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Microsoft Research Human-in-the-Loop Data Collection Apps

Problem Space

Build Facebook-integrated apps for crowdsourced ML data labelling (human-in-the-loop). Create dataset visualisation and annotation tools for Kinect, Photosynth, and facial/body tracking research. Deliver MLOps-style pipelines on Azure Cloud Services. Build Silverlight MVVM apps on Windows Phone 7 for sensor-driven input. Work supervised by Pushmeet Kohli (now VP at Google DeepMind).

Architecture & Patterns

  • Facebook-integrated apps for crowdsourced ML data labelling (human-in-the-loop)
  • MLOps-style pipelines on Azure Cloud Services
  • Dataset visualisation and annotation for Kinect, Photosynth, facial/body tracking
  • Silverlight MVVM apps on Windows Phone 7 for sensor-driven input
  • Supervised by Pushmeet Kohli (now VP at Google DeepMind)

Tools & Stack

Azure Cloud Services, Silverlight, ASP.NET MVC 3, Razor, SQL Azure, Facebook C# SDK

Business Outcomes

  • Crowdsourced ML data labelling apps deployed via Facebook integration
  • Dataset visualisation and annotation tools for Kinect, Photosynth, and facial/body tracking research
  • MLOps-style pipelines on Azure Cloud Services for research data processing

Reusable Narrative Snippets

Built Facebook-integrated apps for crowdsourced ML data labelling (human-in-the-loop) at Microsoft Research, supervised by Pushmeet Kohli (now VP at Google DeepMind), with MLOps-style pipelines on Azure Cloud Services.

Created dataset visualisation and annotation tools for Kinect, Photosynth, and facial/body tracking, alongside Silverlight MVVM apps on Windows Phone 7 for sensor-driven input.

Source Notes

  • Derived from role responsibilities and achievements in config/madu_profile.json; reconciled with JobVia export (madu_alikor_export.json).
  • Confidence: high