Projects
My research develops biologically-inspired frameworks that enhance the robustness and adaptability of artificial neural networks. I have demonstrated the practical relevance of these approaches across diverse domains, including molecular property prediction, biomedical classification, and graph-structured data analysis. These applications are enabled by architectures that integrate homeostatic regulation, multi-scale temporal coordination, and self-repair mechanisms, thereby achieving stable, resilient, and biologically-faithful artificial intelligence systems that maintain reliable performance under perturbation and environmental variability.
Research Implementation
Biologically Inspired Neural Network with Homeostatic Regulation
Published: Scientific Reports (Nature Portfolio), 2025
DOI: 10.1038/s41598-025-09114-8
Research Problem: Artificial neural networks suffer from brittleness under perturbation and lack the self-repair capabilities inherent to biological systems, limiting their reliability in real-world deployments.
Solution Architecture:
- Homeostatic Regulation Layer - Maintains network stability through biological feedback mechanisms
- Adaptive Repair Mechanisms - Self-healing capabilities for damaged or perturbed network components
- Multi-Scale Coordination - Integration across temporal scales for robust function
- Biologically-Principled Design - Grounded in neuroscience and evolutionary biology principles
Validation Domains:
- Molecular property prediction (AIDS, HIV, COX2 datasets)
- Protein analysis and classification
- Biomedical graph-structured data
- Drug discovery applications
Key Contributions:
- First implementation of homeostatic regulation in artificial neural network layers
- Demonstrated superior performance on critical biomedical benchmarks
- Established foundation for biologically-faithful AI architectures
- Validated self-repair mechanisms under various perturbation scenarios
Technical Implementation: PyTorch, Graph Neural Networks, Custom Layer Architecture
Authors: MD Azizul Hakim, Mohammad Ifazul Alam
| View Implementation | Read Paper |
Engineering Applications
My engineering work translates research insights into production-ready systems, demonstrating practical applications of advanced AI technologies across diverse domains.
Competitive Data Science
I actively compete on Kaggle, applying advanced machine learning techniques to complex real-world datasets. This work demonstrates expertise in statistical modeling, feature engineering, and predictive analytics.
Core Competencies:
- Advanced ensemble methods and model stacking
- Deep learning for computer vision and NLP tasks
- Feature engineering and dimensionality reduction
- Hyperparameter optimization and cross-validation strategies
FounderAI - Executive AI Agent Platform
Multi-Agent System for Startup Leadership
Problem Solved: Early-stage founders lack resources for complete executive teams, limiting strategic execution across business functions.
Solution Architecture:
- Multi-Agent Orchestration - CrewAI and LangGraph coordination
- Specialized Executive Agents - CMO, CPO, CTO, COO personas with domain expertise
- Model Context Protocol - Seamless inter-agent communication
- Tool Integration - Market research, financial modeling, technical planning
Agent Capabilities:
- Market positioning and acquisition strategy
- Product roadmap and feature prioritization
- Technical architecture and development planning
- Operations optimization and resource allocation
- Investor pitch and fundraising materials
Technical Stack: LangChain, LangGraph, CrewAI, FastAPI, Streamlit
AI-Driven Tourist Spot Finder
Intelligent Travel Companion with Multi-Agent Architecture
Problem Solved: Traditional travel planning lacks personalization and fails to integrate real-time contextual data.
Solution Architecture:
- FastAPI Backend - Production-grade REST API
- ML Recommendation Engine - Hybrid filtering with collaborative and content-based methods
- LLM Integration - GPT-powered destination insights
- Real-Time Data Agents - Weather, pricing, availability monitoring
- MongoDB Storage - User profiles and preference learning
Key Features:
- Personalized destination recommendations
- Dynamic itinerary generation with contextual awareness
- Multi-modal data integration (text, images, location)
- Real-time constraint satisfaction
Technical Stack: FastAPI, PyTorch, LangChain, MongoDB, Docker
Health-Conscious Food Recommendation System
Personalized Nutrition AI with OCR Integration
Problem Solved: Individuals with health conditions struggle to make informed dietary choices from food labels and nutritional information.
Solution Architecture:
- Advanced OCR Pipeline - Mistral OCR for food label text extraction
- Health Analysis Engine - LLM-powered nutritional assessment
- RAG System - Medical dietary guideline integration
- Personalization Engine - Health condition-aware recommendations
- MongoDB Storage - User health profiles and food database
Key Features:
- Real-time food label analysis
- Medical condition-specific recommendations
- Nutritional database queries with constraints
- Personalized meal planning
Technical Stack: Mistral OCR, LangChain, RAG, MongoDB, Computer Vision
Bengali Text-to-Speech Synthesis
Transformer-Based Language Model Fine-tuning
Problem Solved: Limited accessibility tools for Bengali speakers due to lack of high-quality native TTS systems.
Solution Architecture:
- Pegasus Model Fine-tuning - Transformer optimization for Bengali
- MLOps Pipeline - Automated training and deployment
- Custom Dataset Processing - Bengali corpus preparation
- Production Optimization - Inference performance tuning
Key Features:
- Natural Bengali speech synthesis
- Scalable inference pipeline
- Continuous model improvement through MLOps
- Production API deployment
Technical Stack: Transformers, PyTorch, MLOps, Docker, Kubernetes
Technical Capabilities
Machine Learning & Deep Learning: PyTorch, TensorFlow, Keras, Scikit-learn, Custom Neural Architectures
MLOps & Deployment: MLflow, Docker, Kubernetes, CI/CD, Model Monitoring
Cloud Infrastructure: AWS (SageMaker, EC2, S3), Google Cloud Platform
Specialized Domains: Computer Vision, NLP, Graph Neural Networks, Generative AI
AI Agent Systems: LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, RAG, Vector Databases
Connect
| GitHub | Kaggle | Google Scholar |