London, UK โ 5 August 2026 โ Technion UK congratulates BioCatch and its founders following Visa’s announcement that it will acquire the Israeli fraud prevention company in a $2.4 billion transaction. The acquisition is another landmark moment for Israel’s innovation ecosystem and a proud achievement for the Technion โ Israel Institute of Technology, whose alumni were among BioCatch’s founders.
BioCatch has become a global leader in behavioural biometric intelligence, helping financial institutions detect fraud by analysing how users interact with their devices and banking applications. Today, its technology protects more than 760 million users and serves over 350 financial institutions worldwide, helping stop financial crime before money is lost.
The acquisition further highlights Israel’s position as the Start-Up Nation. Despite a population of fewer than 10 million people, Israel is home to more than 7,000 active startups, giving it one of the highest concentrations of startups per capita in the world. Technion graduates have played a pivotal role in building this ecosystem, founding and leading many of Israel’s most successful technology companies across cybersecurity, fintech, AI, healthcare and semiconductors.
Alan Aziz, CEO of Technion UK, said:“Visa’s acquisition of BioCatch is another outstanding example of how Technion alumni are transforming breakthrough research and entrepreneurial vision into technologies with global impact. We are immensely proud that Technion graduates helped found a company that is protecting hundreds of millions of people from financial fraud. Their success reflects the Technion’s enduring commitment to innovation and reinforces Israel’s position as the Start-Up Nation, where world-changing ideas become world-leading companies.”
Technion has long been recognised as one of the world’s leading science and technology universities, producing generations of entrepreneurs, engineers and innovators whose discoveries and companies continue to shape industries around the globe. The BioCatch acquisition is the latest example of Technion alumni translating cutting-edge research into solutions that improve the security and resilience of the global financial system.
New global ranking places the Technion 25th worldwide, fourth in Europe, and first in Israel in AI Production Capacity. It also places Technion eighth globally for AI entrepreneurship among its graduates
The Technion has been ranked No. 1 in Israel, fourth in Europe, and 25th worldwide in a new index measuring universitiesโ ability to develop talent, research, and entrepreneurship in artificial intelligence. The report also ranks the Technion eighth in the world for the number of university graduates who go on to found AI companies.
Technion President Prof. Uri Sivan said, โDespite the dramatic disparity in resources between the Technion and leading American universities and other top institutions worldwide, the Technion has secured a place among the worldโs leading universities and at the top of the Israeli rankings. Technion researchers were among the pioneers of machine learning, helping lay the foundations for leadership in AI as a whole. This leadership is reflected not only in our research but also in the central role AI now plays across Technion degree programs and in the strong connections we have built with industry in Israel and around the world. AI tools are now essential in the workplace, and we ensure that our students acquire these skills alongside a strong foundation in mathematics, science, and engineering.โ
Prof. Sivan added, โAI is the defining field of our time, and the Technion is at the forefront globally. Our position in this new ranking is consistent with other international indicators, including the CSRankings, which placed the Technion first in Europe for the number of AI publications presented at leading international conferences. These achievements are the result of a long-term, carefully planned strategy made possible by the outstanding people who make up the Technion family: our faculty members and lecturers, leadership and staff, students, administrative teams, distinguished alumni, and, of course, our friends around the world.โ
In addition to its impressive overall ranking, the Technion outperformed many elite universities, including Harvard University, in two key subcategories: the number of faculty members and alumni working in the AI industry, and alumni AI entrepreneurship. In the area of entrepreneurship (educating students who go on to found AI companies), the report notes that, when adjusted for institutional size, the Technionโs alumni entrepreneurship performance is comparable to that of Stanford University, which ranked first overall. The Technion also achieved a stronger overall performance than several elite universities, including Duke, Northwestern, and the University of Pennsylvania, despite operating with a far smaller budget.
