Siavash Khallaghi


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Clarius Mobile Health

Research Engineer • March, 2017 — Present

At my current role, I translate business challenges in the ultrasound domain to a scientific problem. After prototyping an algorithm, I implement the solution as part of our application. I also help out with software development tickets. Below are some of my projects:

  • Virtual demo probe (patent pending).
  • Automatic follicle/bladder segmentation (patent pending).

Copypants Creator Technologies

Machine Learning Scientist • November 2015 — March 2017

I designed and implemented a machine learning framework for an image search service. The company and technology was later acquired by Xperi. Below are some of my projects:

  • Implemented a convolutional neural network for featuren extraction and image matching.
  • Implemented a reinforcement learning agent for matching patches in images.

Lightpath Imaging Labs

Machine Learning Scientist • April 2015 — November 2015

My project is under a non-dislosure agreement. The company was disolved due to lack of funding.


University of British Columbia

PhD, Electrical and Computer Engineering • 2010 — 2015

I was doing research on image-based techniques for prostate interventions. As part of my PhD, I wrote a clinical software to collect B-mode, RF time-series and elastography images during a prostate biopsy. Using this dataset, I developed image registration methods to compensate for motion and deformations during prostate biopsies. I was also involved in machine learning projects for detection of tumors in ultrasound data. My Google Scholar includes a list of published papers from my PhD.

Queen's University

MSc, Electrical and Computer Engineering • 2008 — 2010

My thesis was on image registration methods for spinal interventions. I developed a generative model for spinal anatomy and by fitting and overlaying it on ultrasound images, showed that it could be used to interpret ultrasound images during spinal injections.

Amirkabir University of Technology

BSc, Electrical Engineering • 2004 — 2008

Personal Projects

• October 2016 — Present

PyCPD is a pure numpy implementation of the coherent point drift (CPD) algorithm. I developed it as part of my invited talk at the University of British Columbia.

• November 2017 — December 2017

This is a gym environment for training a reinforcement learning to find matching patches in two identical translated MNIST pairs. The environment consists of a pair of mnist digits against a zero background. The dimension of each image is 40-by-40, mnist digits are standard 28-by-28 pixels. The location of each mnist digit is uniformly random and independent.

• January 2014 — October 2016

GMM-FEM is a point cloud registration technique that I developed as part of my PhD thesis. It is implemented in Matlab, with computationally intensive parts being developed in C++.