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AyushmanRaha/README.md

About

I work across systems engineering, applied machine learning, and embedded software β€” building things where correctness and performance both have to hold under real constraints. That means reasoning carefully about concurrency, memory layout, and failure behavior, whether the target is a multi-core process, a model running entirely on-device, or a microcontroller with a few hundred kilobytes of RAM.


Focus Areas

⚑ Low-Latency & Concurrent Systems

Lock-free data structures built on explicit memory ordering, cache-conscious layouts that avoid false sharing, and durable, zero-copy I/O paths β€” designed to stay correct under concurrent load, not just fast on paper.

🧠 Applied Machine Learning

End-to-end pipelines that go from messy, real-world data to a trained model to a usable output β€” local-first inference, model comparison, and evaluation against ground truth, with as much attention on the engineering around the model as on the model itself.

πŸ”§ Embedded & Real-Time Systems

RTOS-based firmware with clear task separation, control logic kept independent of hardware so it can be unit-tested on a host machine, and fail-safe behavior when sensors or connectivity misbehave.


Selected Work

A lock-free, low-latency message broker library in C++20, built around a cache-line-aligned SPSC ring buffer with explicit acquire/release atomics and a memory-mapped write-ahead log for zero-copy durability. CI covers sanitizers (ASan/UBSan/TSan), static analysis, and a Google Benchmark suite β€” performance is reported only from measured, reproducible runs.

C++20 Lock-Free SPSC mmap WAL


A local-first desktop application for monocular depth estimation. Runs PyTorch and optional ONNX Runtime models entirely on-device to generate depth maps, compare model architectures side by side, evaluate output against ground truth, and export 3D point clouds β€” with no cloud calls or external API keys.

Python PyTorch ONNX Runtime FastAPI Electron


An RTOS-based ESP32 room-sensing and relay-control node. FreeRTOS task separation across sensing, control, and web layers; a control-logic core kept pure and unit-tested independently of hardware; a local dashboard and HTTP API; and fail-safe relay behavior when sensors go stale or faults occur.

C/C++ FreeRTOS ESP32 PlatformIO


Tech Stack

Languages

C++ Python C Java

Systems & Embedded Tooling

Linux CMake Git GoogleTest PlatformIO FreeRTOS

Machine Learning & Data

PyTorch ONNX Runtime XGBoost Scikit-learn OpenCV Pandas NumPy

App & Backend

FastAPI Electron Streamlit Redis

Databases

PostgreSQL MongoDB


Currently Exploring

  • Lock-free / concurrent data structures in modern C++
  • RTOS fundamentals and microcontroller programming (STM32, ESP32)
  • ROS 2 basics for robotics and autonomous systems
  • Applied ML tooling for real-world, messy data

GitHub Stats


Open to systems, applied ML, and embedded engineering conversations β€” let's connect.

Pinned Loading

  1. Aether-Stream Aether-Stream Public

    C++20 low-latency lock-free message broker with SPSC queues, WAL persistence, mmap storage, metrics, CLI demos, benchmarks, and CI.

    C++ 2

  2. DepthLensPro DepthLensPro Public

    Local-first desktop app for monocular depth estimation, model comparison, PyTorch/ONNX benchmarking, ground-truth evaluation, and 3D point-cloud export.

    Python 2

  3. EdgeGuard-ESP32 EdgeGuard-ESP32 Public

    EdgeGuard-ESP32 β€” Built an ESP32-based RTOS-style smart room monitoring node using DHT11, HC-SR04, LDR, relay module, and local web dashboard; implemented task-based firmware, GPIO drivers, state-m…

    C++