Eric W. Tramel Publications Talks ★ Eric W. Tramel,
"Synthetic Data Powering Pretraining ,"
UC Berkeley EE 194/290: Scalable AI (Berkeley, USA) ,
2026.
Eric W. Tramel,
"Generating Zero-Shot Hard-Case Hallucinations: A Synthetic and Open Data Approach ,"
Databricks Data + AI Summit (San Francisco, USA) ,
2025.
E. W. Tramel,
"Federated Learning: Rewards & Challenges of Distributed Private ML ,"
Qcon.ai (San Francisco, USA) ,
2019.
E. W. Tramel,
"(Panelist) Federated Learning: ML with Privacy on the Edge ,"
Fast Forward Labs Webinar ,
2018.
E. W. Tramel,
"Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines ,"
International Traveling Workshop on Interactions between Sparse Models and Technology (iTWIST) ,
2016.
E. W. Tramel,
"Belief Propagation & Approximations: Discrete Tomography ,"
Workshop on Sparse Tomographic Reconstruction: Theoretical and Numerical Aspects ,
2015.
E. W. Tramel,
"Discrete Reconstruction for Electron Tomography ,"
General Congress of the Société Fracaise de Physique ,
2015.
E. W. Tramel,
"Introduction to Compressed Sensing ,"
École de Physique des Houches, «Biophysique: de la Mesure au Modèle en biologie» ,
2015.
E. W. Tramel,
"A probabilistic approach to compressed sensing: Robust Algorithms ,"
International Traveling Workshop on Interactions between Sparse Models and Technology (iTWIST) ,
2014.
Tech Reports & Preprints ★ Nvidia Nemotron Team,
"Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning ,"
arXiv preprint arXiv:2512.20848 ,
2025.
Unlearn.ai DTG Team,
"Digital twin generators for disease modeling ,"
arXiv preprint arXiv:2405.01488 ,
2024.
Constance Beguier & Jean Ogier du Terrail & Iqraa Meah & Mathieu Andreux & Eric W Tramel,
"Differentially private federated learning for cancer prediction ,"
arXiv preprint arXiv:2101.02997 ,
2021.
Constance Beguier & Mathieu Andreux & Eric W Tramel,
"Efficient sparse secure aggregation for federated learning ,"
arXiv preprint ,
2020.
G. Rochette, A. Manoel, E. W. Tramel,
"Efficient Per-Example Gradient Computations in Convolutional Neural Networks ,"
arXiv [cs.LG]: 1912.06015 ,
2019.
P. Courtiol, E. W. Tramel, M. Sanselme, & G. Wainrib,
"Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach ,"
arXiv [stat.ML]: 1802.02212 ,
2018.
B. Goujaud, E. W. Tramel, P. Courtiol. M. Zaslavskiy, & G. Wainrib,
"Robust Detection of Covariate-Treatment Interactions in Clinical Trials ,"
arXiv [stat.AP]: 1712.08211 ,
2017.
Books F.Krzakala, F. Ricci-Tersenghi, L. Zdeborová, R. Zecchina, E. W. Tramel, & L. F. Cugliandolo,
"Statistical Physics, Optimization, Inference, and Message-Passing Algorithms ,"
Oxford University Press ,
2016.
James E Fowler and Sungkwang Mun and Eric W Tramel,
"Block-based Compressed Sensing of Images and Video ,"
Foundations and Trends in Signal Processing ,
2012.
E. W. Tramel,
"Distance-Weighted Regularization for Compressed-Sensing Video Recovery and Supervised Hyperspectral Classification ,"
Mississippi State University (Dissertation) ,
2012.
Book Chapters E. W. Tramel, S. Kumar, A. Giurgiu, & A. Montanari,
"Statistical Estimation: From Denoising to Sparse Regression and Hidden Cliques ,"
Statistical Physics, Optimization, Inference, and Message-Passing Algorithms, Oxford University Press ,
2016.
Journal Articles C Murray & C Kusiak & A Vanderbeek & D Bertolini & E Tramel,
"Boosting Clinical Trial Power in Parkinson's disease (PD) with AI-Generated Digital Twins ,"
MOVEMENT DISORDERS ,
2024.
