Between Computation and Architecture

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Between Computation and Architecture

An algorithm that streamlines and automates architectural form-finding has been developed at the Taub Faculty of Computer Science

A computer rendered image of one of the segmented 3D models computed by the new algorithm.

The use of robots in construction and architectural manufacturing is a vision steadily becoming a reality and is perceived as a key trend in the next revolution in the construction industry. For years, complex architectural projects have been planned by computer. On the ground, however, these projects continue to be executed using construction methods that have remained virtually unchanged for decades.

In recent years, thanks to continuous development, robotic instrumentation has begun to close the gap between the level of planning sophistication and practical execution on-site. Consequently, anyone who has seen videos of robotic manufacturing processes in architectural projects will find   it hard not to be swept up in the tide of enthusiasm. The good ones show robotic arms in motion, lifting building parts that interlock with ease. The pace of production is accompanied by accurate cutting and precise detail.

Fabrication of one of the models from construction paper. (a) Planar hexagonal mesh, (b) 2D face templates for cutting, (c-d) intermediate and (e-f) final constructions

Despite the impressive tempo of the robots and the infinite possibilities inherent in these production processes, human intervention is usually necessary behind the scenes from the production aspect as well as in calculating and planning the various deliverables. This is especially true when architectural planning is based on complex spatial systems such as thin, doubly-curved surfaces, also known as “shells.”

A research group from the Henry and Marilyn Taub Faculty of Computer Science at the Technion – Israel Institute of Technology is working on narrowing the gap between the promise and reality. The researchers, Professor Mirela Ben Chen, Dr. Kacper Pluta, and Michal Edelstein, together with their colleague, Professor Amir Vaxman of Utrecht University, responded to a request from an architect and developed an algorithm that finds automated solutions that meet robotic manufacturing needs for complex surfaces. The researchers created a computational framework that takes as input complex and diverse doubly curved surfaces and computes its segmentation into planar panels. The researchers have shown that the planar segments can be assembled from cardboard, a first step towards robotically manufactured shells made from timber.

Professor Mirela Ben Chen

“It’s important to recognize that industrial robotic manufacturing is not a technological whim,” Prof. Ben Chen explained. “It has numerous advantages in different aspects of sustainability such as material savings, reducing construction time and mitigating the environmental impacts of the construction process. The algorithm we developed can take complex surfaces and break them down into small segments, hexagons, in a way that increases the surface’s mechanical advantages. Further development of the computational tool will enable an optimal implementable solution to be devised.”

“In order for the computational system to be applicative in the ‘real world’ as well, collaboration with architects is necessary,” Prof. Ben Chen continued. “Ultimately, we hope that our research will lead to the development of a system that can compute and manufacture building segments through automation, so that they can be assembled on-site without detracting from or compromising on architectural or structural complexity.”

To read the researchers’ paper in ACM Transactions on Graphics, click here.

Journey to the target tissue

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Journey to the target tissue

Computer science in the service of medicine: Researchers at the Technion and the University of North Carolina present an innovative algorithm that safely and efficiently steers curved surgical needles inside the body while avoiding damage to tissue

Researchers from the Technion and the University of North Carolina (UNC) have developed an algorithm that steers surgical needles along 3D curvilinear trajectories. The researchers – Dr. Oren Salzman of the Taub Faculty of Computer Science at the Technion and Prof. Ron Alterovitz and Mengyu Fu of UNC – announced the development at the recently held virtual 2021 Robotics: Science and Systems Conference.

Dr. Oren Salzman

Numerous medical procedures, such as biopsies and localized therapy delivery for cancer, require that a needle be steered safely through tissue, to the target. Straight needles can “get the job done” when the straight path from the point of entry to the target tissue does not pass through vulnerable tissue, but in many cases, the target tissue is “hidden” behind a bone or vulnerable tissue, and in these cases, the surgeon must avoid anatomical obstacles, a difficult, complex task, most certainly when the body parts involved are vulnerable and sensitive.

