About
Akhilesh Warty
Machine Learning Engineer building real-time perception systems and edge AI deployment pipelines.
I build C++/ROS2 perception systems, optimize low-latency inference on NVIDIA Jetson hardware, and design cloud-based ML experimentation infrastructure.
Experience
Research Assistant — Machine Learning & Data Pipelines
Jun 2025 — PresentBoston University, School of Public HealthBoston, MA
- Built scalable data processing and ML experimentation workflows for 100GB+ biomedical datasets, supporting large-scale model evaluation across longitudinal cohort and health datasets.
- Developed a distributed multiverse experimentation framework evaluating 12,000+ model configurations
- Designed a Python metadata management system standardizing 15,000+ dataset variables for consistent cohort definitions and data lineage
Graduate Student Researcher
Mar 2024 — Jun 2025Boston University AICV LabBoston, MA
Developed computer vision pipelines for medical image analysis, combining classical techniques with deep learning models.
- Built end-to-end pipelines integrating preprocessing, model training, and evaluation
- Trained classification and segmentation models using transfer learning and data augmentation
- Addressed class imbalance and evaluated performance using standard ML metrics
- Applied classical computer vision techniques to improve image quality and feature extraction
Ground Station Software Engineer
Aug 2021 — Dec 2022Space ConcordiaMontreal, QC
Developed software systems for spacecraft telemetry processing and sensor integration in a CubeSat mission.
- Built modular systems for real-time telemetry ingestion and processing from IMU sensors
- Developed tools for data streaming, visualization, and debugging
- Collaborated in an Agile team on a Canadian Space Agency-supported mission
Skills
Python
Primary language for ML systems, data pipelines, and experimentation infrastructure — model training, evaluation, and ETL workflows.
C++
Real-time ROS2 perception nodes, TensorRT/ONNX inference, and CMake-based builds for low-latency edge deployment on NVIDIA Jetson hardware.
Computer Vision
Object detection, tracking, pose estimation, and model evaluation — from architecture design through quantized edge deployment.
Mlops Cloud
Cloud ML experimentation infrastructure — spot-instance training, experiment tracking, and CI/CD for reproducible model pipelines.
Also working with
Areas of interest
Computer vision
Object detection, tracking, pose estimation, and segmentation models.
Edge AI & robotics
Real-time ROS2 perception, TensorRT/ONNX inference, and Jetson deployment.
ML experimentation infra
Experiment tracking, dataset lineage, and reproducible training pipelines.
Real-time systems
Low-latency inference, multi-model pipelines, and strict latency budgets.
Cloud & MLOps
Spot-instance training, CI/CD, and infrastructure as code on AWS.
Data engineering
Large-scale ETL pipelines for biomedical and imaging datasets.