Machine Learning in Cardiology

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Machine Learning in Cardiology

Technion scientists use machine learning for atrial fibrillation risk prediction

Shany Biton

Shany Biton and Sheina Gendelman, two M.Sc. students working under the supervision of Assistant Professor Joachim A. Behar, head of the Artificial Intelligence in Medicine laboratory (AIMLab.) in the Technion Faculty of Biomedical Engineering, wrote a machine learning algorithm capable of accurately predicting whether a patient will develop atrial fibrillation within 5 years. Conceptually, the researchers sought to find out whether a machine learning algorithm could capture patterns predictive of atrial fibrillation even though there was no atrial fibrillation diagnosed by a human cardiologist at the time.

Assistant Professor Joachim Behar

Atrial fibrillation is an abnormal heart rhythm that is not immediately life-threatening, but significantly increases patients’ risk of stroke and death. Warning patients that they are at risk of developing it can give them time to change their lifestyle and avoid or postpone the onset of the condition. It may also encourage regular follow-ups with the patient’s cardiologist, ensuring that if and when the condition develops, it will be identified quickly, and treatment will be started without delay. Known risk factors for atrial fibrillation include sedentary lifestyle, obesity, smoking, genetic predisposition and more.

Sheina Gendelman

Ms. Biton and Ms. Gendelman used more than one million 12-lead ECG recordings from more than 400,000 patients to train a deep neural network to recognize patients at risk of developing atrial fibrillation within 5 years. Then, they combined the deep neural network with clinical information about the patient, including some of the known risk factors. Both the ECG recordings and the patients’ electronic health record were provided by the Telehealth Network of Minas Gerais (TNMG), a public telehealth system assisting 811 of the 853 municipalities in the state of Minas Gerais, Brazil. The resulting machine learning model was able to correctly predict the development of atrial fibrillation risk in 60% of cases, while preserving a high specificity of 95%, meaning that only 5% of persons identified as being potentially at risk did not develop the condition.

“We do not seek to replace the human doctor – we don’t think that would be desirable,” said Prof. Behar of the results, “but we wish to put better decision support tools into the doctors’ hands. Computers are better equipped to process some forms of data. For example, examining an ECG recording today, a cardiologist would be looking for specific features which are known to be associated with a particular disease. Our model, on the other hand, can look for and identify patterns on its own, including patterns that might not be intelligible to the human eye.”

Overview of the experimental setting: digital biomarkers (HRV and MOR), deep learning features (DNN) and clinical data (EMR) are combined together in training a model to predict the future occurrence of atrial fibrillation

Doctors have progressed from taking a patient’s pulse manually, to using a statoscope, and then the ECG. Using machine learning to assist the analysis of ECG recordings could be the next step on that road.

Since ECG is a low-cost routine test, the machine learning model could easily be incorporated into clinical practice and improve healthcare management for many individuals. Access to more patients’ datasets would let the algorithm get progressively better as a risk prediction tool. The model could also be adapted to predict other cardiovascular conditions.

The study was conducted in collaboration with Antônio Ribeiro from the Uppsala University, Sweden and Gabriela Miana, Carla Moreira, Antonio Luiz Ribeiro from the Universidade Federal de Minas Gerais, Brazil.

The study was published in the European Heart Journal – Digital Health.

 

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

 

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

Technion Ranked #1 Europe in Artificial Intelligence (AI)

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Technion Ranked #1 Europe in Artificial Intelligence (AI)

Over the years, the Technion has established itself as a leading academic institution in AI. It is currently ranked 15th in the world, with 100 faculty members engaged in areas across the AI spectrum.

 

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Digital Brochure – AI – 22 July 2021_SMALL

 

The Technion’s efforts to advance the field of artificial intelligence have positioned it among the world’s leaders in AI research and development. CSRankings, the leading metrics-based ranking of top computer science institutions around the world, has ranked the Technion #1 in the field of artificial intelligence in Europe (and of course, in Israel), and 15th worldwide. In the subfield of machine learning, the Technion is ranked 11th worldwide. The data used to compile the rankings is from 2016 to 2021.

One of the innovations that is part of the framework of the Technion’s AI prowess is the Machine Learning and Intelligent Systems (MLIS) research center, which aggregates all AI-related activities.

Professor Shie Mannor

Today, 46 Technion researchers are engaged in core AI research areas, and more than 100 researchers are in AI-related fields: health and medicine, autonomous vehicles, smart cities, industrial robotics, cybersecurity, natural language processing, FinTech, human-machine interaction, and others. Two leading AI researchers co-direct MLIS: Professor Shie Mannor of the Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering and Professor Assaf Schuster of the Henry and Marilyn Taub Faculty of Computer Science.

