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Global Industry Academia Summit-2026, Industry Interaction with Students - Event Theme - AI Week - Infosys Speakers

  • 19 Jan 2026
  • 09:30 am – 04:30 pm
  • Seminar Hall - First Floor (Main Building)
Global Industry Academia Summit-2026, Industry Interaction with Students - Event Theme - AI Week - Infosys Speakers

About the Event

DBUU is organizing a AI week from 19th to 26th of January 2026 on campus. Under this many a events will be scheduled.

This perticular event is the invited interaction with industry experts. The industry in focus is Computing and company in focus is Infosys. Two speakers will grace the occasion.

1) Speaker: Kamalkumar Rathinasamy: Kamal is a Distinguished Technologist at Infosys focusing on Model Engineering, with publications and competition wins at NeurIPS’24, Kaggle AI4Code’22, AAAI-MLPS’21, WMT’20/ACL’20/EMNLP’20, and SQuAD’19.

Session title: Resource dynamics of LLMs. Session details:

This session explores the interplay of model size, training tokens, compute, and memory in large language models. We will examine scaling laws and resource trade-offs that shape efficiency, performance, and deployment feasibility.

(2) Speaker: Harshini Kumarasubramian: Harshini is a Specialist Programmer at Infosys researching on architectural innovations and training methodologies of Language Models. She has co-authored NT-Java-1.1B, Enterprise EM and Enterprise SLM & presented the work on NT-Java-1.1B in NeurIPS'24 Workshop, 'Adaptive Foundation Models'.

Session title: From Models to Agents: Navigating the Agentic Era:

This session introduces the principles and foundations of Agentic AI, where models evolve from passive predictors to autonomous problem-solvers. We will discuss core architectures, design patterns, and emerging applications shaping this new era of intelligence.

Aim of the Event

  • Fundamental understanding of large language models.
  • Explore the interplay of model size, training tokens, compute, and memory in large language models.
  • Understanding of scaling laws and resource trade-offs that shape efficiency, performance, and deployment feasibility.
  • Introduction to the principles and foundations of Agentic AI, where models evolve from passive predictors to autonomous problem-solvers.
  • Understanding of core architectures, design patterns, and emerging applications shaping this new era of intelligence.

Participating Schools

FACULTY

All Faculty Members

School of Engineering & Computing

2nd Year, 3rd Year

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