Umakant Kulkarni

Research Scientist @Nokia Bell Labs

New Providence, NJ, US
MOBILE NUMBERS
+91 *********19

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WORK HISTORY

Jan 2026 — Present

Research Scientist @Nokia Bell Labs

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New Providence, NJ, US

Conducting research at the intersection of cellular networks (5G/6G), distributed systems, network security, and AI/ML to advance next-generation mobile network systems.

EDUCATION

2015 — 2017

Northeastern University

Master’s Degree, Computer Systems Networking and Telecommunications

N/A

Purdue University

Doctor of Philosophy - PhD, Computer Science

2011 — 2015

Pune Vidhyarthi Griha's College Of Engineering And Technology

Bachelor’s Degree, Electrical, Electronics and Communications Engineering

SKILLS

Research3gppC++ElectronicsMicrosoft WordTcl-TkNetworkingManagementPowerpointMicrosoft ExcelPythonMicrosoft PowerpointPcb DesignLinuxLteTelecommunicationsEmbedded SystemsCMicrosoft OfficeMatlabMultisim

ABOUT UMAKANT KULKARNI

I am a Research Scientist in the Mobile Network Systems Department at Nokia Bell Labs. I graduated with a Ph.D. in Computer Science from Purdue University, advised by Prof. Sonia Fahmy. My dissertation developed operator-centric strategies to enhance performance and reliability of cloud-native 5G core and Wi-Fi access networks.Before joining Purdue, I worked as a Software Engineer at Microsoft, focusing on software development, architecture designing and building automation infrastructure for Affirmed Networks’ cloud-native 5G mobile core solution. Prior to that, I completed my master’s in Computer Systems Networking & Telecommunications from Northeastern University, Boston, MA, USA.My research sits at the intersection of cellular networks, distributed systems, and network security with a focus on integrating modern AI-driven techniques into operator networks. I build practical methods for configuration, resource management, orchestration, and elastic scaling to improve the performance and reliability of cloud-native mobile core systems. I am especially interested in how learning-based approaches, including LLMs and other ML techniques, can make cellular networks more efficient, robust, and easier to operate at scale.

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