Publications | Sanketh Vedula

^* Equally-contributing first author, ^† Equally-contributing senior author

2026

Under review

Fold’EM: Direct atomic structure inference from cryo-EM particles

A. Maddipatla, M. Mäeots, M. Pegoraro, N. Dräger, R. Covino, S. Vedula^†, M. Pacesa^†, and A. Bronstein^†

Under review, 2026

Structural BiologyAI for Science

@article{maddipatla2026foldem,
  fields = {structural-biology,ai-for-science},
  kind = {Preprint},
  equal_senior = {Vedula,Pacesa,Bronstein},
  topic = {structural-biology},
  highlight = {maddipatla2026foldem.png},
  title = {Fold'EM: Direct atomic structure inference from cryo-EM particles},
  author = {Maddipatla, Advaith and M{\"a}eots, M{\"a}rt-Erik and Pegoraro, Marco and Dr{\"a}ger, Nikolaus and Covino, Roberto and Vedula, Sanketh and Pacesa, Martin and Bronstein, Alex},
  journal = {Under review},
  year = {2026},
}

bioRxiv

Density-guided AlphaFold3 uncovers unmodelled conformations in β2-microglobulin

A. Maddipatla, S. Vedula, A. Bronstein, and A. Marx

bioRxiv, 2026

Under review at Acta Crystallographica Section D

Structural Biology

Bib

@article{maddipatla2026density,
  fields = {structural-biology},
  kind = {Preprint},
  topic = {structural-biology},
  highlight = {maddipatla2026density.png},
  title = {Density-guided AlphaFold3 uncovers unmodelled conformations in β2-microglobulin},
  note = {Under review at Acta Crystallographica Section D},
  author = {Maddipatla, Sai Advaith and Vedula, Sanketh and Bronstein, Alexander M and Marx, Ailie},
  journal = {bioRxiv},
  pages = {2026--02},
  publisher = {Cold Spring Harbor Laboratory},
  year = {2026}
}

IUS

View-wise spatially-varying Richardson-Lucy (RL) deconvolution for refraction-corrected 3D ultrasound reflection tomography

J. Wiskin, S. Vedula, B. Malik, M. Magno, and A. Bronstein

In Proc. IEEE International Ultrasonics Symposium (IUS), 2026

Computational ImagingMedical Imaging

Bib

@inproceedings{wiskin2026viewwise,
  fields = {computational-imaging,medical-imaging},
  kind = {Journal/Conference},
  topic = {computational-imaging},
  title = {View-wise spatially-varying Richardson-Lucy (RL) deconvolution for refraction-corrected 3D ultrasound reflection tomography},
  author = {Wiskin, J. and Vedula, Sanketh and Malik, B. and Magno, M. and Bronstein, Alex},
  booktitle = {Proc. IEEE International Ultrasonics Symposium (IUS)},
  year = {2026}
}

IUS

Comparison of three methods for deconvolution for refraction-corrected 3D ultrasound reflection tomography

J. Wiskin, S. Vedula, B. Malik, M. Magno, and A. Bronstein

In Proc. IEEE International Ultrasonics Symposium (IUS), 2026

Computational ImagingMedical Imaging

Bib

@inproceedings{wiskin2026comparison,
  fields = {computational-imaging,medical-imaging},
  kind = {Journal/Conference},
  topic = {computational-imaging},
  title = {Comparison of three methods for deconvolution for refraction-corrected 3D ultrasound reflection tomography},
  author = {Wiskin, J. and Vedula, Sanketh and Malik, B. and Magno, M. and Bronstein, Alex},
  booktitle = {Proc. IEEE International Ultrasonics Symposium (IUS)},
  year = {2026}
}

MLHC

Real-world sleep phenotyping in autism: compliance, domain shift, and scalable wearable biomarkers

S. Vedula, A. Kim, M. Kumar, O. Troyanskaya, and G. Sapiro

In Proc. Machine Learning for Healthcare Conference (MLHC), 2026

Health, Wearables & BiosignalsMedical Imaging

Bib

@inproceedings{vedula2026sleep,
  fields = {health-wearables,medical-imaging},
  kind = {Journal/Conference},
  topic = {ml-for-health},
  highlight = {vedula2026sleep.png},
  title = {Real-world sleep phenotyping in autism: compliance, domain shift, and scalable wearable biomarkers},
  author = {Vedula, Sanketh and Kim, A. and Kumar, M. and Troyanskaya, Olga G and Sapiro, Guillermo},
  booktitle = {Proc. Machine Learning for Healthcare Conference (MLHC)},
  year = {2026},
}

