Aspiring Data Scientist / Computer Vision Researcher / Backend Developer

Education

B.Sc Computer Science & Engineering American International University-Bangladesh (Jan 2020-Dec 2023)
H.S.C. Science BAF Shaheen Collage-Dhaka (Jun 2018)

Junior AI Developer CRTVAI (Full-time Remote) (April 2024 - Present)

Designed and implemented the system architecture and API endpoints of an automated quality control AI application to enhance customer care services.
Fine-tuned speech-to-text models, deploying server-less models to minimize third-party transcription costs.
Improved the Voice Activity Detection pipeline and conducted a detailed analysis of speech metrics to enhance accuracy and efficiency.
Achieved a 25% reduction in GPT API calling costs through strategic optimization.
Implemented Background task processing techniques to significantly enhance system performance and reliability.

Researh Assistant in Computer Vision (Full-time) (Sep 2023 - Jan 2024)

Collaborated with the research team to contribute to Computer Vision research project.
Conducted comprehensive research, experiments, and data analysis to support project objectives.
Research Collaboration: Actively participated in a Computer Vision research project, leveraging my skills and knowledge to contribute to the team’s success.
Research Dataset: Contributed to the creation of a research dataset containing 12,000+ labeled images, supporting future computer vision research and applications.
Research and Data Analysis: Reduced research time by 20% by implementing efficient data analysis and documentation practices.
Documentation & Presentation: Successfully presented research progress and insights to the research group and advisor, ensuring clear communication and project transparency.

[Under-Review (Scientific Reports)] Research: High-Accuracy Image Segmentation for Self-Driving Cars
Speech Emotion Recognition using Transfer Learning Approach and Real-Time Evaluation in English and Bengali Language ResearchGate

Built a user-friendly web application for training custom image classification models without writing code.
- Effortlessly create custom models: Train models on their own data with a simple interface, eliminating the need for coding expertise.
- Experiment with diverse models: Select from a variety of pre-trained models or fine-tune existing ones for specific tasks.
- Seamless testing and inference: Upload images or utilize webcam input to test the accuracy and effectiveness of their trained models.
- Published as a PyPI package: Enabling broader accessibility and facilitating quick image classification model training with minimal code for developers.
Tech: Python, TensorFlow, JS, HTML, Bash
Skills: Deep Learning, Web Development, CICD pipeline.

A deep learning project aimed at accurately classifying kidney tumors and stones from medical images.
- The project is designed to integrate state-of-the-art machine learning techniques with robust data management and collaborative tools, providing a significant contribution to medical imaging analysis.
Tech: Python · TensorFlow · DVC · MLOps.
Skills: Deep Learning, Web-Development, Version Control.

Developed a comprehensive web application (ASP.NET Web API) using C# and Entity Framework to foster the AIUB student community. Adhering to SOLID principles and a robust 3-tier architecture, the platform empowers students with:
- Engaging communication: Features for students to connect and share information.
- Streamlined resume building: Efficient tools for creating resumes for the users.
- Job opportunities: Centralized platform for posting and applying to job openings with one-click ease.
- Advanced access control: Admin control over post and job moderation, user management, and security.
Responsibilities included: Full database design, Use-case analysis, Authentication & authorization, Development of features and API endpoints
Tech:ASP.NET, C#, Entity-Framework, Git
Skills: Software Development, Version-controll

Developed a real-time emotion classification system using a custom deep learning model and Flask Web API. This project enables users to:
- Classify emotions in real-time: Leverage their webcam to capture facial expressions and receive instant emotion classification through a user-friendly web interface.
- High-accuracy classification: The custom deep learning model delivers accurate emotion identification, fostering potential applications in various fields.
Tech: Python,TensorFlow, OpenCV, Flask, JavaScript, HTML, CSS.
Skill: Deep Learning, Image processing, Web-development.

Developed a podcast app using PHP to empower podcast enthusiasts. This platform offers:
- Streamlined organization: Efficient tools for managing playlists, and listening progress.
- Seamless podcast experience: Effortlessly discover, subscribe to, and enjoy favorite podcasts, all within a user-friendly interface.
Tech: PHP, JavaScript, Bootstrap, CSS, HTML, Git
Skills: Web development

An end-to-end fashion recomendation system uisng deep learning and machine learning.
- Feature extraction using Resnet50
- Similarity matching using k nearest neighbours (KNN)
Tech: Python, Numpy, Tensorflow, Scikit-learn, Git
Skills: Machine learning

Developed a comprehensive hospital management system to streamline operations and enhance efficiency in multi-department healthcare facilities. This desktop application features:
- Multi-user access control: Granular role-based permissions for doctors, nurses, administrators, and other staff members.
- Comprehensive department management: Dedicated modules for various departments like admissions, billing, pharmacy, and patient records.
- User-friendly interface: Intuitive design for seamless navigation and efficient data management.
Tech: C#, C#-Form , MS-sql, Git
Skill: Desktop application, Database design.
Honors and Awards
Dean’s List Award - for outstanding academic performance in undergrad.