explainable AI technology, and augmented learning.

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