Description
About
NCI Presents: TechTake is an exciting opportunity for international computational and data science leaders to discuss and demonstrate how technology supports research.
Taking place on the last Tuesday of each month, this event will run online in order to reach diverse audiences across the globe and from all fields.
TechTake is designed to prompt engaging and in-depth conversations about both the current state and potential futures of technology to broaden and deepen understanding.
TechTake, with MathWorks: Deep Learning Workflows on clusters and clouds
See how engineers and scientists who work on AI projects know when to move some, or all, of their development to clusters and clouds. You’ll also learn about MathWorks’ cloud options and the advantages they offer across the development stages of a system based on AI. Through a case study of training, tuning, and deploying a semantic segmentation model, you’ll hear about the following topics:
- Managing data efficiently
- Provisioning MATLAB on AWS®—ready-made for deep learning with NVIDIA® GPUs
- Scaling to GPU cloud compute clusters
- Deploying models for unlimited accessibility
You’ll also learn about the following capabilities:
- Using MATLAB Parallel Server™ on AWS for multi-GPU computing
- Deploying the MATLAB Production Server™ pay as you go (PAYG) environment on Azure®
Ms Louvere Walker-Hannon
Louvere Walker-Hannon is a MathWorks Application Engineering Team Leader, who provides direction on technical workflows for various applications and leads a team of other Application Engineers. She has a bachelor’s degree in Biomedical Engineering and a master’s degree in Geographic Information Technology with a specialization in Remote Sensing. Based on her fields of study, she assists with the following topics image processing, computer vision, machine learning, deep learning, geospatial analysis, parallel computing in clusters, deployment in the cloud/edge devices, and data analytics when discussing technical workflows. Louvere has worked in three different engineering roles throughout her 20 plus year career while at MathWorks.
Dr Heather Gorr - MathWorks
Heather Gorr holds a Ph.D. in Materials Science Engineering from the University of Pittsburgh and a Masters and Bachelors of Science in Physics from Penn State University. Since 2013, she has supported MATLAB users in the areas of mathematics, data science, deep learning, and application deployment. She currently acts a Senior Product Marketing Manager for MATLAB, leading technical marketing content in data science, AI, deployment, and advanced MATLAB and Python programming. Prior to joining MathWorks, she was a Research Fellow, focused on machine learning for prediction of fluid concentrations.
Mr Peter Brady
Peter Brady is an application engineer with MathWorks striving to accelerate our customer’s engineering and scientific computing workflows across maths, statistics, and machine learning. Prior to joining MathWorks, Peter worked in computational fluid and thermodynamics as well as high-performance computing for a number of defence and civil contractors as well as a few universities. He has worked in fields as diverse as cavitation, wave/turbulence interactions, rainfall and runoff, nano-fluidics, HVAC and natural convection including scale out cloud simulation techniques. Peter holds a Doctorate in free surface computational fluid dynamics and a Bachelor of Civil Engineering both from the University of Technology Sydney.
Mr Bradley Horton
Bradley Horton is a member of the Academic Customer Success team at MathWorks, helping faculty members better utilize MATLAB and Simulink for education and research. Bradley has supported and consulted for clients on projects in process control engineering, power systems simulation, military operations research, and earthquake impact modelling. Before joining MathWorks, Brad spent 5 years as a systems engineer with the Defence Science & Technology Organisation (DSTO) working as an operations research analyst. Bradley holds a B.Eng. in Mechanical engineering and a B.Sc. in Applied mathematics.
