Machine Learning |https://doost.rice.edu/tags/machine-learning/Machine LearningHugoBlox Kit (https://hugoblox.com)en-usMon, 01 Jan 2024 00:00:00 +0000https://doost.rice.edu/media/icon_hu_702a800cd775dbac.pngMachine Learninghttps://doost.rice.edu/tags/machine-learning/ETHOShttps://doost.rice.edu/projects/ethos/Mon, 01 Jan 2024 00:00:00 +0000https://doost.rice.edu/projects/ethos/<p><strong>ETHOS</strong> develops a comprehensive, multi-dimensional testing framework for machine-learning components in Open RAN software, with the goal of accelerating the deployment of trustworthy, interoperable 5G/6G networks. Funded by the NTIA Public Wireless Supply Chain Innovation Fund, January 2024 – December 2028.</p>Annealed Langevin Dynamics for Massive MIMO Detectionhttps://doost.rice.edu/publications/zilberstein2022langevin/Wed, 01 Jun 2022 00:00:00 +0000https://doost.rice.edu/publications/zilberstein2022langevin/3DMLhttps://doost.rice.edu/projects/3dml/Thu, 01 Oct 2020 00:00:00 +0000https://doost.rice.edu/projects/3dml/<p><strong>3DML</strong> is a community research infrastructure for <em>Data, Design and Deployed</em> validation of machine learning for wireless networks and mobile applications. The platform enables data collection, ML model design, and at-scale deployment for next-generation wireless systems. Funded by NSF CCRI, October 2020 – September 2026. I serve as Co-PI on this project.</p>