Yonina Eldar

Professor and Joseph E. Aoun Chair,  Electrical and Computer Engineering
Affiliated Faculty,  Bioengineering

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  • ISEC 416

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Research Focus

Our research focuses on building radically resource-aware sensing systems that sense only what matters - systems that diagnose, monitor, protect, and connect. By uniting physics, hardware, signal processing, and artificial intelligence, our group develops intelligent, interpretable, and sustainable sensing technologies that improve human life and strengthen security. This vision, which we call Green Intelligence, seeks to redefine the relationship between information, energy, and purpose: sensing not for maximal data, but for maximal insight and information. Our group conducts research at the intersection of artificial intelligence, sensing, signal processing, and biomedical engineering, with a strong emphasis on real-world impact in healthcare, communications, and defense.

Education

  • PhD, Electrical Engineering and Computer Science, MIT, 2002

Honors & Awards

  • 2025 Israel Prize in Engineering Research and Engineering Sciences
  • 2024 Fellow of the Industry Academy within the International Artificial Intelligence Industry Alliance (AIIA)
  • 2023 Member of The Academia Europaea
  • 2023 The Landau Science and Arts Award in Mathematics
  • 2022 Fellow of Asia-Pacific Artificial Intelligence Association (AAIA)
  • 2017 Member of The Israel Academy of Sciences and Humanities
  • 2017 Fellow of the European Association For Signal Processing (EURASIP)
  • 2016 The IEEE Kiyo Tomiyasu Award
  • 2014 IEEE/AESS Fred Nathanson Memorial Radar Award
  • 2013 IEEE Signal Processing Society Technical Achievement Award
  • 2012 Fellow of the Institute of Electrical and Electronics Engineers (IEEE)

Research Overview

Our research focuses on building radically resource-aware sensing systems that sense only what matters - systems that diagnose, monitor, protect, and connect. By uniting physics, hardware, signal processing, and artificial intelligence, our group develops intelligent, interpretable, and sustainable sensing technologies that improve human life and strengthen security. This vision, which we call Green Intelligence, seeks to redefine the relationship between information, energy, and purpose: sensing not for maximal data, but for maximal insight and information. Our group conducts research at the intersection of artificial intelligence, sensing, signal processing, and biomedical engineering, with a strong emphasis on real-world impact in healthcare, communications, and defense.

The Signal Acquisition Modeling Processing and Learning (SAMPL) Lab, headed by Prof. Yonina Eldar, focuses on developing new technologies that more efficiently extract and process signals and information across a wide range of tasks, including medical imaging, radar, communication, scientific and optical imaging and biological inference. The lab also develops model-based methods for artificial intelligence (AI) that aid in obtaining increased information using minimal resources.

Signal processing is the area of science and engineering concerned with the generation, acquisition, representation, transmission and analysis of signals and information using mathematical theory and methods. Signal processing is the power behind our modern lives, enhancing our ability to communicate and share information. The technology in our everyday lives – from computers, cell phones and autonomous vehicles to medical and defense systems – is enabled by signal processing. The performance and effectiveness of these systems depend on the quality and efficiency of signal sampling and processing. In many cases, such as medical applications, automotive and aviation, these capabilities are critical to human health, life and wellbeing.

In the coming years, new and increasingly complex challenges will arise as we become even more dependent on signal processing in our everyday lives. As countless additional tasks and functions are replaced by smart and automated applications, they will require increasingly faster and more powerful technologies. As these new technologies sweep across the planet to billions of people in emerging markets, they will need to become increasingly more efficient and less costly.

While traditional systems treat the sampling and processing stages separately and require sampling at the well-known Nyquist rate, SAMPL lab introduces a paradigm shift in which sampling and processing are jointly designed in order to leverage the inherent properties of signals and tasks during the sampling stage. Thus, we can acquire and process only the information needed for the required task, reduce the sampling and processing rates well below the Nyquist rate, and greatly improve the resolution which can be obtained from a limited number of samples in time, space and frequency.

This approach paves the way to new technologies such as wireless ultrasound, compact portable devices with better imaging quality, fast and quantitative MRI, efficient wideband sensing, high resolution radar, efficient communication systems, joint radar and communication systems for automotive and IoT applications, super resolution microscopy and ultrasound, model-based efficient and interpretable deep networks for medical imaging, communication systems, radar based medical imaging and sensing, and more.

In order to perform the above, SAMPL lab combines theoretical research in the fields of mathematics, information theory, statistical signal processing, AI and computer science, and practical engineering research using our state-of-the-art lab to facilitate the transition from pure theory to prototype systems and clinical studies. Via our Clinical Research arm, we collaborate with physicians across Israel and abroad, to advance healthcare, medical diagnostics and imaging. Via our Technologies arm, we collaborate with industry partners to impact next-generation technologies. In addition, we collaborate closely with researchers in biology and physics in order to advance technology for scientific discovery.

Selected Research Projects

  • AI-driven radar and ultrasound imaging for healthcare and human monitoring.
  • Integrated sensing and communication (ISAC) for next-generation wireless systems.
  • Efficient and “green” sensing architectures and hardware (e.g., novel ADC designs).
  • Model-based deep learning, inverse problems, and task-driven signal acquisition.
  • Intelligent multimodal systems combining physics-based models and machine learning.

Research Centers and Institutes

Selected Publications

  • Y. Zhang, Y. Zhang, L. Zhu, S. Xiao, W. Tang and Y. C. Eldar, “Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications”, IEEE Transactions on Vehicular Technology, vol. 74 (6), pp. 9899-9903, June 2025.
  • H. Chen, J. Li, S. Yang, W. Liu, Y. C. Eldar and C. Yuen, “Near-field Source Localization in 3-D Using Two Parallel Centrally Symmetric Unfold Coprime Array”, IEEE Transactions on Wireless Communications, vol. 24 (6), pp. 4738-4749, June 2025.
  • H. Zhang, N. Shlezinger, F. Guidi, A. Guerra, D. Dardari, M. F. Imani and Y. C. Eldar, “Near-Field Beam Focusing for Wireless Power Transfer With Dynamic Metasurface Antennas”, IEEE Internet of Things Journal, vol. 12 (12), pp. 18596-18605, June 2025.
  • G. Zhang, K. Kang, Y. Cai, Q. Hu, Y. C. Eldar and A. L. Swindlehurst, “O2SC: Realizing Channel-Adaptive Semantic Communication with One-Shot Online-Learning”, IEEE Transactions on Communications, vol. 73 (5), pp. 3268-3282, May 2025.
  • R. Zhang, Y. Shao, M. Li, L. Lu, Y. C. Eldar, “Optical Integrated Sensing and Communication with Light-Emitting Diode”, IEEE Internet of Things Journal, vol. 12 (9), pp. 12896-12911, May 2025.
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Faculty

Aug 19, 2025

New Faculty Spotlight: Yonina Eldar

Yonina Eldar joins the electrical and computer engineering department in August 2025 as a Professor and Joseph E. Aoun Chair.

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