I am a PhD Systems Engineer dedicated to the intersection of hardware acceleration and data science. For over a decade, I’ve built high-performance query engines and numerical computing libraries that squeeze every drop of power out of GPUs and diverse hardware stacks. My focus is on engineering the next generation of data processing operators—ensuring they are not just faster, but more scalable and resilient for the most demanding deep learning and analytics workloads.
- ⚡ Advanced query optimization and indexing strategies, consistently delivering large improvements in query engine throughput and responsiveness.
- 🎯 GPU acceleration, leveraging CUDA and libraries like PyTorch and CuDF to achieve 10x-50x speedups in data-intensive workloads.
- 🔧 Pioneer in PyTorch sparse tensor support, enabling efficient processing of large-scale, sparse datasets.
- 🧠 Algorithm design, creating innovative solutions for complex data processing challenges in big data environments.
- PyTorch - Tensors and dynamic neural networks in Python with strong GPU acceleration.
- Apache Arrow - A development platform for in-memory analytics.
- CuDF - GPU DataFrame library.
- BlazingSQL - A lightweight, GPU-accelerated SQL engine for Python, built on RAPIDS cuDF.
I have a deep passion for data structures and algorithms. What fascinates me most is how they evolve and adapt as they scale—like building a complex bridge with Lego blocks, where each optimization introduces new strengths and challenges. If you’re passionate about these topics too, feel free to connect with me on X (formerly Twitter)!