Sanjiban Sengupta
Sanjiban is a Doctoral Student at CERN, affiliated to the University of Manchester. He is researching on optimization strategies for efficient Machine Learning Inference for the High-Luminosity phase of the Large Hadron Collider at CERN within the Next-Gen Triggers Project. Previously, he was a Summer Student at CERN in 2022, and also contributed at CERN-HSF via the Google Summer of Code Program in 2021. In the development of SOFIE, he was particularly involved in the development of the Keras and PyTorch Parser, storage functionalities, machine learning operators based on ONNX standard, Graph Neural Networks support, etc. Moreover, he volunteered as a Mentor for the contributors of Google Summer of Code 2022, and again in 2023, 2024 and 2025, and the CERN Summer Students of 2023 working on CERN’s ROOT Data Analysis Project.
Previously, Sanjiban spoke at PyCon India 2023 about Python interfaces for Meta’s Velox Engine. He also presented a talk on the Velox architecture at PyCon Thailand 2023. He has been contributing to open-source projects on data science and engineering that includes ROOT, Apache Arrow, Substrait, etc.
Session
At CERN—the European Organization for Nuclear Research—machine learning is applied across a wide range of scenarios, from simulations and event reconstruction to classifying interesting experimental events, all while handling data rates in the order of terabytes per second. As a result, beyond developing complex models, CERN also requires highly optimized mechanisms for model inference.
From the ML4EP team at CERN, we have developed SOFIE (System for Optimized Fast Inference code Emit), an open-source tool designed for fast inference on ML models with minimal dependencies and low latency. SOFIE is under active development, driven by feedback not only from high-energy physics researchers but also from the broader scientific community.
With upcoming upgrades to CERN’s experiments expected to increase data generation, we have been investigating optimization methods to make SOFIE even more efficient in terms of time and memory usage, while improving its accessibility and ease of integration with other software stacks.
In this talk, we will introduce SOFIE and present novel optimization strategies developed to accelerate ML inference and reduce resource overhead.