Aviv A. Rosenberg

Machine Learning • Research • Engineering


Currently a machine learning engineer and data scientist at Sibylla, a fintech startup, where I lead the design and implementation of our machine-learning-based algorithmic trading platform.

Prior to this role, I completed my PhD in the Technion Computer Science Faculty, advised by Prof. Alex Bronstein. I conducted multidisciplinary research focusing on machine learning and statistical tools, with applications in medicine and biology. Some research highlights include: Non-linear and scalable extension for vector quantile regression, a powerful tool allowing general statistical inference by modelling conditional quantiles; Distributional analysis and hypothesis testing for codon-specific protein backbone angles; computationally identifying protein structures defying the “one sequence, one structure” principle; Deep-learning based ECG analysis system focused on clinical applicability and statistical performance. During that time I was also head TA of the CS faculty’s Deep Learning course (2019-2022). I also hold an MSc in Biomedical Engineering and a BSc in Electrical Engineering, both from the Technion.

In addition to my research pursuits, I have a strong technical background in software engineering. Before starting my PhD I worked in multiple technical roles. I have extensive experience writing robust, testable, well-designed code, leading software teams and architecting large-scale systems.


Oct 27, 2023 New paper in PNAS: Our new paper in the domain of computational structural biology was recently published in the Proceedings of the National Academy of Sciences. We performed distributional analysis of protein backbone angles using a novel a cross-peptide-bond Ramachandran plot that captures the conformational preferences of the amino acid pairs, and demonstrate that it conveys biologically meaningful information, not apparent in the traditional Ramachandran plot.
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.
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.
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.
Jul 19, 2020 New paper: Our new paper was published in Nature Scientific Reports.

selected publications


  1. PNAS
    An amino domino model described by a cross peptide bond Ramachandran plot defines amino acid pairs as local structural units
    Aviv A. Rosenberg, Nitsan YehiShalom, Ailie Marx, and Alex M Bronstein
    Proceedings of the National Academy of Sciences, 2023
  2. ICLR
    Fast Nonlinear Vector Quantile Regression
    Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano, and Alex M. Bronstein
    In The Eleventh International Conference on Learning Representations , 2023


  1. NatComm
    Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon
    Aviv A. Rosenberg, Ailie Marx, and Alex M. Bronstein
    Nature Communications, May 2022


  1. PNAS
    Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis
    Yonatan Elul, Aviv A. Rosenberg, Assaf Schuster, Alex M. Bronstein, and Yael Yaniv
    Proceedings of the National Academy of Sciences, May 2021