The AI Production Capacity index, compiled by the U.S.-based public relations firm W5, ranks the worldโs top 50 universities based on their impact on the AI research and industry, including the number of alumni and faculty employed by leading AI companies; entrepreneurship and human capital, such as startup founders, unicorn creation, and venture funding; research volume and quality; computing infrastructure; the depth of AI education; and mentions in generative AI systems.
Scientists at the Technion โ Israel Institute of Technology have developed an artificial intelligence tool that can predict a person’s risk of heart failure up to five years before symptoms appear.
The breakthrough, published in the journal npj Digital Medicine, could help doctors identify high-risk patients much earlier, allowing treatment to begin before serious damage is done.
Heart failure affects around 64 million people worldwide and is one of the leading causes of illness and hospital admissions, particularly in older adults.
The AI model analyses data from a routine 24-hour heart monitor (Holter ECG) and can detect tiny warning signs that are invisible to the human eye.
Professor Joachim Behar, who led the research at the Technion’s Faculty of Biomedical Engineering, said: “By identifying people at risk years before heart failure develops, we have an opportunity to intervene earlier, improve patients’ quality of life and potentially save lives.”
The model was developed using around 70,000 routine heart-monitoring tests from Leumit Health Services and was created in collaboration with researchers and clinicians from Rambam Health Care Campus, Shaare Zedek Medical Center, the Hebrew University of Jerusalem and other leading Israeli medical institutions.
The researchers hope the technology could eventually become part of routine healthcare, helping prevent heart failure before it starts.
The discovery has potential applications in a wide range of fields, including acoustic cloaking and detection, medical imaging, and underwater communications
Prof. Gal Shmuel from the Technionโs Faculty of Mechanical Engineering is part of a research team that recently won a $7.5 million grant from the U.S. Department of Defense (DoD), led by Prof. Andrea Alรน of the City University of New York.
The Multidisciplinary University Research Initiative (MURI) program is a highly competitive DoD grant program designed to support interdisciplinary teams from multiple universities in conducting groundbreaking basic research that contributes to U.S. national security.
The grant will fund the development of a theoretical discovery made by Prof. Shmuel in 2020, in collaboration with Dr. Pernas-Salomรณn, who was then a postdoctoral fellow under his supervision. This research, supported by the Israel Science Foundation (ISF), wasย publishedย in the leading mechanics journalย Journal of the Mechanics and Physics of Solids, and marked a breakthrough in the field of metamaterials โ engineered materials with properties not found in nature.
Prof. Andrea Alรน. Photo: Paula Vlodkowsky
Prof. Gal Shmuel. Photo: Nitzan Zohar
In their paper, the two developed a theory for determining the effective dynamic behavior of electromechanical composite materials โ mixtures of materials whose mechanical and electrical responses are coupled, meaning each depends on the other. According to their theory, by designing such materials in a specific way, the momentum of the composite can be made dependent on the electric field โ a dependency expressed in a unique property that Prof. Shmuel termed the electro-momentum coupling.
The significance of this coupling stems from its role in the balance of momentum in time and space, which is the fundamental physical principle governing the motion of a body and the flow of energy. The electro-momentum coupling designed by the researchers thus offers a controllable degree of freedom for guiding, sensing, and manipulating energy. The coupling has potential applications in a wide range of fields, including acoustic cloaking and detection, medical imaging, and underwater communications.
Following Prof. Shmuelโs theoretical breakthrough, the DoD issued a 2024ย call for researchย aimed at actually creating materials with electro-momentum coupling โ capable of sensing and controlling elastic and acoustic waves via an external electric field.
Schematic illustration of an envisioned directional sensor based on electromomentum metamaterials. By engineering asymmetry inside piezoelectric materials, such devices will convert acoustic signals into direction-dependent electrical signals, enabling compact sensing for applications such as acoustic detection, imaging, and underwater communication.
Prof.ย Andrea Alรนย โ one of the worldโs leading researchers in metamaterials โ contacted Prof. Shmuel to jointly draft a research proposal, bringing together five additional researchers from the U.S. and another from Switzerland. The Department of Defense has now announced that the winning proposal is the one submitted by this team, which includes Prof. Shmuel as an international collaborator. In addition, Prof. Shmuel has received a direct grant from the U.S. Army Research Office to support the theoretical and computational component he leads within the broader collaboration.
