Rudolph Pienaar, PhD

 

Technical Director
Fetal-Neonatal Neuroimaging and Developmental Science Center

Staff Scientist
Boston Children's Hospital
 

PhD Electrical/Electronic Engineering

• Reinforcement Learning in biomechanical control system (self adapting posture balancer)

 

PostDoc

• MRI processing                                                                     
  • Some pre-processing
  • Mostly post-processing

 

 

• 10+ years experience in team leading
  • Lead several small, agile software teams
  • Project development cycle in 6 month periods
• Web-based visualization
  • xtk
  • ami
• Remote computation/coordination
  • CHRIS 

• 5+ years in industry/acadmia collaborative projects
  • Hospital scanner QI
  • Cloud-based radiology
    • Massachusetts Open Cloud
  • GE Healthcare/cloud
    • Image data to/from GE cloud
    • Normative image comparison
  • NVidia
    • ​​Academic research partner
      • Hardware support for in-house cluster

• 5+ years in industry/acadmia collaborative projects
  • GE
    • Learning factory
    • Coolidge.io
  • IBM
    • Partner with PowerAI tools and evaluation
    • IBM Watson
    • Nimbix Advanced HPC
  • RedHat
    • Lead developer with cloud-based scheduling and computation on open cloud

Neurocomputational research

•  Somewhat current
  • Research into brain surface analysis
  • Research into network connectivity
• experience with
  • Machine learning 
    • Reinforcement learning
    • Genetic algorithms

• Lead Architect for software system development
  • medical computational environments
  • understanding data needs and constraints
  • understanding computational requirements for medical settings
    • clinical
    • research
• Design systems to interface with home-grown AI pipelines
• Design systems to interface with industry standard
  AI tools and pipelines

• Design systems to interface with home-grown AI pipelines:
  • Allow for rapid deployment of computational tools
  • Standardized development process
  • Web-based, cloud-based
  • All data handled by system
  • Powerful visualization libraries for input and output data

• Design systems to interface with home-grown AI pipelines:
  • Predictive algorithms
  • NIRS related ML and data mining with Drs Sutin/Lin
  • Monte Carlo simulations of light propagation through brain tissue leveraging GPUs
  • Prostate nerve image segmentation with University of Magdeburg using U-NN

• Design systems to interface with industry standard tools:
  • Python-based tool chain and environment
  • Use available and established data-science frameworks:
    • tensorflow
    • keras
    • opencv-python
    • pandas
    • scikit-image
    • etc

• GE Healthcare:
  • Visualization for Radiology Workbench
  • Interface with GE cloud for data storage
  • Interface with GE AI tools:
    • Learning Factory
    • Route 66
    • Coolidge
      ​

On going and deepening collaboration for systems development.

• Nvidia:
  • Tensor-flow libraries
  • Nvidia openlabs

 

Use of lower-level tools and libraries for specific neural-network training in image analysis tasks.

• IBM:
  • PowerAI systems and software
    • Collaboration on the Massachusetts Open Cloud
       

Use IBM available AI tools for object / scene recognition and develop framework to connect to medical data.

• RedHat, Inc:
  • Collaboration on the Massachusetts Open Cloud
  • Openstack / kubernetes
  • Openswift

Develop backend opensource infrastructure to use framework on industry standard cloud systems.

with compute being "free" to process data where-ever it might naturally collect...

CHRIS is a bio-medical data workflow manage that allows easy and intuitive collection, analysis, and sharing of data between parties.

via here

connect data here

to here...

and compute!

how do I run this on the cloud?

how do I run this on "someone else's computer"?

Main web server local data repository

remote compute nodes

data source (e.g. in hospital PACS)

BCH Team

BU Team

RedHat

Thank you!