(cache)How William Heronemus Kickstarted Wind Energy

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Today, it’s quite possible to see individual atoms in photographs. It’s one of the great triumphs of imaging. What, then, of the inverse? Can you use a single atom to capture an image?

Single atoms are probably not replacing smartphone cameras soon, but an atom can be used to measure light. One research group at the Institute for Molecular Science in Okazaki, Japan, has now used this ability to develop what they call an “atom camera,” which can capture patterns of light far too small to see with standard optical microscopes.

More than a physics demonstration, the atom camera could also be an elegant way to see inside certain quantum computers. The atom camera’s creators are also building quantum computers that use neutral atoms as qubits.

“We expect the atom camera to serve as a valuable diagnostic tool for this effort in our laboratory, and in other similar efforts worldwide as well,” says Kenji Ohmori, a physicist at the Institute for Molecular Science.

Ohmori and colleagues published their work in Nature Communications on 29 May.

The quantum photographer’s guide

The key component of this atom camera is an optical tweezer, an instrument that traps particles by squeezing them with focused laser beams. The instrument has become a common tool of physicists who handle atoms. A tweezer can catch an atom, then move it around or hold it in place. The researchers chilled a rubidium-87 atom to near absolute zero and immobilized it inside an optical tweezer. The atom camera essentially measures how this atom responds to its environment. As light falls on an atom, it imparts energy onto some of the atom’s electrons. This shifts the energy states of those electrons.

By observing these shifts, the researchers could gauge either the light’s intensity or its polarization. They could measure these properties of their tweezer’s light, or they could measure a second pattern of light cast on the atom.

These patterns are much larger than a single atom, so how do you turn measurement into a full image? Because the atom must be kept still, you have to move the pattern itself across the atom. The researchers dragged a pattern 100 nanometers at a time—up, down, or to the side—and measured the intensity or the polarization of the light at each step.

In the end, they had a 2D map of measurements—which they could render into a nanoscale “photograph”. They photographed several different patterns using this method.

The Okazaki researchers aren’t the first to use atoms for measuring light. Since the 1990s, physicists have tried atoms to cheat the diffraction limit of visible light: the tiniest feature that typical optics can see. Atoms are significantly smaller than this, so an atom set up in the proper way could theoretically resolve even tinier details.

As cold-atom physics has grown more sophisticated, more labs have tried their hands (and optical tweezers) at making atoms fit for purpose. In 2022, two groups at the Institute of Photonic Sciences in Barcelona and at University of California, Berkeley separately used rubidium-87 atoms to capture the intensity of oncoming light. The Berkeley group reached a resolution of 300 nm, but they believed their work was only an initial step.

“We envisioned that the method could be made much more sensitive,” says Dan Stamper-Kurn, a physicist who was involved in the aforementioned work, but not the Okazaki group.

In its earlier work, the Berkeley group studied a relatively large shift in energy state. The Okazaki group instead measured a far subtler shift linked to what physicists call a hyperfine transition. This has several advantages. For one, the Okazaki group could measure its light’s polarization, in addition to its intensity. For another, the hyperfine transition is far more sensitive: In theory, the Okazaki group can render features as small as 25 nm. (Smaller than that, quantum uncertainty comes into play.)

The more precisely you know your atom’s position, the better your resolution. This is why the atom must be kept as still as possible.

Qubits calling for photographers

What could an “atom camera” capture? Quite a few things, actually, physicists say.

“There’s a lot of relevance to this, because these so-called optical tweezers are what we use in many experiments nowadays,” says Johannes Zeiher, a physicist at the Ludwig-Maximilians-Universität München in Germany, who was also not involved with the Okazaki group.

Optical tweezers are particularly prized in the world of neutral-atom quantum computers, like the Okazaki group are building. These quantum computers run on atoms such as rubidium-87 chilled to near-absolute-zero inside a vacuum chamber. Optical tweezers can trap the atoms, which act as qubits, and hold them or move them around. Computing with two neutral atoms might involve precisely positioning them and firing a laser to illuminate both.

Such a light beam is almost never uniform. Even a small beam can contain all manner of subtleties, especially quirks of polarization, which can interfere with a qubit and cause it to lose coherence and collapse. It’s crucial, then, for a qubit operator to understand the tiniest details of their light, but physicists today are still searching for a method to reliably do this.

Traditional optics often aren’t suitable for the task of seeing inside a quantum computer’s vacuum chamber, since they too can easily disturb qubits. The challenge becomes even more tedious as neutral-atom quantum computers gain more qubits and become more complex to control.

The atom camera physicists say that their creation, which can map both intensity and polarization at tiny scales, is an enticing alternative.

“Rather than bringing a camera from outside the vacuum chamber, why not use the tools already there inside our quantum playground in the vacuum?” says Takafumi Tomita, a physicist at the Institute for Molecular Science, and another of the authors.

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This 1976 University Experiment Spun Up the U.S. Wind Industry

A nuclear submarine veteran turned a truck axle into a turbine

16 min read

A man and a woman wearing dressy winter coats watch a crew of informally dressed men working on the construction of a wind turbine

With a commanding yet kind presence, Captain William Heronemus [left, in hat] inspired his UMass Amherst team to get the job done.

Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

A half century ago, a scrappy crew at the University of Massachusetts Amherst erected a wind turbine on Orchard Hill, the highest point on campus. It was a frugal production, cobbled together from the rear axle of a Ford truck, a donated generator and microcontroller, a steam pipe, and various handcrafted steel and fiberglass parts, including its 4.5-meter blades.

The team of UMass engineering grad students, faculty advisors, and one precocious undergrad built it to prove that wind energy could keep rural homes toasty in New England’s frigid winters, as a way of trimming U.S. oil dependence—a national imperative in the aftermath of the 1973–1974 energy crisis. To illustrate the point, they also assembled a modular home there on Orchard Hill, and outfitted it with heaters that would be powered by the turbine.

In 1975 and 1976, a crew from the University of Massachusetts Amherst designed and constructed the 25-kilowatt wind turbine that kick-started the U.S. wind industry. Sandy Butterfield

It worked—too well. “We had to open up the doors in the dead of winter. It was just too damn hot,” recalls Michael Edds, who designed the turbine’s electrical system and served as the project’s first resident engineer. Fittingly, they dubbed the turbine the “Wind Furnace.”

The turbine maxed out at 25 kilowatts—puny compared to modern machines that generate up to 26 megawatts, but more than most energy experts expected from wind technology in November 1976. Back then, wind power still conjured up images of quaint Dutch mills and creaky prairie water pumpers. Crafty engineers would soon show that wind power could be so much more. And it all began with the brilliant, commanding, and often polarizing UMass professor leading the Wind Furnace project: William Heronemus.

A retired U.S. Navy captain, Heronemus had joined the UMass faculty in 1967. He’d earned Bronze Stars for valor in World War II, designed and built nuclear submarines, and liaised with the British Royal Navy on the Polaris missile. UMass had recruited Heronemus to do ocean engineering, but the energy crisis and his growing misgivings about nuclear power shifted his attention to renewable energy.

Heronemus, photographed circa 1973, publicly advocated for the buildout of wind turbines, both onshore and off, at immense scale. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

By 1972, Heronemus was advancing detailed designs to deploy wind turbines at immense scale. That year, at the Marine Technology Society’s annual gathering in Washington, D.C., he presented schemes for building thousands of them across the Great Plains as well as a vast grid of massive floating turbines transecting New England’s continental shelf. Wind power, he contended, could generate nearly a fifth of U.S. electricity needs by the year 2000. Never mind that the technology for such an enormous buildout had yet to be commercialized. Espousing grand schemes made Heronemus a quixotic figure.

