AI Research Forum and Summit Focused on Agentic AI Announced

Jithin George designs and builds agentic AI systems that operate with autonomy, context awareness, and real-time decision-making capabilities. 

His work focuses on creating AI agents that can reason, act, and adapt across complex environments—bridging the gap between intent and execution in enterprise and developer workflows.

Munjal Shah is the co-founder and CEO of Hippocratic AI, a company building the first safety-focused large language model for healthcare. 

A serial entrepreneur with a track record in AI and machine learning, he has founded and led multiple startups at the intersection of technology and healthcare, with previous ventures acquired by Google and Alibaba.

Dr. Fatma Tarlaci is an engineering leader with a decade of expertise in AI. Formerly, she served as CTO and VP of Engineering at startups, where she led the development and deployment of robust AI solutions and led high-impact engineering teams. 

As a technical advisor, she helps startups navigate AI adoption and productization, while also training the next generation of AI engineers and data scientists as an Adjunct Assistant Professor in Computer Science at UT Austin. 

She recently stepped into the role of Chief AI Officer at SOAR AI, where she combines strategic leadership with hands-on development and helps guide the technical direction of their AI initiatives.

Munther A. Dahleh is the William A. Coolidge Professor of Electrical Engineering and Computer Science at MIT and the founding director of the MIT Institute for Data, Systems, and Society (IDSS). 

His research explores decision-making under uncertainty, networked systems, and the economics of data, drawing on fields from control theory to distributed learning. 

He leads cross-disciplinary efforts at MIT to understand how data and algorithms shape complex systems, from financial and power networks to social and neural systems.

Pascal Weinberger is an AI entrepreneur and investor whose work spans neuroscience-inspired machine learning, enterprise AI platforms, and moonshot innovation. 

He began his career at the intersection of AI and neuroscience, later joining Google Brain and founding a successful AgTech AI startup. 

He has led AI efforts at Telefonica’s Moonshot Factory, built enterprise-scale AI infrastructure at Augustus Intelligence, and now advises and invests in emerging AI ventures as a Venture Partner at AI Capital.

Saroop Bharwani is the founder of Senso and a longtime builder at the intersection of AI, human behavior, and enterprise systems. 

With a background in computer engineering and neuropsychology, he has spent over a decade applying machine intelligence to predict and influence consumer behavior in large-scale environments. 

His current work focuses on advancing the role of AI in shaping more adaptive and anticipatory financial systems.

Anirudh Narayan focuses on accelerating the adoption of agentic AI by bridging technical innovation with real-world application. 

With a background in growth strategy and data-driven product development, he works at the intersection of AI deployment and business transformation, enabling organizations to unlock new forms of scale and autonomy through intelligent agents.

Dylan Hadfield-Menell is a leading researcher in AI alignment and directs the Algorithmic Alignment Group at MIT CSAIL. His work focuses on ensuring that agentic AI systems behave in ways that reflect human goals, values, and oversight, particularly in multi-agent and human-AI contexts.

As a Schmidt Sciences AI2050 Fellow, he is advancing methods to support the safe, beneficial, and trustworthy deployment of AI in the real world.

Tim Kraska is an Associate Professor at MIT CSAIL and founding co-director of the Data System and AI Lab (DSAIL). His research explores how machine learning can transform the foundations of data systems—improving performance, adaptability, and user interaction. 

From rethinking core components with learned models to building intelligent interfaces for data science, his work enables more autonomous, accessible, and trustworthy AI systems at scale.