Thomas J. Fan
Employers on the program
- 2025Modal1 talk
- 2022–2023Quansight3 talks
- 2019Columbia University4 talks
Appearances by year
- PyData
- SciPy Conference (Scientific Computing with Python)
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| Jul 7, 2025 | SciPy 2025 | SciPy Conference (Scientific Computing with Python) | speaker | Dive into Flytekit's Internals: A Python SDK to Quickly Bring your Code Into Production as Modal Flyte is a Linux Foundation OSS orchestrator built for Data and Machine Learning workflows focused on scalability, reliability, and developer productivity.…Show lessFlyte is a Linux Foundation OSS orchestrator built for Data and Machine Learning workflows focused on scalability, reliability, and developer productivity. Flyte’s Python SDK, Flytekit, empowers developers by shipping their code from their local environments onto a cluster with one simple CLI command. | |
| Nov 6, 2024 | PyData NYC 2024 | PyData | speaker | Pushing Cython to its Limits in Scikit-learn | Video |
| Nov 1, 2023 | PyData NYC 2023 | PyData | speaker | Scikit-learn on GPUs with Array APIScikit-learn was traditionally built and designed to run machine learning algorithms on CPUs using NumPy arrays. In version 1.3, scikit-learn can now run a…Show lessScikit-learn was traditionally built and designed to run machine learning algorithms on CPUs using NumPy arrays. In version 1.3, scikit-learn can now run a subset of its functionality with other array libraries, such as CuPy and PyTorch, using the Array API standard. | Video |
| Jul 10, 2023 | SciPy 2023 | SciPy Conference (Scientific Computing with Python) | speaker | Can There Be Too Much Parallelism? as Quansight Labs Numerical Python libraries can run computations on many CPU cores with various parallel interfaces. When we simultaneously use multiple levels of parallelism,…Show lessNumerical Python libraries can run computations on many CPU cores with various parallel interfaces. When we simultaneously use multiple levels of parallelism, it may result in oversubscription and degraded performance. | |
| Jul 10, 2023 | SciPy 2023 | SciPy Conference (Scientific Computing with Python) | speaker | Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem as Quansight Labs The array API standard (https://data-apis.org/array-api/) is a common specification for Python array libraries, such as NumPy, PyTorch, CuPy, Dask, and JAX.…Show lessThe array API standard (https://data-apis.org/array-api/) is a common specification for Python array libraries, such as NumPy, PyTorch, CuPy, Dask, and JAX. This standard will make it straightforward for array-consuming libraries, like scikit-learn and SciPy, to write code that uniformly supports all of these libraries. | |
| Nov 9, 2022 | PyData NYC 2022 | PyData | speaker | Parallelism in Numerical Python Libraries as Quansight Labs Python libraries can compute on multiple CPU cores using a variety of parallel programming interfaces such as multiprocessing, pthreads, or OpenMP. | Video |
| Jul 11, 2022 | SciPy 2022 | SciPy Conference (Scientific Computing with Python) | speaker | Counting on Poisson Regression with Scikit-learn | |
| Dec 7, 2019 | PyData Austin 2019 | PyData | speaker | Do You Want To Build A Forest? as Columbia University scikit-learn provides two popular ways to build tree ensembles: Gradient Boosting Decision Trees (GBDT) and Random Forests. | Video |
| Nov 4, 2019 | PyData New York City 2019 | PyData | speaker | Deep Dive into scikit-learn's HistGradientBoosting Classifier and Regressor as Columbia University Gradient boosting decision trees (GBDT) is a powerful machine-learning technique known for its high predictive power with heterogeneous data. | Video |
| Nov 4, 2019 | PyData New York City 2019 | PyData | speaker | What's now in NumFOCUS projects? (Part 2) as Columbia University Quick updates from NumFOCUS projects | |
| Jul 8, 2019 | SciPy 2019 | SciPy Conference (Scientific Computing with Python) | speaker | Skorch - A Union of Scikit-learn and PyTorch as Columbia University |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Dharhas Pothina | 5 | — | 2024 |
| James Powell | 5 | — | 2024 |
| Roni Kobrosly | 5 | — | 2024 |
| Hugo Bowne-Anderson | 4 | — | 2025 |
| Gil Forsyth | 4 | — | 2024 |
| Allen Downey | 4 | — | 2023 |
| James A. Bednar | 4 | — | 2023 |
| Julia Signell | 4 | — | 2023 |
| Melissa Weber Mendonça | 4 | — | 2023 |
| Thomas Caswell | 4 | — | 2022 |
| John Kirkham | 3 | 1 | 2025 |
| Hannah Aizenman | 3 | 1 | 2023 |
| Ralf Gommers | 3 | 1 | 2023 |
| Saul Shanabrook | 3 | 1 | 2023 |
| Travis Oliphant | 3 | 1 | 2023 |
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.