Shivam Handa

Papers

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About

Shivam Handa is a researcher advancing the frontier of probabilistic programming, with a focus on making inference both flexible and programmable. His most cited work, "Probabilistic Programming with Programmable Inference" (2018, 37 citations), introduces the concept of inference metaprogramming—a paradigm that replaces rigid, black-box inference algorithms with user-defined, composable inference strategies. This contribution provides new language constructs and a formal foundation, enabling practitioners to tailor inference to specific models and data, dramatically improving efficiency and expressiveness. By demonstrating the first practical implementation of this approach, Handa has helped bridge the gap between probabilistic modeling and real-world deployment. His work empowers researchers and engineers to build custom inference pipelines, reducing reliance on one-size-fits-all solutions. With a total of 47 citations across his top papers, Handa’s research is shaping how probabilistic programming languages are designed and used, making them more accessible and powerful for complex Bayesian modeling tasks. His contributions are particularly influential in fields like machine learning, computational statistics, and AI, where flexible inference is critical.