Not long ago, artificial intelligence felt like something out of science fiction: It lived in futuristic movies and speculative headlines. Today, itโs woven quietly into our daily routines. AI helps us decide where to eat dinner, flags unusual health symptoms, and even drafts our emails.
But while AI has changed daily life, its impact within research universities may be even more profound. At the Technion, a revolution is unfolding. AI is not just another tool in the academic toolbox. It is transforming how research is done and how quickly discovery happens.
Technion President Prof. Uri Sivan describes AI as a kind of โsuperbrain,โ one we are all connected to. This superbrain can process staggering amounts of information, recognize patterns humans would miss, and solve problems at speeds that were unimaginable just a few years ago. For researchers, whose work depends on thinking, analysing, and discovering, AI has become an extension of their own minds.
A Tectonic Shift in Research
Across campus, researchers in fields as diverse as medicine, biology, physics, and mechanical engineering are integrating AI into their daily work. Tasks that once required months or even years of painstaking effort can now be completed in hours. Calculations once done by hand or simulations that took weeks to run are now executed almost instantly.
Prof. Mark Silberstein of the Andrew and Erna Viterbi Faculty of Electrical and Computing Engineering believes this transformation is only beginning. โWeโre seeing a tectonic shift in academic research,โ he said. โSoon, everyone will be using AI for one thing or another.
AI Revolution | Prof. Mark Silberstein
Within a year, he predicts, AI tools will be fully embedded in research across disciplines, and many researchers will build their own custom AI systems tailored to their work. The pace of change, he said, will only accelerate.
What does that look like in practice?
From the Test Tube to the Computer
For generations, scientific breakthroughs were born in laboratories filled with microscopes, test tubes, and experimental animals. Today, many of those experiments are beginning not in physical labs, but inside computers.
Ofer Strichman, professor of computational logic and computer science in the Faculty of Data and Decision Sciences, has watched this evolution firsthand. โEvery year we recruit new faculty,โ he explained, โand you can see how more and more scientists are computational experimentalists. Theyโre doing their research in the computer.โ
AI revolution | Prof. Ofer Strichman
Imagine developing a new drug. Traditionally, scientists tested one compound at a time, often beginning with animals. Itโs slow, expensive, and limited. Now imagine creating a detailed digital simulation of a human organ, a โvirtual organ,โ and testing not just one molecule, but millions of combinations. AI can analyze the results, identify the most promising candidates, and dramatically narrow down what needs to be tested in the lab. Instead of replacing laboratory work, computers supercharge it. Scientists can explore possibilities that would be impossible to test physically, then return to the lab with sharper focus and better odds of success.
Picture a physicist, for instance, trying to predict how 1,000 celestial bodies will move over the next 1,000 years. The math quickly becomes overwhelming. But with powerful computers, each celestial body can be modeled digitally, with the system calculating how every object influences the others. The simulation unfolds in virtual space, revealing patterns no human could calculate by hand.
โNowadays,โ Strichman said, โthe more computing power you have, the better your research results will be. Like having a bigger telescope, computers allow us to see farther.โ
Why Computing Power Matters
Behind every AI breakthrough lies a less glamorous but absolutely essential ingredient: computing power.
For more than 30 years, the Technion has operated a high-performance computing (HPC) facility: essentially a warehouse filled with powerful servers. These systems have long supported researchers running complex simulations, particularly in fields like physics and engineering.
3D render of High Performance Computing Building on Technion campus in Haifa
Traditionally, these computers relied on components called central processing units, or CPUs. You can think of a CPU as the brain of a computer. The Technion currently operates about 6,500 CPUs, and researchers typically wait just a couple of minutes to access one. But AI demands something different.
