Vyas Ramasubramani
Employers on the program
- 2026NVIDIA1 talk
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
- SciPy Conference (Scientific Computing with Python)
- PyCon (national Python conferences)
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| May 13, 2026 | PyCon US 2026 | PyCon (national Python conferences) | speaker | What Python Developers Need to Know About Hardware: A Practical Guide to GPU Memory, Kernel Scheduling, and Execution Models as NVIDIA Python developers building machine learning models often interact with GPUs “indirectly” through frameworks like PyTorch, TensorFlow, and JAX. | Video |
| Jul 7, 2025 | SciPy 2025 | SciPy Conference (Scientific Computing with Python) | speaker | Accelerated DataFrames for all: Bringing GPU acceleration to pandas and PolarsIn Python, data analytics users often prioritize convenience, flexibility, and familiarity over pure performance. The cuDF DataFrame library provides a…Show lessIn Python, data analytics users often prioritize convenience, flexibility, and familiarity over pure performance. The cuDF DataFrame library provides a pandas-like experience with from 10x up to 50x performance improvements, but subtle differences prevent it from being a true drop-in replacement for many users. | |
| Jul 8, 2024 | SciPy 2024 | SciPy Conference (Scientific Computing with Python) | speaker | No-Code-Change GPU Acceleration for Your Pandas and NetworkX WorkflowsWith datasets growing in both complexity and volume, the demand for more efficient data processing has never been higher. Pandas and NetworkX, the go-to Python…Show lessWith datasets growing in both complexity and volume, the demand for more efficient data processing has never been higher. Pandas and NetworkX, the go-to Python libraries for tabular and graph data processing, are very popular for their ease of use and flexibility. | |
| Jul 8, 2019 | SciPy 2019 | SciPy Conference (Scientific Computing with Python) | speaker | Freud: A Software Suite for High-Throughput Analysis of Nanoscale Simulation Data |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Carol Willing | 3 | — | 2026 |
| Sarah Kaiser | 3 | — | 2026 |
| Brigitta Sipőcz | 3 | — | 2025 |
| Eric Ma | 3 | — | 2025 |
| Julie Hollek | 3 | — | 2025 |
| Matthew Feickert | 3 | — | 2025 |
| Nezar Abdennur | 3 | — | 2025 |
| Avik Basu | 2 | — | 2026 |
| Adam Thompson | 2 | — | 2025 |
| Alex Monahan | 2 | — | 2025 |
| Ana Comesana | 2 | — | 2025 |
| Anant Mittal | 2 | — | 2025 |
| Bane Sullivan | 2 | — | 2025 |
| C.A.M. Gerlach | 2 | — | 2025 |
| Cordero Core | 2 | — | 2025 |
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.