According to Prof. Mannor, “for years the Technion has maintained its position as the leading research institute in Israel and Europe in core AI areas. The Technion has a unique ecosystem that includes tens of researchers from various faculties, research centers, and a number of undergraduate and graduate programs in the field.”

Professor Assaf Schuster

“All fields of science, technology, and engineering at the Technion have been upgraded in recent years, applying Technion knowledge in AI fields,” said Prof. Schuster, “Most include components based on information processing and machine learning. Furthermore, the Technion views the dissemination of its acquired knowledge as a mission of national importance for commercial sector. In that regard, the Technion operates in close cooperation with the technology sector in Northern Israel and within its partnership with the prestigious EuroTech Universities Alliance. These partnerships in Israel and worldwide link AI research at the Technion to the vanguard of activity in this field.”

The MLIS center strives toward four main goals: (1) establishing the Technion as a top-5 university in the field of AI worldwide; (2) pooling resources, recruiting researchers, and students from all Technion departments to advance and conduct joint research in the field; (3) connecting Technion researchers with relevant parties in the industry, especially technology companies and other organizations that generate Big Data; (4) Establishing close research collaboration with other prominent research institutes in the AI field in Israel and worldwide.

In May 2021, the Technion entered a long-term collaboration with American software giant PTC, under which the company will transfer its Haifa research campus to the Technion, to advance joint research in AI and manufacturing technology. PTC joins several other organizations that collaborate with the Technion in these fields, among them the technological universities of Lausanne (Switzerland), Eindhoven (Netherlands), Munich (Germany), and the Paris Polytechnique (France) in Europe, as well as Cornell Tech, home of the Jacobs Technion-Corrnell Institute, Waterloo University, and Carnegie Mellon University, which operates the largest center for AI and robotics in the United States.

 

Users Prefer the Warmth of an AI System Over Its Competence

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Users Prefer the Warmth of an AI System Over Its Competence

Study by three Technion researchers has revealed that AI systems’ competence isn’t enough: For users to choose a system, it needs to have warmth.

Spotify or Apple Music? Waze or Google Maps? Alexa or Siri? Consumers choose between artificial intelligence (AI)-based systems every day. How exactly do they choose which systems to use? Considering the amount of money and efforts spent on AI performance enhancement, one might expect competence and capability to drive users’ choices. Instead, a recent study conducted by researchers from the Faculty of Industrial Engineering and Management at the Technion – Israel Institute of Technology shows that the “warmth” of a system plays a pivotal role in predicting consumers’ choice between AI systems.

New research findings from a study featuring more than 1,600 participants, recently published in the Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, offer some insight into the psychology of potential users. The researchers, Zohar Gilad, Prof. Ofra Amir, and Prof. Liat Levontin from the Faculty of Industrial Engineering and Management at the Technion, examined the effects of users’ perception of AI systems’ warmth, that is, the systems’ perceived intent (good or ill), and AI systems’ competence, that is, the systems’ perceived ability to act on those intentions, on the choices they made.

Zohar Gilad

Most of the research done to date regarding warmth perceptions of AI-based systems addressed systems with a virtual or physical

presence, such as virtual agents and robots. The current study, though, focused on “faceless” AI systems, with little or no social presence, such as recommender systems, search engines, and navigation apps. For these types of AI systems, the researchers defined warmth as the primary beneficiary of the system. For example, a navigation system can prioritize collecting data about new routes (benefitting the system) over presenting the best-known route, or vice versa.

Prof. Liat Levontin

The researchers found that the system’s warmth was important to potential users, even more than its competence, and they favored a highly warm system over a highly competent system. This preference for warmth persisted even when the highly warm system was overtly deficient in its competence. For example, when asked to choose between two AI systems that recommend car insurance plans, most participants favored a system with low-competence (“using an algorithm trained on data from 1,000 car insurance plans”) and high-warmth (“developed to help people like them”), over a system with high-competence (“using a state-of-the-art artificial neural network algorithm trained on data from 1,000,000 car insurance plans”) and low-warmth (“developed to help insurance agents make better offers”). That is, consumers were willing to sacrifice competence for higher warmth.

Prof. Ofra Amir

These findings are similar to what is known of human interactions: warmth considerations are often more important than competence considerations when judging fellow humans. In other words, people use similar basic social rules to evaluate AI systems and people, even when assessing AI systems without overt human characteristics. Based on their findings, the researchers concluded that AI system designers consider and communicate the system’s warmth to its potential users.

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, RamanujanMachine.com, 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

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

Meet the Women of Technion

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Maya Angelou once said: “Each time a woman stands up for herself, she stands up for all women.”