Modern AI systems rely heavily on graphics processing units, or GPUs. Originally designed to render video game graphics, GPUs are uniquely suited for the kind of massive, parallel calculations that AI requires. While a CPU handles tasks sequentially, a GPU can perform many calculations simultaneously, making it dramatically faster for AI workloads. The difference is enormous.
GPUs are not only expensive (each unit can cost around $250,000) but they also require specialized infrastructure. They consume large amounts of electricity and generate extraordinary heat, demanding sophisticated cooling systems and advanced networking to allow thousands of units to communicate seamlessly. The Technion currently has only 72 GPUs, which is far from sufficient. Researchers can wait four hours or more for access to one. In a world where speed determines competitiveness, those hours matter.
A Global Race
Around the globe, countries, universities, and technology companies are racing to dominate the AI frontier. Success depends not only on talent and ideas, but also on infrastructure. The institutions that build the most advanced computing systems gain a powerful edge in research, innovation, and economic development.
โThere is an arms race among countries and universities to achieve AI dominance. To be at the forefront of this field, we need to strengthen the capabilities we have at the Technion.โ
Prof. Mark Silberstein
At present, many Technion researchers must rely on industry partnerships to access advanced GPU systems because the University lacks sufficient in-house capacity. While collaboration with industry can be valuable, dependence creates limitations.
Complicating matters, Israelโs recent war with Hamas forced national and institutional priorities to shift and long-term infrastructure investments were necessarily delayed. Now, as the country looks toward rebuilding and strengthening its future, expanding AI infrastructure has become a strategic priority.
The Technion is taking a major step forward with the construction of the Martin and Grace Druan Rosman High-Performance Computer Data Centre. The facility is nearing completion and will provide a state-of-the-art home for next-generation computing.
Supported by Dr. Martin Rosman and Grace Druan Rosman through the American Technion Society, the new centre includes advanced electrical systems, cutting-edge cooling technologies, and high-speed communications networks โ all designed specifically to support powerful GPU-based systems. In simple terms: The building will be ready for the AI era.
Martin and Grace Rosman unveiling the supercomputing centre, 2023
High Stakes for Israel
For Israel, the implications extend far beyond campus. Israelโs reputation as the Startup Nation rests heavily on the strength of its scientific institutions. Many of the countryโs most successful technology companies trace their roots to Technion labs and classrooms. The engineers and entrepreneurs trained here help power Israelโs economy.
If the Technion falls behind in AI research infrastructure, the ripple effects could be significant. Conversely, if it leads, the impact could be transformative: accelerating medical breakthroughs, advancing clean energy solutions, strengthening national security, and fueling new industries.
โThe Technion is committed to educating the best engineers in the world, the most capable entrepreneurs,โ Silberstein said. โIsraelโs brainpower is our competitive advantage.โ
The AI revolution is here and itโs reshaping science, education, and industry. At the Technion, the question is not whether AI will transform research because that transformation is already underway. The question is how boldly and how quickly the University can build the infrastructure needed to lead.
Technion researchers and partners at Tianjin University say managing carbon dioxide, rather than eliminating it, could make low-cost fuel cells more durable and practical for vehicles, drones and remote power systems
Researchers at the Technion-Israel Institute of Technology and Tianjin University in China say they have developed a new approach that could make hydrogen fuel cells more affordable, durable and efficient while allowing them to operate with ambient air.
Their findings, published in Nature Energy, focus on anion-exchange membrane fuel cells, or AEMFCs, which generate electricity through a reaction between hydrogen and oxygen. Unlike some conventional fuel cell technologies, AEMFCs can use cheaper and more abundant materials, potentially reducing system costs.
The technology is being studied for use in transportation, aviation, aerospace, drones, distributed energy systems, backup power and electricity generation in remote areas.
The study was led by Prof. Dario Dekel of the Technionโs Wolfson Faculty of Chemical Engineering and the Nancy and Stephen Grand Technion Energy Program; Prof. Michael Guiver, a polymer membrane expert at Tianjin University; Dr. Karam Yassin, manager of the Technionโs Central Hydrogen Technologies Laboratory; and Dr. Sapir Willdorf-Cohen, a researcher in Dekelโs group.
