Bhaskarjit Sarmah

Bhaskarjit Sarmah

Head of Financial Services AI Research

About

Bhaskarjit Sarmah, Head of Financial Services AI Research at Domyn, leverages over 11 years of data science expertise across diverse industries. Previously, at BlackRock, he pioneered machine learning solutions to bolster liquidity risk analytics, uncover pricing opportunities in securities lending, and develop market regime change detection systems using network science. Bhaskarjit's proficiency extends to natural language processing and computer vision, enabling him to extract insights from unstructured data and deliver actionable reports. Committed to empowering investors and fostering superior financial outcomes, he embodies a strategic fusion of data-driven innovation and domain knowledge in the world's largest asset management firm.

Text-to-SQL is, by most leaderboards, a solved problem. Models post impressive scores and the progress feels real, and then the same architecture gets pointed at a real enterprise warehouse and the numbers fall off a cliff.
Why? The benchmarks we celebrate are built on small, tidy, well-documented schemas with neatly phrased questions and a single obvious answer. Reality is the opposite: hundreds of tables with cryptic names, business terms that map to no column directly, joins that depend on knowledge living only in someone's head, and questions where the same word means three different things depending on who's asking. The way we score these systems doesn't survive the move either, a result that's subtly wrong gets treated the same as one that's obviously right, so the failures that hurt most are exactly the ones our metrics are blind to.

This session looks at where the gap actually lives: in the questions we test against, and in the scores we trust. We'll unpack why ""it works in the demo keeps turning into it broke in production, and what better evaluation could look like.

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In this hands-on workshop, participants will learn how reinforcement learning (RL) is used to train large language model–based agents that can make sequential decisions, interact with environments, call tools autonomously, and improve performance through experience. 
 
We will cover RL fundamentals for LLM agents, extend Markov Decision Processes (MDPs) to agent settings, explore modular RL frameworks, and dive into practical implementations using OpenPipe’s Agent Reinforcement Trainer (ART). By the end, attendees will understand how to design, train, and evaluate RL-based LLM agents for real-world tasks. 

*Note: These are tentative details and are subject to change. 

 

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