Guest Speakers and subjects:
- Nathan Halliday, Hills Road Sixth Form College, Cambridge UK - Watch Recording
HPCC Systems Intern 2020
The Parallel Workflow Engine
Nearly all ECL queries pass through the workflow engine. The workflow engine executes items (sections of the query) sequentially, but there are many situations where it could be beneficial to process items in parallel. This presentation explores the changes that need to be made to the workflow engine, to give an improvement in the running time of each workunit.
Nathan Halliday is a high school graduate who has just completed his A' Levels in the UK. He will start university later this year, having received an offer to study mathematics at St Anne's College, University of Oxford. He is interested in quantum computing, which may influence his future career path.
Jefferson Mao, Lambert High School, Suwannee, Georgia, USA - Watch Recording
HPCC Systems Intern 2020
How to Establish HPCC Systems on the Google Cloud Platform
This presentation covers the steps required to use HPCC Systems on the Google Cloud platform and how he designed a web application for creating a new HPCC Systems cluster on this cloud service.
Jefferson is a 12th grade high school student who has his eyes set on studying business at the University of Pennsylvania in the future. He is already something of an entrepreneur having founded Philosophy Robotics LLC, a software company that produces software for resellers such as automated checkout services, reselling tools and web scraping applications.
He heard about the HPCC Systems intern program when taking part in CodeDay Atlanta, a 24 hour event where student programmers and designers get together to create apps and games. LexisNexis Risk Solutions Group is a sponsor of this annual event which was hosted at our office in Alpharetta in 2019 and Jefferson was a Best in Show prize winner.
Jack Fields, American Heritage School, Delray Beach, Florida, USA - Watch RecordingHPCC Systems Intern 2020
Using the HPCC Systems Generalized Neural Network (GNN) Bundle with TensorFlow to Train a Model to Find Known Faces Leveraging the Robotics API
A previous intern project by American Heritage High School (AHS) student Aramis Tanelus, developed an ROS package to collect data from various sensors and spray it to an HPCC Systems Thor cluster. Aramis also created some ECL code to graph some data with HPCC Systems libraries. (Watch Recording / View Slides / View Poster)
In this presentation, Jack talks about how he has taken this work a step further. His project involves processing some images collected by the Robotics program developed by AHS, with the aim of training a model with the HPCC Systems Machine Learning (ML) General Neural Network (GNN) which support TensorFlow and then applying it to new data.
Jack is a 12th grade high school student who has developed an impressive amount of experience in Java, Python and C++ from the robotics and computer science courses provided by his school and his involvement in the Stallion Robotics Team 5472, run by Tai Donovan (Robotics Program Director and Instructor). If you have attended one of our Community Day Summits in recent years or followed us on social media, you may have seen demonstrations of the impressive robots Tai and the team have built from the ground up over the years. Jack is the Director of Programming for the team, who are currently working on an Autonomous Security Robot (Watch Demo) that can recognise potential risks on a school campus that might otherwise be missed by the human eye. Using object and facial recognition, they can capture faces and recognise them with 93% accuracy using Tensorflow. He submitted a poster into our 2019 Technical Poster Contest showcasing the progress the team has on made on the robot (View Poster / Read Extract).