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.
Sessions
At CERN (European Organization for Nuclear Research), machine learning models are developed and deployed for various applications, including data analysis, event reconstruction, and classification. These models must not only be highly sophisticated but also optimized for efficient inference. A critical application is in Triggers- systems designed to identify and select interesting events from an immense stream of experimental data. Experiments like ATLAS and CMS generate data at rates of approximately 100 TB/s, requiring Triggers to rapidly filter out irrelevant events. This talk will explore the challenges of deploying machine learning in such high-throughput environments and discuss solutions to enhance their performance and reliability.