Highly accomplished and results-driven AI/ML Engineer and Full-Stack Developer with a strong academic background in Computer Science and Engineering. Proven expertise in Large Language Models (LLMs), Natural Language Processing (NLP), Computer Vision, and full-stack web development. Recognized for multiple first-author publications at prestigious international conferences and two Kaggle Bronze Medals (top 6% globally). Adept at problem-solving, competitive programming, and leading technical projects from conception to deployment. Seeking to leverage advanced technical skills and a passion for innovation to drive impactful solutions in a dynamic environment.
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Summary
Actively participated in global machine learning and data science competitions, applying advanced modeling and data analysis techniques to real-world challenges.
Highlights
Awarded Bronze Medal (Top 6.3%) in Image Matching Challenge 2024 by developing a robust multi-model pipeline utilizing COLMAP, LightGlue, SuperGlue, DKM, and LoFTR for high-accuracy 3D reconstruction.
Secured Bronze Medal (Top 5.7%) in ISIC 2024 - Skin Cancer Detection by implementing sophisticated deep learning models that effectively combined image and tabular data with advanced augmentation techniques.
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Summary
Engaged in continuous self-directed learning and advanced problem-solving through competitive programming platforms, enhancing algorithmic and data structure proficiency.
Highlights
Solved over 1500 algorithmic problems across leading platforms including Codeforces, CodeChef, and LeetCode, demonstrating strong analytical and problem-solving capabilities.
Team Captain
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Summary
Led the university's Computer Science and Engineering Department basketball team, fostering a culture of teamwork and competitive excellence.
Highlights
Captained the team to three consecutive championship victories, demonstrating strong leadership, strategic planning, and motivational skills.
Awarded By
Various International Conferences
Recognized for multiple first-author publications presented at prestigious international conferences including CinC-2024, ICCIT-2024, Canadian AI-2025, and COLING-2025, contributing significant research to the AI/ML field.
Awarded By
Kaggle
Awarded Bronze Medal for achieving a top 5.7% ranking globally in a medical imaging competition, demonstrating advanced deep learning and data augmentation strategies.
Awarded By
Kaggle
Awarded Bronze Medal for achieving a top 6.3% ranking globally in a computer vision competition, showcasing expertise in 3D reconstruction techniques.
Awarded By
DL Enigma 1.0, SUST CSE Carnival
Achieved 6th place in a national challenge focused on road object detection for autonomous vehicles, demonstrating advanced computer vision and machine learning skills.
Published by
GenAIDetect Workshop, COLING 2025, Abu Dhabi, UAE
Summary
First Author. Developed an advanced ensemble model integrating RoBERTa, OpenAI detector, and BERT for highly accurate multilingual text detection. Achieved F1-scores of 0.7458 (12th place) for English and 0.7513 (4th place) for multilingual text.
Published by
GenAIDetect Workshop, COLING 2025, Abu Dhabi, UAE
Summary
First Author. Developed an ensemble of RoBERTa-base and OpenAI Detector with inverse perplexity weighting for robust cross-domain AI-generated text detection. Achieved TPRs of 0.826 (10th place) for non-adversarial and 0.801 (8th place) for adversarial scenarios.
Published by
Canadian AI 2025, Calgary, Alberta, Canada
Summary
First Author. Developed CardioFormer, an innovative model incorporating Cross-Channel Patching and Self-Attention mechanisms for ECG analysis. Achieved superior AUROC scores: 96.34% (MIMIC-IV), 89.99% (PTB-XL), and 95.59% (PTB).
Published by
ICCIT 2024, Cox's Bazar, Bangladesh
Summary
First Author. Created CricPose360, a comprehensive dataset, and developed a pose-enhanced YOLOv8 model for real-time cricket shot detection. Achieved 92.21% accuracy and 92.29% F1-score, significantly improving baseline performance by 3%.
Published by
CinC 2024, Karlsruhe, Germany
Summary
First Author. Engineered hybrid UNet-LSTM and ResNet-LSTM models for robust ECG conversion and multilabel classification. Secured 11th place in digitization and 10th place in classification tasks globally.
Fluent
Python, C++, Java, JavaScript, C.
PyTorch, Hugging Face, Scikit-learn.
OpenCV, NumPy, Pandas.
Node.js, Express.js, MySQL, SQLite, FastAPI, Tailwind CSS, EJS, Streamlit.
Model Context Protocol (MCP), Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), Computer Vision, ECG Signal Analysis, Fine-tuning LLMs, n-bit Quantization, LoRA, QLoRA, Prompt Engineering.
ChromaDB, Git, Linux, Arduino, ESP8266.