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Deborah Cohen

Deborah Cohen

Deborah Cohen received the B.Sc. degree in electrical engineering (summa cum laude) in 2010 and the Ph.D. degree in electrical engineering (signal processing) in 2016 from the Technion - Israel Institute of Technology, Haifa, in 2010. Since 2010, she has been a Project Supervisor with the Signal and Image Processing Lab, the High Speed Digital Systems Lab, the Communications Lab and the Signal Acquisition, Modeling and Processing Lab (SAMPL), at the Electrical Engineering Department, Technion. In 2011, Ms. Cohen was awarded the Meyer Foundation Excellence prize. She received the Sandor Szego Award and the Vivian Konigsberg Award for Excellence in Teaching from 2012 to 2016, the David and Tova Freud and Ruth Freud-Brendel Memorial Scholarship in 2014 and the Muriel and David Jacknow Award for Excellence in Teaching in 2015. Since 2014, Ms. Cohen is an Azrieli Fellow. She is currently a research scientist in the Clair team in Google Israel. Her research interests include theoretical aspects of signal processing, compressed sensing, reinforcement learning and machine learning for dialogues.
Authored Publications
Google Publications
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    AI Increases Global Access to Reliable Flood Forecasts
    Asher Metzger
    Dana Weitzner
    Frederik Kratzert
    Guy Shalev
    Martin Gauch
    Sella Nevo
    Shlomo Shenzis
    Tadele Yednkachw Tekalign
    Vusumuzi Dube
    arXiv (2023)
    Preview abstract Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that AI-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time that is similar to or better than the reliability of nowcasts (0-day lead time) from a current state of the art global modeling system (the Copernicus Emergency Management Service Global Flood Awareness System). Additionally, we achieve accuracies over 5-year return period events that are similar to or better than current accuracies over 1-year return period events. This means that AI can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed in this paper was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings. View details
    Preview abstract We study conversational domain exploration (CODEX), where the user’s goal is to enrich her knowledge of a given domain by conversing with an informative bot. Such conversations should be well grounded in high-quality domain knowledge as well as engaging and open-ended. A CODEX bot should be proactive and introduce relevant information even if not directly asked for by the user. The bot should also appropriately pivot the conversation to undiscovered regions of the domain. To address these dialogue characteristics, we introduce a novel approach termed dynamic composition that decouples candidate content generation from the flexible composition of bot responses. This allows the bot to control the source, correctness and quality of the offered content, while achieving flexibility via a dialogue manager that selects the most appropriate contents in a compositional manner. We implemented a CODEX bot based on dynamic composition and integrated it into the Google Assistant. As an example domain, the bot conversed about the NBA basketball league in a seamless experience, such that users were not aware whether they were conversing with the vanilla system or the one augmented with our CODEX bot. Results are positive and offer insights into what makes for a good conversation. To the best of our knowledge, this is the first real user experiment of open-ended dialogues as part of a commercial assistant system. View details
    Preview abstract Complex classifiers may exhibit ``embarassing'' failures in cases that would be easily classified and justified by a human. Avoiding such failures is obviously paramount, particularly in domains where we cannot accept such unexplained behavior. In this work we focus on one such setting, where a label is perfectly predictable if the input contains certain features, and otherwise, it is predictable by a linear classifier. We define a related hypothesis class and determine its sample complexity. We also give evidence that efficient algorithms cannot, unfortunately, enjoy this sample complexity. We then derive a simple and efficient algorithm, and also give evidence that its sample complexity is optimal, among efficient algorithms. Experiments on sentiment analysis demonstrate the efficacy of the method, both in terms of accuracy and interpretability. View details
    Sub-Nyquist Radar Systems: Temporal, Spectral and Spatial Compression
    Yonina C. Eldar
    IEEE Signal Processing Magazine (2018) (to appear)
    SUMMeR: Sub-Nyquist MIMO Radar
    David Cohen
    Yonina C. Eldar
    Alexander M. Haimovich
    IEEE Transactions on Signal Processing, vol. 66 (2018), pp. 4315 - 4330
    Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware
    Shahar Tsiper
    Yonina C. Eldar
    IEEE Signal Processing Magazine, vol. 35 (2018), pp. 137 - 166
    Expediting exploration by attribute-to-feature mapping for cold-start recommendations
    Michal Aharon
    Yair Koren
    Oren Somekh
    Raz Nissim
    RecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems (2017), pp. 184-192
    Spectrum Sharing Radar: Coexistence via Xampling
    Kumar Vijay Mishra
    Yonina C. Eldar
    IEEE Transactions on Aerospace and Electronic Systems, vol. 54 (2017), pp. 1279 - 1296
    CaSCADE: Compressed carrier and DOA estimation
    Shahar Stein Ioushua
    Or Yair
    Yonina C. Eldar
    IEEE Transactions on Signal Processing, vol. 65 (2017), pp. 2645 - 2658
    Sub-Nyquist cyclostationary detection for cognitive radio
    Yonina C. Eldar
    IEEE Transactions on Signal Processing, vol. 65 (2017), pp. 3004 - 3019
    Sub-Nyquist sampling for power spectrum sensing in cognitive radios: A unified approach
    Yonina C. Eldar
    IEEE Transactions on Signal Processing, vol. 62 (2014), pp. 3897-3910