Our lab is supported by a highly skilled and dedicated team of professionals with extensive experience in the fields of Electrical Engineering and Computer Science:
Phone: +972 8 646 15 18
Office: 417, Bldg. 37
I study radio frequency integrated circuits (RFIC). This is usually the juncture responsible for interactions with the physical environment, before information is transformed into digital signals in the realms of communication, sensing, or radar, for example. I find the opportunity to study systems that learn and communicate with their surroundings fascinating. Likewise, that they can be constructed to be as small as a grain or two of rice, is a huge bonus.
Lab Engineer
Phone: 08 646 15 14
Office: 404, 33
Administrative Assistant
Phone: 08 646 15 12
Office: 409, Bldg. 33
Phone: 08 646 17 35
Office: 422, Bldg. 33
Machine Learning and Artificial Intelligence; Communication Systems and Networks; VLSI and Digital Hardware Design
Phone: 08 646 58 90
Office: 418, Bldg. 33
Deep Learning based Graph Generation Techniques and other learning-on-graph subjects
Ph.D.
Co-Advisor: Prof. Oren Idan
Computer Vision; Deep Learning
Ph.D.
Information Theoretical Compression Methods for Foundation Models; Video Foundation Models; Computer Vision
Ph.D.
Co-Advisor: Prof. Kobi Cohen
Computer Vision; Deep Learning; AI
Ph.D.
Graph Neural Networks; Signal Processing; Computer Vision and Image Processing
Ph.D.
Co-Advisors: Prof. Bruno Ribeiro (Purdue), Dr. Moshe Eliasof (Cambridge/BGU)
Using ODEs in Representation Learning for Foundation Models; Graph Neural Networks; Inverse Problems
M.Sc.
Graph Foundation Models; Embedded Systems and Internet of Things (IoT); VLSI and Digital Hardware Design
Co-Advisor: Prof. Oren Idan
Resource-Efficient Learning; Geometric Deep Learning; Power Systems and Renewable Energy
Graph Neural Networks (GNNs); Geometric Deep Learning; VLSI and Digital Hardware Design
Resource-Efficient Learning; Adversarial Robustness; Graph Neural Networks (GNNs); Geometric Deep Learning
Resource-Efficient Learning; Adversarial Robustness; ; VLSI and Digital Hardware Design
Resource-Efficient Learning; Adversarial Robustness
Resource-Efficient Learning; Adversarial Robustness; Geometric Deep Learning
Electrical Engineering Department, MIT, USA
Resource-Efficient Learning; Adversarial Robustness; Graph Neural Networks (GNNs); Geometric Deep Learning
Co-Advisor: Dan Shar; Foundation Models for Uni- and Multi-Modal DataCurrently: TechnionCo-adviser: Dr. Mira Toledano; Resource-Efficient Learning and Adversarial RobustnessCurrently: MIT, USA
