Md Rahatul Ashakin

Bio

Researcher focused on trustworthy, efficient ML for biomedical and other sensitive data. Experience across healthcare and finance, combining reproducible ML pipelines with optimization (classical + quantum-inspired) and privacy-preserving methods for real-world deployment.

Download CV Scholar GitHub LinkedIn

  • Location: Queens, New York, USA
  • Email: ashakin.rahatul@gmail.com
  • Interests: Optimization for large models; robustness & privacy; biomedical (multi-omics) and clinical/financial AI; hardware-aware deployment across cloud/GPU, edge/IoT, and near-term quantum backends.


Summary

I work on AI/ML that is reliable, interpretable, and reproducible, especially for biomedical and other sensitive datasets. My recent work spans multi-omics modeling (e.g., cancer experiments with transformer/GNN-based approaches), optimization-driven model selection (including hybrid quantum–classical methods such as QAOA and VQE in Qiskit), and privacy-preserving analytics (FHE/HE, secure aggregation, and post-quantum cryptography). I also build end-to-end healthcare analytics and clinical NLP pipelines, and I have applied modern NLP tooling to finance (e.g., sentiment/risk-oriented workflows). I’m comfortable with Python, PyTorch, Hugging Face, SQL, and Qiskit, and I prioritize clean, reproducible repos with pinned environments and clear run instructions. For a PhD, I’m interested in optimization for large models, robustness and privacy, and hardware-aware ML systems that run on real infrastructure—cloud/GPU, edge/IoT, and near-term/noisy quantum backends—across NLP, time-series, tabular, and multimodal data.



Experience



Education



Online Certificates



Skills

Programming & Platforms: Python (PyTorch, scikit-learn, NumPy, Pandas) · Git/Linux · Jupyter · Docker (basic) · GPUs · cloud · edge/IoT

Trustworthy ML & Stats: Transformers · GNNs · multimodal fusion · robust training · calibration & uncertainty · ablations & error analysis

Genomics & Functional Genomics: Multi-omics integration · CRISPR/perturbation data · ATAC-seq & regulatory signals · gene & target prioritization

Quantum & Optimization: Qiskit · VQE/QAOA · hybrid classical–quantum workflows · QUBO/Ising mappings · quantum-guided hyperparameter optimization

Privacy & Cryptography: Homomorphic Encryption (CKKS, BGV; OpenFHE/SEAL) · differential privacy · secure aggregation · post-quantum cryptography (KEMs & signatures; liboqs) · threat modeling

Data Engineering & Reproducibility: SQL · ETL pipelines · schema design · MLflow/W&B · environment pinning · unit tests · CI/CD (GitHub Actions) · DVC

Visualization & Writing: Matplotlib · dashboards · LaTeX/Overleaf · figure & table design

Domains: Regulatory genomics · functional genomics (CRISPR & epigenomics) · multi-omics integration · clinical prediction