ICML

Inference-time optimization for experiment-grounded protein ensemble generation

A. Maddipatla, A. Rzayev, M. Pegoraro, M. Pacesa, P. Schanda, A. Marx, S. Vedula^†, and A. Bronstein^†

In Proc. International Conference on Machine Learning (ICML), 2026

Structural BiologyAI for Science

Bib HTML

@inproceedings{maddipatla2026inference,
  fields = {structural-biology,ai-for-science},
  kind = {Journal/Conference},
  equal_senior = {Vedula,Bronstein},
  topic = {structural-biology},
  highlight = {maddipatla2026inference.png},
  title = {Inference-time optimization for experiment-grounded protein ensemble generation},
  author = {Maddipatla, Advaith and Rzayev, Anar and Pegoraro, Marco and Pacesa, Martin and Schanda, Paul and Marx, Ailie and Vedula, Sanketh and Bronstein, Alex M},
  booktitle = {Proc. International Conference on Machine Learning (ICML)},
  year = {2026}
}

Nat. Biotech.

Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles

A. Maddipatla, N. Sellam, M. Bojan, V. Masalitin, S. Vedula^†, P. Schanda^†, A. Marx^†, and A. Bronstein^†

Nature Biotechnology, 2026

Structural BiologyAI for Science

Bib

@article{maddipatla2025experiment,
  fields = {structural-biology,ai-for-science},
  kind = {Journal/Conference},
  equal_senior = {Vedula,Schanda,Marx,Bronstein},
  topic = {structural-biology},
  highlight = {maddipatla2025experiment.png},
  title = {Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles},
  author = {Maddipatla, Advaith and Sellam, Nadav Bojan and Bojan, Meital and Masalitin, Volodymyr and Vedula, Sanketh and Schanda, Paul M and Marx, Ailie and Bronstein, Alexander M},
  journal = {Nature Biotechnology},
  year = {2026},
}

ICLR Workshop

On the role of drug representations in single-cell perturbation modeling

S. Vedula^*, M. Pegoraro^*, L. Halika, M. Danziger, A. Bronstein, and M. Rosen-Zvi

In ICLR 2026 Learning Meaningful Representations of Life (LMRL) Workshop, 2026

Structural BiologyAI for Science

Bib

@inproceedings{pegoraro2026role,
  fields = {structural-biology,ai-for-science},
  kind = {Journal/Conference},
  equal_first = {Vedula,Pegoraro},
  topic = {ml-for-health},
  title = {On the role of drug representations in single-cell perturbation modeling},
  author = {Vedula, Sanketh and Pegoraro, Marco and Halika, Lion and Danziger, Michael M and Bronstein, Alexander and Rosen-Zvi, Michal},
  booktitle = {ICLR 2026 Learning Meaningful Representations of Life (LMRL) Workshop},
  year = {2026}
}

ICLR

Representing local protein environments with machine learning force fields

S. Vedula^*, M. Bojan^*, A. Maddipatla, N. Sellam, A. Rzayev, F. Napoli, P. Schanda, and A. Bronstein

In The Fourteenth International Conference on Learning Representations (ICLR), 2026

Structural BiologyAI for Science

Bib

@inproceedings{bojan2026representing,
  fields = {structural-biology,ai-for-science},
  kind = {Journal/Conference},
  equal_first = {Vedula,Bojan},
  topic = {structural-biology},
  highlight = {bojan2026representing.png},
  title = {Representing local protein environments with machine learning force fields},
  author = {Vedula, Sanketh and Bojan, Meital and Maddipatla, Sai Advaith and Sellam, Nadav and Rzayev, Anar and Napoli, Federico and Schanda, Paul and Bronstein, Alexander},
  booktitle = {The Fourteenth International Conference on Learning Representations (ICLR)},
  year = {2026}
}

