Welcome to my personal website which serves to showcase my portfolio. I am currently a PhD candidate in the School of Mechanical and Materials Engineering (MME) at Washington State University (WSU), Pullman. My expected graduation date is Summer 2023. I completed my Master’s Degree in Statistics from WSU in Fall 2022. I am actively looking for full time positions in Data Science, Machine Learning or similar roles.
My PhD research has been initially focused on implementing a Monte Carlo based probabilistic model in C++ and Fortran for studying bioparticle transport across blood-brain barrier (BBB). Lately, I have been working on utilizing physics informed neural networks (PINNs) for solving inverse heat transfer problems. I have also studied complex polymer/solvent/nonsolvent systems for industrial applications using molecular dynamics simulations.
Pursuing MS in Statistics has fueled my interest in exploring the expansive world of machine learning and data science to decipher intriguing, real-life problems.
PhD in Mechanical Engineering, Summer 2023
Washington State University
MS in Statistics, 2022
Washington State University
BSc in Mechanical Engineering, 2017
Bangladesh University of Engineering and Technology
The amazing, 12 weeks long, remote internship experience is summarized below.
Worked within Agile software development environment by utilizing CI/CD tools and best practices.
Incorporated new NLP functionalities (text summarization and emotion detection) to the existing codebase and wrote necessary code for unit testing and model benchmarking against relevant metrics.
Contributed towards building a REST API (with FastAPI) having different NLP endpoints hosted by multiple AWS services such as S3, ECR, Lambda and API Gateway (using Pulumi IAC). Tested the endpoints in Postman and deployed to dev using Octopus Deploy.
Worked on several other ongoing projects in the Data Science team to facilitate easier implementation by other organizations across the company. In the process, acquired hands on experience with Docker, Spark clusters in Databricks notebooks, Azure services such as DataLake, DevOps and ADF.
Current research is focused on the following.
Used finite volume method (FVM) for solving an inverse heat transfer problem using Bayesian Inference machine learning technique.
Customized loss functions to deploy physics informed neural networks for complex unsteady, multi-dimensional heat transfer problems.
Developed and further improved a probabilistic model based on Monte Carlo method written in C++ and Fortran programming language.
Utilized the stochastic model for studying key parameters for drug delivery through blood brain barrier (BBB) as an aid for neurodegenerative diseases such as Alzheimer’s and Parkinson’s.
Studied design parameters and relevant characteristic properties for manufacturing functional nanoparticle for drug delivery.
Investigated complex polymer/solvent/nonsolvent systems for industrial applications using atomistic models and molecular dynamics simulations.
Assisted the instructor with preparing course materials, conducting the class and grading homework as well as exams. Guided students with in-class performance and homework.