He also vigorously attacked the commercialization of nuclear power, creating enemies within electric utilities and U.S. government agencies that saw nuclear technology as the future. They didn’t appreciate his claims that a cleaner energy future via wind was ready to be tapped, and that the push for nuclear power and its radiological risks was unnecessary. As author and energy analyst Peter Asmus put it in his 2000 book, Reaping the Wind: “William Heronemus was a dangerous man suggesting an audacious departure from the status quo.”

The UMass Amherst wind turbine generated most of the energy to heat a modular home through the cold, windy winters on Orchard Hill. Solar thermal panels provided some heat during windless periods. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

What happened on Orchard Hill in 1976 marked Heronemus’s turn from provocateur to changemaker. The success of the experimental turbine set off waves of technological and industrial developments that forever changed the energy landscape. Within a few years, the students he trained and the entrepreneurs he inspired were building the world’s first modern wind farms and leading the Great California Wind Rush—the market that turned wind craft into an industry that’s still growing fast half a century later.

Globally, annual wind generation more than tripled between 2015 and 2025, according to data from Ember Energy, a think tank based in London. It will best nuclear’s global output by the end of this year, Ember predicts. And it all started with Heronemus, says Robert Thresher, longtime former director of wind research at the National Renewable Energy Laboratory (NREL) in Golden, Colo. (a U.S. Department of Energy lab rebranded late last year as the National Laboratory of the Rockies). “In my mind he was the father of the people that went out and really made the industry what it is today,” he says.

William Heronemus and the History of Wind Power

I got to know Captain Heronemus posthumously, interviewing his contemporaries and sifting through boxes delivered to the UMass Amherst archival research center’s 25th-floor reading room. During three visits there since 2023, I have discovered clues to his life, thinking, and research process amid the writings where he pitched his big ideas to the world. His papers include proposals to governments, utilities, and deep-pocketed philanthropists and investors, including Jane Fonda and Goldman-Sachs. Papers reveal the internationalism and commitment to service that took Heronemus on renewable-energy consulting trips to Pakistan, Cuba, Côte d’Ivoire, and beyond. Records show meetings with corporate powerhouses like Boeing and Grumman Aerospace and calls on politicians, including the senator and presidential hopeful Ted Kennedy. Postcards from former students exude gratitude.

Heronemus sits with a mock-up of a multirotor turbine in his cramped office in Marston Hall, UMass Amherst’s main engineering building. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

I learned that Heronemus turned his attention from ocean engineering to energy a few years after arriving at UMass, when he saw the growing string of nuclear power plants going up along the Connecticut River, which flows past Amherst en route to Long Island Sound. The U.S. government had picked nuclear power as an antidote to the 1970s oil crises, and Northeast utilities had jumped in big. But Heronemus and other UMass engineers worried that the riverside reactors’ waste heat would threaten the river’s ecosystem and bounty.

The advent of cooling towers to blow off heat into the air addressed the thermal pollution concern but created another: water depletion. (Nuclear plants consume about 60 million gallons of water per day, per reactor, on average.) And Heronemus perceived other nuclear power liabilities, stemming from his experience with nuclear propulsion on Navy ships. As a design engineer and head of construction and repair for a shipyard, he valued the military’s zero-accident standard for reactors but also knew the high cost of adhering to it. He argued that building expanded versions of the Navy’s pressurized water reactors to power cities and factories couldn’t be both safe and economical.

In 1971, Heronemus designed an offshore turbine with three rotors, but the first big multirotor prototype wouldn’t be built for another four decades. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

He predicted—accurately, as it turned out—that costs would rise sharply as the nuclear industry addressed safety and environmental concerns. “Each plant costs more than its predecessor. The shipyards involved with nuclear reactors came to that conclusion years ago,” he wrote in a 1973 research proposal. He also argued that the risks inherent in nuclear reactors and their radioactive waste were unnecessary given Earth’s abundant solar and wind energy resources. He broadcast those views wherever and whenever he could: before congressional committees, at U.S. Atomic Energy Commission hearings, at academic conferences, in media interviews, and even at Rotary Club luncheons.

At a 1973 licensing hearing for the proposed 820-MW Shoreham Nuclear Power Plant on Long Island, N.Y., for example, Heronemus called affordable nuclear energy a “myth.” He detailed, in its stead, a floating wind power system that could be moored off Long Island and sized to deliver more than four times as much electricity as the Shoreham plant. Each of the 640 floating platforms would carry six rotors and crank out up to 12 MW, some of which would power electrolyzers to generate hydrogen. The hydrogen would be fed to power plants or fuel cells to produce electricity when the wind wasn’t blowing. This seemingly futuristic idea drew on his Navy experience with water-splitting electrolyzers, which supplied the oxygen that enabled subs to remain submerged for months at a time, and NASA’s use of hydrogen fuel cells to power the Apollo missions.

More than five decades later, his vision for offshore wind power is big business. Floating platforms are now widely accepted as the future of offshore wind, as necessity pushes the industry to build in deeper waters. Testing began on the first floating electrolysis platforms in 2023, and multirotor turbine prototypes are in development in China, Norway and Scotland.

The UMass Amherst Wind Turbine Legacy

Photos in the UMass archives invariably capture Heronemus in jacket and tie, usually standing bolt straight. That commanding affect, plus his World War II veteran pedigree, Cold War engineering credentials, and his informed, pugnacious attacks made him a hard target for his adversaries in the nuclear establishment. He certainly wasn’t your typical antinuclear activist.

Wielding his Cold War engineering credentials and often dressed in a suit and tie, Heronemus fought hard against nuclear energy, arguing that wind was a far safer and cost-competitive resource.Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

But brutal candor in public settings probably won him as many enemies as friends. Consider his presentation at the IEEE Power and Energy Society’s 1974 winter meeting, where Heronemus suggested scrapping the utilities’ then nuclear-focused research arm, the Electric Power Research Institute. That stance no doubt created discomfort for the engineers in attendance who were involved in EPRI projects, or who aspired to be.

It’s hard to say whether Heronemus’s campaign slowed nuclear development. The industry was already struggling with cost overruns when, in 1979, a reactor at Three Mile Island in Pennsylvania partially melted down and slammed the brakes on further expansion.

What is certain is that Heronemus spurred investment in wind power. When he started talking up wind in the early ’70s, even fellow travelers in the fledgling renewable energy movement were writing it off. As future White House science advisor John Holdren opined in a 1971 Sierra Club book: “There are few places in the world where the wind is strong enough and steady enough to make harnessing it for the large-scale production of power at all interesting.”

Heronemus dreamed up networks of wind turbines over and along highways after driving down the Garden State Parkway to a conference in Cape May, New Jersey. Ellen Heronemus

Heronemus countered the naysayers by quickly forging expert consensus around wind power’s immense potential, playing a key role as the sole wind expert on a 1972 federal panel on renewable energy. That joint National Science Foundation–NASA panel concluded that, in fact, wind could meet up to 19 percent of projected U.S. power demand by the year 2000.

Congress listened, sort of. After most Persian Gulf states restricted oil shipments to the United States in 1973, congressional appropriators dedicated US $1.8 million to wind-power research and development for 1974—up from zero—and by 1976 it had bumped that to $22 million. (For comparison, Congress gave nuclear power $714 million in 1976.)

Heronemus’s vision for a massive highway wind-power scheme was inspired in part by the wind-power advocate Percy Thomas, who in the 1940s and 1950s “talked a lot about how fresh New Jersey winds are,” he told the New York Times in 1974. “I got to thinking about what Thomas had said and how wind energy could be captured there.” Ellen Heronemus

The bulk of the funding for wind power flowed to big aerospace firms and to NASA, financing an ultimately fruitless attempt to leap straight to megawatt-scale wind turbines. UMass struggled to grab a slice of the leftovers to pursue Heronemus’s offshore wind system. Professors and students who worked with Heronemus told me they felt they’d been blackballed as payback for his activism and antagonism.

