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Somnath Ma­zum­dar

Tenure Track Assistant Professor

Subjects
Digitalisation Machine learning Technology Security

Primary research areas

Privacy-Enhancing Computation (Data Focused)

It enables secure data processing and sharing while safeguarding confidentiality and meeting regulatory standards, fostering trusted collaboration across organizations.

Transfer learning for value creation

It enhances AI model accuracy even with limited domain-specific data, enabling businesses to unlock new insights and transform data into a tangible competitive advantage.

Cloud-Native Performance Engineering

It is essential for achieving seamless scalability, efficiency, and cost-effectiveness. By embedding performance engineering early in the lifecycle, organizations can ensure resilient services, ultimately turning performance into a decisive strategic advantage.

I explore problems, choose the tech, and drive innovation

My research confronts one of society’s greatest technological imperatives: creating digital ecosystems where privacy, security, and trust are not compromises, but guarantees. By advancing blockchain, cloud/fog/IoT platforms, and machine learning models, I aim to redefine how sensitive data and code are protected during computation, ensuring that security scales seamlessly with innovation. The impact reaches far beyond technical domains: empowering individuals with sovereignty over their data, enabling companies to operate responsibly under strict regulations, and fostering transparent systems that strengthen public trust. I am driven by the vision of a digital future where progress and ethics advance hand in hand. As threats to privacy and cybersecurity intensify, I see an urgent need for solutions that do more than mitigate risks. They must transform how society interacts with technology

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