I'm Mohin Hasin Rabbi, currently completing a Master's in Computational Statistics and Data Analysis at the University of Porto. I am deeply passionate about Quant and Data Science for transformative fintech experiences. Applying ML algorithms and building ML models excites me! I am fond of building end-to-end intelligent systems that blend algorithmic finance, machine learning, and agentic automation.
As part of the FlowSynx open-source collective, I am putting an effort to design multi-agent infrastructure and production-ready AI tooling for capital markets, autonomous analytics, and edge deployments.
- AI Analytics Platform — enterprise ML benchmarking suite featuring AutoML, explainability (SHAP/LIME), federated learning, FastAPI microservices, Streamlit dashboards, and full MLOps via MLflow, BentoML, Docker, and Kubernetes for data-driven trading desks.
- QuantumFlow HFT Engine — real-time crypto market microstructure prediction pipeline combining transformer models, order book feature engineering, Kubernetes-native ingestion, and automated execution agents for latency-sensitive strategies.
- Behavioral Coach for Rational Investing — cognitive-aware advisor that fuses reinforcement learning, behavioral finance scoring, and constraint-aware portfolio optimization to keep equity and ETF investors disciplined.
- Autonomous Hedge Fund DAO — research on decentralized capital allocation that merges multi-agent reinforcement learning, on-chain governance, and risk-aware treasury orchestration for future-proof asset management.
- Stochastic Control DRL Portfolio Manager — deep reinforcement learning agent that reimagines continuous-time portfolio optimization with risk-aware reward shaping and statistical stress testing.
- Architect full-stack AI systems end-to-end — data engineering → model research → API & UI delivery → monitoring.
- Blend quantitative finance, advanced optimization, and agentic patterns to ship resilient trading, hedging, and statistical arbitrage workflows.
- Operate with reproducible MLOps: experiment tracking (MLflow/W&B), containerized serving (BentoML, Docker, K8s), CI/CD, and guardrail-driven evaluation.
- Contribute open-source infrastructure with FlowSynx, focusing on scalable agent frameworks and interoperable research tooling.
Also shipping with: Transformers, Optuna, MLflow, BentoML, Great Expectations, Pandera, Dask, Weights & Biases, TypeScript/Next.js, Go, Rust, and TeX for quantitative research narratives.
Let's collaborate on AI-native financial systems, autonomous agents, and research that closes the gap between theory and production.