MS
Malachi Schram
Pacific Northwest National Laboratory, Thomas Jefferson National Accelerator Facility, Carleton University, Bubble Technology Industries
Research areas: Physics and Astronomy · Nuclear and High Energy Physics · Particle physics theoretical and experimental studies · Quantum Chromodynamics and Particle Interactions · Particle Detector Development and Performance
0000-0002-3475-2871
Employment
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Pacific Northwest National Laboratory Chief Scientist2026 - Present
- 2021 - 2026
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Pacific Northwest National Laboratory Scientist2012 - 2021
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Bubble Technology Industries Scientist2011 - 2012
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McGill University Postdoct Scholar2007 - 2011
Education
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Carleton University Ph.D/Particle Physics2003 - 2008
Projects & Funding
Projects & funding information is unavailable.
Publications (39)
- Continual Learning for Particle Accelerators Save
- Toward an event-level analysis of hadron structure using differential programming Save
- Applying Transfer Learning for Street‐Scale Nuisance Flood Forecasting in Coastal‐Urban Environments Save
- Explainable physics-based constraints on reinforcement learning for accelerator optimization Save
- Uncertainty based Online Ensemble on Non-Stationary Data for Fusion Science Save
- Geometric GNNs for charged particle tracking at GlueX Save
- Outlook towards deployable continual learning for particle accelerators Save
- Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City Save
- Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators Save
- SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver Save
- UNCERTAINTY-AWARE DEEP LEARNING FRAMEWORK FOR FORECASTING COASTAL WATER LEVEL IN VIRGINIA BEACH Save
- Distance preserving machine learning for uncertainty aware accelerator capacitance predictions Save
- Robust errant beam prognostics with conditional modeling for particle accelerators Save
- A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia Save
- Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex Save
- Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory Save
- Uncertainty aware anomaly detection to predict errant beam pulses in the Oak Ridge Spallation Neutron Source accelerator Save
- Physics with CEBAF at 12 GeV and future opportunities Save
- Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders Save
- Colloquium : Machine learning in nuclear physics Save