// mission
Building interpretable machine learning systems at the intersection of behavioral modeling,
social engineering detection, and socio-technical resilience.
## Who I Am
I work at the intersection of AI, behavioral modeling, and socio-technical resilience — building systems that are not only accurate but explainable and grounded in human reality.
I'm a BSc Computer Science & Engineering student at Dhaka International University (CGPA 3.76 / 4.00), graduating December 2027. My research focuses on detecting social engineering through interpretable behavioral anomaly modeling, predicting system failures using socio-economic and IoT data, and building continual learning frameworks for dynamic environments.
As President of DIU Computer Programming Club and an active IEEE & IEEE Computer Society Member, I combine research depth with community leadership — recognized as a Regional Winner (Barisal Region), Global Nominee, and Honorable Mention for the Global Finalist at NASA International Space Apps Challenge 2025 with Team Polaris.
fig.0 — research_methodology_framework.svg
# Core Mathematical Formulations & Architectures
L = -log(e^(z_i·z_j/τ) / ∑ e^(z_i·z_k/τ))
ϕ_i(v) = ∑ [|S|!(|N|-|S|-1)!/|N|!] Δv_i(S)
η = Bη + Γξ + ζ (CFI=0.982, RMSEA=0.031)
Softmax(QK^T/√d_k)V || FPN C2f Head
✓ Formulations underlying 10+ accepted papers in behavioral ML, social engineering detection, and AI resilience.
Accepted, under review, and ongoing research across ML, AI, socio-economics, and computer vision.
Research prototypes and engineering systems spanning ML, CV, and full-stack development.
A comprehensive guide for AI/ML researchers covering research methodology, paper writing, experimentation design, and navigating the academic publication process. Available on ResearchGate.
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NASA NEO data platform with real-time 3D visualization. Regional Winner (Barisal Region), Global Nominee, and Honorable Mention for the Global Finalist at NASA International Space Apps Challenge 2025 with Team Polaris.
fig.3 — meteorshield_pipeline.svg
Distributed, scalable platform integrating LLM agents and resilient proxy routing for automated content generation.
fig.5 — distributed_llm_pipeline.svg
XGBoost model trained on IoT time-series data to predict food spoilage (F1: 0.89). Dashboard visualizes waste reduction impact.
fig.6 — spoilage_prediction_model.svg
TypeScript full-stack engine analyzing developer workflows, integrating GitHub services with burnout tracking algorithms.
fig.7 — burnout_telemetry.svg
ML model identifying high-risk urban segments using traffic patterns and accident history (Precision: 84%).
fig.2 — risk_synthesis.svg
Hierarchical Temporal-Spatial Pattern Fusion model for advanced data analysis and forecasting in dynamic environments.
fig.1 — hierarchical_fusion.svg
Social Engineering Tactic Detection system using DistilBERT + SHAP attribution to identify and explain psychological manipulation tactics in text. MIT Licensed · 1★ on GitHub.
fig.9 — tactic_detection_pipeline.svg
AI-powered smart health monitoring dashboard with real-time vitals tracking, predictive analytics, and ML-driven health risk assessment. Live demo on GitHub Pages.
fig.10 — health_monitoring.svg
Python-based intelligent system with live deployment on Vercel. 2★ on GitHub. Incorporates ML pipelines and an API-driven backend for real-world research-grade inference.
fig.11 — inference_pipeline.svg
AI-powered supplementary educational platform enhancing university coursework with intelligent assistance and interactive tracking.
fig.8 — educational_assistant_loop.svg
Real-time cryptocurrency portfolio management application for tracking live market data and analyzing asset performance.
fig.9 — market_api_stream.svg
Novel AI architectures created, programming languages, ML/DL frameworks, and engineering tools.
Original AI models & empirical frameworks designed in research publications:
Core languages used for research pipelines, ML modeling, and production code:
Core scientific compute & deep learning stacks:
Development environments, automation, & infrastructure:
Led Team Polaris to Regional Winner at the Barisal Region, then Global Nominee and Honorable Mention for the Global Finalist at NASA International Space Apps Challenge 2025. Combined expertise in Machine Learning, full-stack development, data analysis, and UI/UX to engineer a winning planetary defense solution — MeteorShield.
Open for research collaborations, engineering challenges, or just a friendly conversation about AI and its impact on humanity.
meetmehedi1@gmail.com