UMass finally caught a funding break when Heronemus dialed back his ambitions and proposed the 25-kW unit for Orchard Hill. A $130,000 federal grant landed in early 1975, and $150,000 more the following year. It was a “trivial” sum, according to team member Sandy Butterfield, who would later become chief engineer for wind-turbine testing at NREL. “They gave us just enough to fail,” says Butterfield.

A crane erects the “Wind Furnace” in November 1976. Sandy Butterfield

But the project triumphed, resulting in Wind Furnace 1, or WF-1 (pronounced “woof one”). The young engineers behind it credit their success to the confidence, sense of mission, and structure that Heronemus gave them. The self-described “hippies” called Heronemus “the Captain” out of both affection and respect.

As team member Edds puts it: “What showed in his demeanor and his actions was discipline, and it sort of rubbed off on us. We didn’t always dress like the Captain, but we knew we had to be disciplined, to be prepared, and just do the job.”

From Helicopter Rotor to Wind Turbine

Team WF-1 got a quick start, thanks to earlier, privately financed work by a couple of doctoral students, including Forrest “Woody” Stoddard. Stoddard had been designing helicopter rotors for the U.S. Air Force when Heronemus invited him to come work on wind power in 1972. Stoddard set about adapting helicopter-rotor theory to the closely related wind rotors, and his aerodynamics modeling proved essential to the engineering of the entire machine.

Woody Stoddard [far right, in hat] designed the fiberglass blades with Ted Van Dusen. The team assembled the blades in a campus shop, and when it was time to squeegee epoxy from the blades, it was all hands on deck. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

As WF-1’s de facto chief designer, Stoddard likely supported the team’s early choice to mimic a helicopter’s ability to “pitch” its blades. To fly forward, a helicopter continuously adjusts the lift created by each blade, turning the airfoil on its long axis to reduce lift as it swings past the front of the aircraft. Doing so tilts the nose down and moves the vehicle forward. In WF-1’s case, blades pitched to regulate torque, helping get the rotor spinning in low winds and then easing off to protect the machine in dangerously high winds.

Repurposing a truck axle to mechanically couple WF-1’s rotor and generator was one of several design elements borrowed from engineers at McGill University in Montreal. Production of WF-1’s fiberglass blades got started at UMass in 1974 under the direction of doctoral student Ted Van Dusen. A competitive rower, he had a side hustle making ultralight composite boats—a trade that had stalled his doctoral work at MIT but was an accelerant for WF-1.

The federal funds in 1975 allowed Heronemus to really spin up the project and recruit a squad of students to engineer the balance of WF-1’s components. They made good use of the UMass engineering machine shop and received guidance from faculty, including mechanical engineering professors Duane Cromack and Jon McGowan. But it was the dozen or so students who really cranked out the parts.

Most were master’s students, like Butterfield, who designed the blade-pitching mechanics. Edds, the team’s only electrical engineer, had come to UMass to learn ocean engineering, only to be diverted into handling WF-1’s generator. Louis Manfredi, another ocean engineering student, teamed up with master’s student Jim Sexton on the nacelle housing the generator and drivetrain. Fred Antoon adapted the truck axle. Brian Kuhn did drawings.

WF-1 contained a mechanism that pitched its blades to regulate torque in response to wind speed, a feature that became an industry standard.Sandy Butterfield

An 18-year-old freshman, Dan Handman, came aboard and soon made himself indispensable. When he approached Heronemus to introduce himself, Heronemus handed him three months’ worth of anemometer readings punched into recording paper, and told him to turn it into 15-minute averages. Figuring there had to be a more efficient method for analyzing wind speeds, Handman asked around and found a wind-averaging machine from an earlier student project. A month or so later, he’d installed it in a cabinet near Heronemus’s office and wired it to an anemometer on Orchard Hill.

Handman’s primary role on WF-1 was setting up its computerized control system, which tracked wind speed and sent commands to Butterfield’s pitch mechanism. The controls also tracked the generator’s speed and adjusted the current to its rotor windings, in accordance with calculations by Edds. Tweaking the current ensured that power demand from the electric heaters installed in the home below didn’t stop the rotor in weak winds.

Sandy Butterfield, part of the 1970s “UMass Mafia” team that built WF-1, became a wind-power entrepreneur and a top engineer at the National Renewable Energy Laboratory in Golden, Colo. Sandy Butterfield

The finished WF-1 really cranked up the heat, some of which was stored by heating water in tanks in the modular house’s basement, to be circulated through baseboards in windless periods. It turned out WF-1 was unusually efficient at capturing wind energy because its rotor could change speed with the wind, keeping the blades close to an aerodynamic optimum.

This varying rotor speed meant that the frequency of the electric power WF-1 produced also varied. Turbines linked to power lines must strive for the opposite—a steady output that synchronizes with the grid’s frequency—primarily 50 or 60 hertz. But it suited the home’s low-tech heating scheme just fine. (Electronic converters let today’s turbines have it all by ingesting a variable wave and outputting a new wave that’s synced to the grid.)

The Great California Wind Rush

In 1977, with WF-1’s success in hand, Heronemus projected that 3 million homes like the one on Orchard Hill could soon slash U.S. heating oil demand by 90 million barrels a year. That never happened, but an industry was born, starting with a Burlington, Mass. startup called US Windpower—the first “credible” U.S. turbine manufacturer, according to Thresher, who is now an emeritus researcher at the National Laboratory of the Rockies.

Belgian-made WindMaster turbines erected at Altamont Pass signaled the internationalism of the California wind rush. UMass team member Woody Stoddard conducted engineering analyses of many early designs deployed there.Bettman/Getty Images

Boston-area entrepreneurs Russell Wolfe and Stanley Charren launched US Windpower with Stoddard and Van Dusen after visiting Heronemus in 1974 and liking what they heard. They adapted WF-1’s design to make it suitable for grid-connected operation, building and breaking prototypes before erecting the world’s first grid-connected wind farm in 1980—20 turbines on a mountain in New Hampshire. California’s water authority placed an order for 100 MW of wind power, and in 1981 US Windpower began installing hundreds of turbines in Altamont Pass, east of San Francisco.

As more firms jumped to California, drawn by state government incentives, WF-1’s creators and the next cohort of UMass grads assumed important roles in the nascent market. Seven joined Energy Sciences, a startup cofounded by Butterfield. More joined U.S. Windpower. Stoddard left that company to start a consulting firm and ended up advising some of Denmark’s modern wind pioneers, which rapidly expanded thanks to the California market. Those early Danish firms made relatively simple, sturdy machines that subsequently scaled up and dominated globally for several decades — until China embraced wind power.

The California wind power boom peaked in 1986, after which energy prices collapsed and incentives faded. Most manufacturers were bankrupted by equipment failures and financial challenges, making the 1990s a tough time for wind power’s pioneers. Many UMass wind engineers, like Butterfield, joined Thresher’s operation at NREL, culling everything they could from the California experience.

“An entire generation of U.S. wind engineers got their graduate training, at least in part, using the Wind Furnace.”—Harold Wallace

There, Heronemus’s protégés became known as the “UMass Mafia.” Thresher says it attests to the crew’s impact: “There were others. But that UMass Mafia were really leaders in the field. I think that’s the heritage we got from Bill Heronemus. Those people were so impactful and the education they got [with Heronemus] was the key.” What Heronemus began at the university became the UMass Wind Energy Center, which has awarded over 300 graduate degrees.

