PeopleofSuperintelligence

Researcher

Jennifer Ngadiuba

Jennifer Ngadiuba is a particle physicist at Fermilab who brings fast machine learning into the real-time trigger systems of the Large Hadron Collider. After physics degrees in Milan and a University of Zurich PhD on the CMS experiment, she turned to machine learning as a CERN fellow in 2017 and coauthored the 2018 paper that introduced hls4ml, a package for running neural networks on FPGA chips. With Maurizio Pierini, she argued in 2021 for anomaly-detection algorithms in the LHC's level-one triggers, and in spring 2024 her team installed such a tool in the CMS trigger system. Her honours include a Schmidt Futures AI2050 Early Career Fellowship in 2022 and the 2024 IUPAP Early Career Scientist Prize in Particle Physics. She is an associate scientist with a Wilson Fellowship at Fermilab.

Badges

Timeline

  1. By 2013

    Milan studies and a Zurich PhD

    She earns bachelor's and master's degrees at the University of Milano-Bicocca and, as a master's student, works at Fermilab on tracking-detector R&D for the CMS upgrade. Her CERN research begins in 2013 with PhD studies at the University of Zurich, where she searches CMS data for new heavy resonances decaying to dibosons.

  2. 2017–2018

    The hls4ml library

    During a three-year CERN fellowship, she turns to machine learning in 2017. In 2018, she coauthors the paper that introduces hls4ml, a package that builds neural networks into FPGAs for LHC trigger systems with latencies around 100 ns.

  3. 2020

    Millikan postdoc at Caltech

    Italian-Nigerian and living in Geneva, she works at CERN in 2020 as a Robert A. Millikan Postdoctoral Scholar at Caltech, with support from Fermilab's Machine Intelligence group. In June 2020, she coauthors a method for automatically quantizing deep neural networks that reaches nanosecond inference on FPGAs while cutting resource use by a factor of 50.

  4. 2021

    Wilson Fellow at Fermilab

    She becomes a Wilson Fellow at Fermilab in 2021. In an August 2021 CERN Courier article with Maurizio Pierini, she argues that anomaly-detection algorithms belong in the level-one triggers of LHC experiments, built with hls4ml and Google's QKeras tool.

  5. April 2022

    Teaching hls4ml at DeepLearn

    In April 2022, she teaches ultra-low-latency neural-network inference on FPGAs with the hls4ml library at the DeepLearn 2022 Spring school in Guimarães, Portugal. She is a co-founder of the fast machine learning organization, a research collective of physicists, engineers and computer scientists.

  6. 2022–2023

    AI2050 and AI4HEP awards

    In late 2022, she becomes one of 15 Schmidt Futures AI2050 Early Career Fellows, with up to $300,000 over two years. She also wins a U.S. Department of Energy AI4HEP award of $2.96M over three years, which Fermilab announces in June 2023, to build physics-informed edge AI for CMS, the Deep Underground Neutrino Experiment and accelerator technology.

  7. 2024

    An IUPAP early career prize

    In spring 2024, she and her colleagues install their anomaly-detection tool in the CMS trigger system and collect a first data set. In July 2024, she receives the IUPAP's 2024 Early Career Scientist Prize in Particle Physics at ICHEP in Prague for ultra-fast machine learning and its use in anomaly detection.

  8. As of October 2026

    Associate scientist at Fermilab

    She is an associate scientist with a Wilson Fellowship at Fermilab, applying AI to detector systems and data reduction at the LHC. In October 2025, she gives Caltech's physics colloquium on anomaly detection and real-time triggers, and in January 2026 she coauthors a study of a self-driving trigger that adjusts its thresholds in real time.

Career

  1. Since 2021Fermi Research AllianceWilson Fellow
  2. 2020California Institute of TechnologyRobert A. Millikan Postdoctoral Scholar
  3. From 2017European Organization for Nuclear ResearchFellow
  4. Fast Machine Learning OrganizationCo-founder

Education

  1. From 2013University of ZurichPhD
  2. University of Milano-BicoccaBachelor's degree

From the biography.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes California Institute of Technology. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. People shaping AI work at a median of 3 organizations over a career: 3 for university starters and institute and lab starters, versus 2 for company startersIncludes California Institute of Technology. Across 6,853 people shaping AI with a dated work role, we estimate a median of 3 parent organizations over a career (mean 3.6), comparing role groups, starting sectors and starting decades from the 1950s to 2020–2026.
  3. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes European Organization for Nuclear Research. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes California Institute of Technology. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Jennifer Ngadiuba - Wikipedia en.wikipedia.org. Checked 20 October 2026.
  2. Fermilab Fellow Jennifer Ngadiuba receives Early Career Scientist Prize in Particle Physics news.fnal.gov. Checked 20 October 2026.
  3. Community Perspective - Jennifer Ngadiuba - AI2050 ai2050.schmidtsciences.org. Checked 20 October 2026.
  4. IUPAP Early Career Award Winner - Jennifer Ngadiuba | CMS Experiment cms.cern. Checked 20 October 2026.
  5. Untangling Particles with Artificial Intelligence caltech.edu. Checked 20 October 2026.
  6. [1804.06913] Fast inference of deep neural networks in FPGAs for particle physics arxiv.org. Checked 20 October 2026.
  7. [2006.10159] Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors arxiv.org. Checked 20 October 2026.
  8. Hunting anomalies with an AI trigger - CERN Courier cerncourier.com. Checked 20 October 2026.
  9. Jennifer Ngadiuba - DeepLearn 2022 Spring deeplearn.irdta.eu. Checked 20 October 2026.
  10. Fermilab researcher receives two prestigious awards in AI and ML research news.fnal.gov. Checked 20 October 2026.
  11. Jennifer Ngadiuba - AI2050 ai2050.schmidtsciences.org. Checked 20 October 2026.
  12. Physics Colloquium - Caltech Physics physics.caltech.edu. Checked 20 October 2026.
  13. [2601.08910] Towards a Self-Driving Trigger at the LHC: Adaptive Response in Real Time arxiv.org. Checked 20 October 2026.