Amir Toranjsimin

Biomedical AI Engineer · Full-Stack Developer

Summary

I build clinical AI, and I build the software a clinician actually opens. Those are usually two jobs held by two different people, and the handoff between them is where most medical AI quietly dies.

The research half is nine years of EEG, MRI and CT diagnostics. A reinforcement-learning approach to skin cancer diagnosis, published through IEEE. An autism screening framework built deliberately light, so it runs where there is no GPU budget. Quantitative EEG for bipolar classification, blockchain for medical image integrity, and a book on QEEG processing.

The engineering half is everything that happens after the paper. I took a precision health platform from an empty repository to production: a genomic engine holding just under a million classified variants, ACMG classification cross-referenced against ClinVar and GWAS Catalog, polygenic risk scores across a few hundred conditions, and role-based access for admins, doctors, technicians and patients. FastAPI and PostgreSQL underneath, Next.js on top, containerised and cloud-deployed with audit logging and HIPAA and GDPR constraints designed in from the first commit.

Core skills

Language: Python · MATLAB · TypeScript · JavaScript · SQL
Machine Learning & AI: PyTorch · TensorFlow · Keras · scikit-learn · CNNs & LSTMs · Reinforcement Learning · Self-Supervised Learning · Transfer Learning · Hugging Face · Claude · OpenAI
Signal & Image Processing: EEG · ECG · PPG · EEGLAB · Wavelet Transform · OpenCV · Medical Imaging · DICOM · Hough Transform
Backend & Data: FastAPI · PostgreSQL · NumPy · pandas · Matplotlib · Docker · Redis · NGINX
Frontend & Product: React · Next.js · Tailwind CSS · Motion · D3 & Recharts · Three.js · jsPDF · Vercel
Design & Workflow: Figma · UI/UX Design · Design Systems · Motion Design · Data Visualization · Git · GitHub · Jupyter
Domain: Clinical Genomics · ACMG Classification · Pharmacogenomics · ClinVar & GWAS Catalog · Polygenic Risk Scores · HIPAA & GDPR

Experience

Founding Engineer — end to end — Precision Health Platform

2024 — Present · Independent · Remote

Built the largest system I have shipped: patients register, complete intake, then upload blood work, microbiome panels and raw genotype files, and the platform runs continuous analysis rather than issuing a single report.
Wrote the genomic engine at its core — close to a million classified variants, ACMG-guideline classification cross-referenced against ClinVar and GWAS Catalog, polygenic risk scores across a few hundred conditions, plus drug-gene interaction analysis.
Built parsers for lab and microbiome reports including MITOswab and GI Effects, turning dense lab output into something a clinician can read, in English or Persian.
Designed the AI layer as an ensemble: several models run together where that measurably improved reliability, and a single model handles the tasks where it did not.
Shipped a personalised medical assistant that adapts to user feedback, plus a layer where a physician configures their own specialised agent for their patients.
Role-based access control and audit logging across admins, doctors, technicians and patients; interactive 2D and 3D visualisations mapping findings onto the body; exportable patient-ready clinical reports.
Containerised and cloud-deployed, with HIPAA and GDPR constraints designed in from the first commit rather than retrofitted.

AI & Signal Processing Engineer — Freelance — Upwork

2019 — Present · Freelance · Top Rated · Contract

Applied AI and signal processing for clients, most often in healthcare-adjacent domains. Top Rated with a consistent record of delivered projects.
Built a reinforcement learning model for optimising power generator scheduling across a 72-hour horizon.
Debugged a self-supervised contrastive learning pipeline for EEG motor imagery classification, then built a separate full pipeline from raw signal to trained model.
Found and fixed a subtle filtering bug in that pipeline that had been quietly corrupting results before anyone noticed the numbers were wrong.

Researcher — Medical Signal & Image Processing — Biomedical Research

2017 — 2024 · Research · Academic

Published across cancer diagnosis, autism detection, bipolar classification and blockchain applications in healthcare — 19 citations, h-index 2.
Authored a book on EEG and quantitative EEG (QEEG) processing and its clinical applications.
Ran diagnostic pipelines over EEG, MRI, CT, ECG and PPG data, from preprocessing and artefact removal through to trained, validated models.

Publications — 19 citations, h-index 2

Cancer diagnosis based on combination of artificial neural networks and reinforcement learning
A. Toranj Simin, S. M. G. Baygi, A. Noori · 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), 2020
DOI 10.1109/ICSPIS51611.2020.9349530
Robust low complexity framework for early diagnosis of autism spectrum disorder based on cross wavelet transform and deep transfer learning
A. Toranjsimin, S. Zahedirad, M. H. Moattar · SN Computer Science 5(2), 231, 2024
DOI 10.1007/s42979-023-02564-9
A novel deep learning approach for bipolar disorder classification via quantitative EEG
S. Zahedirad, A. Toranjsimin · TechRxiv / Authorea, 2024
DOI 10.22541/au.172489715.51548126/v1
Securing medical image integrity on the blockchain: a zero-watermark approach using IPFS and Ethereum
M. Afkhami, M. Baghani, A. Toranjsimin, H. Mahrooghi · Authorea, 2024
DOI 10.22541/au.173391945.55724873/v1

Book · Author. EEG and Quantitative EEG (QEEG) Processing and Clinical Applications

Applied research

Parkinson's disease detection — CNN + LSTM over MRI brain scans, targeting early-stage patterns
Epilepsy detection system — Full pipeline simulated through to LCADC hardware converter integration
Fracture detection in X-rays — Hough-transform image analysis for long bone fractures
Lung condition diagnosis from CT — Comparative study of GoogLeNet against Inception-v3
EEG clustering and pattern analysis — Preprocessing, feature extraction and k-means, backed by PSD analysis
QRS detection in noisy ECG — Robust heartbeat detection validated on the MIT-BIH Arrhythmia Database
Automated hip MRI classification — Preparation, augmentation and enhancement, then normal vs osseous lesion
ADHD brain signal classification — Signal processing and ML over EEG recorded during visual attention tasks
PPG signal analysis — ECG annotation techniques applied to peak detection in photoplethysmography
Face recognition using SVD — Singular value decomposition applied to a medical imaging context
Liver segmentation — Deep learning outlining liver regions in CT automatically
Heart sound classification — Segmentation and classification of recordings to flag abnormal patterns
Generator scheduling optimisation — Reinforcement learning over a 72-hour scheduling horizon
EEG motor imagery classification — Self-supervised contrastive pipeline debugged, then rebuilt end to end

Education

Master's degree, Biomedical Engineering

Sadjad University of Technology

Bachelor's degree, Biomedical Engineering

Biomedical Engineering

Continued learning: Python Programming · Statistical Analysis with SPSS · ReactJS Development · MATLAB for AI & Graphics