Dr. Jennifer Ngadiuba is Associate Scientist with Wilson Fellowship at the Fermi National Accelerator Laboratory, the leading facility for particle physics research in the United States. She is specialized in the application of AI to particle physics towards more intelligent detector systems, data reduction and data analysis strategies for an efficient extraction of the most fundamental physics information from the multitude of data collected at the Large Hadron Collider (LHC), the world’s highest-energy particle physics experiment located at the CERN laboratory (Switzerland-France).

AI2050 Project

Through this fellowship, Jennifer is applying AI to particle physics by working towards more intelligent detector systems, data reduction and data analysis strategies. This will allow for an efficient extraction of the most fundamental physics information from the data collected at the Large Hadron Collider. AI can use patterns in the data from millions of collisions to find rare insights similar to finding a needle in a haystack. This will advance Hard Problem 4 (Opportunities) by empowering the experiments with innovative strategies that have the ability to expand their physics reach and hopefully lead to new scientific discoveries.

Project Artifacts

AI2050 Community Perspective — Jennifer Ngadiuba (2024)

A. Gandrakota, L. Zhang, A. Puli, K. Cranmer, J. Ngadiuba, R. Ranganath, and N. Tran. Robust anomaly detection for particle physics using multi-background representation learning. arXiv. 2023.

R. Liu, A. Gandrakota, J. Ngadiuba, M. Spiropulu, and J. Vlimant. Fast particle-based anomaly detection algorithm with variational autoencoder. NeurIPS. 2023.

R. Liu, A. Gandrakota, J. Ngadiuba, M. Spiropulu, and J. Vlimant. Efficient and robust jet tagging at the LHC with knowledge distillation. NeurIPS. 2023.