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About

Deep learning, inside the clinic.

I'm Digvijay Patil — an AI/ML engineer building systems that integrate directly into the PACS/DICOM workflows radiologists use every day.

At aycan Medical Systems (acquired by the Paratus healthcare IT group), I conceived, designed, deployed, and maintain the entire AI/ML stack — six production systems serving hospitals, clinical-trial organizations, and FDA-regulated workflows. My background pairs an M.S. in Robotics & AI with deep, hands-on expertise in DICOM standards and vendor-neutral imaging architecture.

The throughline is closing the gap between academic AI and the reading room: models that are accurate, run on-premise for HIPAA compliance, and survive contact with real clinical data and real radiologist workflows.

Research interests

Where I'm pointed.

The path

Career timeline

  1. AI/ML Engineer — aycan Medical / Paratus

    2023 — Present

  2. Software Engineering Intern — Revolutionary Integration

    2022 — 2023

  3. M.S. Robotics & AI — University at Buffalo

    2021 — 2022

  4. Engineer — EV / IoT startup

    2019 — 2021

  5. Software Engineer — SureClaim (medical NLP)

    2019 — 2020

Education

  • M.S. Management Information Systems

    University at Buffalo (SUNY)

    incoming 2026

  • M.S. Engineering Science — Robotics & AI

    University at Buffalo (SUNY)

    2021 — 2022

  • B.E. Computer Science & Engineering

    Visvesvaraya Technological University

    2015 — 2019

Stack

  • ML / AI

    PyTorch · nnU-Net · Florence-2 · ConvNeXt · CatBoost · Whisper · MediaPipe · Transformers

  • Medical imaging

    pydicom · pynetdicom · nibabel · GDCM · NIfTI · DICOM C-FIND/MOVE/STORE · GSPS · MedDream

  • LLM / inference

    Ollama · vLLM (dual-GPU) · llama-cpp · EmbeddingGemma · GGUF quantization · on-prem

  • Systems

    Python · JavaScript · C++ · Docker · WireGuard · PostgreSQL · Flask · HL7 v2.x · Cloudflare

GPU bench

Optimized across the fleet.

From Apple Silicon MPS to datacenter Pascal — the same model, tuned to whatever's under it. A GGUF quantization ladder (Q8 → Q4 → Q2) and dual-GPU tensor parallelism stretch 7B–70B models onto prosumer hardware.

Apple M1

Metal · MPS

unified memory

dev

Apple M4 Pro

Metal · MPS

unified memory

dev

RTX 5070

CUDA · Blackwell

12 GB GDDR7

work

RTX 5090

CUDA · Blackwell

32 GB GDDR7

personal

Dual Tesla P40

CUDA · Pascal

48 GB · tensor-parallel

server