2025

bioRxiv

An Evidence-Grounded Research Assistant for Functional Genomics and Drug Target Assessment

K. Sokolova, D. Kosenkov, K. Nallamotu, S. Vedula, D. Sokolov, G. Sapiro, and O. Troyanskaya

bioRxiv, 2025

Under review

AI for Science

Bib HTML

@article{sokolova2025evidence,
  fields = {ai-for-science},
  kind = {Preprint},
  topic = {ml-for-health},
  title = {An Evidence-Grounded Research Assistant for Functional Genomics and Drug Target Assessment},
  note = {Under review},
  author = {Sokolova, Ksenia and Kosenkov, Dmitri and Nallamotu, Keerthana and Vedula, Sanketh and Sokolov, Daniil and Sapiro, Guillermo and Troyanskaya, Olga G},
  journal = {bioRxiv},
  pages = {2025--12},
  publisher = {Cold Spring Harbor Laboratory},
  year = {2025}
}

CSBJ

Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment

S. Vedula, A. Bronstein, and A. Marx

Computational and Structural Biotechnology Journal, 2025

Structural Biology

Bib

@article{vedula2025improving,
  fields = {structural-biology},
  kind = {Journal/Conference},
  topic = {structural-biology},
  highlight = {vedula2025improving.png},
  title = {Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment},
  author = {Vedula, Sanketh and Bronstein, Alex M and Marx, Ailie},
  journal = {Computational and Structural Biotechnology Journal},
  publisher = {Elsevier},
  year = {2025}
}

EcoEvoRxiv

TwisstNTern 2: Ternary analysis of topology weights from tree sequences

H. Lifchitz, S. Vedula, A. Pal, D. Shipilina, and S. Stankowski

EcoEvoRxiv, 2025

Bib

@article{lifchitz2025twisstntern,
  kind = {Preprint},
  topic = {other},
  title = {TwisstNTern 2: Ternary analysis of topology weights from tree sequences},
  author = {Lifchitz, Hila and Vedula, Sanketh and Pal, Arka and Shipilina, Daria and Stankowski, Sean},
  journal = {EcoEvoRxiv},
  year = {2025}
}

ICMI

From Lab to Wrist: Bridging Metabolic Monitoring and Consumer Wearables for Heart Rate and Oxygen Consumption Modeling

B. Gahtan, S. Vedula, G. Samuelly Leichtag, E. Kodesh, and A. Bronstein

In Proceedings of the 27th International Conference on Multimodal Interaction, 2025

Health, Wearables & Biosignals

Bib

@inproceedings{gahtan2025lab,
  fields = {health-wearables},
  kind = {Journal/Conference},
  topic = {ml-for-health},
  highlight = {gahtan2025lab.png},
  title = {From Lab to Wrist: Bridging Metabolic Monitoring and Consumer Wearables for Heart Rate and Oxygen Consumption Modeling},
  author = {Gahtan, Barak and Vedula, Sanketh and {Samuelly Leichtag}, Gil and Kodesh, Einat and Bronstein, Alex},
  booktitle = {Proceedings of the 27th International Conference on Multimodal Interaction},
  pages = {60--77},
  year = {2025}
}

ICML

Inverse problems with experiment-guided AlphaFold

A. Maddipatla^*, N. Sellam^*, M. Bojan, S. Vedula^†, P. Schanda^†, A. Marx^†, and A. Bronstein^†

In Proc. International Conference on Machine Learning (ICML), 2025

Structural BiologyAI for Science

Bib

@inproceedings{maddipatla2025inverse,
  fields = {structural-biology,ai-for-science},
  kind = {Journal/Conference},
  equal_first = {Maddipatla,Sellam},
  equal_senior = {Vedula,Schanda,Marx,Bronstein},
  topic = {structural-biology},
  highlight = {maddipatla2025inverse.png},
  title = {Inverse problems with experiment-guided AlphaFold},
  author = {Maddipatla, Advaith and Sellam, Nadav Bojan and Bojan, Meital and Vedula, Sanketh and Schanda, Paul and Marx, Ailie and Bronstein, Alex M},
  booktitle = {Proc. International Conference on Machine Learning (ICML)},
  year = {2025}
}