WF-1 now rests in the Smithsonian Institution’s collections in Washington, D.C. It earned its place there, as Smithsonian’s only modern wind turbine, because it represents wind energy’s revival, according to Harold Wallace, Smithsonian’s curator for electricity collections. “An entire generation of U.S. wind engineers got their graduate training, at least in part, using the Wind Furnace,” he says.

Heronemus didn’t get to witness the production of the massive offshore machines that he foresaw. He lost his long fight with cancer in November 2002, at the age of 82, even as former students and family members were racing to patent his multirotor and floating turbine designs.

Had he lived longer, the Captain would almost certainly have railed against current U.S. energy policy. The U.S. government has never backed wind power as generously as he’d hoped. Wind supplied 10 percent of U.S. generation last year—that’s half the share in Europe—with offshore turbines providing only a tiny sliver. Federal support for wind power has been in a stop-go cycle since Ronald Reagan’s administration, and it’s hit a low again under President Donald Trump, who has vowed to stop wind power cold. As Trump boasted to oil executives in January: “We have not approved one windmill since I’ve been in office, and we’re going to keep it that way.”

Under Trump, stop-work orders have disrupted offshore projects from Massachusetts to Virginia, contributing to a nearly $600 million loss in 2025 for GE Vernova’s wind business. GE Vernova is the only major wind turbine manufacturer remaining in the United States, and it too can be traced back to Heronemus via a US Windpower patent.

In stark contrast, European and Asian countries have been going big on offshore wind and are now developing floating wind farms to push into deeper waters. China might be the one to finally conjure up Heronemus’s favored wind design: floating platforms bearing massive multirotor machines. In 2024, Zhongshan-based turbine maker Ming Yang Smart Energy Group deployed a two-rotor offshore prototype. The company says its next iteration will generate a whopping 50 MW—a twin-headed beast that would be the world’s most powerful wind machine.

That will be a bittersweet moment for the U.S. wind industry and Captain William Heronemus’s UMass Mafia, for whom such massive machines are a dream come true. Joanne Carroll, a retired member of the UMass Mafia, says she remembers the very moment, her freshman year, when Heronemus’s dream became hers. While he was lecturing in Introduction to Engineering about the hidden costs of coal-fired power, Heronemus walked to the window and said: “‘But out there there’s wind, and you can harvest that energy,’” Carroll recalled. “And I remember thinking: That’s what I want to do with my life.”

The author would like to give special thanks to UMass professor emeritus James Manwell for his assistance with this story.

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The Life and Work of Bill Heronemus, Wind Engineering Pioneer | Wind Energy Center ›

What It Takes for Future-Ready Power Distribution

A bolder vision for distribution for an increasingly complex grid

4 min read

Utility workers inspect electrical equipment beside a service truck on a grassy site

Black & Veatch sees that leading utilities are no longer debating whether to modernize — they’re deciding how quickly they can do it, and how to do it at scale.

Black & Veatch

This sponsored article is brought to you by Black & Veatch.

The biggest challenge facing utilities today isn’t what it seems. It’s not demand, even as load growth accelerates. It’s not extreme weather, even as “major events” become routine. It’s not cybersecurity, even as connections expand across the grid.

Nick Lehnert, Associate Vice President, Distribution Grid Leader, Black & Veatch.

Black & Veatch

The real challenge is this: Distribution systems were designed for a different reality.

Long gone are the days of predictable demand, one-way power flow and isolated disruptions. At Black & Veatch, we see that leading utilities are no longer debating whether to modernize. They’re deciding how quickly they can do it, and how to do it at scale.

Across grid modernization programs globally, three truths consistently emerge. They define what it takes to prepare the distribution system for what’s next:

1. Outage response is not a resilience strategy

Resilience is being redefined in real time. A strategy centered on mobilizing crews and restoring service as quickly as possible is reactive, and increasingly insufficient.

Resilience has to shift upstream into integrated system design. That starts with hardening. Stronger poles, undergrounding and structural upgrades all have a role, particularly in high-risk corridors. We’re also seeing meaningful gains from how the network is configured and how quickly it can respond without waiting on manual intervention.

This is where distribution automation programs can change outcomes. Strategically placed reclosers, automated switches and fault indicators help contain disruptions before they spread. When combined with feeder reconfiguration and updated protection strategies, distribution automation investments allow utilities to set more aggressive recovery targets and achieve measurable reductions in outage duration and customer impact.

2. Future-readiness depends on DERs at scale

Forecasting is less and less reliable. Only 19 percent of utilities report strong confidence in their ability to predict future load growth, according to the Black & Veatch 2025 Electric Report. Distributed Energy Resources (DERs) like solar, storage, EVs and behind-the-meter generation are exciting solutions; but they fundamentally change how the system operates. Power is no longer just delivered. It’s injected, stored and redirected in ways the system was never designed to manage.

At scale, these challenges show up quickly — particularly on feeders where distributed generation is approaching or exceeding hosting capacity. Protection coordination becomes more difficult when fault current comes from multiple directions. Voltage becomes less predictable as generation fluctuates throughout the day. And planning models must now account for highly variable, location-specific behavior.

Distribution modernization is fundamentally changing how the system is designed and operated so it can absorb disruption, manage bi-directional flows and respond in real time.

Adapting to bi-directional power flow requires more than incremental updates. Leading utilities are responding by building flexibility into the system, moving beyond static assumptions toward dynamic hosting capacity and interconnection studies, planning that incorporates DER, EV adoption and localized load growth, and infrastructure aligned with the communications and control needed to manage it.

3. The edge must be intelligent, visible and secure

As system stress and complexity increase, utilities need far greater visibility and control over the network. Historically, utilities relied on customer calls, Supervisory Control and Data Acquisition (SCADA) at the substation level and field crews to understand what was happening on the system. That model doesn’t hold up. You can’t effectively manage a system you can’t see. Plus, the most critical events are increasingly happening beyond the substation — on feeders, laterals, and at the edge where DER and customer behavior are interacting with the grid.

Grid-edge technologies have become essential. Sensors, Advanced Metering Infrastructure (AMI) and automated switching provide the raw data and control needed to move from reactive to proactive operations. In more advanced deployments, utilities are creating centralized control environments that allow operators to see and manage the distribution system in near real time. That capability is enabled by:

Advanced communications networks to form the backbone of real-time grid visibility
Distribution Management System (DMS) and Outage Management System (OMS) to enable faster, more coordinated system response
Analytics, AI and machine learning to improve situational awareness, anticipate system conditions, and support operational decision-making

The same connectivity enabling this real-time visibility and control also introduces new vulnerabilities, blurring the line between physical and cyber risk, yet many utilities manage them separately. Only 22 percent have unified teams in place, even as threats continue to rise, including a 50 percent increase in substation attacks and growing exposure to malware and ransomware, according to the Black & Veatch 2025 Electric Report. Cybersecurity and resilient network design must be embedded into the architecture from the outset—not layered on after the fact.

See what bolder vision looks like

Distribution modernization is fundamentally changing how the system is designed and operated so it can absorb disruption, manage bi-directional flows and respond in real time.

To learn about a successful program, check out Georgia Power’s recent grid modernization program. Black & Veatch partnered with the utility on large-scale infrastructure upgrades. The results? Outages are down 76 percent, restoration times have improved by more than 80 percent and communities across Georgia are powered by a grid built to meet the future head-on.

When the state faced the most destructive storm in the company’s history, Hurricane Helene, Georgia Power deployed a rapid response team that utilized its “smart grid” and restored power to more than 1 million customers within days.

A grid built to meet the future head-on—that’s the result of bolder vision.

Defining Autonomy for Wellness Robots in Senior Care

What defines a wellness robot as a category

1 min read

An examination of how socially assistive wellness robots could support the seven dimensions of senior wellness, and how a framework can measure their autonomy.

