Machine LearningArtificial CognitionAutonomous LLM Cybersecurity

Hello, I'mAshiq SazidMd. Ashiq Ul Islam Sajid

Ashiq Sazid, formally Md. Ashiq Ul Islam Sajid, is a Computer Science graduate focused on machine learning, data science, and AI backend systems, currently contributing to research and product work at Sparktech and Moodifai.

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✨ Objective

Ashiq Sazid is a BRAC University Computer Science graduate with a focus on machine learning and data science.

I have published 13 research papers and currently have 4 more under review. Alongside research, I build solo and collaborative machine learning systems, contribute to open-source projects on GitHub and continue preparing submissions for a Q1 journal and an A* conference. I also recently earned the AI Prompt Engineer Level 1 certification from AI CERTs.

13Published papers
4Papers under review
2Active AI projects
🎓 Education

BRAC University

BSc in Computer Science

Coursework: Data Structures and Algorithms, Operating Systems, Artificial Intelligence, Neural Networks, Assembly Languages, Parallel, Distributed and High-Performance Computing, Image Processing, Computer Architecture, Comparative Learning Algorithms, Pattern Recognition, Computational Theory, Natural Language Processing, Blockchain and Cryptocurrencies.

💼 Experience

Backend engineering for AI products and research.

Sparktech

AI Backend Developer

Moodifai

AI/ML Backend Developer

  • USA-based startup and research company.
  • Moodsinger - system for capturing and analyzing lyrical and audio sentiment from songs.
  • TheraMuse - adaptive music therapy platform for children with Down syndrome, individuals with ADHD, and patients with dementia.
  • Built around 32 clinically informed parameters with Q-Learning and Linear Thompson Sampling for real-time personalization.
  • Uses culturally grounded Bengali folk and classical datasets and recently secured $3 million in VC funding under Dr. Mustak Ibn Ayub, University of Oxford.
📄 Publications

Research spanning LLMs, medical imaging, recommendation systems, and explainable AI.

Scientific Reports, Q1 Journal · Under review

Federated Fine-Tuning Frameworks for Privacy-Preserving Distributed Large Language Intelligence

Md. Ashiq Ul Islam Sajid, et al.

IEEE Xplore, France · Nov 2024

Optimizing Multimodal Transformers for Medical Image Captioning: Enhancing Automated Descriptions via AI Systems

Md. Ashiq Ul Islam Sajid, et al.

ICAII, Washington, DC, USA · Oct 2025

Vertical AI for Kidney Stone Detection: Knowledge-Distilled CNNs with Student-Teacher Model for Ultrasound Imaging

Md. Ashiq Ul Islam Sajid, et al.

ICMLA, Boca Raton, FL, USA · Nov 2025

XAI-PredictFare: Comparative Flight Fare Prediction using Machine Learning Models with Dual Explainability through LIME and SHAP

Md. Ashiq Ul Islam Sajid, et al.

Springer, Australia · Oct 2024

Enhancing User Experience by Tackling the Cold Start Challenge in Product Recommendation System

Md. Ashiq Ul Islam Sajid, et al.

IEEE, Bahrain · Mar 2025

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment

Md. Ashiq Ul Islam Sajid, et al.

View the full publications archive
✨ Projects

Applied ML and data science work.

Selected machine learning and data science builds across research, product, and experimentation work.

Machine Learning

Qwen2.5 LoRA Fine-Tuning

LoRA fine-tuning workflow and experiments built around Qwen2.5 models.

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Machine Learning

MedViT-HoVer++ (ViT)

Transformer-guided framework for multitask nucleus segmentation, classification, and count regression in histopathology images.

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Military-grade YOLOv8 pipeline built with Bangladesh Army collaboration, optimized for blurry CCTV inference.

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Machine Learning

Brain Tumor Segmentation

Automated brain tumor detection and segmentation using 3D U-Net and TensorFlow.

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MuseScore extension using Mistral 7B plus custom models trained on 200 videos and 1,000 audio samples.

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Knowledge-distilled CNNs for ultrasound kidney stone detection.

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Predicts popularity and sentiment from lyrics and audio using ML models and Qwen3 8B.

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Chatbot built with Django, JavaScript, and SQLite.

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Machine Learning

Property AI

Django-based RAG assistant with FAISS search for natural property recommendations using location and weather context.

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Machine Learning

Smokebot

Django, React, SQL, and GPT-4 chatbot that tracks interruptions and responds to the latest customer intent.

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Car parking system built with Laravel and MySQL.

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Computer Graphics

Enemy Attacking Ball Game

Computer graphics game project built with PyOpenGL.

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Machine learning model for car price prediction.

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ML analysis and prediction for drug addiction data.

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Price prediction for BMW vehicles using machine learning.

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Key skills
across the stack.

Tools and frameworks used across AI backend, research, and open-source work.

Programming and BackendPython, Django, Flask, FastAPI, Git, Linux.
Machine LearningTensorFlow, PyTorch, Keras, scikit-learn, Hugging Face Transformers.
LLMs and RAGLangChain, LlamaIndex, fine-tuning, RAG, prompt engineering, evaluation and alignment, DeepSeek, LLaMA, Qwen, GPT, Ollama.
Data Science and DatabasesPandas, NumPy, Matplotlib, Seaborn, MySQL, SQLite, SQL, MongoDB.
Cloud and Data SystemsAWS, Azure, Kafka, Docker, MySQL, MongoDB, SQLite.
Blockchain and Web3Solidity, JavaScript, Truffle, Ethereum, Hyperledger Fabric, Ganache, MetaMask, Web3.js, ERC-721.

Let's collaborate together.

Based in Dhaka, Bangladesh. Open to AI/ML backend roles, research collaborations, and product-focused ML work.