Tianqi Chen is an Assistant Professor in the Machine Learning Department and Computer Science Department at Carnegie Mellon University and the Chief Technologist of OctoML. His research focuses on the intersection of machine learning and systems.
Tianqi's PhD thesis is titled "Scalable and Intelligent Learning Systems," which he completed in 2019 at the University of Washington. We discuss his influential work on machine learning systems, starting with the development of XGBoost,an optimized distributed gradient boosting library that has had an enormous impact in the field. We also cover his contributions to deep learning frameworks like MXNet and machine learning compilation with TVM, and connect these to modern generative AI.
- Episode notes: www.wellecks.com/thesisreview/episode48.html
- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter
- Follow Tianqi Chen on Twitter (@tqchenml)
- Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
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Tianqi Chen is an Assistant Professor in the Machine Learning Department and Computer Science Department at Carnegie Mellon University and the Chief Technologist of OctoML. His research focuses on the intersection of machine learning and systems.
Tianqi's PhD thesis is titled "Scalable and Intelligent Learning Systems," which he completed in 2019 at the University of Washington. We discuss his influential work on machine learning systems, starting with the development of XGBoost,an optimized distributed gradient boosting library that has had an enormous impact in the field. We also cover his contributions to deep learning frameworks like MXNet and machine learning compilation with TVM, and connect these to modern generative AI.
- Episode notes: www.wellecks.com/thesisreview/episode48.html
- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter
- Follow Tianqi Chen on Twitter (@tqchenml)
- Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
[30] Dustin Tran - Probabilistic Programming for Deep Learning
The Thesis Review
1 hour 2 minutes 50 seconds
4 years ago
[30] Dustin Tran - Probabilistic Programming for Deep Learning
Dustin Tran is a research scientist at Google Brain. His research focuses on advancing science and intelligence, including areas involving probability, programs, and neural networks.
Dustin’s PhD thesis is titled "Probabilistic Programming for Deep Learning", which he completed in 2020 at Columbia University.
We discuss the intersection of probabilistic modeling and deep learning, including the Edward library and the novel inference algorithms and models that he developed in the thesis.
- Episode notes: https://cs.nyu.edu/~welleck/episode30.html
- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter
- Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html
- Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
The Thesis Review
Tianqi Chen is an Assistant Professor in the Machine Learning Department and Computer Science Department at Carnegie Mellon University and the Chief Technologist of OctoML. His research focuses on the intersection of machine learning and systems.
Tianqi's PhD thesis is titled "Scalable and Intelligent Learning Systems," which he completed in 2019 at the University of Washington. We discuss his influential work on machine learning systems, starting with the development of XGBoost,an optimized distributed gradient boosting library that has had an enormous impact in the field. We also cover his contributions to deep learning frameworks like MXNet and machine learning compilation with TVM, and connect these to modern generative AI.
- Episode notes: www.wellecks.com/thesisreview/episode48.html
- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter
- Follow Tianqi Chen on Twitter (@tqchenml)
- Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