Technion women have been trailblazers for generations. In 1924, at a time when academia was closed off to women in many countries, Technion’s first class of 17 students included one female.

Today, the percentage of women undergraduate students at Technion is 37%; graduate students 32%; and doctoral students 44%. This number continues to rise, with Technion’s commitment to equal ratios and empowering women in the fields of STEM.

Meet some of the Technion women who are making critical advances in human health, leading the digital revolution and developing the technologies of tomorrow.

FORBES 30 UNDER 30 ISRAEL

Technion PhD student Alona Shagan was recently featured in the Forbes Israel list for promising technological entrepreneurs.

Together with Prof. Boaz Mizrahi, Shagan developed a hot-glue and a novel adhesive to adhere human tissue that has been seriously injured. the new concept will lead to the development of devices that will reduce the use of stitches, staples and pins, speed up the healing process and reduce scarring.

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Prof. Lilac Amirav and Renewable Engergy

Prof. Lilac Amirav, Technion Alumna, discusses her work in nano-scale photo-catalysis and the future of renewable energy. Prof. Amirav is a member of  the Grand Technion Energy Project (GTEP) and the Schulich Faculty of Chemistry.

 

Resolving Antibiotic Resistance

Michelle Heymann and Diane Abensur were recent immigrants to Israel (Heymann from Brazil and Abensur from France) when they met while studying for in the Technion’s MBA program. Together they founded medical start-up Nanosynex, which has developed a method of precisely adapting antibiotics to infections to enable more effective and faster treatment.

The Next Cancer Killer

Prof. Marcelle Machluf, a Technion alumnus, received a $5 million investment from “aMoon”, an Israeli health-tech and life sciences fund, to commercialize her cancer-fighting NanoGhost technology. Prof. Machluf’s research uses a revolutionary bio-medical nano-technology which targets specific tumors to shrink the deadliest forms of cancer. 

Professor Asya Rolls: A Pioneer in Psychology

Can emotions affect health? Is it possible that thoughts impact the body’s ability to cope with disease? Assoc. Prof. Asya Rolls’ ground-breaking research on how thoughts and emotions impact health has been recognized with numerous young scientist awards. Most recently, Rolls successfully dramatically shrank brain tumors by activating the neural reward system.

 

A Leader In Research

Dr. Shulamit Levenberg, Dean of the Faculty of Biomedical Engineering at Technion-Israel Institute of Technology was named by Scientific American as a “Research Leader” in tissue engineering, for her seminal work on vascularization of engineered tissues. She is founder and chief scientific officer of two start-up companies in the areas of cultured meat and nano-liter arrays for rapid antimicrobial susceptibility testing.

Grand Technion Energy Program

Prof. Sabrina Spatari is leading the Grand Technion Energy Program Life Cycle Assessment which analyzes new renewable energy technologies at an early stage of development and analyzes whether or not these technologies have the potential to contribute large scale environmental benefits to society.

 

She has Reading on the Brain

Technion alumna and Professor Tzipi Horowitz-Kraus, is the Director of the Educational Neuro-imaging Center in the Technion Faculties of Education in Science and Technology, and Biomedical Engineering. Prof. Horowitz-Kraus believes that “reading is not an intuitive process”. She uses her lab to assess and predict whether or not kids will have reading difficulties with the help of electroencephalogram (EEG).

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How Data can change the World

Dr. Kira Radinsky, Technion alumna, discusses the potential for artificial intelligence (A.I.) to predict events and patterns in disease, genocide, riots, and drug effects based on data analysis. Radinksy joined eBay in 2016 after they acquired of her company, SalesPredict. She has since become a pioneer in the field of data science through her machine learning solutions which are transforming the future of e-commerce.

 

Dr. Silva Behar-Harpaz: Inspiring a Love of Math & Physics

Dr. Silvia Behar Harpaz received her Ph.D. in physics from the Technion, during which she spent time at the Large Hadron Collider (LHC) project in Switzerland, which is the world’s largest particle accelerator. She has also taught at Technion’s campus in China and is always inspired to teach to students who are “highly motivated, hard-working and invested in learning”. She has been an inspiration to many of her students as she continues to receive many awards for her excellence in teaching.

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These are just some of the inspirational Technion women whose passion, curiosity and intelligence will create critical advancements in science and technology, while inspiring the next generation of women to take on whatever challenges they choose!  

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Inspired by the Brain

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Prof. Shahar Kvatinsky (on the left) and the doctoral student Loai Danial

‘Hardware neural network’ to make Artificial Intelligence tech faster, cheaper

Researchers at the Technion and TowerJazz have developed a revolutionary technology that can turn TowerJazz’s commercial flash memory components into memristors—devices that contain both memory and computing power. The technology, which was inspired by the operation of the human brain, significantly accelerates the operation of artificial intelligence (AI) algorithms.

