Portfolio of
ISHWAR SONI
Computer Vision & Motion Processing Engineer
Specializing in human motion synthesis and processing large-scale datasets (AMASS). I build robust preprocessing pipelines and intelligent systems that bridge research and production.
Engineering Mindset
I enjoy solving confusing technical problems more than building demo projects. My work revolves around making messy systems reliable, whether it's normalizing AMASS motion datasets or deploying prediction models to production.
/Engineering mindset: I prioritize reliability and reproducibility.
/Debugging complex systems: I dive deep into coordinate transforms and data inconsistencies.
/Working with messy data: I build robust pipelines to handle real-world edge cases.
/Shipping over showing off: I value deployed, working systems over theoretical perfection.
Experience
Semantic Labs
Worked on large-scale AMASS motion datasets and BVH ↔ SMPL-H preprocessing pipelines.
- Built and optimized BVH ↔ SMPL-H preprocessing pipelines for large-scale datasets.
- Fixed critical coordinate system normalization issues affecting downstream models.
- Resolved orientation flips and scaling bugs in motion data.
- Implemented motion stabilization and smoothing algorithms.
- Optimized data pipeline performance for faster processing.
Featured Projects
A collection of technical case studies focusing on system architecture, data pipelines, and scalable infrastructure.
StableMotion / Motion Processing System
Technologies
A robust system for preprocessing and stabilizing human motion data for ML training.
The Challenge
Raw motion data from various sources (AMASS, custom captures) often has inconsistent coordinate systems, global orientation errors, and jitter.
The Solution
Implemented automated coordinate detection and transformation modules, along with a custom smoothing algorithm.
Project Impact: Clean training data is the bottleneck for detailed motion generation models. This pipeline automated weeks of manual cleanup.
End-to-end ML production system for real estate price prediction.
The Challenge
Existing models lacked a production-ready interface and deployment strategy.
The Solution
Optimized model serialization and implemented robust input validation in FastAPI.
Project Impact: Demonstrates full-stack ML engineering capability, from EDA to Dockerized deployment.
A reusable, modular machine learning pipeline architecture.
The Challenge
Spaghetti code in notebooks leading to data leakage and reproducibility issues.
The Solution
Implemented custom Scikit-learn transformers and pipelines.
Project Impact: Proves ability to write clean, maintainable, and leak-proof ML code.
High-volume corporate data extraction and structuring system.
The Challenge
Need to aggregate data for 10,000+ companies from diverse sources.
The Solution
Implemented retry logic, rotation, and robust parsing strategies.
Project Impact: Shows capability in data engineering and handling unstructured data at scale.
Technical Skills
Computer Vision & Motion
- Human Motion Analysis
- Pose Estimation
- SMPL
- SMPL-H
- BVH
Machine Learning & Data
- NumPy
- Pandas
- Scikit-learn
- Model Evaluation
- Data Engineering
Programming & Tools
- Python
- Git
- GitHub
- Jupyter Notebook
- Docker
- FastAPI