Joint Research Meeting

ISBIS StatFin 2026

7-11 December, 2026

Workshop

The StatFin2026 Workshop aims to provide participants exposure to the Principles, Techniques and Tools of Data Science including their applications to Macroeconomics, Finance and Complexity of Economics.

Date and Venue 7-8 Dec, 2026 Chennai Mathematical Institute

Workshop 1: How to Train your Language Models

Workshop 2: Asset Pricing Meets Econometrics: Classical and Bayesian Regression in Practice

Workshop 1: How to Train your Language Models

By Sudhir Kumar Coriolis Technologies, Pune, India and Sourish Das Chennai Mathematical Institute

In this workshop, you will learn how to build and adapt your own Small Language Models (SLMs) for domain-specific tasks. We will cover two practical approaches:

1. Few-shot prompting to get strong performance with minimal examples, and
2. Parameter-efficient fine-tuning of an open-source SLM using LoRA (Low-Rank Adaptation).

You will also learn how to create and use synthetic data for fine-tuning, testing, and validation, and how to assess model quality with simple, reliable evaluation checks. By the end of the workshop, you will have a clear, end-to-end workflow for selecting an SLM, preparing data, tuning the model, and validating it for real use cases.

Workshop 2: Asset Pricing Meets Econometrics: Classical and Bayesian Regression in Practice

By M.A. Rahman, IIT Kanpur

This workshop provides a structured introduction to classical and Bayesian approaches to linear regression, with applications to financial econometrics. Using stock price data for Amazon, participants will learn to estimate and interpret the Capital Asset Pricing Model (CAPM) and the Fama–French three-factor model under both frameworks. The workshop is designed for advanced undergraduate students, graduate students and researchers in economics, finance, and statistics.

1. Foundations and Classical Linear Regression
2. Bayesian Linear Regression in Depth

By the end of the workshop, participants will understand the conceptual and practical differences between classical and Bayesian regression methods, and will be able to implement both approaches in empirical settings. The workshop consists of four lectures of approximately 1.5-2 hours each, combining theoretical exposition with applied illustration. Supporting materials, including datasets and code templates, will be provided.

Prerequisite Participants are expected to have a basic background in probability, statistics, and linear regression.