.. _acknowledgement:
Acknowledgements
================
It takes a lot of talent and dedication to build the AI products that drive our mission of connecting the
world's professionals to make them more productive and successful. We would like to thank the following folks for their
instrumental support in the development of DuaLip:
.. container:: custom-title
Pytorch-based DuaLip Solver
We thank the following contributors for their work on the design, implementation,
and evaluation of the Pytorch-based DuaLip solver, including algorithmic development, system
architecture, and experimental validation:
- `Gregory Dexter `_
- `Aida Rahmattalabi `_
- `Sangana Garg `_
- `Qingquan Song `_
- `Zhipeng Wang `_
.. container:: custom-title
Spark-based DuaLip Solver
We acknowledge the contributors to the original Spark-based DuaLip solver for establishing the
core ideas, Spark-based implementations, and experimental groundwork that informed and
enabled the Pytorch-based DuaLip solver redesign:
- `Rohan Ramanath `_
- `Kinjal Basu `_
- `Yao Pan `_
- `Miao Cheng `_
- `Sathiya Keerthi `_
- `Amol Ghoting `_
- `Konstantin Salomatin `_
- `Kenneth Tay `_
- `Borja Ocejo `_
- `Ayan Acharya `_
We also thank the following individuals and teams for their support through detailed and thoughtful discussions, infrastructure, technical review, or advisory contributions:
- `Shaunak Chatterjee `_
- `Ankan Saha `_
- `Souvik Ghosh `_
- `Shenyinying (Ruby) Tu `_
- `Yuan Gao `_
- `Deepak Kumar `_
- `Lingjie Weng `_
- `Shipeng Yu `_
- `Hema Raghavan `_
- `Romer Rosales `_
- `Suju Rajan `_
- `Igor Perisic `_
.. container:: custom-title
Additional Support
Finally, we would also like to thank `Rahul Mazumder `_ for the great collaboration during the
algorithmic development of the solver across both Spark-based and Pytorch-based versions of the solver.