Against this backdrop, in recent years, medical needles with bevel tips were developed. These needles are controlled by rotating them at their base. The problem is that directing these needles is neither simple nor intuitive, and steering them manually involves numerous risks. This has led to the development of “motion planning algorithms” designed to accurately and safely direct the needle. These algorithms have displayed impressive capabilities, and yet, since these are invasive procedures, the degree of precision required is very high; otherwise, the systems will not be granted regulatory approval.

The development presented by the researchers at the conference illustrates the importance of computer science in solving problems related to medicine and biomedical engineering. On the basis of relevant medical images such as a computed tomography (CT) or magnetic resonance imaging (MRI) scan, the new algorithm computes the optimal trajectory that will lead the needle to the target while avoiding damage to various anatomical obstacles. As opposed to existing algorithms, the new algorithm provides a “completeness” guarantee that the needle can indeed reach the specified target while avoiding those tissues, and if no such safe motion plan exists, it will inform the user accordingly. Moreover, it computes plans faster compared to rival steerable needle motion planners and with a higher success rate. According to the researchers, the technology presented at the conference is a new algorithmic foundation that is expected to lead to additional applications based on automated steerable needles.

Three views of the lung environment. The needle steers to targets (green) while avoiding anatomical obstacles including large blood vessels (red), bronchial tubes (brown), and the lung boundary (gray)

The research was funded by the US National Institutes of Health (NIH), the Israeli Ministry of Science and Technology, and the US-Israel Binational Science Foundation (BSF).

Dr. Oren Salzman joined the Technion staff in the summer of 2019 following a postdoctoral fellowship in the Robotics Institute at Carnegie Mellon University. He is head of the Computational Robotics Lab (CRL) in the Taub Faculty of Computer Science.

This E-Skin Knows What Movement You Make

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This E-Skin Knows What Movement You Make

Technion scientists created a wearable motion sensor capable of identifying bending and twisting

The new device


Professor Hossam Haick

Technion scientists have produced a highly stretchable electronic material and created a wearable sensor capable of precisely identifying bending and twisting motion. It is essentially an electronic skin capable of recognizing the range of movement human joints normally make, with up to half a degree precision. This breakthrough is the result of collaborative work between researchers from different fields in the Laboratory for Nanomaterial-Based Devices, headed by Professor Hossam Haick from the Wolfson Faculty of Chemical Engineering. It was recently published in Advanced Materials and was featured on the journal’s cover.

Yehu David Horev

Prof. Haick’s lab is focused on wearable devices for various uses. Currently existing wearable motion sensors can recognize bending movement, but not twisting. Existing twisting sensors, on the other hand, are large and cumbersome. This problem was overcome by Ph.D. candidate Yehu David Horev and postdoctoral fellow Dr. Arnab Maity. Mr. Horev found a way to form a composite material that was both conductive (and thus, usable as a sensor) and flexible, stretchable, breathable, and biocompatible, and that did not change its electrical properties when stretched. Dr. Maity then solved the mathematics of analyzing the received signal, creating an algorithm capable of mapping bending and twisting motion – the nature of the movement, its speed, and its angle. The novel sensor is breathable, durable, and lightweight, allowing it to be worn on the human body for prolonged periods.

“This sensor has many possible applications,” Prof. Haick stated. “It can be used in early disease diagnosis, alerting of breathing alterations, and motor system disorders such as Parkinson’s disease. It can be used to assist patients’ motor recovery and be integrated into prosthetic limbs. In robotics, the feedback it provides is crucial for precise motion. In industrial uses, such sensors are necessary in monitoring systems, putting them at the core of the fourth industrial revolution.”

“Electrically conductive polymers are usually quite brittle,” explained Mr. Yehu about the challenge the group had overcome. “To solve this, we created a composite material that is a little like fabric: the individual polymer ‘threads’ cannot withstand the strain on the material, but their movement relative to each other lets it stretch without breaking. It is not too different from what lends stretch to t-shirts. This allows the conductive polymer withstand extreme mechanical conditions without losing its electrical properties.”

