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Publications and Preprints
Machine Learning
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning K. Liu, W. Zhu, Y. Shen, S. Liu, N. Razavian, K. Geras, C. Fernandez-Granda. Conference on Computer Vision and Pattern Recognition (CVPR) 2023
Deep Probability Estimation S. Liu, A. Kaku, W. Zhu, M. Leibovich, S. Mohan, B. Yu, H. Huang, L. Zanna, N. Razavian, J. Niles-Weed, C. Fernandez-Granda. International Conference on Machine Learning (ICML) 2022
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations S. Liu, K. Liu, W. Zhu, Y. Shen, C. Fernandez-Granda. Conference on Computer Vision and Pattern Recognition (CVPR) 2022
Adaptive Test Allocation for Outbreak Detection and Tracking in Social Contact Networks P. Batlle, J. Bruna, C. Fernandez-Granda, V. M. Preciado. SIAM Journal on Control and Optimization, 60(2), pp.S274-S293. 2022
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training S. Liu, X. Li, Y. Zhai, C. You, Z. Zhu, C. Fernandez-Granda, Q. Qu. Proc. 34th Conference on Neural Information Processing Systems (NeurIPS) 2021 Code
Early-Learning Regularization Prevents Memorization of Noisy Labels S. Liu, J. Niles-Weed, N. Razavian, C. Fernandez-Granda. Proc. 34th Conference on Neural Information Processing Systems (NeurIPS) 2020 Code. Video
Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization A. Kaku, S. Mohan, A.Parnandi, H. Schambra, C. Fernandez-Granda. 2020
Data-driven Estimation of Sinusoid Frequencies G. Izacard, S. Mohan, C. Fernandez-Granda. Proc. 33rd Conference on Neural Information Processing Systems (NeurIPS) 2019, 5128-5138. Website. Example notebook. Code
A Learning-Based Framework for Line-Spectra Super-resolution G. Izacard, B. Bernstein, C. Fernandez-Granda. International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2019, 3632-3636
Super-resolution via Transform-invariant Group-sparse Regularization, C. Fernandez-Granda, E. J. Candès. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013 Supplementary material. Matlab code
Image and Video Denoising
Evaluating Unsupervised Denoising Requires Unsupervised Metrics A. Marcos-Morales, M. Leibovich, S. Mohan, J. L. Vincent, P. Haluai, M. Tan, P. A. Crozier, C. Fernandez-Granda. International Conference on Machine Learning (ICML) 2023
Deep denoising for scientific discovery: A case study in electron microscopy S. Mohan, R. Manzorro, J. L. Vincent, B. Tang, D. Y. Sheth, D. S. Mattesson, E. P. Simoncelli, P. A. Crozier, C. Fernandez-Granda. IEEE Transactions on Computational Imaging 8, 585-597. 2022 Website with code and data
Adaptive Denoising via GainTuning S. Mohan, J. L. Vincent, R. Manzorro, P. A. Crozier, C. Fernandez-Granda, E. P. Simoncelli. Proc. 34th Conference on Neural Information Processing Systems (NeurIPS) 2021
Unsupervised Deep Video Denoising D. Y. Sheth, S. Mohan, J. L. Vincent, R. Manzorro, P. A. Crozier, M. M. Khapra, E. P. Simoncelli, C. Fernandez-Granda. Proc. IEEE-CVF International Conference on Computer Vision 2021 Website with code and data
Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise J. L. Vincent, R. Manzorro, S. Mohan, B. Tang, D. Y. Sheth, E. P. Simoncelli, D. S. Mattesson, C. Fernandez-Granda, P. A. Crozier. Microscopy and Microanalysis 27 6 1431-1447. 2021
Robust and Interpretable Blind Image Denoising via Bias-free Convolutional Neural Networks S. Mohan, Z. Kadkhodaie, E. Simoncelli, C. Fernandez-Granda. Proc. International Conference on Learning Representations (ICLR) 2020. Website. Code. Video