The main challenges in developing AEMFCs have been improving power output, energy efficiency, performance and durability.
Until now, carbon dioxide in ambient air has largely been treated as a contaminant that harms performance and shortens fuel cell durability. The researchers propose what they call โCO2 management,โ arguing that carbon dioxide should not be viewed only as an obstacle but as a factor that can be controlled and, under some conditions, used to improve fuel cell stability.
โFor years, carbon dioxide has been considered one of the main challenges facing AEM fuel cells,โ Dekel said. โOur work shows that the picture is more nuanced. Under certain conditions, carbon dioxide may contribute to the long-term stability of fuel cell materials. By learning how to manage CO2 rather than simply eliminate it, we can pave the way toward affordable, durable and high-performance fuel cells capable of operating directly with ambient air.โ
The researchers said the findings could help speed the adoption of hydrogen fuel cells in passenger vehicles, trains, drones, ships, distributed energy systems and autonomous power technologies.
The research was supported by the Nancy and Stephen Grand Technion Energy Program, the Israel Science Foundation, the Israeli Council for Higher Education and other funding partners.
Technion-led study combines deep learning and mathematical modelling to produce dynamic MRI images at up to one frame per second.
A group of researchers from the Technion in Israel and the United States has reported a breakthrough in MRI scanning that could significantly improve breast cancer diagnosis, according to a paper published in Nature Communications.
The researchers developed a new method, called ELITE, that accelerates and enhances MRI scans used in breast cancer imaging, a disease diagnosed in approximately 2.3 million people each year, most of them women. The approach combines artificial intelligence with advanced mathematical modelling to enable dynamic MRI imaging at what the researchers describe as unprecedented speed and accuracy.
Dr. Eddy Solomon.ย (Leo DeLuca)
The international study brings together expertise in engineering, MRI physics, artificial intelligence and clinical radiology.
Dr. Eddy Solomon of the Technionโs Faculty of Biomedical Engineering, the studyโs lead author, said the research focuses on dynamic MRI, a key tool in breast cancer diagnosis. Dynamic MRI is primarily used for screening high-risk populations and is characterised by high sensitivity, with more than 90% accuracy, compared with roughly 50-60% for ultrasound and mammography combined.
However, MRI technology has long faced a fundamental limitation: producing highly detailed images requires relatively long scan times, making it difficult to track the movement of contrast material through tissue in real time. Traditional MRI systems typically generate one image every one to two minutes at best, limiting the ability to capture the rapid dynamics of contrast agents.
Dr. Solomon and his colleagues sought to bridge this gap by combining mathematical modeling that identifies structural and functional patterns in different tissues with a deep neural network (ResNet) trained to remove noise and distortions. The system also reconstructs missing information from undersampled measurements.
The result, according to the researchers, is the ability to generate one image per second.
The improved temporal resolution allows clinicians to track the movement of contrast agents almost continuously. This, the researchers say, could improve the detection of small tumours, help distinguish more accurately between benign and malignant growths, and better characterise tumour biology, including blood flow and vascular permeability.
In a study involving 54 patients, the researchers reported improved tumour visibility compared with existing methods, higher image quality, and strong diagnostic sensitivity.
They also said that shorter scan times could increase the number of patients that can be scanned using a given MRI system, potentially improving access to imaging services.
The findings are presented as a step toward faster and more precise MRI-based cancer diagnostics, though further validation and clinical deployment would be required before broader adoption.
Large language models are an innovative tool transforming a wide range of tasks, including translation, text comprehension, and code generation. However, these models also have shortcomings that require improvement, including biases, disregard for instructions, and โhallucinationsโ (i.e. the generation of inaccurate information).
