Aviv A. Rosenberg

I’m a PhD student in the VISTA Lab at the Technion Computer Science Faculty, advised by Prof. Alex Bronstein. In addition, I’m currently working as a machine learning engineer and data scientist at Sibylla.

My research interests include deep learning, differentiable optimization, physiological signal processing (e.g. ECG), and recently computational structural biology. I hold an MSc in Biomedical Engineering and a BSc in Electrical Engineering, both from the Technion. Currently head TA of the CS faculty’s Deep Learning course.

In addition to a my research pursuits, I have a strong technical background in software development. 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.

news

Jun 14, 2021	New paper in PNAS about deep learning in cardiology.
Aug 24, 2020	CHE Data Science Scholarship awarded.
Jul 19, 2020	Our new paper was published in Nature Scientific Reports.

selected publications

Frontiers

PhysioZoo: a novel open access platform for heart rate variability analysis of mammalian electrocardiographic data

Behar, Joachim A, Rosenberg, Aviv A, Weiser-Bitoun, Ido, Shemla, Ori, Alexandrovich, Alexandra, Konyukhov, Eugene, and Yaniv, Yael

Frontiers in Physiology 2018

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Nat. Sci. Rep.

Signatures of the autonomic nervous system and the heart’s pacemaker cells in canine electrocardiograms and their applications to humans

Rosenberg, Aviv A, Weiser-Bitoun, Ido, Billman, George E, and Yaniv, Yael

Nature Scientific Reports 2020

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Heart rate and heart rate variability (HRV) are mainly determined by the autonomic nervous system (ANS), which interacts with receptors on the sinoatrial node (SAN; the heart’s primary pacemaker), and by the “coupled-clock” system within the SAN cells. HRV changes are associated with cardiac diseases. However, the relative contributions of the ANS and SAN to HRV are not clear, impeding effective treatment. To discern the SAN’s contribution, we performed HRV analysis on canine electrocardiograms containing basal and ANS-blockade segments. We also analyzed human electrocardiograms of atrial fibrillation and heart failure patients, as well as healthy aged subjects. Finally, we used a mathematical model to simulate HRV under decreased “coupled-clock” regulation. We found that (a) in canines, the SAN and ANS contribute mainly to long- and short-term HRV, respectively; (b) there is evidence suggesting a similar relative SAN contribution in humans; (c) SAN features can be calculated from beat-intervals obtained in-vivo, without intervention; (d) ANS contribution can be modeled by sines embedded in white noise; (e) HRV changes associated with cardiac diseases and aging can be interpreted as deterioration of both SAN and ANS; and (f) SAN clock-coupling can be estimated from changes in HRV. This may enable future non-invasive diagnostic applications.
PNAS

Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis

Elul, Yonatan, Rosenberg, Aviv A., Schuster, Assaf, Bronstein, Alex M., and Yaniv, Yael

Proceedings of the National Academy of Sciences 2021

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The use of artificial intelligence (AI) in medicine, particularly deep learning, has gained considerable attention recently. Although some works boast superior capabilities compared to clinicians, actual deployments of AI systems in the clinic are scarce. We describe four important gaps on the machine-learning side responsible for this discrepancy by first formulating them in a way that is actionable by AI researchers and then systematically addressing these needs. Aiming beyond the search for better model architectures or improved accuracy, we focus directly on the challenges of clinical usefulness as stated by medical professionals in the literature. Our results show that deep-learning systems can be robust, trustworthy, explainable, and transparent while retaining the superior level of performance these algorithms are known for.Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in the daily practice of medicine largely due to several crucial unmet needs of healthcare practitioners. These include lack of explanations in clinically meaningful terms, handling the presence of unknown medical conditions, and transparency regarding the system’s limitations, both in terms of statistical performance as well as recognizing situations for which the system’s predictions are irrelevant. We articulate these unmet clinical needs as machine-learning (ML) problems and systematically address them with cutting-edge ML techniques. We focus on electrocardiogram (ECG) analysis as an example domain in which AI has great potential and tackle two challenging tasks: the detection of a heterogeneous mix of known and unknown arrhythmias from ECG and the identification of underlying cardio-pathology from segments annotated as normal sinus rhythm recorded in patients with an intermittent arrhythmia. We validate our methods by simulating a screening for arrhythmias in a large-scale population while adhering to statistical significance requirements. Specifically, our system 1) visualizes the relative importance of each part of an ECG segment for the final model decision; 2) upholds specified statistical constraints on its out-of-sample performance and provides uncertainty estimation for its predictions; 3) handles inputs containing unknown rhythm types; and 4) handles data from unseen patients while also flagging cases in which the model’s outputs are not usable for a specific patient. This work represents a significant step toward overcoming the limitations currently impeding the integration of AI into clinical practice in cardiology and medicine in general.All study data are included in the article and/or SI Appendix.