Abstract: Data-driven techniques help boost agricultural productivity by increasing yields, reducing losses and cutting down input costs. However, these techniques have seen sparse adoption owing to high costs of manual data collection and limited connectivity solutions. In this talk we will describe our innovations that leverage Internet of Things, Artificial Intelligence, and Edge Compute in the Farm and Space, to help make affordable digital agriculture solutions. We will also present our product based on this research, which is in preview, and can be used by partners to build their digital agriculture solutions.

Bio: Ranveer Chandra is the CTO of Agri-Food, Managing Director of Research for Industry, and Head of Networking Research at Microsoft Research in Redmond, WA. Ranveer’s research has shipped in multiple Microsoft products, including XBOX, Azure, Windows, and Visual Studio. He started the FarmBeats project in 2015, which shipped as a Microsoft product in 2019. Bill Gates has featured Ranveer’s FarmBeats work on GatesNotes and was invited to present to the Secretary of Agriculture. He has also presented to the FCC Chairman on TV White Spaces. Ranveer has published over 100 research papers and has over 150 patents granted by the USPTO. He is an IEEE Fellow, and has won several awards, including “Best Paper in Computer Science” awards at conferences, as well as the MIT Technology Review “Top Innovators Under 35” award in 2010. He was recently recognized by Newsweek magazine as one of “America’s 50 most disruptive innovators in 2021”. Ranveer has an undergraduate degree from IIT Kharagpur, India, and a Ph.D. in Computer Science from Cornell University.

Abstract: Advances in neural network revolutionized modern machine intelligence, but important challenges remain when applying these solutions in IoT contexts; specifically, in cost-sensitive applications on lower-end embedded devices. The talk discusses challenges in offering real-time machine intelligence services at the edge to support applications in resource constrained environments. The intersection of IoT applications, real-time requirements, and AI capabilities motivates several important research directions. For example, how to support efficient execution of machine learning components on low-cost edge devices while retaining inference quality and offering confidence estimates in results? How to reduce the need for expensive manual labeling of IoT application data? How to improve the responsiveness of AI components to critical real-time stimuli in their physical environment? How to prioritize and schedule the execution of intelligent data processing workflows on edge-device GPUs? How to exploit data transformations that lead to sparser representations of external physical phenomena to attain more efficient learning and inference? The talk discusses recent advances and presents evaluation results in the context of different real-time edge AI applications.

Bio: Tarek Abdelzaher received his Ph.D. in Computer Science from the University of Michigan in 1999. He is currently a Sohaib and Sara Abbasi Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign. He has authored/coauthored more than 300 refereed publications in real-time computing, distributed systems, sensor networks, and control. He serves as an Editor-in-Chief of the Journal of Real-Time Systems for over 10 years, and has served as Associate Editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Embedded Systems Letters, the ACM Transaction on Sensor Networks, and the Ad Hoc Networks Journal, among others. Abdelzaher's research interests lie broadly in understanding and influencing performance and temporal properties of networked embedded, social and software systems in the face of increasing complexity, distribution, and degree of interaction with an external physical environment. Tarek Abdelzaher is a recipient of the IEEE Outstanding Technical Achievement and Leadership Award in Real-time Systems (2012), the Xerox Award for Faculty Research (2011), as well as several best paper awards. He is a fellow of IEEE and ACM.

Abstract: In this paper we will articulate the motivation for and the characteristics of the end-to-end computing, communications, and storage infrastructure which will ideally host distributed applications for Cyber-Physical Systems found in modern Automotive, Transportation, Industrial Automation deployments, among other verticals. We will emphasize the necessary decoupling between the infrastructure and the applications running over it, in the spirit of the Software-Defined paradigms which are progressively adopted, from networking to computing to storage. We will specifically focus on the technologies required at the critical boundary between physical systems and cyber space, between Operational technologies (OT) and Information Technologies (IT), where a challenging cultural and technological convergence needs to fully unfold. The success of the Digital Transformation rests in a significant way on this complex convergence process.

Bio: Flavio Bonomi is a Technology or Board of Director Advisor for multiple startups in the Edge Computing space, where he is considered one of the pioneers. Flavio is focusing on the positioning of Edge Computing at the the critical boundary where requirements typical of embedded computing (security, real-time and safety) are incorporated into modern networked, virtualized, containerized and intelligence rich computing.

Flavio Bonomi Co-founded Nebbiolo Technologies, a Silicon Valley startup delivering the first complete Edge Computing software platform for the Industrial Automation market, opening the path towards what is now called Software Defined Manufacturing.

He spent 14 years at Cisco Systems as a Cisco Fellow and VP, where he led the vision and technology initiative for Cisco’s forward-looking initiatives. Prior to Cisco, Flavio spent 5 years with Silicon Valley startups and 10 years at AT&T Bell Labs. During his career, Flavio directly contributed to fundamental technology inflections in the fields of Data Networking, Computing, and Industrial IoT.

Flavio has PhD and MS degrees in Electrical Engineering from Cornell University and a Laurea Summa Cum Laude Electrical Engineering from University of Pavia, Italy.