Benjamin Bengfort
where else to find them
what they talk about
all 5 in the filters
where they keep going back
no series more than once — all 6 in the filters
on a stage with
- Rebecca Bilbroonce
- Sean Murphyonce
who talks about the same things
- Paco Nathan8 talks
- Vincent D Warmerdam22 talks
- Eric J. Ma8 talks
- Alon Nir5 talks
- Andreas Dewes3 talks
- Christopher Hoult5 talks
| title | conference | date |
|---|---|---|
| Practical Multi Armed Bandits | PyData Virginia 2025 | 2025-04-18 |
Concrete takeaways: implemented systems combine epsilon-greedy with Thompson sampling, compute updates in NumPy and SciPy, and store minimal state for fast… | ||
| Data Structures for Data Privacy: Lessons Learned in Production | PEPR '22 | 2022-06-23 |
Decentralized protocol implements a peer‑to‑peer PKI and a global directory yellow pages to mutually authenticate VASPs, uses gRPC/protobuf plus mTLS, and… | ||
| Visual Diagnostics for More Effective Machine Learning | PyData Miami 2019 | 2019-01-09 |
Demonstrates a concrete library and patterns: the Yellowbrick Visualizer that plugs into the scikit‑learn API, producing feature, classification, clustering… | ||
| Dynamics in Graph Analysis Adding Time as a Structure for Visual and Statistical | PyData DC 2016 | 2016-10-07 |
Claims that embedding time structurally reduces traversal cost and clarifies dynamics are demonstrated with concrete tools and counts: NetworkX and graph_tool… | ||
| Dynamics in Graph Analysis: Adding Time as a Structure for Visual and Statisti | PyData Carolinas 2016 | 2016-09-14 |
Claims that embedding time as a structural element improves graph analytics and visualization, implemented with NetworkX and Graph Tool and tooling called… | ||
| Natural Language Processing and Hadoop | Strata Conference New York + Hadoop World 2013 | 2013-10-28 |
Shows how to run NLP at scale by pairing classic pipeline steps — tokenization, parsing, feature extraction and model training — with Hadoop to process… | ||
| nothing matches | ||