I am an avid open source contributor mostly based on Stackoverflow. I like to be up-to-date with web based frameworks my recent favourite framework would be FastAPI.

Projects:

• Classification and Segmentation of Breast Cancer using Mask R-CNN This was a government funded project which I did during my bachelor year. The reason I find this project to be my all time favourite is due to the fact that it was the point where I started understanding deep learning vivdely. And another factor could be that I was fully involved in making each step of this project, whether it be data gathering, analysis, engineering, training and then finally making a fully functional web based application, deplyoing the front and backend. I was also able to publish this research on the web, you can check it here

• Machine Learning based Models For Early Screening & Detection Of Hematologic Cancer Using Cell Population Data For the completion of my bachelow degree program I had to complete a 2 semester long thesis. This research was the topic and implementation of my thesis which can be further explored. This project was done in collaboration with NIBD(National Institute of Blood Diseases). I was yet again involved in making this project end to end. From data gathering, analysis, feature extraction, feature selection, feature engineering, training and then further developing a web based interface on Vue.js and backedn using Express.js along with Flask for machine learning inferences.

• Visual Landmarks Recognition of Urban Structures using Convolutional Neural Network During my 4th semester I landed an incredile research internship at the National Center for Artificial Intelligence(NCAI, NED-UET). I contributed towards an ongoing visual positioning system project by introducing Convolution Neural Network technique in order to classify building. I was also able to publish a part of this huge project which can be further explored here

Further more my current tech stack would be,

• FastAPI and Flask
• Pyspark for ETL and maintaining a data lake
• AWS, EC2, S3, Lambda, DynamoDB, Kinesis etc
• GCP, Kubernetes Engine, Cloud Functions, Compute Engine, BigQuery, Artifact Registry etc
• Airbyte, open source contribution particularly to make an in-house tool for a startup in Hamburg
• Databricks, Delta Tables etc
• Redash, Superset etc
• Docker and Kubernetes, on-prem and cloud managed

I recenlty got hands on with end-to-end pipelining with kubernetes on Google Cloud Docker and K8s deployment pipelining