One of the advanced API based and Cloud-AI-backed Banking Analytics, and Instant Lending solutions provider

• Tech stack: Python, Azure Repos.Pipelines.Functions.ML-Workspace.Compute.,Terraform, Docker, Postman API, NLP
• Setting up DS-RND development space on azure & management of 16+counting APIs for the data-science team.
• Owning 3 services end to end and optimizing others for better performance, monitoring and formatting by automating them using azure yaml pipelines.
• Improved legacy API calls by 5x and saved server cost to the company.

Research group for AI in Medical Imaging, lead by Prof. Wachinger; develops machine learning models for healthcare

• Tech stack: Python, Flask, Docker, PyTorch, scikit-learn/Pandas/Matplotlib/NumPy/NiBabel, Nginx, AWS
• Offered to work full-time starting in August 2020 after successfully working together on Master thesis
• Implemented state of the art deep learning models (Quicknat, U-Nets) for liver and spleen segmentation using nnU-net, Octave-Conv, SWA, SWAG, Thick-slices, Multi-phases
• Developed data pre-processing pipelines to prepare 23.000+ MRI scans for input into deep learning segmentation
• Built web application using Python/Flask/Docker to visualise MRI scans and its segmentations/heatmaps predicted by machine learning models; deployed using AWS EC2/S3/Lambda

• Tech stack: Python, Keras, Pandas/Matplotlib/Numpy
• Collected, cleaned and prepared datasets of facial expressions (e.g. cry, yawn) to be used to classify emotional reactions of movie viewers and measure which pieces of a movie are engaging
• Developed deep learning CNN models to classify 8 basic facial expressions from Cohn-Kanade dataset

quant-ip.com is a data provider serving large financial institutions

• Tech stack: Python, Flask, TypeScript/JavaScript, Angular, Ionic
• Built web application to visualise financial data and innovation score using Flask/Plotly at Bloomberg Magic
• Developed web and mobile application in Flask, Angular and Ionic to analyse and visualise company innovations and growth, based on patent data

American software company for customer relationship management and digital process automation

• Tech stack: JavaScript, HTML5, CSS3
• Solved client issues in their web applications using JavaScript, HTML and CSS
• Trained 5 new employees as part of Pega recruitment and UI technology training group

Indian IT services and consulting company, largest company by market capitalization in India

• Tech stack: JavaScript/TypeScript, Angular, Node, MongoDB, HTML, CSS, PHP, MySQL
• Awarded TCS GEMS award for excellent coding skills among 500 co-workers at the Qualcomm account
• Managed and improved data consistency of 200.000+ records across 500 MySQL tables for Qualcomm application
• Developed user & administration interface and CRON tasks for Qualcomm learning management web application
• Built hybrid app with Angular used by employees of Qualcomm and TCS

• Master Thesis which was accepted at MLMI2020 - https://arxiv.org/pdf/2008.12680.pdf (machine learning conference on medical imaging): Analysed deep learning model accuracy using PyTorch/scikit-learn/NumPy; compared 4 state of the art Bayesian neural networks for image segmentation and developed improvements
• i-Graph project: Wrote web application in TypeScript using RASA-NLU to translate language into SQL queries
• Expression prediction project: Detected emotions on image and video data using PyTorch and OpenCV
• Allianz HackaTUM-2019: Won 1st prize out of 37 teams for detecting cracks on metal using image analysis

• Above-average student (top 15%, GPA 8.3); wrote bachelor thesis detecting cracks in railway tracks based on data