Technion

Machine learning and statistical methods for problems in life and physical sciences

S. Vedula

Technion – Israel Institute of Technology, 2025

Ph.D. Thesis, Computer Science Department, Technion

Computational ImagingStructural BiologyAI for Science

Bib

@phdthesis{vedula2025phd,
  fields = {computational-imaging,structural-biology,ai-for-science},
  kind = {Thesis},
  research = {false},
  topic = {other},
  title = {Machine learning and statistical methods for problems in life and physical sciences},
  author = {Vedula, Sanketh},
  school = {Technion -- Israel Institute of Technology},
  note = {Ph.D. Thesis, Computer Science Department, Technion},
  year = {2025}
}

US Patent

Scalable Graph-Based Approach To Accelerating Recommendation Model Inference

H. Ye, S. Vedula, A. Bronstein, R. Dreslinski, T. Mudge, and N. Talati

2025

US Patent App. 18/613,387

Deep LearningSystems & Hardware

Bib

@misc{ye2025scalable,
  fields = {deep-learning,systems-hardware},
  kind = {Patent},
  research = {false},
  topic = {other},
  title = {Scalable Graph-Based Approach To Accelerating Recommendation Model Inference},
  author = {Ye, Haojie and Vedula, Sanketh and Bronstein, Alex and Dreslinski, Ronald and Mudge, Trevor and Talati, Nishil},
  note = {US Patent App. 18/613,387},
  year = {2025}
}

2024

NeurIPS W

Generative modeling of protein ensembles guided by crystallographic electron densities

A. Maddipatla^*, N. Sellam^*, S. Vedula, A. Marx, and A. Bronstein

In Machine Learning for Structural Biology Workshop, NeurIPS, 2024

Structural BiologyAI for Science

Bib

@inproceedings{maddipatla2024generative,
  fields = {structural-biology,ai-for-science},
  kind = {Journal/Conference},
  equal_first = {Maddipatla,Sellam},
  topic = {structural-biology},
  highlight = {maddipatla2024generative.png},
  title = {Generative modeling of protein ensembles guided by crystallographic electron densities},
  author = {Maddipatla, Sai Advaith and Sellam, Nadav Bojan and Vedula, Sanketh and Marx, Ailie and Bronstein, Alex},
  booktitle = {Machine Learning for Structural Biology Workshop, NeurIPS},
  year = {2024}
}

bioRxiv

Seeing Double: Molecular dynamics simulations reveal the stability of certain alternate protein conformations in crystal structures

A. Rosenberg, S. Vedula, A. Bronstein, and A. Marx

bioRxiv, 2024

Structural Biology

Abs

Proteins jiggle around, adopting ensembles of interchanging conformations. Here we show through a large-scale analysis of the Protein Data Bank and using molecular dynamics simulations, that segments of protein chains can also commonly adopt dual, transiently stable conformations which is not explained by direct interactions. Our analysis highlights how alternate conformations can be maintained as non-interchanging, separated states intrinsic to the protein chain, namely through steric barriers or the adoption of transient secondary structure elements. We further demonstrate that despite the commonality of the phenomenon, current structural ensemble prediction methods fail to capture these bimodal distributions of conformations.Competing Interest StatementThe authors have declared no competing interest.

ICML Workshop

Scalable unsupervised alignment of general metric and non-metric structures

S. Vedula, V. Maiorca, L. Basile, F. Locatello^†, and A. Bronstein^†

arXiv preprint arXiv:2406.13507 (AI for Science Workshop, ICML), 2024

Deep Learning

Bib

@article{vedula2024scalable,
  fields = {deep-learning},
  kind = {Journal/Conference},
  equal_senior = {Locatello,Bronstein},
  topic = {other},
  title = {Scalable unsupervised alignment of general metric and non-metric structures},
  author = {Vedula, S. and Maiorca, V. and Basile, L. and Locatello, F. and Bronstein, A.},
  journal = {arXiv preprint arXiv:2406.13507 (AI for Science Workshop, ICML)},
  year = {2024},
}