What makes this achievement more important is that the materials the group used are very cheap, resulting in an inexpensive sensor. “If we make a device that is very expensive, only a small number of institutions in the Western World can afford to use it. We want the technological advances we achieve to benefit everyone, regardless of their geographic location and socio-economic status,” said Prof. Haick. True to his word, among the laboratory’s other projects is a tuberculosis-diagnosing sticker patch, sorely needed in developing countries.

Dr. Arnab Maity

The scientists who contributed to this study are Yehu David Horev, Dr. Arnab Maity, Dr. Youbin Zheng, Yana Milyutin, Dr. Muhammad Khatib, Dr. Ning Tang, and Prof. Hossam Haick from the Department of Chemical Engineering and Russell Berrie Nanotechnology Institute at the Technion-Israel Institute of Technology; Miaomiao Yuan from the Eighth Affiliated Hospital, Sun Yat-sen University, China; Dr. Ran Yosef Suckeveriene from the Department of Water Industry Engineering at the Kinneret Academic College; and Prof. Weiwei Wu from the School of Advanced Materials and Nanotechnology at Xidian University, China.

Click here for the paper in Advanced Materials


When Homework is an Opportunity to Help

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When Homework is an Opportunity to Help

Technion Computer Science students joined the Technion Social Hub to make their last-semester projects ones that help communities and NGOs

Friends for Health team

Come end of term, the Henry and Marilyn Taub Faculty of Computer Science at the Technion held its annual projects fair, showcasing projects by undergraduate students in their final year. Given freedom to choose how they will apply the principles learnt in the course of their studies, multiple groups chose to help the community, and more than one partnered with the Technion Social Hub to do so.

The students’ projects and the partnership with the Technion Social Hub present a unique opportunity: small NGOs often find themselves in need of a software solution but can neither find an existing platform to cover their particular needs, nor afford custom software. This is just the demand students can meet, at the same time gaining valuable experience in servicing a client.

This year, three such projects stood out among the rest.

First, a project for Friends for Health, by Ido Yam, Tal Manheim, Yaakov Sherma, Illay Hai, and Daniel Shapiro, guided by Eytan Singher and Itai Dabran. Friends for Health is a non-profit organization dedicated to helping people who cannot afford the life-saving medication they need. Their stock comes from donors who have unused medications that they are willing to donate. The Technion team wrote for the organization a computerized interface – for the patients, it guides them to the appropriate form for receiving the specific medication they need; for donors – organizing the donation, adding the possibility for medications to be collected from the donor’s home (necessary for medications with strict storage requirements); for employees – management functions. These functions, which we take for granted in a commercial company, were previously done manually in this so important non-profit. The students’ software would considerably shorten waiting times and enable Friends for Health to assist more patients.

Social Delivery team

A similar project was provided for Social Delivery by Lior Zelikman, Alex Chirkov, Yagel Meir and Tal Neoran, guided by Eytan Singher and Itai Dabran. Social delivery is an initiative offering logistical solutions for connecting between excess stocks of various objects (furniture, textiles, etc.) and NGOs needing those objects. Recently they are also looking into adding companies replacing office furniture as potential donors. The students digitized for the first time the initiative’s donations, and requests and storage tracking. The new interface even lets donors see their past donations and which NGOs they had helped.

Hadar Social Network team

A project of a different sort is the Hadar Social Network, by Haneen Jeries, Hussein Abu Jabal, Sami Hammoud and Haitham Kablan, guided by Elazar Gershoni and and Itai Dabran. Hadar is a disadvantaged neighbourhood in the city of Haifa, where the Technion is located. In the past years, the neighbourhood has come to organize, people assisting each other however they can. But everything was happening over disjointed WhatsApp conversations, making it difficult to keep track of what was going on, or administrate the interactions. The students partnered with the neighbourhood council to create a dedicated app, providing a smooth and secure process of connecting volunteers with people who need help. An administrator can ensure the security of the interaction, and a community social worker can use it to provide assistance.

