CV

Education

• PhD in Medical Image Analysis, University of Copenhagen, Denmark (2018)
• MSc in Electrical Engineering, Chalmers University, Sweden (2015)
• Bachelor in Electronics and Communication Engineering, BMS Institute of Technology, India (2009)

Work experience

• Sep 2020 – Ongoing
  • Role: Assistant Professor
  • Employer: University of Copenhagen
• Jan 2019 – Aug. 2020
  • Role: Post-doc
  • Employer: University of Copenhagen, Copenhagen
• Aug 2011 – July 2013
  • Role: Lecturer
  • Employer: BMS Institute of Technology, Bangalore
• Nov 2009 – July 2011 * Role: Networking Engineer
  • Employer: MRO-TEK, Bangalore
• Freelance writer
  • Columnist for The Hindu, Frontline
  • Nov 2011 - Ongoing

Awards

• Runner-up Best Paper Award at International Conference of Medical Imaging with Deep Learning (2020). Interview.
• Swedish Institute Scholarship covering MSc expenses (2013)
• Limca Book of Records Award: Team Member of India’s smallest satellite project - StudSat (2011)

Skills

• Reviewer at high impact journals and conferences such as IEEE Transactions of Medical Image Analysis, Medical Image Analysis, MICCAI, MIDL, NeurIPS
• Blogger

Publications

Carbon footprint driven deep learning model selection for medical imaging Permalink

Deep ensemble model for segmenting microscopy images in the presence of limited labeled data Permalink

Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks Permalink

Detection of foraging behavior from accelerometer data using U-Net type convolutional networks Permalink

Segmenting two-dimensional structures with strided tensor networks Permalink

Dynamic β-VAEs for quantifying biodiversity by clustering optically recorded insect signals Permalink

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients Permalink

Multi-layered tensor networks for image classification Permalink

Locally orderless tensor networks for classifying two- and three-dimensional medical images Permalink

Characterising the Atomic Structure of Mono-Metallic Nanoparticles from X-Ray Scattering Data Using Conditional Generative Models

Uncertainty quantification in medical image segmentation with Normalizing Flows Permalink

Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Lung Segmentation from Chest X-rays using Variational Data Imputation

Tensor Networks for Medical Image Classification

Raghavendra Selvan, Erik Dam, "Tensor Networks for Medical Image Classification." In the proceedings of International Conference on Medical Imaging with Deep Learning (MIDL), 2020.

Locomotor deficits in ALS mice are paralleled by loss of V1-interneuron-connections onto fast motor neurons

Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks

Segmentation of roots in soil with U-Net

Increasing Accuracy of Optimal Surfaces Using Min-Marginal Energies Permalink

Extracting tree-structures in CT data by tracking multiple statistically ranked hypotheses

A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs

Mean field network based graph refinement with application to airway tree extraction

Extraction of Airways from Volumetric Data Permalink

Extraction of Airways using Graph Neural Networks

Extraction of airways with probabilistic state-space models and Bayesian smoothing

Extraction of airway trees using multiple hypothesis tracking and template matching

A batch algorithm for estimating trajectories of point targets using expectation maximization Permalink

Bayesian tracking of multiple point targets using expectation maximization Permalink