Anna A Vidovszky & Charles K Fisher & Anton D Loukianov & Aaron M Smith & Eric W Tramel & Jonathan R Walsh & Jessica L Ross,
"Increasing acceptance of AI‐generated digital twins through clinical trial applications ,"
Clinical and Translational Science ,
2024.
Jean Ogier du Terrail & Armand Leopold & Clément Joly & Constance Béguier & Mathieu Andreux & Charles Maussion & Benoît Schmauch & Eric W Tramel & Etienne Bendjebbar & Mikhail Zaslavskiy & Gilles Wainrib & Maud Milder & Julie Gervasoni & Julien Guerin & Thierry Durand & Alain Livartowski & Kelvin Moutet & Clément Gautier & Inal Djafar & Anne-Laure Moisson & Camille Marini & Mathieu Galtier & Félix Balazard & Rémy Dubois & Jeverson Moreira & Antoine Simon & Damien Drubay & Magali Lacroix-Triki & Camille Franchet & Guillaume Bataillon & Pierre-Etienne Heudel,
"Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer ,"
Nature Medicine ,
2023.
Huili Chen & Jie Ding & Eric Tramel & Shuang Wu & Anit Kumar Sahu & Salman Avestimehr & Tao Zhang,
"ActPerFL: Active personalized federated learning ,"
Amazon Science ,
2022.
Mathieu Andreux & Jean Ogier Du Terrail & Constance Beguier & Eric W Tramel,
"Siloed federated learning for multi-centric histopathology datasets ,"
Springer International Publishing ,
2020.
M. Zaslavskiy, S. Jégou, E. W. Tramel, & G. Wainrib,
"ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors ,"
Computational Toxicology ,
2019.
★ E. W. Tramel, M. Gabrié, A. Manoel, F. Caltagirone, F. Krzakala,
"Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines ,"
Physical Review X ,
2018.
Boshra Rajaei & Eric W Tramel & Sylvain Gigan & Florent Krzakala & Laurent Daudet,
"Intensity-only optical compressive imaging using a multiply scattering material and a double phase retrieval approach ,"
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ,
2016.
Florent Krzakala & Federico Ricci-Tersenghi & Lenka Zdeborová & Riccardo Zecchina & Eric W Tramel & Leticia F Cugliandolo,
"Statistical Physics, Optimization, Inference, and Message-Passing Algorithms: Lecture Notes of the Les Houches School of Physics: Special Issue, October 2013 ,"
Oxford University Press ,
2016.
E. W. Tramel, A. Dremeau, & F. Krzakala,
"Approximate Message Passing with Restricted Boltzmann Machine Priors ,"
Journal of Statistical Mechanics: Theory and Experiment (JSTAT) ,
2016.
C. Chen, W. Li, E. W. Tramel, & J. E. Fowler,
"Reconstruction of Hyperspectral Imagery from Random Projections using Multihypothesis Prediction ,"
IEEE Transactions on Geoscience and Remote Sensing ,
2014.
C. Chen, W. Li, E. W. Tramel, M. Cui, S. Prasad, & J. E. Fowler,
"Spectral-Spatial Preprocessing Using Multihypothesis Prediction for Noise-Robust Hyperspectral Image Classification ,"
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ,
2014.
M. Trocan, E. W. Tramel, J. E. Fowler, & B. Pesquet-Popescu,
"Compressed-Sensing Recovery of Multiview Image and Video Sequences using Signal Prediction ,"
Multimedia Tools and Applications ,
2014.
W. Li, E. W. Tramel, S. Prasad, & J. E. Fowler,
"Nearest Regularized Subspace for Hyperspectral Classification ,"
IEEE Transactions on Geoscience and Remote Sensing ,
2013.
Patents Mathieu Galtier & Mathieu Andreux & Camille Marini & Eric W Tramel & Inal Djafar & Jean du Terrail,
"Systems and methods for administrating a federated learning network ,"
US Patent Application US20240046147A1 ,
2024.
Pierre Courtiol & Eric W Tramel & Marc Sanselme & Gilles Wainrib,
"Systems and methods for image classification ,"
US Patent Application US20240346651A1 ,
2024.
Pierre Courtiol & Eric W Tramel & Marc Sanselme & Gilles Wainrib,
"Systems and methods for image classification ,"
US Patent US11482022B2 ,
2022.
Ankit Mohan & Siu-Kei Tin & Eric W Tramel,
"Systems and Methods for Compressive Light Sensing Using Multiple Spatial Light Modulators ,"
US Patent US9160900B2 ,
2015.