VLDB

Everest: GPU-Accelerated System For Mining Temporal Motifs

Y. Yuan, H. Ye, S. Vedula, W. Kaza, and N. Talati

Proceedings of the International Conference on Very Large Databses (VLDB), 2024

GraphsSystems & Hardware

Bib

@article{yuan2023everest,
  fields = {graphs,systems-hardware},
  kind = {Journal/Conference},
  topic = {other},
  title = {Everest: GPU-Accelerated System For Mining Temporal Motifs},
  author = {Yuan, Y. and Ye, H. and Vedula, S. and Kaza, W. and Talati, N.},
  year = {2024},
  journal = {Proceedings of the International Conference on Very Large Databses (VLDB)},
  publisher = {VLDB Endowment},
}

VLDB

Demystifying graph sparsification algorithms in graph properties preservation

Y. Chen, H. Ye, S. Vedula, A. Bronstein, R. Dreslinski, T. Mudge, and N. Talati

Proceedings of the International Conference on Very Large Databses (VLDB), 2024

Graphs

Bib

@article{chen2023demystifying,
  fields = {graphs},
  kind = {Journal/Conference},
  topic = {other},
  title = {Demystifying graph sparsification algorithms in graph properties preservation},
  author = {Chen, Y. and Ye, H. and Vedula, S. and Bronstein, A. and Dreslinski, R. and Mudge, T. and Talati, N.},
  journal = {Proceedings of the International Conference on Very Large Databses (VLDB)},
  volume = {17},
  number = {3},
  pages = {427--440},
  year = {2024},
  publisher = {VLDB Endowment},
}

AISTATS

Vector Quantile Regression on Manifolds

M. Pegoraro, S. Vedula, A. Rosenberg, I. Tallini, E. Rodola, and A. Bronstein

In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2024

Deep LearningOptimal Transport

Bib

@inproceedings{pegoraro2023vector,
  fields = {deep-learning,optimal-transport},
  kind = {Journal/Conference},
  topic = {uncertainty},
  highlight = {pegoraro2023vector.png},
  title = {Vector Quantile Regression on Manifolds},
  author = {Pegoraro, M. and Vedula, S. and Rosenberg, A. and Tallini, I. and Rodola, E. and Bronstein, A.},
  booktitle = {Proc. International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year = {2024},
}

US Patent

Method and system for training a machine learning procedure to analyze a radar signal

A. Bronstein, T. Weiss, N. Peretz, S. Vedula, and A. Feuer

2024

US Patent App. 18/376,465

Computational Imaging

Bib

@misc{bronstein2024method,
  fields = {computational-imaging},
  kind = {Patent},
  research = {false},
  topic = {computational-imaging},
  title = {Method and system for training a machine learning procedure to analyze a radar signal},
  author = {Bronstein, A. and Weiss, T. and Peretz, N. and Vedula, S. and Feuer, A.},
  year = {2024},
  note = {US Patent App. 18/376,465}
}

2023

ICML Workshop

Continuous Vector Quantile Regression

S. Vedula^*, I. Tallini^*, A. Rosenberg, M. Pegoraro, E. Rodola, Y. Romano, and A. Bronstein

In ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023

Deep LearningOptimal Transport

Bib

@inproceedings{vedula2023continuous,
  fields = {deep-learning,optimal-transport},
  kind = {Journal/Conference},
  equal_first = {Vedula,Tallini},
  topic = {uncertainty},
  highlight = {vedula2023continuous.png},
  title = {Continuous Vector Quantile Regression},
  author = {Vedula, S. and Tallini, I. and Rosenberg, A. and Pegoraro, M. and Rodola, E. and Romano, Y. and Bronstein, A.},
  booktitle = {ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems},
  year = {2023},
}

ICLR

Fast Nonlinear Vector Quantile Regression

A. Rosenberg^*, S. Vedula^*, Y. Romano, and A. Bronstein

In Proc. International Conference on Learning Representations (ICLR), 2023

Deep LearningOptimal Transport

Bib HTML

@inproceedings{rosenberg2023fast,
  fields = {deep-learning,optimal-transport},
  kind = {Journal/Conference},
  equal_first = {Vedula,Rosenberg},
  topic = {uncertainty},
  highlight = {rosenberg2023fast.png},
  title = {Fast Nonlinear Vector Quantile Regression},
  author = {Rosenberg, A. and Vedula, S. and Romano, Y. and Bronstein, A.},
  booktitle = {Proc. International Conference on Learning Representations (ICLR)},
  year = {2023},
}

