AUKIK AURNAB
CHIEF TECHNOLOGY OFFICER
SKILLS & EXPERTISE
System and Cloud Architect
Stack and Tools
Docker, AWS EC2, Azure VM, S3 Bucket, Azure Blob Storage, Github, Selenium, Playwright, Jest, Supertest, Cloudflare, Cloudfront, Nginx, Wireshark, Fiddler, GitHub
Web Development
Stack and Tools
Express.js, Node.js, Prisma ORM, React, SvelteKit, Tailwind CSS, FastApi, Python Flask, Postgresql, Mongodb, Langchain, OpenAI
Teaching
Courses
CSE110: Programming Language I (Python), CSE111: Programming Language II (Object Oriented Programming)
Github Contributions (Last 5 months)
Projects
Website Bismo
Bismo is an organization that provides financial services. I built their website using React to showcase their services and help them reach a wider audience. The website is user-friendly and easy to navigate, making it simple for users to find the information they need.
Mr Market
A stock market analyzing web application built using react, nodejs, mongodb, express and python flask rest api. Stock market data is obtained on runtime through webscrapping using selenium and necessary analyzing tools were made by taking Rule#1 book as reference.
DigiProd
An ecommerce full stack web application following MVC architecture built using express, nodejs, ejs and mongodb.
EDUCATION & CERTIFICATION
BSc. in Computer Science and Engineering
Brac University (2022)
CGPA: 3.93
Higher Secondary Certificate in Science
Notre Dame (2018)
GPA: 5.00
Secondary School Certificate in Science
South Point (2016)
GPA: 5.00
Microsoft Certified: Azure Data Fundamentals
Issued by Microsoft on April 2023
Show CertificationMachine Learning Specialization
Issued by Coursera on May 2023
Credential ID: PVF5P5TEDFYW
Show CertificationUnsupervised Learning, Recommenders, Reinforcement Learning
Issued by Coursera on May 2023
Credential ID: 5SWQ2TPXLYYS
Show CertificationAdvanced Learning Algorithms
Issued by Coursera on April 2023
Credential ID: HY5EKRU3G7KY
Show CertificationSupervised Machine Learning: Regression and Classification
Issued by Coursera on Mar 2023
Credential ID: WT5MDAYVTXT7
Show CertificationUdemy Katonic MlOps
Issued Mar 2023
Credential ID: UC-0ec6aa52-91ac-45fa-ab4b-9c6e34df3d5d
Show CertificationPROFESSIONAL EXPERIENCE
Chief Technology Officer
Bismo
May 2023 - Present
Job Responsibilities
System Engineer
allDoc
Nov 2022 - May 2023
Job Responsibilities
Student Tutor
Brac University
May 2021 - April 2022
Courses
Job Responsibilities
PUBLICATIONS
“Comparative analysis of machine learning techniques in optimal site selection.”
Abstract: Site selection is a crucial aspect of many businesses, as a company’s location can significantly impact its success. In recent years, machine learning techniques have been increasingly used to assist with optimal site selection by providing data-driven predictions about the potential success of a given location. Machine learning techniques can be used to assist in the process of selecting the optimal site by analyzing the patterns in data such as demographics, lifestyle services, and geographic features. In this paper, we compare several machine learning techniques for their performance in optimal site selection for features extracted from Open Street Map (OSM) data, WorldPop population data, and Bing satellite imagery. A target dataset corresponding to the features extracted was collected from Yelp data on restaurant check-ins, and this was used as a parameter to determine the human engagement rate of that location with the businesses in that area. Our analysis methods included SVR, Random Forest, XGBoost, Ridge Regression, Lasso Regression, and ElasticNet. The satellite imagery collected from Bing maps were used to train CNN architectures such as; VGG16, VGG19, ResNet, DenseNet, and InceptionV3 and the results were compared. We evaluated the techniques using several metrics, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error(MedAE), Max Error(ME), and Median Absolute Deviation(MAD). We used algorithm strategies that performed the best in related works for this research. One meta model was also implemented in this work by an ensemble learning technique known as stacking. The model that performed the best for the data collected was then determined by looking at the error scores of different models. This work provides an insight into the strengths and limitations of each technique and recommendations for practitioners considering the use of machine learning in site selection. This study demonstrates the potential of machine learning for improving site selection processes and highlights the importance of considering multiple approaches.
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CONTACT ME!
Email Me: aurnab@bismoapp.com
© 2023. Created by Aukik Aurnab
ABOUT ME
I am a lifelong learner who finds joy in acquiring knowledge. My interests often align with my career, making the learning process feel less like work and more like a personal adventure. I am not just confined to my field of work, I love to gather diversified knowledge on various fields. I value freedom and prefer to do things in my own way. This doesn’t mean I disregard advice. In fact, I take advice from people based on their credibility over the topic. This approach helps me make informed decisions and be efficient in my work. One of my defining traits is that I love playing the long-term game. I believe in making decisions that will benefit me in the long run, rather than seeking instant gratification. In essence, I am someone who loves learning, values freedom and efficiency, and is always planning for the future.