Conference Papers Enmao Diao & Eric W Tramel & Jie Ding & Tao Zhang,
"Semi-supervised federated learning for keyword spotting ,"
2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) ,
2023.
Jie Ding & Eric Tramel & Anit Kumar Sahu & Shuang Wu & Salman Avestimehr & Tao Zhang,
"Federated learning challenges and opportunities: An outlook ,"
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ,
2022.
★ Huili Chen & Jie Ding & Eric W Tramel & Shuang Wu & Anit Kumar Sahu & Salman Avestimehr & Tao Zhang,
"Self-aware personalized federated learning ,"
Advances in Neural Information Processing Systems (NeurIPS) ,
2022.
A. Manoel, F. Krzakala, E. W. Tramel, & L. Zdeborová,
"Streaming Bayesian Inference: Theoretical Limits and Mini-batch Approximate Message-Passing ,"
Proc. Allerton Conf. on Comm. Control & Comp. ,
2017.
B. Rajaei, E. W. Tramel, S. Gigan, F. Krzakala, & L. Daudet,
"Intensity-only Optical Compressive Imaging Using a Multiply Scattering Material: A Double Phase Retrieval System ,"
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP) ,
2016.
E. W. Tramel, A. Manoel, F. Caltagirone, M. Gabrié, & F. Krzakala,
"Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines ,"
Proc. IEEE Info. Theory Workshop ,
2016.
J. Barbier, E. W. Tramel, & F. Krzakala,
"Scampi: A Robust Approximate Message-Passing Framework for Compressive Imaging ,"
Proc. Int. Mtg. on High-Dimensional Data Driven Science (HD^3) ,
2015.
★ M. Gabrié, E. W. Tramel, & F. Krzakala,
"Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer Free Energy ,"
Neural Information Processing Systems (NeurIPS) ,
2015.
★ A. Manoel, E. W. Tramel, F. Krzakala, & L. Zdeborová,
"Sparse Estimation with the Swept Approximated Message-Passing Algorithm ,"
Proc. Int. Conf. on Machine Learning (ICML) ,
2015.
Andre Manoel & Florent Krzakala & Eric W. Tramel & Lenka Zdeborova,
"Swept Approximate Message Passing for Sparse Estimation ,"
Proceedings of ICML ,
2015.
Wei Li & Saurabh Prasad & Eric W Tramel & James E Fowler & Qian Du,
"Decision fusion for hyperspectral image classification based on minimum-distance classifiers in thewavelet domain ,"
2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP) ,
2014.
F. Krzakala, A. Manoel, E. W. Tramel, & L. Zdeborová,
"Variational Free Energies for Compressed Sensing ,"
Proc. IEEE Int. Symp. on Information Theory (ISIT) ,
2014.
W. Li, S. Prasad, E. W. Tramel, J. E. Fowler, & Q. Du,
"Decision Fusion for Hyperspectral Image Classification Based on Minimum-Distance Classifiers in the Wavelet Domain ,"
IEEE China Summit on Signal and Info. Processing ,
2014.
Chen Chen and Eric W Tramel and James E Fowler,
"Compressed-sensing Recovery of Images and Video Using Multihypothesis Predictions ,"
Asilomar Conference on Signals, Systems, and Computers ,
2011.
J. E. Fowler and S. Mun, & E. W. Tramel,
"Multiscale Block Compressed Sensing with Smoothed Projected Landweber Reconstruction ,"
Proc. European Signal Processing Conf. (EUSIPCO) ,
2011.
E. W. Tramel & J. E. Fowler,
"Video Compressed Sensing with Multihypothesis ,"
Proc. of the IEEE Data Compression Conf. (DCC) ,
2011.
M. Trocan, T. Maugey, E. W. Tramel, J. E. Fowler, & B. Pesquet-Popescu,
"Compressed Sensing of Multiview Images using Disparity Compensation ,"
Proc. of the IEEE Int. Conf. on Image Processing (ICIP) ,
2010.
M. Trocan, T. Maugey, E. W. Tramel, J. E. Fowler, & B. Pesquet-Popescu,
"Multistage Compressed-Sensing Reconstruction of Multiview Images ,"
Proc. of the IEEE Int. Workshop on Multimedia Signal Processing (MMSP) ,
2010.