ASPLOS

GRACE: A Scalable Graph-Based Approach To Accelerating Recommendation Model Inference

H. Ye, S. Vedula, Y. Chen, Y. Yeng, A. Bronstein, R. Dreslinski, T. Mudge, and N. Talati

In 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2023

Deep LearningSystems & Hardware

Bib

@inproceedings{ye2023grace,
  fields = {deep-learning,systems-hardware},
  kind = {Journal/Conference},
  topic = {other},
  title = {GRACE: A Scalable Graph-Based Approach To Accelerating Recommendation Model Inference},
  author = {Ye, H. and Vedula, S. and Chen, Y. and Yeng, Y. and Bronstein, A. and Dreslinski, R. and Mudge, T. and Talati, N.},
  booktitle = {28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)},
  year = {2023}
}

2022

MICRO

Mint: An Accelerator For Mining Temporal Motifs

N. Talati, H. Ye, S. Vedula, K. Chen, Y. Chen, D. Liu, Y. Yuan, D. Blaauw, A. Bronstein, T. Mudge, and R. Dreslinski

In 55th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2022

GraphsSystems & Hardware

Bib

@inproceedings{talati2022mint,
  fields = {graphs,systems-hardware},
  kind = {Journal/Conference},
  topic = {other},
  title = {Mint: An Accelerator For Mining Temporal Motifs},
  author = {Talati, N. and Ye, H. and Vedula, S. and Chen, K.Y. and Chen, Y. and Liu, D. and Yuan, Y. and Blaauw, D. and Bronstein, A. and Mudge, T. and Dreslinski, R.},
  booktitle = {55th IEEE/ACM International Symposium on Microarchitecture (MICRO)},
  pages = {1270--1287},
  year = {2022}
}

2021

MLSP

Joint optimization of system design and reconstruction in MIMO radar imaging

T. Weiss, N. Peretz, S. Vedula, A. Feuer, and A. Bronstein

In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021

Computational Imaging

Bib

@inproceedings{weiss2021joint,
  fields = {computational-imaging},
  kind = {Journal/Conference},
  topic = {computational-imaging},
  highlight = {weiss2021joint.png},
  title = {Joint optimization of system design and reconstruction in MIMO radar imaging},
  author = {Weiss, T. and Peretz, N. and Vedula, S. and Feuer, A. and Bronstein, A.},
  booktitle = {2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)},
  pages = {1--6},
  year = {2021},
  organization = {IEEE}
}

MSML

Spectral Geometric Matrix Completion

A. Boyarski^*, S. Vedula^*, and A. Bronstein

In Proceedings of the Second Mathematical and Scientific Machine Learning Conference (MSML), 2021

Deep Learning

Bib

@inproceedings{boyarski2021spectral,
  fields = {deep-learning},
  kind = {Journal/Conference},
  equal_first = {Vedula,Boyarski},
  topic = {other},
  title = {Spectral Geometric Matrix Completion},
  author = {Boyarski, A. and Vedula, S. and Bronstein, A.},
  booktitle = {Proceedings of the Second Mathematical and Scientific Machine Learning Conference (MSML)},
  year = {2021},
}

MELBA

PILOT: Physics-informed learned optimal trajectories for accelerated MRI

T. Weiss, O. Senouf, S. Vedula, O. Michailovich, M. Zibulevsky, and A. Bronstein

The Journal of Machine Learning for Biomedical Imaging, 2021

Computational ImagingMedical Imaging

Bib HTML

@article{weiss2021pilot,
  fields = {computational-imaging,medical-imaging},
  kind = {Journal/Conference},
  topic = {computational-imaging},
  highlight = {weiss2021pilot.png},
  title = {PILOT: Physics-informed learned optimal trajectories for accelerated MRI},
  author = {Weiss, T. and Senouf, O. and Vedula, S. and Michailovich, O. and Zibulevsky, M. and Bronstein, A.},
  journal = {The Journal of Machine Learning for Biomedical Imaging},
  volume = {1},
  issue = {April 2021 issue},
  year = {2021},
}

