Jaya Venkatesh
Employers on the program
- 2025–2026NVIDIA3 talks
The year around now
Talks in the past 12 months and announced talks ahead, one row per conference, January to December. Hover a mark for the talk.
Appearances by year
- PyData
- SciPy Conference (Scientific Computing with Python)
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| Jul 13, 2026 | SciPy 2026 | SciPy Conference (Scientific Computing with Python) | speaker | Accelerating Geospatial Analysis with GPUs as NVIDIA Geospatial analysis relies on raster data — n-dimensional arrays where each cell holds a spatial measurement. The scale of modern remote sensing data makes…Show lessGeospatial analysis relies on raster data — n-dimensional arrays where each cell holds a spatial measurement. The scale of modern remote sensing data makes CPU-based workflows impractical, but raster operations are naturally parallelizable and well suited for GPU acceleration. | |
| Jul 13, 2026 | SciPy 2026 | SciPy Conference (Scientific Computing with Python) | tutorial | Deploying and debugging GPU accelerated Python workloads (Room HSEC 2-110) as NVIDIA As GPU acceleration becomes essential for scaling Python workloads, many developers face new challenges: understanding installation, managing dependencies, and…Show lessAs GPU acceleration becomes essential for scaling Python workloads, many developers face new challenges: understanding installation, managing dependencies, and deploying GPU-enabled environments. Even experienced Python users can struggle to integrate GPUs effectively or troubleshoot performance issues. | |
| Dec 8, 2025 | PyData Boston 2025 | PyData | speaker | Accelerating Geospatial Analysis with GPUs as NVIDIA Geospatial analysis often relies on raster data, n‑dimensional arrays where each cell holds a spatial measurement. Many raster operations, such as computing…Show lessGeospatial analysis often relies on raster data, n‑dimensional arrays where each cell holds a spatial measurement. Many raster operations, such as computing indices, statistical analysis, and classification, are naturally parallelizable and ideal for GPU acceleration. | Video |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Jacob Tomlinson | 2 | 3 | 2026 |
| Naty Clementi | 2 | 3 | 2026 |
| Benjamin Batorsky | 2 | — | 2026 |
| Daina Bouquin | 2 | — | 2026 |
| Eric Ma | 2 | — | 2026 |
| Katrina Riehl | 2 | — | 2026 |
Speakers with the same name are merged into one entry, so this page may combine more than one person. Affiliations are shown as each conference listed them.