Stroke Rehabilitation
Data-Driven Quantitation of Movement Abnormality after Stroke A. Parnandi, A. Kaku, A. Venkatesan, N. Pandit, E. Fokas, B. Yu, D. Nilsen, C. Fernandez-Granda, H. Schambra. Bioengineering 10(6), p.648 2023
StrokeRehab: A Benchmark Dataset for Sub-second Action Identification A. Kaku, K. Liu, A. Parnandi, H. Rajamohan, K. Venkataramanan, A. Venkatesan, A. Wirtanen, N. Pandit, H. Schambra, C. Fernandez-Granda. Proc. 35th Conference on Neural Information Processing Systems (NeurIPS) Datasets And Benchmarks Track. 2022 Data
PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training A. Parnandi, A. Kaku, A. Venkatesan, N. Pandit, A. Wirtanen, H. Rajamohan, K. Venkataramanan, D. Nilsen, C. Fernandez-Granda, H. Schambra. PLOS Digital Health 1(6), p.e0000044. 2022 Data
Towards data-driven stroke rehabilitation via wearable sensors and deep learning A. Kaku, A. Parnandi, A. Venkatesan, N. Pandit, H. Schambra, C. Fernandez-Granda. Proceedings of Machine Learning Research 126 143-171. Machine Learning in Healthcare (MLHC) 2020. Video
Data-driven Medicine
Dictionary learning for integrative, multimodal, and scalable single-cell analysis Y. Hao, T. Stuart, M. Kowalski, S. Choudhary, P. Hoffman, A. Hartman, A. Srivastava, G. Molla, S. Madad, C. Fernandez-Granda, R. Satija. Nature Biotechnology 1-12. 2023
Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs S. Liu, A. V. Masurkar, H. Rusinek, J. Chen, B. Zhang, W. Zhu, C. Fernandez-Granda, N. Razavian. Scientific reports 12(1) 1-12. 2022
Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning W. Zhu, C. Fernandez-Granda, N. Razavian. Proceedings of Medical Imaging with Deep Learning (MIDL). 2022
Cramer-Rao bound-informed training of neural networks for quantitative MRI X. Zhang, Q. Duchemin, K. Liu, S. Flassbeck, C. Gultekin, C. Fernandez-Granda, J. Asslaender. Magnetic Resonance in Medicine 88 (1) 436-448. 2022
Analysis of Transfer Learning for Select Retinal Disease Classification R. Gelman, C. Fernandez-Granda. Retina 42 1 174-183. 2022
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis K. Liu, Y. Shen, N. Wu, J. Chledowski, C. Fernandez-Granda, K. Geras. Proceedings of Medical Imaging with Deep Learning (MIDL). 2021
An artificial intelligence system for predicting the deterioration of
COVID-19 patients in the emergency department F. E. Shamout, Y. Shen, N. Wu, A. Kaku, J. Park, T. Makino, S. Jastrzkebski, D. Wang, B. Zhang, S. Dogra, M. Cao, N. Razavian, D. Kudlowitz, L. Azour, W. Moore, Y. W. Lui, Y. Aphinyanaphongs, C. Fernandez-Granda, K. J. Geras. npj Digital Medicine 4 80. 2021. Website
Optimized dimensionality reduction for parameter estimation in MR fingerprinting via deep learning Q. Duchemin, K. Liu, C. Fernandez-Granda, J. Asslaender. Proc. 28th Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2020
On the Design of Convolutional Neural Networks for Automatic Detection of Alzheimer's Disease S. Liu, C. Yadav, C. Fernandez-Granda, N. Razavian. Proceedings of Machine Learning Research 116 171–183. NeurIPS Machine Learning for Healthcare (ML4H) workshop 2019. Code
Time-Series Analysis via Low-Rank Matrix Factorization Applied to Infant-Sleep Data S. Liu, M. Cheng, H. Brooks, W. Mackey, D. J. Heeger, E. G. Tabak, C. Fernandez-Granda. NeurIPS Machine Learning for Healthcare (ML4H) workshop 2019. Website (code and dataset)