US Patent

Systems and methods for ultrasonic imaging

O. Senouf, S. Vedula, A. Bronstein, M. Zibulevsky, G. Zurakhov, and O. Michailovich

US Patent 11,158,052, 2021

Computational ImagingMedical Imaging

Bib

@article{senouf2021systems,
  fields = {computational-imaging,medical-imaging},
  kind = {Patent},
  research = {false},
  topic = {computational-imaging},
  title = {Systems and methods for ultrasonic imaging},
  author = {Senouf, O. and Vedula, S. and Bronstein, A. and Zibulevsky, M. and Zurakhov, G. and Michailovich, O.},
  journal = {US Patent 11,158,052},
  year = {2021}
}

2020

CDMRI

Towards learned optimal q-space sampling in diffusion MRI

T. Weiss, S. Vedula, O. Senouf, O. Michailovich, and A. Bronstein

In Computational Diffusion MRI, 2020

Computational ImagingMedical Imaging

Bib

@inproceedings{weiss2020towards,
  fields = {computational-imaging,medical-imaging},
  kind = {Journal/Conference},
  topic = {computational-imaging},
  highlight = {weiss2020towards.png},
  title = {Towards learned optimal q-space sampling in diffusion MRI},
  author = {Weiss, T. and Vedula, S. and Senouf, O. and Michailovich, O. and Bronstein, A.},
  booktitle = {Computational Diffusion MRI},
  year = {2020},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {13--28}
}

MICCAI-W

3D FLAT-Feasible Learned Acquisition Trajectories for Accelerated MRI

J. Alush-Aben, L. Ackerman, T. Weiss, S. Vedula, O. Senouf, and A. Bronstein

In International Workshop on Machine Learning for Medical Image Reconstruction, MICCAI, 2020

Computational ImagingMedical Imaging

Bib

@inproceedings{alush20203d,
  fields = {computational-imaging,medical-imaging},
  kind = {Journal/Conference},
  topic = {computational-imaging},
  highlight = {alush20203d.png},
  title = {3D FLAT-Feasible Learned Acquisition Trajectories for Accelerated MRI},
  author = {Alush-Aben, J. and Ackerman, L. and Weiss, T. and Vedula, S. and Senouf, O. and Bronstein, A.},
  booktitle = {International Workshop on Machine Learning for Medical Image Reconstruction, MICCAI},
  year = {2020}
}

ICASSP

Joint learning of Cartesian undersampling and reconstruction for accelerated MRI

T. Weiss, S. Vedula, O. Senouf, A. Bronstein, O. Michailovich, and M. Zibulevsky

In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

Computational ImagingMedical Imaging

Bib

@inproceedings{weiss2019joint,
  fields = {computational-imaging,medical-imaging},
  kind = {Journal/Conference},
  topic = {computational-imaging},
  highlight = {weiss2019joint.png},
  title = {Joint learning of Cartesian undersampling and reconstruction for accelerated MRI},
  author = {Weiss, T. and Vedula, S. and Senouf, O. and Bronstein, A. and Michailovich, O. and Zibulevsky, M.},
  booktitle = {ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year = {2020}
}

Technion

Learning-based design of ultrasound imaging systems

S. Vedula, A. Bronstein, and M. Zibulevsky

M.Sc. Thesis, Computer Science Department, Technion, 2020

Computational ImagingMedical Imaging

Bib

@article{vedula2020learning,
  fields = {computational-imaging,medical-imaging},
  kind = {Thesis},
  research = {false},
  topic = {computational-imaging},
  title = {Learning-based design of ultrasound imaging systems},
  author = {Vedula, S. and Bronstein, A. and Zibulevsky, M.},
  year = {2020},
  journal = {M.Sc. Thesis, Computer Science Department, Technion}
}

2019

MICCAI-W

Self-supervised learning of inverse problem solvers in medical imaging

O. Senouf^*, S. Vedula^*, T. Weiss, A. Bronstein, O. Michailovich, and M. Zibulevsky

In Domain adaptation and representation transfer and medical image learning with less labels and imperfect data, MICCAI, 2019

