"Friction in data sharing is a large challenge for large scale machine learning. Emerging technologies in domains such as biomedicine, health and finance benefit from distributed deep learning methods which can allow multiple entities to train a deep neural network without requiring data sharing or resource aggregation at one single place. The talk will explore the main challenges in data friction that make capture, analysis and deployment of ML. The challenges include siloed and unstructured data, privacy and regulation of data sharing and incentive models for data transparent ecosystems. The talk will compare distributed deep learning methods of federated learning and split learning. Our team at MIT has pioneered a range of approaches including automated machine learning (AutoML), privacy preserving machine learning (PrivateML) and intrinsic as well as extrinsic data valuation (Data Markets). One of the programs at MIT aims to create a standard for data transparent ecosystems that can simultaneously address the privacy and utility of data.
Bio: Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on AI and Imaging for health and sustainability. They span research in physical (e.g., sensors, health-tech), digital (e.g., automated and privacy-aware machine learning) and global (e.g., geomaps, autonomous mobility) domains. He received the Lemelson Award (2016), ACM SIGGRAPH Achievement Award (2017), DARPA Young Faculty Award (2009), Alfred P. Sloan Research Fellowship (2009), TR100 Award from MIT Technology Review (2004) and Global Industry Technovator Award (2003). He has worked on special research projects at Google [X], Apple Privacy Team and Facebook and co-founded/advised several companies. Project page [ Ссылка ]" Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on Machine Learning and Imaging for health and sustainability. They span research in physical (e.g., sensors, health-tech), digital (e.g., automated and privacy-aware machine learning) and global (e.g., geomaps, autonomous mobility) domains.
In his recent role at Facebook, he launched and led innovation teams in Digital Health, Health-tech, Satellite Imaging, TV and Bluetooth bandwidth for Connectivity, VR/AR and ‘Emerging Worlds’ initiative for FB.
At MIT, his co-inventions include camera to see around corners, femto-photography, automated machine learning (auto-ML), private ML, low-cost eye care devices (Netra,Catra, EyeSelfie), a novel CAT-Scan machine, motion capture (Prakash), long distance barcodes (Bokode), 3D interaction displays (BiDi screen), new theoretical models to augment light fields (ALF) to represent wave phenomena and algebraic rank constraints for 3D displays(HR3D).
Before MIT, he co-invented techniques for AR, Computational Photography, Shader Lamps (projector-AR), composite RFID (RFIG), multi-flash non-photorealistic camera for depth edge detection, quadric transfer methods for multi-projector curved displays.
He received the Lemelson Award 2016 and ACM SIGGRAPH Achievement Award 2017, Technology Review TR100 award 2004 (which recognizes top young innovators under the age of 35), Global Indus Technovator Award (top 20 Indian technology innovators worldwide) 2003, Alfred P. Sloan Research Fellowship award 2009 and Darpa Young Faculty award 2010. Other awards include Marr Prize honorable mention 2009, LAUNCH Health Innovation Award, presented by NASA, USAID, US State Dept+ NIKE, 2010, Vodafone Wireless Innovation Award (first place) 2011.
His work has appeared in NYTimes, CNN, BBC, NewScientist, TechnologyReview and several technology news websites..
His invited and keynote talks include TED, Wired, TEDMED, Darpa Wait What, MIT Technology Review, Google SolveForX and several TEDx venues.
His co-authored books include Spatial Augmented Reality, Computational Photography and 3D Imaging (both under preparation).
He has worked on special research projects at Google [X] and Facebook and co-founded/advised several startups. He launched REDX.io, a platform for young innovators to explore AI-for-Impact. He frequently consults for dynamic organizations to conduct ‘SpotProbing’ exercises to spot opportunities and probe solutions.
He holds 82+ US patents.
[Personal webpage [ Ссылка ]] [ Ссылка ]
Specialties: Health-tech, Digital health, Computer Vision, Machine Learning, Imaging, Optics, Displays, Sensors, Medical Imaging, RFID, Projector, VR-AR, Computation Photography, HCI, Tech-Transfer, Ventures, Startups This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at [ Ссылка ]