These projects were by no means the only ones that sought to combine a homework assignment with a chance to do good. Other groups dealt with fair trade, donation of excess food from restaurants, accessibility mapping and more. For Technion students, academic success and helping those less privileged go hand in hand.

Nature Communications

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Nature Communications:
Technion Researchers Develop Innovative Rapid Imaging Technology

Visualizing the movement of C. elegans with the new technology. Creating such videos had not been previously feasible with SPI technologies.
Professor Amir Rosenthal

Researchers at the Technion – Israel Institute of Technology have developed an innovative rapid imaging technology and demonstrated its performance in reconstructing the movement of a minute animal. Published in Nature Communications, the development project was headed by Professor Amir Rosenthal, doctoral student Evgeny Hahamovich, and master’s student Sagi Monin of the Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering.

Doctoral student Evgeny Hahamovich

The research team’s technology is based on the innovative SPI (single-pixel imaging) concept – the production of high-quality images using a device equipped with only a single detector. This concept, which enables photographs to be taken without a camera, has vast potential for diverse applications, such as the development of components of warning systems in autonomous vehicles or enhanced image depth in microscopy of biological tissues.

Master’s student Sagi Monin

SPI is based on the illumination of an object with encoded light patterns, generally by means of a projector. Based on the properties of the light reflected and propagated by the object, the image of the object can be produced using reconstruction algorithms. The problem is that to date, these systems have been hampered by significant limitations, one of them being the slow image acquisition rate, which is the result of the fact that the projectors themselves are slow. This has, until now, limited use of the systems to photographing stationary objects.


Visualizing the movement of C. elegans with the new technology. Creating such videos had not been previously feasible with SPI technologies.

The Technion research team broke through this limitation by applying a new method for spatially encoding light at unprecedented frequencies – 2.4 MHz as opposed to 22 kHz, which is the maximum frequency currently available in SPI technology. This represents an improvement of more than a hundredfold in projection rates and image acquisition rates. By using a rotating device fitted with a coding mask, the researchers created a completely new illumination pattern and an SPI microscope with unprecedented capabilities.

To demonstrate the system’s capabilities, the research group produced videos with a frame rate of 72 FPS (frames per second). The films accurately depict the complex movement of the nematode worm, C. elegans, an impossible achievement using currently available SPI technology.



The study was funded by the Ollendorf Minerva Center.


Click here for the paper in Nature Communications

Your Chance of Finding Quality Scientific Information on Google Depends on the Language You Search In

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Your Chance of Finding Quality Scientific Information on Google Depends on the Language You Search In

There is a saying that all the world’s knowledge is available at our fingertips – just a quick Google search away. But what happens when users search for information in their own language? For example, when searching for a scientific term, do search engines provide English-, Hebrew- and Arabic-speaking students with the same level of access to quality scientific information? This question is addressed by a new study, conducted at the Technion – Israel Institute of Technology and recently published in Public Understanding of Science.

Professor Ayelet Baram-Tsabari

The study found that search results for terms in English are of better quality than those provided for equivalent terms in Hebrew and Arabic. Additionally, most of the differences between the languages pertained to pedagogical aspects of quality, that is, the extent to which the content was geared towards young users, rather than the scientific aspects, such as the accuracy of the content. Some of the largest differences between the languages were found for terms related to nutrition and metabolism, such as “carbohydrate,” “protein,” “enzyme,” and “metabolism.”


These findings are based on the top Google Search results presented to users in Israel for 30 basic scientific terms in three languages: Hebrew, Arabic, and English. The terms pertained to three scientific domains: biology, chemistry, and physics. Each search result’s overall quality was determined using scientific criteria, such as content accuracy, the author’s authority, and the use of sources; pedagogical criteria, such as references to everyday life and the quality of audiovisual materials; and criteria specific to online content, such as recency and interactivity.