These challenges are a major focus of the research group led by Dr. Haggai Maron from the Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering at the Technion, in collaboration with researchers from other universities and NVIDIA. Recently, three papers by the group were accepted to the most prestigious conferences in computational learning: ICLR 2026, NeurIPS 2025, and AAAI 2026. The papers were led by Ph.D. student Guy Bar-Shalom (co-advised by Prof. Ran El-Yaniv) and postdoctoral researcher Dr. Fabrizio Frasca, in collaboration with Dr. Yftah Ziser (University of Groningen and NVIDIA).
In the photo, from left to right: Guy Bar-Shalom, Dr. Haggai Maron, Fabrizio Frasca
Dr. Maron and his team propose a new research direction for identifying failures and flaws in text generated by large language models. Instead of attempting to fully understand how the model operates at every level (something that remains beyond the current reach of the research community), the authors suggest a more pragmatic, inexpensive, and faster approach. Their method is based on building and deploying new machine-learning systems on top of the modelsโ internal computations, in a way that leverages the complex internal structure of those computations. The goal is for these learning systems to detect and utilize hidden information embedded within these computations, even if humans do not fully understand it. The key achievement is demonstrating the possibility of externally and inexpensively monitoring and diagnosing risks. This approach enables users to supervise the model, predict its behavior, and control it without fully understanding the entire mechanism.
The research addresses one of the most critical challenges of the AI era: how to identify when a large language model is making mistakes, fabricating information, or deviating from expected behavior. The methods developed at the Technion provide rapid and effective diagnostics that do not depend on understanding the entire mechanism or the modelโs training process.
The new approach opens broad practical possibilities, including the development of warning systems, quality assurance tools, and safety standards for language models used in medicine, research, education, regulation, and other fields. This marks an important step toward the responsible integration of artificial intelligence into critical systems and toward making AI tools more reliable.
This series of studies is part of a broader research program in Dr. Maronโs laboratory, where the group investigates how patterns can be learned from new types of data that can be extracted from trained models, such as their weights and signals used during training.
Time to Move technology gives users control over motion in AI-generated videos without retraining models or requiring massive computing power
Researchers at the Technion-Israel Institute of Technology have developed a technology that allows users to control movement in AI-generated videos using simple mouse gestures, without requiring large computing resources or retraining on massive video datasets.
The system, called Time to Move, or TTM, was developed by Dr. Or Litany of the Henry and Marilyn Taub Faculty of Computer Science, together with Prof. Ron Kimmel and students Asaf Singer, Noam Rotstein and Amir Mann.
Litany presented the research last month at the International Conference on Learning Representations, or ICLR 2026, in Brazil. The conference is considered one of the leading global gatherings in deep learning and artificial intelligence.
The technology is designed to address one of the key limitations of AI video generation: the difficulty of precisely controlling how objects and characters move over time. โOur development solves one of the main limitations of AI-based video generation: the difficulty of precisely controlling the movement of objects and characters over time,โ Litany said.
He said TTM can be integrated as a plug-in into existing video models and does not require retraining. Unlike earlier approaches that require model-specific adaptation and significant computing power, the Technion system operates without additional computational cost, he said.
โIn doing so, it helps democratise AI video creation by expanding access beyond giant companies such as Google and Meta,โ Litany said.
The key innovation behind the technology is a method called dual-clock denoising, which refines motion while balancing the userโs intended movement with natural-looking video results.
Experiments conducted by the researchers showed that TTM matched training-based methods and outperformed them in motion accuracy and realism, according to the Technion. The system also allows users to edit the appearance of objects and add new objects to a scene, capabilities not offered by some earlier trained methods.
Researchers said the technology represents a step toward more intuitive and controllable tools for generative video.
Litany joined the Technionโs computer science faculty as a senior lecturer in 2023 after being selected as an Azrieli Faculty Fellow and a Taub Fellow. He previously completed postdoctoral fellowships at Stanford University and FAIR at Meta and has worked on computer vision technologies.
For decades, theย Energy Tower by Dan Zaslavskyย was one of the most audacious clean-energy ideas never built. And it was the first story we covered when Green Prophet was founded in 2007!
