| Jul 1, 2023 |
New workshop papers in ICML:
Two of our new papers have been accepted to the ICML 2023 workshop Frontiers4LCD.
In the first paper we propose a novel approach for estimating high-dimensional conditional quantile functions on manifolds.
In the second paper we propose a novel continuous formulation of vector quantile regression that allows for accurate, scalable, differentiable, and invertible estimation of nonlinear conditional vector quantile functions.
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| May 22, 2023 |
PhD defense completed:
I have successfully defended by PhD thesis, as part of my doctoral degree
requirements. I would like to sincerely thank the committee members, Prof. Ron
Kimmel (Technion), Prof. Joel L. Sussman (Weizmann) and Prof. Mickey
Scheinowitz (TAU) for the interesting and engaging discussion.
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| Feb 1, 2023 |
New paper in ICLR:
Our paper,
“Fast Nonlinear Vector Quantile Regression” has been published
in the International Conference for Learning Representations (ICLR 2023).
In this work, we extend Vector Quantile Regression to support non-linear
specification, while ensuring monotonicity and scaling to millions of samples.
|
| Dec 20, 2022 |
New paper in Scientific Reports:
Our recent work,
has been published. Building on our previous work, we apply machine learning approaches to demonstrate that
protein structures carry information about their genetic coding.
|
| Jun 6, 2022 |
Featured: Our recent work about the
association between protein structure and synonymous genetic coding was featured
on the Technion website! See english and hebrew.
|
| May 20, 2022 |
New paper in Nature Communications:
Our paper,
“Codon-specific Ramachandran plots show amino acid backbone conformation
depends on identity of the translated codon” has been published
in Nature Communications.
In this work we applied powerful statistical methods to uncover novel
associations between synonymous genetic coding and protein structure.
|
| Dec 8, 2021 |
Scholarship:
I have been awarded with the Gutwirth Excellence Scholarship based on my PhD
research.
This scholarship is intended for PhD students at Technion, based on academic
excellence and research achievements, where priority is given to students
whose academic achievements indicate a high potential for further career as
independent researchers.
|
| Jul 23, 2021 |
Featured:
Our paper about clinically-relevant deep-learning-based ECG analysis was featured on
the Technion website!
See english and
hebrew.
|
| Jun 14, 2021 |
New paper in PNAS:
Our recent work paper was published
in PNAS. We tackle the issues of real-world clinical applicability of deep-learning based ECG analysis.
|
| Aug 24, 2020 |
Scholarship: I have been awarded the Technion Machine Learning and Intelligent Systems
(MLIS) Scholarship (funded by the Council for
Higher Education) for outstanding research in multi-disciplinary Data Science.
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| Jul 19, 2020 |
New paper: Our new paper was
published in Nature Scientific Reports.
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