Title:  FarmBeats: AI & IoT for Agriculture
https://cornell.zoom.us/webinar/register/WN_0iAhy0iZQqKmwPOHjS3dlA

Abstract: 

Data-driven techniques can boost agricultural productivity by increasing yields, reducing losses and cutting down input costs. However, these techniques have seen low adoption due to high costs of sensors, manual data collection and limited connectivity solutions. We are developing an end-to-end IoT platform for agriculture called FarmBeats. Our system enables seamless data collection from various sensors, cameras and drones. Our system design explicitly accounts for weather related power and Internet outages, which has enabled six month long deployments in two US farms.

Bios:

Ranveer Chandra is a Principal Researcher at Microsoft Research where he is leading an Incubation on IoT Applications. His research has shipped as part of multiple Microsoft products, including VirtualWiFi & low power Wi-Fi in Windows since 2009, Energy Profiler in Visual Studio, and the Wireless Controller Protocol for XBOX One.  Ranveer is leading the FarmBeats, battery research, and TV white space projects at Microsoft Research. He has published over 80 papers, and filed over 100 patents, with over 80 granted by the USPTO. He has won several awards, including best paper awards at ACM CoNext 2008, ACM SIGCOMM 2009, IEEE RTSS 2014, USENIX ATC 2015, and Runtime Verification 2016 (RV’16), the Microsoft Research Graduate Fellowship, the Microsoft Gold Star Award, the MIT Technology Review’s Top Innovators Under 35, TR35 (2010) and Fellow in Communications, World Technology Network (2012). Ranveer has an undergraduate degree from IIT Kharagpur, India and a PhD from Cornell University.

Sudipta Sinha is a researcher at Microsoft Research. He received his M.S. and Ph.D. degrees in Computer Science from the University of North Carolina at Chapel Hill in 2005 and 2009, respectively. His research interests lie broadly in computer vision and robotics. He works on topics related to 3D scene reconstruction from images and video such as structure from motion, SLAM, stereo, optical flow, scene flow, multi-view stereo, photometric stereo,  image-based localization and 6D object detection and tracking. He is interested in applications ranging from dense 3D scanning, augmented reality (AR) and UAV-based aerial photogrammetry and dense 3D mapping. He was a member of the UNC Chapel Hill team that received the best demo award at CVPR 2007 for one of the first scalable, real-time, vision-based urban 3D reconstruction systems. He has served as a program co-chair for 3DV 2017, was/is an area chair for 3DV 2016, ICCV 2017 and 3DV 2018 and has served on program committees at computer vision conferences. He is also an associate editor for the Computer Vision and Image Understanding (CVIU) Journal.