What Attendees will Learn

IEEE Rolls Out Large Language Models Virtual Training Course

Learn how to design, secure, and deploy LLMs

3 min read

A middle-aged Black man taking a virtual coding class in his home office.

iStock

AI type:ti education ieee educational activities large language models ieee products and services type:ti

Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.

While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising.

The LLM technology market is expected to grow by about 33 percent every year through 2030, according to MarketsandMarkets. The rapid expansion suggests that proficiency in implementing and securing the models is transitioning from a niche into a core requirement for technologists.

More than just a better search engine

To use LLMs effectively, technical professionals must move beyond treating them as conversational robots. At a fundamental level, the AI systems are built on the transformer architecture, a framework that replaced the older method of processing data in a fixed, sequential order. Unlike earlier models that analyzed information one step at a time, transformers use self-attention mechanisms to ingest vast datasets simultaneously.

For technical professionals, LLMs are core architectural elements that are fundamentally changing how digital infrastructures are built and maintained.

Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.

Four ways LLMs are changing jobs

Here are areas that integrate large language models.

Moving past basic prompts. Developers are using application program interfaces (APIs) to connect LLMs directly to their databases and software tools. Employing the APIs allows AI to perform work such as executing code or searching through internal repositories.

Fixing the “hallucination” problem. LLMs are at risk of hallucinations, which are generated facts or code that looks correct but actually is wrong or broken. To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a company’s database.

Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up “private” instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.

The future of collaboration. By automating repetitive coding tasks and summarizing thousands of pages of documentation, LLMs let engineers spend more time on high-level designs and solving important issues.

Online course program helps with mastering the tech

The gap between people who use AI and those who understand how to build with it is growing wider. To help technical professionals stay ahead, IEEE offers a five-course online program, Large Language Models Demystified, available through the IEEE Learning Network.

The program, developed by IEEE Educational Activities in partnership with the IEEE Computer Society, is built for people who want to understand the “how” and the “why” behind the technology. Rather than just teaching basic prompting, the curriculum dives into the engineering behind generative AI, including:

Evolution, impact, and hands-on exercises: the shift from statistical methods to modern transformers, including hands-on model optimization.
Understanding transformer architectures: the mathematical core of self-attention and positional encoding, implemented in NumPy and Python.
Architectural analysis and implementation: advanced LLM design with practical model-building exercises.
Training and modeling with PyTorch: end-to-end pipelines in PyTorch, leveraging parameter-efficient techniques such as low-rank adaptation and quantization.
Optimization, alignment, and deployment: performance scaling, reinforcement learning from human feedback (RLHF), group-relative policy optimization, RAG, and agentic AI.

Upon completion of the program, participants earn professional development credits and a digital badge from IEEE to verify their expertise.

Enroll in the course program on the IEEE Learning Network.

Organizations looking to prepare their teams to work on LLMs can connect with an IEEE content specialist to discuss group enrollment and tailored training paths.

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LLM 101: Foundations and Practical Application - Young Professionals ›

Why Orbital Data Centers Are Harder Than Silicon Valley Thinks

Shedding heat will require ingenious new designs

10 min read

Globe surrounded by zeroes and ones on a blue background

Edmon de Haro

“Space computing, the final frontier, has arrived,” Nvidia CEO Jensen Huang declared at the Nvidia GTC conference in March.

Indeed, the idea of data centers in orbit has gone from science fiction to a serious spending category. Elon Musk’s SpaceX has acquired xAI (also Musk’s) and is planning a constellation of space-based data centers. Google, not to be outdone, announced Project Suncatcher in partnership with Planet, planning to launch two satellites equipped with Google Tensor Processing Unit (TPU) AI chips by early 2027. Startup Starcloud has already filed a proposal with the Federal Communications Commission for an 88,000-satellite constellation for orbital data centers. As Starcloud’s filing suggests, these companies are all proposing fleets of satellites numbering in the thousands, each housing a rack or multiple racks of AI-grade GPUs, interconnected with each other through free-space optical links and communicating back to Earth via microwave links, either directly or through other satellites.

	SpaceX/xAI	Starcloud	Google Project Suncatcher
Status	FCC filing, January 2026; AI1 design, June 2026	First satellite (H100) launched late 2025; second due October 2026	Research phase; 2-satellite demo with Planet Labs planned early 2027
Proposed scale	Up to 1 million satellites	5 GW total across ~100 launches	81-satellite clusters
Per satellite power	Up to 150 kW	40 MW per launch container	Not specified
Chips	Custom D3 chip from Terafab consortium or other	Off-the-shelf GPUs (Nvidia H100/Blackwell)	Google Trillium TPU v6e
Orbit	500–2,000 km LEO; sun-synchronous shells at 50-km intervals	Dawn-dusk sun-synchronous LEO (>99% sunlight)	Dawn-dusk sun-synchronous LEO (~650 km)
Cooling	Liquid circulating through radiators	Liquid circulating through radiators about half the size of solar arrays	No design published
Connectivity	Optical links to Starlink mesh, then to ground	Laser links to Starlink/Kuiper/Kepler; physical “data shuttle” modules for bulk data	Free-space optical links; radio for pilot mission ground links
Cost parity expected	Musk says 2–3 years	~$8M per 40-MW cluster over 10 years vs. $167M terrestrial (modeled)	Mid-2030s when launch hits <$200/kg (per Google’s own paper)
Key dependency	Terafab chip fab; Starship reusability	Starship reusability; off-the-shelf GPU radiation tolerance at scale	Launch cost reduction; thermal management solutions
Sources: www.datacenterdynamics.com; http://www.cnbc.com/; www.infoq.com; www.datacenterdynamics.com; services.google.com; starcloudinc.github.io; research.google/blog; x.com

Meet NASA Low Outgassing Standards With Adhesives for Aerospace and Optical Systems

Learn how outgassing affects optical, semiconductor, and aerospace systems — and how to prevent it

2 min read

Illustration of molecules leaving a surface as it transforms into an ordered graphene lattice

Generic adhesives allow volatile molecules to escape through a loosely bonded polymer network (left). NASA-compliant low outgassing adhesives use a highly cross-linked structure to keep contamination in check (right).

Master Bond

This sponsored article is brought to you by Master Bond.

Outgassing is the release of volatile substances from a cured adhesive over time. These released materials, which may include residual solvents, unreacted monomers, or other chemical species, can deposit on nearby surfaces, causing contamination that interferes with sensitive components.

What Is Outgassing and How Is It Measured?

The industry standard for measuring outgassing is ASTM E595, developed by NASA. This test exposes a cured sample to 125 °C at high vacuum (10⁻⁵ to 10⁻⁶ torr) for 24 hours, measuring Total Mass Loss (TML) and Collected Volatile Condensable Materials (CVCM). To meet NASA low outgassing requirements, materials must exhibit less than 1 percent TML and less than 0.1 percent CVCM.

Optical assemblies need contamination-free bonding and prevention of fogging the optics to maintain clarity. High-vacuum scientific equipment, semiconductor manufacturing tools, and aerospace electronics also demand low outgassing materials.

Key Applications

Low outgassing adhesives are essential wherever contamination could compromise performance and this is particularly relevant for space and satellite systems. Optical assemblies, including cameras, telescopes, and laser systems, need contamination-free bonding and prevention of fogging the optics to maintain clarity.

High-vacuum scientific equipment, semiconductor manufacturing tools, and aerospace electronics also demand low outgassing materials. Even terrestrial optical devices benefit from reduced outgassing to ensure long-term reliability.