Hybrid-State Free Precession for Measuring Magnetic Resonance
Relaxation Times in the Presence of B0 Inhomogeneities V. Kobzar, C. Fernandez-Granda, J. Asslaender. Proc. 27th Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2019 (selected for oral presentation)
Multicompartment magnetic resonance fingerprinting S. Tang, C. Fernandez-Granda, S. Lannuzel, B. Bernstein, R. Lattanzi, M. Cloos, F. Knoll, J. Asslaender. Inverse Problems 34 (9) 1–35. 2018
Multi-Compartment MR Fingerprinting via Reweighted-l1-norm Regularization S. Tang, J. Asslaender, L. Tanenbaum, R. Lattanzi, M. Cloos, F. Knoll, C. Fernandez-Granda. Proc. 25th Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2017
Robustness of compressed sensing parallel MRI in the presence of inaccurate sensitivity estimates C. Fernandez-Granda, T. Thüring, J. Sénégas. Third International ISMRM Workshop on Parallel MRI 2009
L1-norm regularization of coil sensitivities in non-linear parallel imaging reconstruction C. Fernandez-Granda, J. Sénégas. Proc. 17th Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2009 (selected for oral presentation)
Climate
Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model P. Perezhogin, L. Zanna, C. Fernandez-Granda. Journal of Advances in Modeling Earth Systems, 15 (10),
e2023MS003681. 2023
Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model C. Zhang, P. Perezhogin, C. Gultekin, A. Adcroft, C. Fernandez-Granda, L. Zanna. Journal of Advances in Modeling Earth Systems, 15 (10),
e2023MS003697. 2023
Benchmarking of machine learning ocean subgrid parameterizations in an idealized model A. S. Ross, Z. Li, P. Perezhogin, C. Fernandez-Granda, L. Zanna. Journal of Advances in Modeling Earth Systems, 15(1), e2022MS003258. 2023
Theory of Inverse Problems
A Sampling Theorem for Deconvolution in Two Dimensions J. McDonald, B. Bernstein, C. Fernandez-Granda. SIAM Journal on Imaging Sciences. 13 (4), 1754-1780. 2020
Sparse Recovery Beyond Compressed Sensing: Separable Nonlinear Inverse Problems B. Bernstein, S. Liu, C. Papadaniil, C. Fernandez-Granda. IEEE Transactions on Information Theory. 66 (9), 5904–5926. 2020
Deconvolution of point sources: a sampling theorem and robustness guarantees B. Bernstein, C. Fernandez-Granda. Communications on Pure and Applied Mathematics. 72 (6), 1152–1230. 2019 Code for proofs. Code for simulations
Demixing sines and spikes: robust spectral super-resolution in the presence of outliers C. Fernandez-Granda, G. Tang, X. Wang, L. Zheng. Information and Inference 7 (1), 105–168. 2017 Matlab code
Super-resolution of point sources via convex programming C. Fernandez-Granda. Information and Inference 5 (3), 251–303. 2016
Matlab scripts: Demixing of sines and spikes. Super-resolution of signals with a common support. Proof of main theorem
Information and Inference Best Paper Prize
Super-resolution from noisy data E. J. Candès, C. Fernandez-Granda. Journal of Fourier Analysis and Applications 19 (6), 1229–1254. 2013 Matlab script
Towards a mathematical theory of super-resolution E. J. Candès, C. Fernandez-Granda. Communications on Pure and Applied Mathematics 67 (6), 906–956. 2013 Matlab script
SIAM Student Paper Prize
Support detection in super-resolution C. Fernandez-Granda. Proceedings of the 10th International Conference on Sampling Theory and Applications (SampTA), 145-148. 2013
Lecture notes
Other work
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