Systems engineer with a PhD in computer science and over a decade of hands-on experience accelerating analytics, I've zeroed in on GPU-accelerated query engines and high-performance numerical computing stacks. Throughout my career, I've crafted sophisticated parallel software that taps into the raw power of diverse hardware accelerators—like GPUs, pushing them to their limits in real-world applications. I'm excited to dive in and elevate the functional depth, raw speed, scalability, and rock-solid reliability of those essential data processing operators at the heart of analytics engines or deep learning stacks.
- ⚡ 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)!