EP30-2 is a versatile system can be used in a variety of applications in aerospace, electronic, optical and specialty OEM industries, especially when optical clarity and low outgassing are important criteria.Master Bond

Ensuring Low Outgassing Performance Through Proper Handling

Achieving specified outgassing performance requires attention to storage, mixing, and curing. For two-part systems, use the correct mix ratio and mix thoroughly to ensure complete reaction. Follow recommended cure schedules — adding heat, even at modest temperatures of 150-200 °F, significantly improves cross-linking and reduces outgassing. For UV-curable adhesives, ensure complete cure by using the correct lamp wavelength (typically 365 nm), adequate intensity, and proper exposure time with no shadowed areas.

Troubleshooting Outgassing Issues

If contamination appears on optical surfaces or outgassing test results are higher than expected, an incomplete cure might be one of the root causes. The first step is to verify that the adhesive has fully hardened to its specified Shore hardness. The next step is to consider adding or extending heat cure to improve cross-linking.

Master Bond Product Recommendations

Master Bond offers a range of adhesives meeting NASA low outgassing requirements. EP30-2 and EP21TCHT-1 are some examples of two-part epoxy systems that have been successfully deployed in demanding vacuum applications, including ultra-high vacuum environments.

For applications requiring UV cure, Master Bond provides specialty UV formulations such as UV16 meeting ASTM E595, as well as dual-cure systems (UV plus heat) such as UV22DC80-10F for assemblies where shadows prevent complete UV exposure. These dual-cure products initiate with UV light and complete curing with heat as low as 180 °F (80 °C).

Direct-to-Cell Technology: Enabling Satellite Connectivity for Legacy Devices

How DTC works as a spaceborne cell tower

1 min read

Direct-to-cell technology uses LEO satellites as spaceborne cell towers. It delivers LTE services to existing smartphones without hardware changes, bridging global coverage gaps.

What Attendees will Learn

Video Friday: Do Robots Even Need Legs?

Your weekly selection of awesome robot videos

3 min read

White service robot on a square mobile base working at a countertop in a modern wooden kitchen

In the kitchens we’re going to, we don’t need legs.

Genesis

Video Friday robot videos lunar rover inspection robots robot hands robot ai

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

RSS 2026: 13–17 July 2026, SYDNEY

Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUE

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Enjoy today’s videos!

Eno is our first agentic robot: an AI agent and a general-purpose robot working as one system. It reasons, plans, and acts in the real world. Human in capability, not in form. Every detail with a purpose, reduced to what matters. Designed not to resemble us, but to extend us. Eno is built end to end at Genesis.

[ Genesis ]

Engineers from NASA’s Jet Propulsion Laboratory are field-testing advanced capabilities for potential future Moon and Mars rovers. In the Colorado Desert near Plaster City, California, teams used a prototype rover called ERNEST (Exploration Rover for Navigating Extreme Sloped Terrain) to test software for a potential future long-range lunar mission. The software enables the rover, developed at JPL, to operate autonomously and travel extreme distances with minimal intervention from human operators.

ERNEST is a lot more capable than it may look; here’s some recent research showing the kinds of terrain it can handle:

[ NASA's Jet Propulsion Lab ]

Table tennis can produce moments that are difficult even for experienced players to anticipate…like when the ball clips the net and suddenly changes direction. For the Ace research project at Sony AI, these events were a key test of the system’s ability to operate reliably in unpredictable real-world conditions. Ace addresses this uncertainty by simulating counterfactual ball trajectories in real time. In the video, the green overlays show these alternative paths the system considers while planning its response.

And check out some of these rallies that the robot has with Miyuu Khiara.

[ Sony AI ]

This video of an ANYmal deployment in a concrete plant is worth watching because it makes explicit how quadrupeds make money in inspection contexts: Among other things, “a cracked crusher foundation [was] caught before a week-long shutdown, avoiding roughly $630,000 in lost production.” That pays for a lot of robots.

[ ANYbotics ]

A lot of interesting footage here from GITAI’s prep for a robotic satellite servicing demo mission. The thruster test-firing isn’t a robot, exactly, but it may be the coolest part.

[ GITAI ]

Anyone who’s tried to take a half decent photo underwater knows that it’s basically impossible, so let’s try and teach robots to cope.

[ Bi-AQUA ]

Thanks, Masato!

Handling delicate, irregular or unpredictable objects is one of the hardest problems left in automation, and one of the most important. It’s what’s holding back the next wave of robots from doing more in the real world. That’s why we’re working with PSYONIC on a new approach. Their Ability Hand, worn by hundreds of people every day, captures real-world data on touch, pressure and grip. Our GoFa cobot brings the industrial-grade accuracy and repeatability to turn that human data into reliable robotic performance.

[ ABB Robotics ]

Sanctuary AI has achieved world-class performance on a complex wire-plugging production task with a global Tier 1 automotive supplier. In this demonstration, Sanctuary AI’s Physical AI successfully performs a high-speed wire-plug insertion task, achieving a validated task success rate of over 99.5% with a cycle time of just 2.54 seconds, meeting live production benchmarks established by the customer.

WHY IS THIS STRESSING ME OUT SO MUCH?

[ Sanctuary ]

This video is quite obviously fake, but I suppose maybe there’s a market for extra beefy quadrupeds? Maybe?

[ Kepler ]

I cannot overstate how much I do not want any robot to look at what I’m wearing and then attempt to sell me things based on what it thinks it can guess about my personality or interests.

[ MagicLab ]

I am here for fed-up robots learning how to move boxes by just kicking them.

[ ATARI Lab ]

Ah, yes, very useful and very important robots that make me very uncomfortable.

[ Paper ]

I built GrowBot ( a ~6”, two-servo bipedal robot) that runs entirely on a $15 Raspberry Pi Zero 2 W, ~$100 in parts. An LLM drives it directly: it reads the raw IMU stream with no translation layer and narrates its own motion (“rocked side to side like a baby”), riding on a 50-Hz reinforcement-learning walk policy trained in sim and transferred to the real body.

The idea here is to build an open course around this project, Brit says, “so everyone can experience physical AI right now in a low-risk way.”

[ GrowBot ]

Thanks, Brit!

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NYU’s Quantum Institute Bridges Science and Application

Newly launched NYUQI seeks solutions in quantum computing, sensing, and communications

6 min read

NYUQI aims to foster collaboration between physicists, engineers, materials scientists, computer scientists, biologists, and chemists vital to quantum research into one holistic operation.

NYU Tandon

This sponsored article is brought to you by NYU Tandon School of Engineering.

Within a 6 mile radius of New York University’s (NYU) campus, there are more than 500 tech industry giants, banks, and hospitals. This isn’t just a fact about real estate, it’s the foundation for advancing quantum discovery and application.

While the world races to harness quantum technology, NYU is betting that the ultimate advantage lies not solely in a lab, but in the dense, demanding, and hyper-connected urban ecosystem that surrounds it. With the launch of its NYU Quantum Institute (NYUQI), NYU is positioning itself as the central node in this network; a “full stack” powerhouse built on the conviction that it has found the right place, and the right time, to turn quantum science into tangible reality.

Proximity advantage is essential because quantum science demands it. Globally, the quest for practical quantum solutions — whether for computing, sensing, or secure communications — has been stalled, in part, by fragmentation. Physicists and chemical engineers invent new materials, computer scientists develop new algorithms, and electrical engineers build new devices, but all three often work in isolated academic silos.

