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<!doctype html>
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<h3><b>Hedda</b> Cohen Indelman</h3>
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<p>
I am a Ph.D. student under the supervision of Professor Tamir Hazan at the Technion - Israel Institute of Technology. My research is focused on learning and inference in high-dimensional structured models, with a special emphasis on learning matching representations. Motivated by structured prediction problems such as graph matching, I study efficient learning of constrained structured spaces, gradients of randomly perturbed structured models, and theory-driven relaxation techniques thereof. I am especially interested in marrying optimization techniques, probability and perturbations models, as well as classical ML to solve discrete data problems. The advantages of my research is demonstrated on well-known structured prediction benchmarks.
</p>
<p>
I am also an AI research scientist at GE Healthcare, focusing on bridging the gap between AI innovations and medical imaging. Previously, I was a research intern at Amazon, and have more than 10 years of experience in data science and analytics.
</p>
<br><br>
<i class="fa fa-envelope"></i> heddacohenind dot mail at gmail dot com</a>
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<h2 class="mb-5">News</h2>
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<ol>
<li>Our paper "InDi: Informative and Diverse Sampling for Dense Retrieval" won the <u>Best Paper Award</u> at ECIR 2024!</li>
<li>Our paper "Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models" has been accepted for <u>oral presentation</u> at EMBC 2024</li>
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<section id="publications" class="service section">
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<h2 class="mb-5">Publications</h2>
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<ol>
<h3>2024</h3>
<li id="paper" class="mb-1"> <b>On The Statistical Representation Properties Of The Perturb-Softmax And The Perturb-Argmax Probability Distributions</b>. <u>Hedda Cohen Indelman</u>, Tamir Hazan. <em></em> </li>
[
<a href="https://arxiv.org/pdf/2406.02180" target="_blank">PDF,</a>
<a class="bibtex-link" href="#">BibTeX</a>
]
<div class="bibtex-content" style="display: none; padding: 10px; background-color: #f0f0f0; border: 1px solid #ccc; border-radius: 4px;">
<br>
<pre style="line-height: 1.5; font-family: 'Courier New', monospace;">
<pre>
@misc{indelman2024statistical,
title={On The Statistical Representation Properties Of The Perturb-Softmax And The Perturb-Argmax Probability Distributions},
author={Hedda Cohen Indelman and Tamir Hazan},
year={2024},
eprint={2406.02180},
archivePrefix={arXiv},}
</pre>
</div>
<li id="paper" class="mb-1"> <b>Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models</b>. <u>Hedda Cohen Indelman</u>, Elay Dahan, Angeles M Perez-Agosto, Carmit Shiran, Doron Shaked, Nati Daniel. <em>Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBC), 2024</em> </li>
[
<a href="https://arxiv.org/pdf/2404.16325" target="_blank">PDF,</a>
<a class="bibtex-link" href="#">BibTeX</a>
]
<div class="bibtex-content" style="display: none; padding: 10px; background-color: #f0f0f0; border: 1px solid #ccc; border-radius: 4px;">
<br>
<pre style="line-height: 1.5; font-family: 'Courier New', monospace;">
<pre>
@misc{indelman2024semantic,
title={Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models},
author={Hedda Cohen Indelman and Elay Dahan and Angeles M. Perez-Agosto and Carmit Shiran and Doron Shaked and Nati Daniel},
year={2024},
eprint={2404.16325},
archivePrefix={arXiv},}
</pre>
</div>
<li id="paper" class="mb-1"> <b>Learning Latent Partial Matchings with Gumbel-IPF Networks</b>. <u>Hedda Cohen Indelman</u>, Tamir Hazan. <em>International Conference on Artificial Intelligence and Statistics (AISTATS), 2024</em> </li>
[
<a href="https://proceedings.mlr.press/v238/cohen-indelman24a/cohen-indelman24a.pdf" target="_blank">PDF,</a>
<a href="https://github.com/HeddaCohenIndelman/Learning-Latent-Partial-Matchings-with-Gumbel-IPF-Networks" target="_blank">Code,</a>