Computational ImagingMedical Imaging

Bib

@inproceedings{senouf2019self,
  fields = {computational-imaging,medical-imaging},
  kind = {Journal/Conference},
  equal_first = {Vedula,Senouf},
  topic = {computational-imaging},
  highlight = {senouf2019self.png},
  title = {Self-supervised learning of inverse problem solvers in medical imaging},
  author = {Senouf, O. and Vedula, S. and Weiss, T. and Bronstein, A. and Michailovich, O. and Zibulevsky, M.},
  booktitle = {Domain adaptation and representation transfer and medical image learning with less labels and imperfect data, MICCAI},
  pages = {111--119},
  year = {2019},
  publisher = {Springer, Cham}
}

MIDL

Learning beamforming in ultrasound imaging

S. Vedula^*, O. Senouf^*, G. Zurakhov, A. Bronstein, O. Michailovich, and M. Zibulevsky

In Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning (MIDL), 2019

Computational ImagingMedical Imaging

Bib PDF

@inproceedings{vedula2018learning,
  fields = {computational-imaging,medical-imaging},
  kind = {Journal/Conference},
  equal_first = {Vedula,Senouf},
  topic = {computational-imaging},
  highlight = {vedula2018learning.png},
  title = {Learning beamforming in ultrasound imaging},
  author = {Vedula, S. and Senouf, O. and Zurakhov, Grigoriy and Bronstein, A. and Michailovich, O. and Zibulevsky, M.},
  booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning (MIDL)},
  pages = {493--511},
  year = {2019},
  editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom},
  volume = {102},
  series = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
  url = {https://proceedings.mlr.press/v102/vedula19a.html}
}

2018

MICCAI

High frame-rate cardiac ultrasound imaging with deep learning

O. Senouf, S. Vedula, G. Zurakhov, A. Bronstein, M. Zibulevsky, O. Michailovich, D. Adam, and D. Blondheim

In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018

Computational ImagingMedical Imaging

Bib

@inproceedings{senouf2018high,
  fields = {computational-imaging,medical-imaging},
  kind = {Journal/Conference},
  topic = {computational-imaging},
  highlight = {senouf2018high.png},
  title = {High frame-rate cardiac ultrasound imaging with deep learning},
  author = {Senouf, O. and Vedula, S. and Zurakhov, G. and Bronstein, A. and Zibulevsky, M. and Michailovich, O. and Adam, Dan and Blondheim, David},
  booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  pages = {126--134},
  year = {2018},
  organization = {Springer}
}

MICCAI-W

High quality ultrasonic multi-line transmission through deep learning

S. Vedula, O. Senouf, G. Zurakhov, A. Bronstein, M. Zibulevsky, O. Michailovich, D. Adam, and D. Gaitini

In International Workshop on Machine Learning for Medical Image Reconstruction, MICCAI, 2018

Computational ImagingMedical Imaging

Bib

@inproceedings{vedula2018high,
  fields = {computational-imaging,medical-imaging},
  kind = {Journal/Conference},
  topic = {computational-imaging},
  highlight = {vedula2018high.png},
  title = {High quality ultrasonic multi-line transmission through deep learning},
  author = {Vedula, S. and Senouf, O. and Zurakhov, G. and Bronstein, A. and Zibulevsky, M. and Michailovich, O. and Adam, D. and Gaitini, D.},
  booktitle = {International Workshop on Machine Learning for Medical Image Reconstruction, MICCAI},
  pages = {147--155},
  year = {2018},
  organization = {Springer}
}

2017

arXiv

Towards CT-quality ultrasound imaging using deep learning

S. Vedula^*, O. Senouf^*, A. Bronstein, O. Michailovich, and M. Zibulevsky

arXiv preprint arXiv:1710.06304, 2017

Computational ImagingMedical Imaging

Bib

@article{vedula2017towards,
  fields = {computational-imaging,medical-imaging},
  kind = {Preprint},
  equal_first = {Vedula,Senouf},
  topic = {computational-imaging},
  highlight = {vedula2017towards.png},
  title = {Towards CT-quality ultrasound imaging using deep learning},
  author = {Vedula, S. and Senouf, O. and Bronstein, A. M and Michailovich, O. V and Zibulevsky, M.},
  journal = {arXiv preprint arXiv:1710.06304},
  year = {2017}
}