Gregory Gabadadze, NYU’s dean for science, NYU physicist and Quantum Institute Director Javad Shabani, and Juan de Pablo, Anne and Joel Ehrenkranz Executive Vice President for Global Science and Technology and executive dean of the Tandon School of Engineering.Veselin Cuparić/NYU

NYUQI’s premise is that breakthroughs happen “at the interfaces between different domains,” according to Juan de Pablo, Executive Vice President for Global Science and Technology at NYU and Executive Dean of the NYU Tandon School of Engineering. The Institute is built to actively force those necessary collisions — to integrate the physicists, engineers, materials scientists, computer scientists, biologists, and chemists vital to quantum research into one holistic operation. This institutional design ensures that the hardware built by one team can be immediately tested by software developed by another, accelerating progress in a way that isolated departments never could.

NYUQI’s premise is that breakthroughs happen at the interfaces between different domains. —Juan de Pablo, NYU Tandon School of Engineering

NYUQI’s integrated vision is backed by a massive physical commitment to the city. The NYUQI is not just a theoretical concept; its collaborators will be housed in a renovated, million-square-foot facility in the heart of Manhattan’s West Village, backed by a state-of-the-art Nanofabrication Cleanroom in Brooklyn serving as a high-tech foundry. This is where the theoretical meets physical devices, allowing the Institute to test and refine the process from materials science to deployment.

NYUQI will be housed in a renovated, million-square-foot facility in the heart of Manhattan’s West Village.Tracey Friedman/NYU

Leading this effort is NYUQI Director Javad Shabani, who, along with the other members, is turning the Institute into a hub for collaboration with private and public sector partners with quantum challenges that need solving. As de Pablo explains, “Anybody who wants to work on quantum with NYU, you come in through that door, and we’ll send you to the right place.” For New York’s vast ecosystem of tech giants and financial institutions, the NYUQI offers a resource they can’t build on their own: a cohesive team of experts in quantum phenomena, quantum information theory, communication, computing, materials, and optics, and a structured path to applying theoretical discoveries to advanced quantum technologies.

Solving the Challenge of Quantum Research

The NYUQI’s integrated structure is less about organizational management, and more about scientific requirement. The challenge of quantum is that the hardware, the software, and the programming are inherently interconnected — each must be designed to work with the other. To solve this, the Institute focuses on three applications of quantum science: Quantum Computing, Quantum Sensing, and Quantum Communications.

For Shabani, this means creating an integrated environment that bridges discovery with experimentation, starting with the physical components all the way to quantum algorithm centers. That will include a fabrication facility in the new building in Manhattan, as well as the NYU Nanofab in Brooklyn directed by Davood Shahjerdi. New York Senators Charles Schumer and Kirsten Gillibrand recently secured $1 million in congressionally-directed spending to bring Thermal Laser Epitaxy (TLE) technology — which allows for atomic-level purity, minimal defects, and streamlined application of a diverse range of quantum materials — to NYU, marking the first time the equipment will be used in the U.S.

NYU Nanofab manager Smiti Bhattacharya and Nanofab Director Davood Shahjerdi at the nanofab ribbon-cutting in 2023. The nanofab is the first academic cleanroom in Brooklyn, and serves as a prototyping facility for the NORDTECH Microelectronics Commons consortium.NYU WIRELESS

Tight control over fabrication, and can allow researchers to pivot quickly when a breakthrough in one area — say, finding a cheaper, more reliable material like silicon carbide — can be explored for use across all three applications, and offers unique access to academics and the private sector alike to sophisticated pieces of specialty equipment whose maintenance knowledge and costs make them all-but-impossible to maintain outside of the right staffing and environment.

The NYU Nanofab is Brooklyn’s first academic cleanroom, with a strategic focus on superconducting quantum technologies, advanced semiconductor electronics, and devices built from quantum heterostructures and other next-generation materials.NYU Nanofab

That speed and adaptability is the NYUQI’s competitive edge. It turns fragmented challenges into holistic solutions, positioning the Institute to solve real-world problems for its New York neighbors—from highly secure data transmission to next-generation drug discovery.

Testing Quantum Communication in NYC

The integrated approach also makes the NYUQI a testbed for the most critical near-term applications. Take Quantum Communications, which is essential for creating an “unhackable” quantum internet. In an industry first, NYU worked with the quantum start-up Qunnect to send quantum information through standard telecom fiber in New York City between Manhattan and Brooklyn through a 10-mile quantum networking link. Instead of simulating communication challenges in a lab, the NYUQI team is already leveraging NYU’s city-wide campus by utilizing existing infrastructure to test secure quantum transmission between Manhattan and Brooklyn.

The NYUQI team is already leveraging NYU’s city-wide campus by utilizing existing infrastructure to test secure quantum transmission between Manhattan and Brooklyn.

This isn’t just theory; it is building a functioning prototype in the most demanding, dense urban environment in the world. Real-time, real-world deployment is a critical component missing in other isolated institutions. When the NYUQI achieves results, the technology will be that much more readily available to the massive financial, tech, and communications organizations operating right outside their door.

NYUQI includes a state-of-the-art Nanofabrication Cleanroom in Brooklyn serving as a high-tech foundry.NYU Tandon

While the Institute has built the physical infrastructure and designed the necessary scientific architecture, its enduring contribution will be the specialized workforce it creates for the new quantum economy. This addresses the market’s greatest deficit: a lack of individuals trained not just in physics, but in the integrated, full-stack approach that quantum demands.

By creating a pipeline of 100 to 200 graduate and doctoral students who are encouraged to collaborate across Computing, Sensing, and Communications, the NYUQI is narrowing the skills gap. These will be future leaders who can speak the language of the physicist, the materials scientist, and the engineer simultaneously. This commitment to interdisciplinary talent is also fueled by the launch of the new Master of Science in Quantum Science & Technology program at NYU Tandon, positioning the university among a select group worldwide offering such a specialized degree.

Interdisciplinary education creates the shared language and understanding poised to make graduates coming from collaborations in the NYUQI extremely valuable in the current landscape. Quantum challenges are not just technical; they are managerial and philosophical as well. An engineer working with the NYUQI will understand the requirements of the nanofabrication cleanroom and the foundations of superconducting qubits for quantum computing, just as a physicist will understand the application needs of an industry partner like a large financial institution. In a field where the entire team must be able to communicate seamlessly, these are professionals truly equipped to rapidly translate discovery into deployable technology. Creating a talent pipeline at scale will provide a missing link that converts New York’s vast commercial energy into genuine quantum advantage.

NYUQI: Building Talent, Technology, and Structure

The vision for the NYUQI is an act of strategic geography that plays directly into the sheer volume of opportunity and demand right outside their new facility. By building the talent, the technology, and the structure necessary to capitalize on this dense environment, NYU is not just participating in the quantum race, it is actively steering it.

Attendees of NYU’s 2025 Quantum Summit.Tracey Friedman/NYU

The initial hypothesis for the NYUQI was simple: the ultimate advantage lies in pursuing the science in the right place at the right time. Now, the institute will ensure that the next wave of scientific discovery, capable of solving previously intractable problems in finance, medicine, and security, will be conceived, built, and tested in the heart of New York City.

Finite-Element Approaches to Transformer Harmonic and Transient Analysis

How finite-element simulation supports pre-fabrication performance evaluation of transformers

1 min read

Explore structured finite-element methodologies for analyzing transformer behavior under harmonic and transient conditions — covering modelling, solver configuration, and result validation techniques.

What Attendees will Learn

Tensordyne Claims Massive Speed and Power Improvement Over Nvidia

The startup uses logarithmic math to speed up inference

4 min read

Tensordyne’s Napier pods fit 72 of its new AI chips in a system that takes up one-quarter of a server rack.

Tensordyne

inferencing AI hardware nvidia mathematics

If simulations are to be believed, startup Tensordyne’s new AI chip could crush the performance of market leader Nvidia in terms of energy efficiency and latency for inferencing. The company just sent the plans for its first chip to be manufactured, with commercial sales of a 72-chip system scheduled for the second half of 2027. Tensordyne claims its 72-chip system can run large LLMs four times as fast using one-fifth the power compared to a 72-Nvidia GB300 system. However, real systems won’t be around to back these figures up until the end of the year.

