Vision
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"Where the mind is without fear and the head is held high, where knowledge is free, where the world has not been broken up into fragments by narrow domestic walls
Where words come out from the depth of truth, where tireless striving stretches its arms towards perfection, where the clear stream of reason has not lost its way into the dreary desert sand of dead habit
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Where the mind is led forward by thee, into ever-widening thought and action, into that heaven of freedom, my Father, let my country awake."
~Rabindranath Tagore
Nothing conveys it better than my favorite poem since my childhood
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The Journey
My skills across lifecycle of data from its ingestion, analysis, modeling and beyond....
Data Engineering
Ingestion of data via pipelines marks its inception into the world of Data Science. Here it is securely stored in various databases and warehouses. Pipelines are setup to process and transfer it as necessary
My Skills:
Extensive experience in designing solutions on GCP and AWS databases like RDS, BigQuery, CloudSql, MongoDB and Firestore.​
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Developed scalable cloud-based ETL pipelines for various applications like streaming-data processing (Apache Kafka, AWS Kinesis, GCP Dataflow) and big data processing (GCP Dataproc and AWS EMR)
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Researched on various dataset versioning tools like DVC, Pachyderm and integrated them into ETL and ML pipelines.
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Adept with de-identification, ingestion, storage and integration of medical data formats like FHIR, HL7 and DICOM using GCP Cloud Healthcare API.
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Built a pipeline that identified false positives returned from GCP Cloud Healthcare API that detects images with PHI information on it.
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Data Analytics
Next step is to visualize and analyze the ingested data.
Data is cleaned, transformed and made ready with newly calculated features for modeling
My Skills:
Integrated visualization tools like Tableau, PowerBI and Looker to cloud databases on GCP, AWS and Azure.
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Excelled at graduate-level statistical inference course covering topics like regression, ANOVA and hypothesis testing.​
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In depth knowledge of probability distributions and Bayesian probability concepts
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Extensive experience in analyzing and visualizing medical image formats of DICOM and NIFTI using open source softwares like 3D slicer.
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Adept with feature engineering techniques for both temporal and relational data-sets.
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Machine Learning
Pivotal step! The cleaned and processed data is modeled using traditional ML algorithms or Deep Learning methods.
My Skills:
Well versed with​ building 2D and 3D Convolutional Neural Networks for object classification, detection and segmentation using U-Net, YOLO and Faster-RCNN architectures
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Designed and developed solutions for sentiment analysis, machine translation and sentence completion using LSTMs and BERT​
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Developed a parametrized 3D GAN for generation of realistic CT images based on user input
Proficient in ​deep learning frameworks of Tensorflow and PyTorch with 3+ years of experience. Currently exploring the JAX framework.
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Technical experience and elementary research in novel techniques like Federated Learning and Self-supervised Learning.
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Highly skilled in employing traditional ML algorithms of SVM, XGBoost, Random Forest, DBSCAN, KNN and K-means clustering
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Researched and experimented with loss functions like CL-Dice, Betti number and Hausdorff distance for 3D thin-object segmentation as part of a NIH funded project called HubMap
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Experience in building and deploying Document-OCR and object detection models using GCP AutoML.
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ML OPS
Components...Assemble! After all the components of the project are built, they are fixed together in a pipeline to ensure a smooth and scalable execution.
My Skills:
Integrated tools that enable experiment tracking, metric visualization and dataset versioning into a deep learning training pipeline
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Developed a Python wrapper to train deep learning models using multi-node distributed learning with multi-gpu or TPU accelerators.
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Led the project to design, build and deploy an end-to-end MLOPs pipeline using Kubeflow-based Vertex-AI pipeline on GCP
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Co-authored a guide for the best practices and tools used for design, development and deployment of a deep learning based SAMD (software as a medical device)
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Well versed with model compression and inference optimization (Tensor RT, ONNX). Currently experimenting with GPU and deep-learning focused languages like Titron and Julia.
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Management and Sales
Data's journey culminates at this stage where an application/system modeled on it is pitched as a solution to a client's problem. To ensure viable and efficient development of such applications, project management is of utmost importance
My Skills:
Acted as the technical consultant for multiple proposals of the Go-to-Market (GTM) team across varied domains like self-supervised learning, predictive maintenance, and image indexing.
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Converted 8 out of 10 sales pitched and effectively generated revenue of $1.5 million+ for the company
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Drafted Statement of Works for multiple proof of concepts and development projects.
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Well versed with different project management methodologies like agile, kanban and waterfall using JIRA and Azure Boards
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Prepared detailed proposals with project timelines, team requirements, deliverables list, cost estimations and other dependencies in response to numerous Request for Proposals.
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Integrated Azure Boards with development pipeline to automate ticket-updating, project documentation and metrics visualization.
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