<a class="bibtex-link" href="#">BibTeX</a>
]
<div class="bibtex-content" style="display: none; padding: 10px; background-color: #f0f0f0; border: 1px solid #ccc; border-radius: 4px;">
<br>
<pre style="line-height: 1.5; font-family: 'Courier New', monospace;">
<pre>
@InProceedings{pmlr-v238-cohen-indelman24a,
title = { Learning Latent Partial Matchings with {G}umbel-{IPF} Networks },
author = {Cohen Indelman, Hedda and Hazan, Tamir},
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
pages = {1513--1521},
year = {2024},
editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
volume = {238},
series = {Proceedings of Machine Learning Research},
month = {02--04 May},
publisher = {PMLR}}
</pre>
</div>
<li id="paper" class="mb-1"> <b>InDi: Informative and Diverse Sampling for Dense Retrieval</b>. Nachshon Cohen, <u>Hedda Cohen Indelman</u>, Yaron Fairstein, Guy Kushilevitz. <em> European Conference on Information Retrieval, (ECIR), 2024</em> </li>
[
<a href="https://assets.amazon.science/39/b7/5ce986a64af6a9c21d163aedf307/indi-informative-and-diverse-sampling-for-dense-retrieval.pdf" target="_blank">PDF,</a>
<a class="bibtex-link" href="#">BibTeX</a>
]
<div class="bibtex-content" style="display: none; padding: 10px; background-color: #f0f0f0; border: 1px solid #ccc; border-radius: 4px;">
<br>
<pre style="line-height: 1.5; font-family: 'Courier New', monospace;">
<pre>
@InProceedings{10.1007/978-3-031-56063-7_16,
author="Cohen, Nachshon
and Cohen-Indelman, Hedda
and Fairstein, Yaron
and Kushilevitz, Guy",
title="InDi: Informative and Diverse Sampling for Dense Retrieval",
booktitle="Advances in Information Retrieval",
year="2024",
publisher="Springer Nature Switzerland",
pages="243--258",
isbn="978-3-031-56063-7"
}
</pre>
</div>
<h3>2023</h3>
<li id="paper" class="mb-1"> <b>Learning Constrained Structured Spaces with Application to Multi-Graph Matching</b>. <u>Hedda Cohen Indelman</u>, Tamir Hazan. <em>International Conference on Artificial Intelligence and Statistics (AISTATS), 2023</em> </li>
[
<a href="https://proceedings.mlr.press/v206/indelman23a/indelman23a.pdf" target="_blank">PDF,</a>
<a href="https://github.com/HeddaCohenIndelman/Learning-Constrained-Structured-Spaces-with-Application-to-Multi-Graph-Matching" target="_blank">Code,</a>
<a class="bibtex-link" href="#">BibTeX</a>
]
<div class="bibtex-content" style="display: none; padding: 10px; background-color: #f0f0f0; border: 1px solid #ccc; border-radius: 4px;">
<br>
<pre style="line-height: 1.5; font-family: 'Courier New', monospace;">
<pre>
@InProceedings{pmlr-v206-indelman23a,
title = {Learning Constrained Structured Spaces with Application to Multi-Graph Matching},
author = {Indelman, Hedda Cohen and Hazan, Tamir},
booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
pages = {2589--2602},
year = {2023},
editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
volume = {206},
series = {Proceedings of Machine Learning Research},
month = {25--27 Apr},
publisher = {PMLR},}
</pre>
</div>
<h3>2021</h3>
<li id="paper" class="mb-1"> <b>Learning randomly perturbed structured predictors for direct loss minimization</b>. <u>Hedda Cohen Indelman</u>, Tamir Hazan. <em>International Conference on Machine Learning (ICML), 2021</em> </li>
[
<a href="https://proceedings.mlr.press/v139/indelman21a/indelman21a.pdf" target="_blank">PDF,</a>
<a href="https://github.com/HeddaCohenIndelman/PerturbedStructuredPredictorsDirect" target="_blank">Code,</a>
<a class="bibtex-link" href="#">BibTeX</a>
]
<div class="bibtex-content" style="display: none; padding: 10px; background-color: #f0f0f0; border: 1px solid #ccc; border-radius: 4px;">
<br>
<pre style="line-height: 1.5; font-family: 'Courier New', monospace;">
<pre>
@InProceedings{pmlr-v139-indelman21a,
title = {Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization},
author = {Indelman, Hedda Cohen and Hazan, Tamir},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {4585--4595},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},}
</pre>
</div>
</ol>
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Machine Learning 2 (097209) head TA (Winter 2024 and 2023)
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