Dr. Aviv Sharon

According to Kawther Zoubi, who conducted the study as part of her masters’ thesis in the Technion’s Faculty of Education in Science and Technology, “these findings help us understand the digital divide and the social factors that affect our ability to develop science literacy. Our understanding of science depends on the environment we live in and the extent to which we have access to quality scientific information. This depends on our proficiency in different languages.”

Professor Ayelet Baram-Tsabari of the Faculty of Education in Science and Technology, who oversaw the study, added that, “The scientific and educational communities must act to mitigate the digital divide. We all have the right to access quality scientific information in our language.”

Click here for the paper in Public Understanding of Science.


Click here for video demonstrating the research

The Heart of the Matter: Deep Learning in Medicine

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The Heart of the Matter: Deep Learning in Medicine

Technion researchers laid down the principles for a clinically viable way to develop AI-based tools for medicine, and demonstrated how to use them to develop practical systems for the cardiology discipline

In recent years, meteoric progress has been made in the world of deep learning, but at the present time, there are virtually no medical products on the shelf that use this technology. Consequently, doctors continue to employ the same tools used in previous decades.

Prof. Yael Yaniv

To find a solution to this problem, the research group of Professor Yael Yaniv of the Faculty of Biomedical Engineering joined forces with the research groups of Professors Alex Bronstein and Assaf Schuster of the Taub Faculty of Computer Science. Now, under their joint supervision, research by doctoral students Yonatan Elul and Aviv Rosenberg has been published in Proceedings of the National Academy of Sciences of the United States of America (PNAS). In the article, the authors demonstrate an AI-based system that automatically detects disease on the basis of hundreds of electrocardiograms, which are currently the most widespread technology employed for the diagnosis of cardiac pathology.

Prof. Alex Bronstein

The new system automatically analyzes the electrocardiograms (ECGs) using augmented neural networks – the most prominent tool in deep learning today. These networks learn different patterns by training on a large number of samples, and the system developed by the researchers was trained on more than 1.5 million ECG segments sampled from hundreds of patients in hospitals in different countries.

Doctoral student Yonatan Elul

The electrocardiogram, developed more than a century ago, provides important information on conditions affecting the heart, and does so quickly and non-invasively. The problem is that the printouts are presently interpreted by a human cardiologist, and thus, their interpretation is, by necessity, pervaded by subjective elements. As a result, numerous research groups worldwide are working on the development of systems that will automatically interpret the printouts efficiently and accurately. Moreover, these systems are able to identify pathological conditions that human cardiologists, regardless of their experience, will not be able to detect.

Doctoral student Aviv Rosenberg

The system developed by the Technion researchers was built according to requirements defined by cardiologists, and its output includes an uncertainty estimation of the results, indication of suspicious areas on the ECG wave, and alerts regarding inconclusive results and increased risk of pathology not observed in the ECG signal itself. The system demonstrates sufficient sensitivity in providing alerts regarding patients at risk of arrhythmia even when the arrhythmia is not demonstrated in the ECG printout, and the rate of false alarms is negligible. Moreover, the new system explains its decisions using the accepted cardiology terminology.

The researchers hope this system can be used for cross-population scanning for the early detection of those who are at risk of arrhythmia. Without this early diagnosis, these people have an increased risk of heart attack and stroke.

Prof. Assaf Schuster

The study was headed by Prof. Yael Yaniv, director of the Bioelectric and Bio-energetic Systems Laboratory at the Faculty of Biomedical Engineering at the Technion; Prof. Alex Bronstein, director of the VISTA Laboratory at the Taub Faculty of Computer Science; Prof. Assaf Schuster of the Learning at Scale Laboratory (MLL) at the Taub Faculty of Computer Science and co-director of the MLIS Center (Machine Learning & Intelligent Systems); Yonatan Elul, a doctoral student in the laboratories of Professors Bronstein, Yaniv, and Schuster who completed his bachelor’s degree in Biomedical Engineering and his master’s degree at the Faculty of Computer Science at the Technion; and Aviv Rosenberg, a doctoral student in the laboratory of Professors Bronstein and Yaniv who completed his B.Sc. at the Viterbi Faculty of Electrical and Computer Engineering and his M.Sc. at the Faculty of Biomedical Engineering.

