Suneeta Mall

Rambling of a curious engineer & data scientist

Oct 2021

Sometime early this year, I deployed a brand new thoroughly tested, and profiled application onto a self-managed Kubernetes cluster. Suffice to say, all hell broke loose. The pods were getting OOMKilled with error code 137 left and right. This sparked a massive crime scene investigation and some interesting insights were discovered. I blogged about this in May, 2021. I have made some more progress on this since then, as a sidekick.

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May 2019

Reproducibility crisis is real in data-science. This crisis has been recognized in data-science research and several efforts e.g. International Conference on Learning Representations has been underway in improving reproducibility in data science research. Dr. Joelle Pineau, an Associate Professor at McGill University and lead for Facebook’s Artificial Intelligence Research lab (FAIR) in her talk covered the importance of reproducibility.

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March 2019

Significant amount of effort has been invested in improving the quality of breast imaging modalities (for example, mammography) to increase the accuracy of breast cancer detection. Despite that, about 4-34% of cancers are still missed during mammographic examination of cancer of the breast. This indicates the need to explore a) The features of the lesions that are missed, and b) Whether the features of missed cancers contribute to why some cancers are not ‘looked at’ (search error) whereas others are ‘looked at’ but still not reported. In this visual search study, we perform feature analysis of all lesions that were missed by at least one participating radiologist.

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May 2018

At Nearmap, we have been working on an open source CI/CD solution for Kubernetes based workload called KCD for Kubernetes Continous Delivery (Formally known as CVManager (Container Version Manager)). We had oppertunity to present this solution at KubeCon EU Copenhagen, Denmark 2018. For more details of this talk see link and associated blog with detailed info can be found here.

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March 2018

Visual search, the process of detecting and identifying objects using the eye movements (saccades) and the foveal vision, has been studied for identification of root causes of errors in the interpretation of mammography. The aim of this study is to model visual search behaviour of radiologists and their interpretation of mammograms using deep machine learning approaches. Our model is based on a deep convolutional neural network, a biologically-inspired multilayer perceptron that simulates the visual cortex, and is reinforced with transfer learning techniques.

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