» Book your place at this event
Join us for the second Machine Intelligence Garage meetup where you’ll have the chance to meet experts from Cray and EPCC.Focused on High Performance Computing, this meetup will give you the opportunity to learn more about two supercomputing powerhouses: EPCC and Cray.
Who should attend?
Register to attend if you are:
- Company developing AI-enabled products
- A researcher in the field of AI
- Data scientist
- Interested in AI
Machine learning models are being used in an increasing number of applications, meanwhile their complexity and the amount of data for their training is growing rapidly.
High performance computing (HPC) is providing ways of overcoming these barriers. This meetup will cover how HPC can help advance AI development.
A little more info
- 6 pm – Registration, drinks and pizza
- 6.30 pm – EPCC – Speaker Adrian Jackson
- 7.15 pm – Cray – Speaker Rajesh Anantharaman
- 8 pm – Drinks and networking
- 9 pm – Close
Head of AI Product Strategy, Cray
Rajesh heads AI Product Strategy at Cray and is responsible for AI product roadmap, customer engagement and partner strategy. Previously, he led AI strategy, product and data science at Samsung for an AI lab focused on Deep Reinforcement Learning and applications to multiple verticals including Manufacturing and Marketing. Rajesh also held multiple positions at NVIDIA including Product Marketing for the Automotive Business and Engineering for High Speed Mixed Signal IP. He has an MBA in Technology Innovation & Marketing from UC Berkeley Haas School of Business and a Masters in Electrical Engineering from Stanford.
Research Architect, EPCC
Adrian Jackson is a research architect at EPCC, where he works with academics and businesses on high performance and parallel computing in addition to large scale data analysis. He spends most of his time trying to improve time to solution for a wide range of scientific problems, from computational fluid dynamics, to simulations of the heart, as well as researching the best methods for exploiting new hardware, such as many core processors, or non-volatile, persistent memory. Recently he has been actively working with industry on data analysis and feature extraction for large scale datasets.