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Indian-Origin Couple Donates $20 Million To University Of North Texas For AI, Advanced Analytics And Student Research | Watch

New York: An Indian-origin couple has donated USD 20 million to its alma mater in Texas, giving a push to studies on artificial intelligence and advanced analytics at the institute, underscoring contribution by immigrants to help build the “American dream”. Anuradha and Vikas Sinha are paying opportunity forward with a $20M gift to establish UNT’s new College of Artificial Intelligence and…

Indian-Origin Couple Donates $20 Million To University Of North Texas For AI, Advanced Analytics And Student Research | Watch

An Indian-origin couple, Anuradha and Vikas Sinha, have donated $20 million to the University of North Texas (UNT) to establish a new College of Artificial Intelligence and Advanced Analytics. This generous gift will create opportunities for students and faculty in AI, intelligent systems, data analytics and related fields, reflecting the Sinhas' belief in UNT's role in shaping the future.

The college will be organized into two academic divisions: Division of Applied Intelligence and Informatics, focusing on building and deploying intelligent systems, and Division of Information and Learning Applications, examining how these systems operate within human and institutional contexts. The Sinhas' investment in UNT has grown over time, with previous gifts establishing the Anuradha and Vikas Sinha Department of Data Science and the Sinha Innovation Lab for Data Science.

UNT's president, Harrison Keller, emphasized the importance of artificial intelligence in shaping the future, stating that the Sinha College will prepare students to build, study and apply these technologies responsibly. The $20 million gift will support six permanent endowments, including those for college leadership, student scholarships, endowed professorships, entrepreneurship and incubation, advanced research and emerging technologies.

Written by urgent.news from Free Press Journal's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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