Dinner, Networking & Presentation on the Cognitive Telescopic Network

A Great Evening with Food, Drink and Networking

5:30 PM   Network with your peers

6:00 PM  Presentation

7:00 PM  Dinner and More Networking

Menu:  Appetizers - Antipasto Salad, Shrimp Calypso and Charred Crudite', Pop and Water

Entrees; - Beef Stroganoff Over Egg Noodles, Basted Cod with Autumn Succotash, Wild Mushroom Rissoto, Garlic Mashed Potatoes and Heirloom Caesar Salad

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Topic:  The Cognitive Telescopic Network (IoT / AI Case Study)

Cognitive Telescopic Network (CTN): Telescopic follow-up of transient astronomical events is one of the most desirable and scientifically useful activities in modern observational astronomy. From a scientific perspective, pinpointing a transient event can be essential for discovering more about the source, either by directing more powerful telescopes to observe, or to maintain a near continuous record of observations as the transient evolves. Very often transients are poorly localized on the sky and telescopes have a limited field of view – thus pin-pointing the transient source is often a daunting task. Perhaps most importantly, the number of large telescopes on the planet is small, and they cannot be commandeered to randomly search for every transient of interest.

Modern sub-meter class telescopes, of the sort often owned by universities and increasingly by amateurs, if properly directed, could play an important role in enabling transient follow-up. Modern technology gives them the ability to be automated and controlled remotely and to make useful imaging observations that will enable follow-up work by other, larger telescopes. The Cognitive Telescope Network (CTN) will be a framework that takes notifications of transient events and intelligently instructs a network of sub-meter telescopes mapped into a grid and observe a large region of the sky that likely contains the transient event, based on the geo-location, weather and properties of the individual telescopes. The goal of CTN is to collect the data from this network of small telescopes, evaluate and classify that data to identify the most likely candidates for the transient being hunted and deliver the results to the astronomer community for further analysis by larger telescopes for directed and focused observations.

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Speaker:  Arunava (Ron) Majumdar

Ron is a Watson and Cloud architect with over 20 years of experience in Software design and development. He leads the Asset Portfolio Strategy for the IBM Watson and Cloud Platform and is the lead for the Chicago Center for Advanced Studies. He has been involved with large scale design, architecture and implementation for IBM clients, helping them successfully through the project lifecycle. He has architected High Availability and Disaster Recovery solutions with IBM integration products and worked on performance testing and securing client environments.

Ron started as a software engineer working with Object Oriented Programing languages, Middleware integration technologies and Relational Databases. He is currently working on Watson services, Internet-of-Things, Micro-services, API Economy, Hybrid Integration and Pattern-based automation. He is deeply involved with moving workloads to the cloud and Application Modernization. Ron has several patents and published assets to his credit and is collaborating with Research faculty and Universities on innovative ideas and their implementations with emerging technologies. He is also leading several efforts for a comprehensive innovation strategy for IBM in the Greater Chicago area.