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Home | Andhra Pradesh | Nit Andhra Pradesh Inks An Mou With Linkedin

NIT Andhra Pradesh inks an MoU with LinkedIn

Hyderabad: National Institute of Technology (NIT) Andhra Pradesh (AP) has recently signed a Memorandum of Understanding (MoU) with LinkedIn for a joint research in the area of intelligent cloud computing. The MoU was signed by Prof. CSP Rao, Director, NIT-AP, and Naga Krishna R. Sathvalli, Director, Business Systems Program, LinkedIn. Chidambaran Kollengode, Director, Data and […]

By Telangana Today
Updated On - 30 January 2022, 06:50 PM
NIT Andhra Pradesh inks an MoU with LinkedIn
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Hyderabad: National Institute of Technology (NIT) Andhra Pradesh (AP) has recently signed a Memorandum of Understanding (MoU) with LinkedIn for a joint research in the area of intelligent cloud computing.

The MoU was signed by Prof. CSP Rao, Director, NIT-AP, and Naga Krishna R. Sathvalli, Director, Business Systems Program, LinkedIn. Chidambaran Kollengode, Director, Data and AI Platforms division of LinkedIn spearheads this research collaboration on behalf of LinkedIn.


Under the agreement, a research grant will be awarded to the principal investigator Dr. Karthick Seshadri, Assistant Professor, NIT-AP and Co-Investigator Dr. Nagesh Bhattu Sristy, Assistant Professor, NIT-AP, for solving open research problems in enabling intelligent and elastic cloud computing.

Speaking about the initiative, Prof. Rao said the association of the two organisations would enable joint research in the niche area of intelligent cloud computing and in fostering research to address industry-relevant problems by academia.

“I appreciate the researchers at NIT-AP for providing extensive training to their research team on Statistical Modeling, Cloud Computing and Machine Learning, to impart the necessary technical know-how for meeting the objectives of the first MoU,” Prof. Rao said.

“Scaling is a big challenge in a cloud environment and truly elastic model is typically viewed as a financial optimization problem; however, we have attempted to explore the technical implications underlying this problem. To address the problem of scaling, there is a need for an efficient workload characterization model. I am happy to remark that this collaboration has been promising and fruitful in our steps to realize a truly elastic cloud,” Kollengode said.


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