The not-so-secret sauce behind the outsized efficiency of Tensordyne’s new chip, Napier, is how it does matrix multiplication, the main math of AI. It takes advantage of the fact that the logarithm of A times B equals the logarithm of A plus the logarithm of B.

“We’ve turned multipliers into adders,” explains Gilles Backhus, a Tensordyne founder and vice president of AI. Adders are smaller and more energy-efficient logic circuits than those that do multiplication, he says. So Napier can pack more compute into a smaller area and still save on power.

New kinds of numbers

That such a thing was possible has long been known, but there wasn’t a good way to use it, because converting back and forth between logarithmic numbers and the floating point numbers that describe neural networks took too much time and energy and introduced too many inaccuracies. Not anymore, according to Backhus.

“So far no one has figured out how to do the linear to logarithm and logarithm to linear conversion as we have,” he says. “And that’s actually the crux of that whole thing. Our engineers have figured out ways to do this very elegantly and very very accurately and cheaply on silicon.”

The importance of number formats hasn’t been lost on the AI industry. Speaking at IEEE Hot Chips in 2023, Nvidia chief scientist Bill Dally attributed the majority of the improvement in the company’s GPUs at the time to the use of shorter number formats and the smaller circuits they require.

Researchers have also worked on circuits to compute with alternative formats, such as the logarithm-like posit and more recently its scientific-computing counterpart the takum. However, these formats have not reached mainstream adoption mostly because their hardware implementation is so different from traditional floating point.

Inference Demands Influence Architecture

Market trends, including the rise of AI agents, mean inference—the execution of neural network models—is becoming more important than training new large language models (LLMs). Factors like the cost and the speed at which answers are delivered are starting to dominate, and that’s led AI companies to look for system architectures that are a better fit for that.

Tensordyne executives say they saw this coming and engineered their computers to meet it.

Tensordyne’s Napier AI chip includes 144 gigabytes of HBM, but the real power comes from its unusual math.Tensordyne

There are two main parts to executing an LLM: prefill and decode. In the prefill stage the model takes in the input text and turns it into tokens, the basic units it can work with, and builds a kind of working memory about the input, called the key-value cache. It’s a computationally heavy task.

Decode is where the LLM generates its output tokens, the answer or response to your input. Each new token is predicted using the previous token and the key-value cache. This sequential nature can make decode a slower process, and it’s more dependent on memory and network latency than computing power.

So AI chip makers are starting to build systems with those two different demands in mind. Nvidia is touting a system where a server rack full of B300 GPUs handles prefill and several racks of its Groq 3 processors do the decode. Amazon Web Services is combining a rack of its Trainium AI chips for prefill with several racks of Cerebras’s wafer-scale computers for decode.

Tensordyne says its system can handle both jobs. “We’re optimizing for two hard challenges here at the same time,” says R.K. Anand, chief product officer and co-founder of Tensordyne. “We’re the first company proving that you can do both without going to multiple vendors and multiple racks.”

The dense compute needed for prefill comes from the logarithmic math. The needs of decode come from 144-gigabytes of high-bandwidth memory and a custom 1-microsecond-latency network called Tensordyne Napier Link.

In a “pod” system that fits in one-quarter of a standard rack, Tensordyne packs in 72 Napier chips, 8 Intel Xeon CPUs, and 64 terabytes of solid-state storage. A four-pod rack working on a 2-trillion parameter LLM would deliver 1,300 tokens per-second per-user at a cost of US $11 for 1 million tokens, while consuming 120 kilowatts of power, the company claims, with one pod crunching out prefill and three working on decode. To get similar tokens per-second per-user numbers, a nine-rack Rubin and Groq 3 system would likely consume 1.5 megawatts, according to Tensordyne.

Whether or not these numbers really hold up will have to wait until later in the year. Tensordyne plans to have a beta version available through the cloud for customers to work with. It expects to begin shipping systems to customers about a year from now.

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Entering a New Era of Modeling and Simulation

Companies using simulation have a lot to gain, but software skills are a limiting factor. Apps open the playing field.

6 min read

COMSOL

This is a sponsored article brought to you by COMSOL.

Computer modeling and simulation has been used in engineering for many decades. At this point, anyone working in R&D is likely to have either directly used simulation software or indirectly used the results generated by someone else’s model. But in business and in life, “the best laid plans of mice and men can still go wrong.” A model is only as useful as it is realistic, and sometimes the spec changes at a pace that is difficult to keep up with or is not fully known until later in the development process.

Picosecond Accuracy in Multi-channel Data Acquisition

Learn about clocking, synchronization, and triggering

1 min read

Timing accuracy is vital for multi-channel synchronized sampling at high speed. In this webinar, we explain challenges and solutions for clocking, triggering, and timestamping in Giga-sample-per-second data acquisition systems.

Learn more about phase-locked sampling, clock and trigger distribution, jitter reduction, trigger correction, record alignment, and more.

Timing Trick Cuts Energy Used in LLM Training by Up to 14 Percent

Adjusting clocking frequency during computation can save energy without affecting performance

3 min read

Abstract illustration of a pixelated cube leaking vibrant colors onto a dark grid.

Eric Frommelt

training ai energy LLMs clock speeds processor clock

OpenAI’s fourth large language model (LLM), GPT-4, took an estimated 50 gigawatt-hours to train, or the equivalent of 5,000 American homes’ yearly power consumption. That was in 2023. Since then, the computational resources used to train frontier LLMs have only increased, though direct power usage numbers are hard to come by.

Now, a research group at the University of Twente in the Netherlands has shown that you can save up to 14 percent of the energy used in LLM training without sacrificing speed by cleverly adjusting the clock frequency of the GPU during computation. Jeffrey Spaan, Ph.D. candidate at University of Twente and lead author on the article, presented the results at the Computing Frontiers conference in Catania, Sicily, last month.

Quantum Leap: Sydney’s Leading Role in the Next Tech Wave

As the country’s leading innovation hub, Sydney is rapidly emerging as a global leader in quantum technology

4 min read

A group of people in a research lab stand around a quantum device consisting of metal chambers, pipes, and wires.

One of several leading quantum startups in Sydney, Silicon Quantum Computing was founded by Michelle Simmons [front, left], a professor of physics at the University of New South Wales (UNSW) and Director of the Centre for Quantum Computation and Communication Technology in Australia.

BESydney

This is a sponsored article brought to you by BESydney.

Australia plays a crucial role in global scientific endeavours, with a significant contribution recognized and valued worldwide. Despite comprising only 0.3 percent of the world’s population, it has contributed over 4 percent of the world’s published research.

This Operating System Reveals a Chip’s Dark Secrets

Fractal gives researchers a new tool to investigate hardware flaws

4 min read

Conceptual illustration of a computer chip floating against a techno grid background.

Gabriel Maragaño

operating systems security apple amd Intel

Modern operating systems are packed with defenses against security vulnerabilities. But what if those defenses could be removed? Doing so would offer a clear view of the chip running the OS. It would also expose hardware vulnerabilities the OS normally obscures.

A new hand-coded OS, Fractal, provides that clear view. Built from scratch by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Fractal is designed to probe the architecture of the chip that runs it. To prove that point, the OS, presented in May at the 2026 IEEE Symposium on Security and Privacy in San Francisco, was used to uncover a previously unknown vulnerability in Apple’s M1 chip.

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New Device Can Take Photographs With a Single Atom

What seems like a fun physics experiment could be a quantum computing life-saver

The quantum photographer’s guide

Qubits calling for photographers

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