Ravnoor Gill

Postdoctoral Imaging Scientist


  • Imaging data scientist with a passion for leveraging data science and AI to build clinically-accessible and interpretable algorithms in healthcare.
  • With 7+ years of experience in medical imaging and collaborating with subject matter experts, I have honed my skills in deep learning, computer vision, and data-centric AI to deliver impactful solutions that improve patient outcomes. I understand the importance of precision and accuracy in healthcare, and I am committed to developing algorithms that can provide reliable and clinically-relevant insights.
  • Whether it's analyzing medical images, detecting anomalies in patient data, or predicting treatment outcomes, I'm dedicated to pushing the boundaries of what's possible with AI in healthcare.


Postdoctoral Scientist in Neuroimaging of Epilepsy

Data-centric explainable machine learning in MRI-negative focal epilepsies using multi-contrast MRI

  • Multi-Task Multi-Modal Learning - designed and implemented a multi-task hippocampal subfield segmentation and seizure focus lateralization pipeline in temporal lobe epilepsy

Machine Learning & Signal Processing Intern

HelpWear Inc

01/2021 - 04/2021

Algorithm development for arrhythmia detection in a wearable single-lead ECG monitoring solution at a MedTech startup

  • Developed algorithms for supervised beat classification, unsupervised anomaly detection and active learning strategies based on public and private datasets
  • Data Management: integrated, curated, and annotated heterogeneous sources of cardiac electrophysiology datasets for training data
  • DataOps: Modernized data storage by improving storage, archival and performance efficiencies by migrating to compressed binary HDF5 containers
  • Demonstrated the business/clinical value-add of the machine learning solution to the relevant stakeholders
  • Co-authored a successful grant application valued at CA$20,000

Doctoral Research Assistant

McGill University

09/2015 - 08/2022

Multi-contrast MR imaging analysis using machine learning for clinical decision support in MRI-negative epilepsy

  • Byaesian Lesion Detection with Risk Stratification: developed and validated a fully convolutional deep learning pipeline with uncertainty quantification (to provide diagnostic confidence) for lesion detection in focal cortical dysplasia across 9 epilepsy centres worldwide
  • Segmentation with Clinical Diagnosis: built a hippocampal subfield segmentation and focus lateralization pipeline in temporal lobe epilepsy
  • Multimodal Data Synthesis: implemented generative adversarial networks to synthesize missing modalities (T2-weighted from T1-weighted MRI and vice-versa)
  • Multimodal Data Reconstruction: implemented reconstruction of multimodal magnetic resonance fingerprinting using deep learning based regression models
  • Conducted a large-scale systematic review and meta-analysis to demonstrate the heterogeneity in the clinical and operating definition of MRI-negative epilepsies

Graduate Research Assistant

Characterized disruptions of functional connectivity in experimental models of epilepsy using electrophysiology (EEG) and resting-state functional MRI (rsfMRI)

  • Performed surgeries in seizure-induced Long-Evans rats to implant depth electrodes to record intracortical EEG recordings
  • Leveraged rsfMRI at 9.4 Tesla to characterize extensive disruptions of the functional networks beyond the hippocampus in a graph theoretical framework
  • Contributed to the RF-coil design for a volumetric receive coil compatible with MRI cradle to immobilize the rats


McGill University

09/2015 - 04/2022
Montréal, Québec

Ph.D. Neuroscience

Thesis: Quantitative imaging of cortical malformations in MRI-negative epilepsy

University of Western Ontario

09/2012 - 01/2015
London, Ontario

M.Sc. Neuroscience

Thesis: Resting-state functional network disruptions in a rodent model of mesial temporal lobe epilepsy

Panjab University

07/2008 - 05/2012
Chandigarh, India

B.Eng. Biotechnology

Software (Open-Source)

Validation of a deep Learning algorithm for detecting lesions in MRI-negative focal cortical dysplasia with uncertainty quantification for assigning diagnostic confidence across 9 tertiary epilepsy care centers

Principles Implemented: Two-Stage Patch-Based Detector, 3D Bayesian Convolutional Neural Networks, Model Uncertainty using Monte Carlo Dropout

Technology Stack: Python, Keras, Theano, Docker

Web app to generate textures maps from MRI using Advanced Normalization Tools. The pipeline generates quantitative 3D maps derived from T1-weighted MRI or computer-aided detection of focal cortical dysplasia (FCD)

Principles Implemented: Relative Intensity and Gradient Magnitude

Technology Stack: Python, Plotly Dash, Flask, ANTsPy, Docker

V-Net architecture implementation for brain extraction (removal of skull, dura mater, and cerebellum) using either T1-weighted alone or in conjunction with T2-weighted FLAIR MRI images in malformations of cortical development

Principles Implemented: 3D Convolutional Neural Networks, 3D Fully Connected Conditional Random Fields

Technology Stack: Python, PyTorch, Docker

Brain - 01/2023

H. M. Lee, S.-J. Hong, R. Gill, B. Caldairou, I. Wang, J.-g. Zhang, F. Deleo, and others

Brain - 10/2022

H. M. Lee, F. Fadaie, R. Gill, B. Caldairou, V. Sziklas, J. Crane, S.-J. Hong, and others

Neurology - 10/2021

R. S. Gill, H.-M. Lee, B. Caldairou, S.-J. Hong, C. Barba, F. Deleo, L. D'Incerti, and others

NeuroImage - 01/2020

H. M. Lee, S.-J. Hong, R. Gill, B. Caldairou, I. Wang, J.-g. Zhang, F. Deleo, and others

Epilepsia - 01/2020

A. Bernasconi, F. Cendes, W. H. Theodore, R. S. Gill, M. J. Koepp, R. E. Hogan, G. D. Jackson, and others

International Conference on Medical Image Computing and Computer Assisted Intervention - 01/2019

R. S. Gill, B. Caldairou, N. Bernasconi, and A. Bernasconi

NeuroImage: Clinical - 01/2017

R. S. Gill, S. M. Mirsattari, and L. S. Leung

Selected Awards

Fonds de Recherche du Québec - Santé

10/2017 - 12/2020
Government of Québec

PhD Scholarship (CA$70,000)

Young Investigator Award

American Epilepsy Society Meeting, Virtual

Merit Award (US$2,000)

Grass Foundation Young Investigator Award

American Epilepsy Society Meeting, Baltimore, MD, USA

Merit Award (US$2,000)

MICCAI Society NIH Travel Award

International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Granada, Spain

Merit Award (US$850)

OHBM Merit Abstract Award

Organization for Human Brain Mapping (OHBM) Meeting, Singapore

Merit Award (US$2,000)