Dan Zaslavsky date unknown
Conceived by Dr. Phillip Carlson and championed by Professor Dan Zaslavsky of the Technion in Israel, the Energy Tower proposed something almost magical: spray seawater into the top of a giant desert tower, cool the hot air, let it plunge downward at high speed, and generate electricity through turbines at the base. The hotter and drier the desert, the better it would work. Zaslavsky envisioned towers over 1,000 metres tall rising from the Negev, Jordan Valley, and Red Sea region, generating power day and night while potentially producing fresh water.
The Energy Tower
The idea never made the leap from drawings and engineering studies to full-scale construction. We have the original PDF proposal and science โ>ย LINK HERE
Theย UN advertised its potential in 2001ย but noted then that the $20M USD cost to build it was limiting. But nearly two decades after most people stopped talking about it, the concept is quietly evolving in two unexpected places: China and Iran. The concept let dreamers dream and doers do โ figuring out more pleasing designs and engineering.
The Downdraft Energy Tower
China turns the Energy Tower into a climate machine
The Chinese methane paper, on the other hand, is much closer to the original Energy Tower because it explicitly describes spraying water into the top of the tower to create the downdraft, exactly as Carlson and Zaslavsky envisioned.
In 2023, researchers from the University of Edinburgh, Wuhan University of Technology and other institutions revisited the downdraft Energy Tower concept with a new purpose: removing methane from the atmosphere. Their study proposed that the humid air released from a downdraft tower could increase the formation of hydroxyl radicals, the atmosphereโs primary cleanser and the main natural sink for methane.
Downdraft Energy Tower (DET)
The researchers estimated that a tower 1,200 metres high and 400 metres in diameter could generate roughly 380 MW of electricity while simultaneously helping remove atmospheric methane. They calculated that a single Jordan-based tower could remove approximately 12.5 tonnes of methane per day under ideal conditions.
Whether those numbers hold up in practice remains to be seen. No commercial-scale downdraft Energy Tower has yet been built. But the research marks a remarkable shift. The tower is no longer viewed merely as a power plant. It is being reimagined as a tool for climate remediation.
Iran transforms the tower into a vertical oasis
Iranian Energy Tower
Meanwhile, a team of Iranian architects received an Honorable Mention in the 2025 Skyscraper Competition for their โRegenerative Towerโ proposal on Iranโs Makran coast.
Unlike Zaslavskyโs energy-focused concept, the Iranian project imagines the tower as an entire ecosystem. The design combines wind energy generation, atmospheric water harvesting, food production, housing and climate adaptation in a single 200-metre structure.
The towerโs twin wind shafts generate energy. A butterfly-like exoskeleton captures moisture from the air. Vertical farms produce vegetables, fruit and medicinal crops. Residential rings provide shaded housing inspired by traditional Baluchi architecture. The project claims it could generate up to 15,000 litres of water per day while recycling nearly all of its water in a closed-loop system.
Iranโs Energy Tower
Although the project does not explicitly employ the classic evaporative downdraft system developed by Carlson and Zaslavsky, its philosophy is strikingly similar: use desert heat, wind and humidity not as obstacles but as resources.
What links these projects is not simply a tower. It is a way of thinking.
Carlson and Zaslavsky believed deserts should not be viewed as barren landscapes waiting for resources to be imported. They believed deserts themselves contained enormous untapped energy. Heat, dryness, wind and seawater could be transformed into electricity, water and prosperity.
Chinaโs methane-removal research expands the concept into the realm of climate engineering. Iranโs Regenerative Tower expands it into urban design and community resilience.
Neither project has yet delivered a functioning tower. But both suggest that Zaslavskyโs dream may have been ahead of its time. From the engineering literature, Carlson appears to have been an American engineer/inventor, and the concept emerged in the United States before being adopted and extensively studied in Israel during the 1970sโ1990s. The Israeli work is much better documented than Carlsonโs own biography.
Nearly half a century after its invention, Dan Zaslavskyโs giant Energy Tower may finally be finding its moment.
