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The Future of Computer Science and Engineering in the Age of AI

Artificial Intelligence & Data Science

The Future of Computer Science and Engineering in the Age of AI

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29 Apr 2026 18:30 - 20:00 (IST)
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Speakers

Dr. Sudipta Roy
Dr. Sudipta Roy
Associate Professor, Artificial Intelligence & Data Science, Jio Institute, India
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Dr. Larry Birnbaum
Dr. Larry Birnbaum
Professor, Computer Science, Northwestern University, USA
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Sunil
Dr. Sunil Kumar Vuppala
AI Partner of ArisGlobal, Former Director of Data Science at Ericsson, Top 10 Data Scientists in India
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Dinesh
Dr. Dinesh Manocha
Paul Chrisman Iribe Professor of Computer Science, Electrical and Engineering, Distinguished University Professor, College Park, Maryland, United States
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Session Brief: 

Now a days, Artificial Intelligence is not just another subfield of computer science, it is reshaping the very foundations of how computing is designed, built, and applied. From software development to systems architecture, from data to decision-making, AI is blurring traditional boundaries within CSE.

This shift raises deeper questions:

  • What remains core to computer science in an AI-first world?

  • How do we balance enduring fundamentals with rapidly evolving technologies?

  • What should the next generation of engineers know, build, and question?
     

This panel brings together diverse perspectives to explore how CSE must evolve-academically, technically, and philosophically to remain relevant, rigorous, and impactful in the age of AI.

Key Discussion Areas

1. What is the “core” of CSE in an AI-first world?
Are algorithms, systems, and theory still central, or is AI redefining the foundation itself?
2. AI: Tool or Paradigm Shift?
Is AI an additional layer, or the lens through which all computing will be designed and built?
3. The Changing Role of Engineers
With AI generating code, does the role shift from coding to system design, validation, and orchestration?
4. From Models to Real-World Systems
Are we preparing students to deploy scalable, reliable, production-grade AI systems, not just train models?
5. Security, Privacy, and Trust by Design
How do we ensure AI systems are secure, robust, and privacy-preserving from the outset?