The project was sponsored by the Ministry of Science and Technology and the Technion Hiroshi Fujiwara Cyber Security Research Center and the Israel Cyber Directorate.


Click here for the article in PNAS

Mathematical Conjectures: The Ramanujan Machine

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The Ramanujan Machine

 Using AI and computer automation, Technion researchers have developed a “conjecture generator” that creates mathematical conjectures, which are considered to be the starting point for developing mathematical theorems. They have already used it to generate a number of previously unknown formulas. The study, which was published in the journal Nature, was carried out by undergraduates from different faculties under the tutelage of Assistant Professor Ido Kaminer of the Andrew and Erna Viterbi Faculty of Electrical Engineering at the Technion.

The project deals with one of the most fundamental elements of mathematics – mathematical constants. A mathematical constant is a number with a fixed value that emerges naturally from different mathematical calculations and mathematical structures in different fields. Many mathematical constants are of great importance in mathematics, but also in disciplines that are external to mathematics, including biology, physics, and ecology. The golden ratio and Euler’s number are examples of such fundamental constants. Perhaps the most famous constant is pi, which was studied in ancient times in the context of the circumference of a circle. Today, pi appears in numerous formulas in all branches of science, with many math aficionados competing over who can recall more digits after the decimal point: 3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679821480865132823066470938446095505822317253594081284811174502841027019385211055596446229489549303820…

The Technion researchers proposed and examined a new idea: The use of computer algorithms to automatically generate mathematical conjectures that appear in the form of formulas for mathematical constants.

A conjecture is a mathematical conclusion or proposition that has not been proved; once the conjecture is proved, it becomes a theorem. Discovery of a mathematical conjecture on fundamental constants is relatively rare, and its source often lies in mathematical genius and exceptional human intuition. Newton, Riemann, Goldbach, Gauss, Euler, and Ramanujan are examples of such genius, and the new approach presented in the paper is named after Srinivasa Ramanujan.

Ramanujan, an Indian mathematician born in 1887, grew up in a poor family, yet managed to arrive in Cambridge at the age of 26 at the initiative of British mathematicians Godfrey Hardy and John Littlewood. Within a few years he fell ill and returned to India, where he died at the age of 32. During his brief life he accomplished great achievements in the world of mathematics. One of Ramanujan’s rare capabilities was the intuitive formulation of unproven mathematical formulas. The Technion research team therefore decided to name their algorithm “the Ramanujan Machine,” as it generates conjectures without proving them, by “imitating” intuition using AI and considerable computer automation.

According to Prof. Kaminer, “Our results are impressive because the computer doesn’t care if proving the formula is easy or difficult, and doesn’t base the new results on any prior mathematical knowledge, but only on the numbers in mathematical constants. To a large degree, our algorithms work in the same way as Ramanujan himself, who presented results without proof. It’s important to point out that the algorithm itself is incapable of proving the conjectures it found – at this point, the task is left to be resolved by human mathematicians.”

The conjectures generated by the Technion’s Ramanujan Machine have delivered new formulas for well-known mathematical constants such as pi, Euler’s number (e), Apéry’s constant (which is related to the Riemann zeta function), and the Catalan constant. Surprisingly, the algorithms developed by the Technion researchers succeeded not only in creating known formulas for these famous constants, but in discovering several conjectures that were heretofore unknown. The researchers estimate this algorithm will be able to significantly expedite the generation of mathematical conjectures on fundamental constants and help to identify new relationships between these constants.

As mentioned, until now, these conjectures were based on rare genius. This is why in hundreds of years of research, only a few dozens of formulas were found. It took the Technion’s Ramanujan Machine just a few hours to discover all the formulas for pi discovered by Gauss, the “Prince of Mathematics,” during a lifetime of work, along with dozens of new formulas that were unknown to Gauss.

According to the researchers, “Similar ideas can in the future lead to the development of mathematical conjectures in all areas of mathematics, and in this way provide a meaningful tool for mathematical research.”