Published in the Nature Electronics journal, the research was led by doctoral student Loai Danial and Professor Shahar Kvatinsky of the Andrew & Erna Viterbi Faculty of Electrical Engineering at the Technion, in collaboration with Prof. Yakov Roizin and Dr. Evgeny Pikhay from TowerJazz and Prof. Ramez Daniel of the Faculty of Biomedical Engineering at the Technion.

From the outset, the ability of computers to solve computational problems has been superior to that of humans. Yet for decades, when it came to identifying images, classifying image attributes and making decisions, computers lagged behind humans. In recent years, artificial intelligence has begun to narrow this gap and has managed to carry out complex operations by means of training based on examples. For the past few decades, vast resources have been devoted to developing artificial intelligence on the software level. This investment has generated a quantum leap in AI effectiveness in many and varied fields, among them medicine, intelligent transportation, robotics and agriculture.

Artificial intelligence is fueled by data, and specifically by extremely large data sets known as big data. For this reason, the major breakthrough in the field of artificial intelligence had to “wait” for dramatic improvements in computing power. Yet hardware lagged behind these rapid developments in software performance, such that the development of hardware that would meet the demands of AI software was delayed for years. Such hardware must work well in terms of speed, low power demand, accuracy, area and cost. These requirements are very difficult to satisfy with the traditional hardware model based on digital computation.

The digital model limits hardware performance in two main contexts: 1) Digital hardware has difficulty performing many operations in parallel, for it was originally intended to perform a relatively small number of operations. 2) This type of hardware can provide great accuracy only at the cost of extremely high energy and time consumption. As a result, the researchers say innovative hardware is needed that will meet the needs of the artificial intelligence era.

According to Prof. Kvatinsky: “One of the major challenges that AI poses to hardware engineers is how to implement complex algorithms that require a) storage of massive amounts of data in the computer memory, b) rapid retrieval from memory, c) performing many computations in parallel, and d) high accuracy. Standard digital platforms hardware (processors) is not suited for this for the reasons mentioned above.”

This is the background for the new technology described in the article published in Nature Electronics. “Our technology transforms hardware that is digital in nature into a neuromorphic platform—an analog infrastructure of sorts that resembles the human brain,” said Prof. Kvatinsky. “Just as the brain can perform millions of operations in parallel, our hardware is also capable of performing many operations in parallel, thus accelerating all associated operations.”

Doctoral student Loai Danial goes on to explain: “I am personally interested in neuromorphic computations, both as a computer engineering student and as someone who lost his father to a rare neurological disease. The brain has always served as an inspiration for computational systems, and my challenge is to use engineering tools to understand the computational mechanism of brain operations. In the current research we showed that an electrical chip based on standard commercial technology has two critical abilities: associative memory that, like the brain, operates based on features rather than index searching, and the ability to learn.”

Associative memory, which is familiar to us from human thought, means, for example, that when we see eyes we do not search some clause in an index of items to find a match for an eye but rather identify the eye associatively. This mechanism is rapid, efficient and energy-saving. Moreover, as with the brain, the system’s ability to learn improves as the links between the synapses and the nerve cells change and are updated.

According to Prof. Roizin of TowerJazz: “The new technology is easy to implement and transforms TowerJazz’s transistors, originally designed to store data only, into memristors—units that contain not only memory but also computing ability. Because the memristors are situated on existing TowerJazz transistors, they immediately interface with all the devices the transistors work with. The new technology has been tested under real conditions, demonstrating that it can be implemented in building neural hardware networks, thus significantly improving the performance of commercial artificial intelligence systems. Like the brain, the improved system excels in its ability to store data over the long term and in its very low energy consumption.”

According to Prof. Ramez Daniel, formerly an electrical engineer at TowerJazz and now a member of the Technion Faculty of Biomedical Engineering: “The computing power of the improved device stems from its ability to function in the sub-conduction area, or to put it more simply, in a way that resembles natural biological mechanisms. As a result, we have achieved high efficiency with low output, similar to mechanisms that developed in nature over billions of years of evolution.”

Technion researchers Eric Herbelin, Nicolas Wainstein, Vasu Gupta and Nimrod Wald from Prof. Kvatinsky’s research group participated in the research.

This research was supported by the Planning and Budgeting Committee (PBC), the KAMIN grant from the Israel Innovation Authority, the Andrew Viterbi and Erna Finci Viterbi Scholarship for Graduate Students and the European Research Council (ERC) starting grant. Recently, Loai Danial presented this research at the Nature Conference in China and was awarded the prize for the best paper award at the conference.