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January 13-17, 2026
Upcoming event: Explorations in Statistics, Probability, Learning, and Optimization Research.
We are delighted to announce the finalists for exSPLOre 26 Best Young Researcher Paper Competition. After a competitive review process, the committee has selected the following papers:
These finalists are invited to present their research at exSPLOre 2026 (Jan 13–17, 2026) at Ashoka University. Two winners will be determined following the presentations.
Congratulations to all the finalists!
Rapid advancements in data, compute, and algorithms are profoundly transforming our world, and we’re still in the early stages of this data science, machine learning, and AI revolution.
At SCDLDS, we conduct high-level applied and methodological research in data and learning sciences to aid in informed decision-making and address complex societal challenges. Initially, our work is focused on four key areas:
In addition to our research, we are committed to teaching. We train young minds through engaging coursework and projects, exposing them to a deep and varied mathematical toolkit that helps them uncover hidden patterns in large and complex datasets.
We look at many of the important challenges in financial mathematics through the lens of latest developments in machine learning. Some of the problems of interest include data driven portfolio risk measurement including market, credit and operational risk for large banks. Portfolio optimization while managing the return-risk trade off. Pricing and hedging high dimensional options. Measuring systemic risk in the banking system, and so on.
We are developing machine learning based models that combine historical data with output from physics based numerical weather prediction models to improve monsoon weather prediction in India. We are also using ideas from extreme value theory to develop algorithmic methodologies to predict weather related extreme events including high precipitation, heat waves, cyclones, flooding, etc.
Reinforcement learning finds wide applications in diverse areas including healthcare, autonomous vehicles, robotics, financial trading and portfolio risk measurement, traffic modelling. In the center we focus on methodological issues associated with the underlying reinforcement learning models to arrive at accurate and computationally efficient algorithms for massive RL models, exploiting latest developments in machine learning.
Infectious diseases such as Covid have been a subject of great deal of analysis through mathematical modelling, simulation and machine learning models. In the centre we will collect relevant data and develop cutting edge agent based simulation models models to help study disease spread and predict its evolution trajectory.