The research team has launched a website,, which is intended to inspire the public to be more involved in the advancement of mathematical research by providing algorithmic tools that will be available to mathematicians and the public at large. Even before the article was published, hundreds of students, experts, and amateur mathematicians had signed up to the website.

The research study started out as an undergraduate project in the Rothschild Scholars Technion Program for Excellence with the participation of Gal Raayoni and George Pisha, and continued as part of the research projects conducted in the Andrew and Erna Viterbi Faculty of Electrical Engineering with the participation of Shahar Gottlieb, Yoav Harris, and Doron Haviv. This is also where the most significant breakthrough was made – by an algorithm developed by Shahar Gottlieb – which led to the article’s publication in Nature. Prof. Kaminer adds that the most interesting mathematical discovery made by the Ramanujan Machine’s algorithms to date relates to a new algebraic structure concealed within a Catalan constant. The structure was discovered by high school student Yahel Manor, who participated in the project as part of the Alpha Program for science-oriented youth. Prof. Kaminer added that, “Industry colleagues Uri Mendlovic and Yaron Hadad also participated in the study, and contributed greatly to the mathematical and algorithmic concepts that form the foundation for the Ramanujan Machine. It is important to emphasize that the entire project was executed on a voluntary basis, received no funding, and participants joined the team out of pure scientific curiosity.”

Prof. Ido Kaminer is the head of the Robert and Ruth Magid Electron Beam Quantum Dynamics Laboratory. He is a faculty member in the Andrew and Erna Viterbi Faculty of Electrical Engineering and the Solid State Institute. Kaminer is affiliated with the Helen Diller Quantum Center and the Russell Berrie Nanotechology Institute.

Click here for the paper in Nature

Prof. Ziv the First Israeli to win Medal of Honor

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IEEE Medal of Honor to Technion Living Legend Dist. Prof. Ziv

Distinguished Professor Jacob Ziv

Distinguished Professor Jacob Ziv from the Viterbi Faculty of Electrical Engineering at the Technion wins the IEEE Medal of Honor for 2021

Prof. Ziv is the first Israeli to win the Medal of Honor – the most prestigious award given by the IEEE and one of the most prestigious in the world of technology

The IEEE Medal of Honor, which is one of the most prestigious awards in technology, has been granted to Distinguished Prof. Jacob Ziv (Emeritus) of the Viterbi Faculty of Electrical Engineering at Technion – Israel Institute of Technology. Dist. Prof Ziv is the first Israeli to have won this honor from the International Institute of Electrical and Electronics Engineers. Dist. Prof. Ziv is a world pioneer in the field of information theory, and he is co-inventor of both the Lempel-Ziv algorithm and the Wyner-Ziv algorithm. He receives the medal for his “Fundamental contributions to information theory and data compression technology, and for distinguished research leadership.”

IEEE is the world’s largest technical-professional organization, with about half a million members in 150 countries. The association’s Medal of Honor has been awarded to a single winner each year since 1917, in recognition of an exceptional contribution to science and technology. This is IEEE’s most prestigious award and one of the most prestigious in the world of technology, honoring scientists whose exceptional achievements have left a mark for years on technology, society, and engineering.
The winners of the medal have included individuals who have shaped the fields of information, communication, electronics and computing. They include: Claude Shannon, father of information theory; Guglielmo Marconi, inventor of wireless; Gordon Moore, who drafted Moore’s Law; Andrew Grove, who was CEO and chairman of Intel; Harry Nyquist, one of the most prominent figures in communication and system theory; and founders of Qualcomm Dr. Irwin Jacobs and Prof. Andrew Viterbi (who made a significant contribution to the Electrical Engineering Faculty at the Technion which has since been named after him and his late wife Erna).

According to Dean of the Andrew & Erna Viterbi Faculty of Electrical Engineering Prof. Nahum Shimkin: “There is no one more worthy of this award than Distinguished Prof. Ziv. This is a great honor for the Faculty and the Technion as well.”

