Ramasuri Narayanam

Short CV: Ramasuri Narayanam is a Senior Research Scientist at Adobe Research, having joined in September 2021 after a decade at IBM Research – India. He is a recognized expert in computational game theory and network science, with about 35 peer-reviewed publications in top-tier CS venues and over 40 granted USPTO patents. An IEEE Senior Member and ACM Distinguished Speaker, he has delivered 30+ invited talks, including 11 in the last five years. He plays key leadership roles in the research community, serving as Area Chair for AAMAS 2025 and contributing to major AI conferences like AAAI, IJCAI, AAMAS, and WWW. At Adobe, he has significantly advanced the AEP AI Assistant, particularly in NL-to-SQL and data value enhancement. He has organized four workshops, including the upcoming ICLR 2025 workshop. Previously, at IBM, he developed AI-driven analytics solutions for telcos, retail, and social media analytics. His hobbies include driving, traveling, and exploring new places.

Title of Talk 1: Building AI Co-Pilots: Design, Models, and Research Challenges

Synopsis: I propose an innovative and novel topic for this talk in which the aim is to discuss the practical aspects associated with Co-Pilots and the underlying scientific aspects. Toward this end, this talk provides conceptual underpinnings of the use of Large Language Models (LLMs) in the design of Co-Pilots and AI Assistants. It will further bring out how LLMs can enhance and complement the vanilla from Natural Language Processing (NLP) and Machine Learning (ML) in the design of co-pilots. 

Title of Talk 2: Strategic and Computational Aspects of Social Network Science

Synopsis: This lecture provides the conceptual underpinnings of the use of game theoretic models as well as online multi-agent learning models in social network analysis and brings out how these models supplement and complement existing approaches for social network analysis. The first part of the lecture provides rigorous foundations of relevant concepts in game theory, mechanism design, network science, and online learning in multi-agent network systems. The second part of the lecture brings out how game theoretic approach and online multi-agent learning approach help analyze key problems in network science better and also how to apply these technical concepts to problem solving in a rigorous way. In particular, it presents a comprehensive study of a few contemporary and pertinent problems in social networks such as social network formation games, social network monetization, design of incentive mechanisms, and economics of networks.

Title of Talk 3: Social Network Analysis: Introduction, Key Problems, Applications

Synopsis: This lecture provides introduction to social network science and then proceeds to highlight key representative problems in this area. Finally, it presents several applications of social network analysis along with key resources to conduct in this area. Below is a detailed description of the materials in this lecture.