SaveFood — Behavioral ML for Food Waste
Trained XGBoost on IoT time-series data to predict spoilage (F1: 0.89). Dashboard visualizes waste reduction impact and socio-economic benefits.
XGBoost
IoT
SHAP
AI/ML Researcher
Pioneering Social Integrity through Behavioral AI
I develop interpretable machine learning frameworks to detect social engineering, model behavioral risk, and predict socio-technical system failures.
NASA Champion
Space Apps 2025 Global Nominee & Winner (Barisal)
BIM 2025
Accepted Publications in Socio-Economic ML
IEEE Member
IEEE & IEEE Computer Society Member
Team Polaris — NASA Space Apps Challenge 2025 Champions, Global Nommine & Honarable Mention
I was part of Team Polaris, which won the Barisal Division championship. Our team combined expertise in ML, full-stack development, data analysis, and UI/UX design to create a winning solution.
Research Vision
I work at the intersection of Artificial Intelligence, behavioral modeling, and socio-technical system resilience. My research focuses on developing systems that are not only accurate but explainable, fair, and grounded in real human behavior.
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Detecting social engineering through interpretable behavioral anomaly modeling.
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Predicting system failures using socio-economic and IoT data integration.
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Building continual learning frameworks for dynamic, evolving environments.
Collaborative Success
Leading Team Polaris to NASA Space Apps Victory.
Research Projects
SafeRoads — Geospatial Risk Prediction
ML model to identify high-risk urban segments using traffic patterns and accident history data (Precision: 84%).
Geospatial
ML
Ensemble
NASA WINNER
MeteorShield — Planetary Defense
NASA NEO data platform with real-time 3D visualization. Champion, NASA International Space Apps Challenge 2025.
NASA
API
Three.js
AgroHub — IoT & ML Smart Agriculture
Real-time farm monitoring and ML-driven decision support system for smallholder farmers to optimize yield.
IoT
Python
Selected Publications
Focused on Socio-Economic ML & Behavioral Analysis
Accepted
BIM 2025
A Socio-Economic Machine Learning Framework for Predicting Programmer Retention
Hasan, M. M., Rakib, R., Molla, M. A., Borhan, R., Based, M. A.
Accepted
BIM 2025
Behavioral and Demographic Feature Fusion for Developer Attrition Modeling
Hasan, M. M., Mahin, A. A., Chakraborty, S., Afrose, M., Mia, M. A., Based, M. A.
Under Review
EWC-RL++: A Modular and Adaptive Continual RL Framework
Mahin, A. A., Hasan, M. M., et al.
Under Review
Explainable Hybrid ML for Seismic Metadata
Molla, M. A., Hasan, M. M., et al.
Technical Arsenal
Languages
Python
C / C++
SQL
ML/AI
Scikit-learn
TensorFlow
PyTorch
Data
Pandas
NumPy
Matplotlib
Specialized
SHAP
Geospatial
AutoML
Memberships
- IEEE Student Member
- IEEE Computer Society
- BASIS Students' Forum
Status
Open for Research Collaborations