“This is a great honor for Dist. Prof. Ziv and the Technion,” said Technion President Prof. Uri Sivan. “His groundbreaking scientific and applied contributions are a source of inspiration for the best engineers in the world. His research in the Faculty of Electrical Engineering has brought about a significant revolution that laid the foundations for the Israeli Startup Nation.”

Born in 1931, Prof. Ziv, completed a bachelor’s and master’s degree in electrical engineering at the Technion, followed by a doctorate at MIT (1962). After some eight years of research and development at Raphael and Bell Labs in the United States, he joined the Technion faculty. Over the years he has held senior positions including Vice President of the Technion for Academic Affairs, Chairman of the Planning and Budgeting Committee, and President of the Israeli Academy of Sciences. He is a member of the US National Academy of Sciences and the American Academy of Arts and Sciences.

Dist. Prof. Ziv has won many prestigious awards, including the Israel Defense Award (twice), the Israel Prize in Exact Sciences (1993) the Marconi Award (1995), the Richard Heming Medal (1995), the Shannon Award (1997), the Frontiers of Knowledge Award from the BBVA Foundation (2009), and the EMET Prize (2017).

In 1977, Prof Ziv and Prof. Abraham Lempel of the Faculty of Computer Science published the initial version of the Lempel-Ziv algorithm, and in 1978 the second version. Both versions served as the basis for essential compression technologies including PNG, TIFF, ZIP and GIF and played a major role in PDF (for documents) and MP3 (for music) formats. This is an information compression algorithm that enables lossless compression, regardless of the structure of the data and without prior knowledge of the statistical properties of the data. Based on this algorithm, many of the compression technologies currently used today in memory devices, computers and smartphones were developed.

The Lempel-Ziv algorithm has opened the way for unprecedented technology, enabling the transfer of visual and other information at high speed without loss of information. In 2004, the Institute of Electrical and Electronic Engineers (IEEE) announced that the Lempel-Ziv algorithm is “a milestone in electronics and computer engineering” and that it “made a significant contribution to making the internet an effective means of global communication.”

Dist. Prof. Ziv also participated in the development of the Wyner-Ziv algorithm in Bell Laboratories. This algorithm, which is now part of Microsoft’s operating system, allows the compression of many images from different cameras, and their simultaneous transmission (for example in sports events).

Technion Waterloo Research Alliance

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Technion Waterloo Research Alliance

University of Waterloo President, Feridun Hamdullahpur, with Technion’s Immediate Past President, Peretz Lavie

When University of Waterloo President, Feridun Hamdullahpur, first met with Peretz Lavie, Technion’s immediate Past President, he quickly knew there was potential for a very special relationship between the two universities.  Both are world class institutions known for academic and research excellence, innovation, and nation-building entrepreneurship. But what really inspired President Hamdullahpur was the opportunity to build a partnership based on Israel and Canada’s shared democratic values and a common devotion to solve the challenges of the 21st century. 

The Technion Waterloo Research Alliance formally began in June 2011, with a focus on 3 areas of national and global importance: Quantum Computing, Water, and Nano-sciences.  An initial round of seed funding was bolstered by a generous gift from Gerry Schwartz and Heather Reisman which enabled the alliance to expand in both scope and capacity.  Since then, collaborative research teams have produced numerous joint publications, created new intellectual property and start-up initiatives, and partnered with industry, resulting in funding that has more than tripled beyond the initial investment.

Renewed funding from the Schwartz Reisman Foundation continues to support the alliance, and the partnership is attracting new philanthropic visionaries who recognize the incredible potential of this unique collaboration. Further research efforts have focused on Quantum Security, and partnership agreements are currently in progress to support research in AI & Medicine, Photonics, and Smart Cities.

Clearly President Hamdullahpur’s intuition was correct; nearly a decade since that fateful meeting with Peretz Lavie, the Technion Waterloo partnership continues to grow, yielding fruitful joint ventures and scientific advancements that further the global good.

University of Waterloo researchers visit the Technion in 2018