Bio
Research: Information theoretic analysis of neural models, explainable AI, detecting bias
Background: Software development at USGS, ML researcher at US Army Corps of Engineers, Data Scientist in private sector
Recent Works:
- M. Mattei, M. J. Meni, R. Hillner, E. Christ, K. Chandra, V. Chivukula, and R. T. White (2024). Evaluating Performance of Physics-Informed Neural Networks for Predicting 3D Blood Flow Patterns. (Submitted)
- M. J. Meni, R. T. White, M. L. Mayo, and K. Pilkiewicz (2024). Entropy-based Guidance of Deep Neural Networks for Accelerated Convergence and Improved Performance. Accepted at Information Sciences.
- K. Taylor, M. J. Meni, and R. T. White (2024). PEEKing into the Universe. Conference for Undergraduate Women in Physics (CUWiP). Poster presentation.
- M. J. Meni, T. Mahendrakar, R. T. White, M. L. Mayo, and K. Pilkiewicz (2024). Taking a PEEK into YOLOv5 for Satellite Component Recognition via Entropy-based Visual Explanations. AIAA SCITECH Forum 2024.
- M. J. Meni and R. T. White. Information-Informed Neural Networks: Probabilistically Interpretable Neural Decisionmaking. Invited talk at Frontiers in Geoscience Seminar, Earth and Environmental Sciences (EES) division and the Center for Space and Earth Science (CSES), Los Alamos National Lab, Los Alamos, NM, Aug 13, 2023.
- M. J. Meni, K. Daust, T. Jia, and R. T. White (2023). Wind Forecasting with Recurrent Neural Networks. PIMS Workshop on Forecasting and Mathematical Modeling for Renewable Energy. (Poster presentation).
- M. N. Attzs, T. Mahendrakar, M. J. Meni, R. T. White, and I. Silver (2023). A comparison of tracking-by-detection algorithms for real-time satellite component tracking. 37th Annual Small Satellite Conference.
