Xiangyi Cheng Is Bringing AR to Classrooms and Hospitals

When Xiangyi Cheng published her first journal paper as a principal investigator in IEEE Access in 2024, it marked more than a professional milestone. For Cheng, an IEEE member and an assistant professor of mechanical engineering at Loyola Marymount University, in Los Angeles, it was the latest waypoint in a career shaped by curiosity, persistence, and a belief that technology should serve people—not the other way around.

The paper’s title was “Mobile Devices or Head-Mounted Displays: A Comparative Review and Analysis of Augmented Reality in Healthcare.”

XIANGYI CHENG

Employer

Loyola Marymount University, in Los Angeles

Title

Assistant professor of mechanical engineering

Member grade

Member

Alma maters

China University of Mining and Technology; Texas A&M University

Cheng’s work spans robotics, intelligent systems, human-machine interaction and artificial intelligence. It has applications in patient-specific surgical planning, an approach whereby treatment is customized to the anatomy and clinical needs of each individual.

Her research also covers wearables for rehabilitation and augmented-reality-enhanced engineering education.

The throughline of her career is sound judgment based on critical thinking. She urges her students to avoid the temptation to accept the answers they’re given by AI without cross-checking them against their own foundational understanding of the subject matter.

“AI can give you ideas,” Cheng says, “but it should never lead your thinking.”

That principle—honed through uncertainty, disciplinary shifts, and hard-earned confidence—has made Cheng an emerging voice in applied intelligent systems and a thoughtful educator preparing students for an AI-saturated world.

From Xi’an to Beijing: A mind drawn to mathematics

Cheng, born in Xi’an, China, grew up in a household shaped by her parents’ disparate careers. Her father was a mining engineer, and her mother taught Chinese and literature at a high school.

“That contrast between logical and literary thinking helped me understand myself early,” Cheng says. “I liked math, and STEM felt natural to me.”

Several teachers reinforced her inclination, she says, particularly a math teacher whose calm, fair approach emphasized reasoning over punishments such as detention for misbehavior or failure to complete assignments.

“It wasn’t about being right,” Cheng says. “It was about thinking clearly.”

She moved to Beijing in 2011 to attend the China University of Mining and Technology , where she studied mechanical engineering. After graduating with a bachelor’s degree in 2015, she was unsure where the field would take her.

An IEEE paper changed her trajectory

Later in 2015, she traveled to the United States to study at Case Western Reserve University, in Cleveland.

She initially viewed the move as exploratory rather than a long-term commitment.

“I wasn’t thinking about a Ph.D.,” she says. “I wasn’t even sure research was for me.”

That uncertainty shifted in 2017, when Cheng submitted her “IntuBot: Design and Prototyping of a Robotic Intubation Device” paper to the IEEE International Conference on Robotics and Automation (ICRA)—which was accepted.

“AI can give you more possibilities, but thinking is still our responsibility.”

Intubation is a procedure in which an endotracheal tube is inserted into a patient’s airway—usually through the mouth—to help them breathe. Because placing the tube correctly is not simple and usually must be done quickly, it requires training. That’s why research into robotic or assisted intubation systems focuses on improving speed, accuracy, and safety.

She presented her findings at ICRA in 2018, giving her early exposure to a global research community.

“That acceptance gave me confidence,” she recalls. “It showed me I could contribute to the field.”

Her advisor at Case Western encouraged her to switch from the mechanical engineering master’s program to the Ph.D. track. When the advisor moved to Texas A&M University, in College Station, in 2019, Cheng decided to transfer. She completed her Ph.D. in mechanical engineering at Texas A&M in 2022.

Although she didn’t earn a degree from Case Western, she credits her experience there with clarifying her professional direction.

Shortly after graduating with her Ph.D., Cheng was hired as an assistant professor of mechanical engineering at Ohio Northern University, in Ada. She left in 2024 to become an assistant professor at Loyola Marymount.

Engineering for the body—and the classroom

Cheng’s research focuses on human-centered engineering, particularly in health care. One of her major projects addresses syndactyly, a congenital condition in which a newborn’s fingers are fused at birth. Surgeons rely on their experience to estimate the size and shape of skin grafts to be taken from another part of the body for the corrective surgery.

She is developing technology to scan the patient’s hand, extract anatomical landmarks, and use finite element analysis—a computer-based method for predicting how a physical object will behave under real-world conditions—to determine the optimal graft size and shape.

Xiangyi Cheng designs human-centered intelligent systems with applications in health care and education.Xiangyi Cheng

“Everyone’s hand is different,” Cheng says. “So the surgery should be personalized.”

Another project centers on developing smart gloves to assist with hand rehabilitation, pairing the unaffected hand with the injured one so the person’s natural motion can help guide therapy.

She also is exploring augmented reality in engineering education, using immersive visualization and AI tools to help students grasp three-dimensional concepts that are difficult to convey through traditional learning tools. Such visualization lets students see and interact with a digital world as if they’re inside it instead of viewing it on a flat screen.

Teaching balance in an AI-driven world

Despite working at the forefront of AI-enabled systems, Cheng cautions her students to be judicious in their use of the technology so that they don’t rely on it too heavily.

“AI is not always right and perfect,” she says. “You still need to be able to judge whether the answers it provides are correct.”

As AI continues to reshape engineering, Cheng remains grounded in a simple principle, she says: “We should use these tools. But we should never let them replace our judgment. AI can give you more possibilities, but thinking is still our responsibility.”

In her lab and classroom, Cheng prioritizes independent thinking, critical evaluation, and persistence. Many of her research students are undergraduates, and she encourages them to take ownership of their work—planning ahead, testing ideas, and learning from failure.

“The students who succeed don’t give up easily,” she says.

What she finds most rewarding, she says, is watching students mature. Reserved first-year students often become confident seniors who can present complex work and manage demanding projects.

“Getting to witness that transformation is why I teach,” she says.

For students considering engineering, Cheng offers straightforward advice: “Focus on mathematics. Engineering looks hands-on, but math is the foundation behind everything.”

With practice and persistence, she says, students can succeed and find meaning in the field.

Why IEEE continues to matter

Cheng joined IEEE in 2017, the year she submitted her first paper to ICRA. The organization has remained central to her professional development, she says.

She has served as a reviewer for IEEE journals and conferences including Robotics and Automation Letters, Transactions on Medical Robotics and Bionics, Transactions on Robotics, the International Conference on Intelligent Robots and Systems, and ICRA.

IEEE’s interdisciplinary scope aligns naturally with her work, she says, adding that the organization is “one of the few places that truly welcomes research across boundaries.”

More personally, IEEE helped her see a future she had not initially imagined.

“That first conference was a turning point,” she says. “It helped me realize I belonged.”

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This Power Grid Pioneer’s EV Prediction Came 100 Years Too Soon

Charles Proteus Steinmetz was a towering figure in the early decades of electrical engineering, easily the intellectual equal of Thomas Edison and Nikola Tesla—men he considered his friends. One of Steinmetz’s most significant achievements was to quantify and characterize the phenomenon of magnetic hysteresis—the behavior of magnetism in materials—and then devise a simple law that allowed for predictable transformer and motor design. He also established a revolutionary framework for analyzing AC circuits, which is still taught today in power engineering. And from 1893, he served as chief consulting engineer at General Electric at a pivotal moment for the young company and for the U.S. effort to expand its power grid. For these and other accomplishments, he was well known in his time, even if he’s not exactly a household name today.

Steinmetz was also an evangelist for electric vehicles. In March 1920, he typed out his thoughts, comparing the pros and cons of EVs to the gasoline-propelled alternative. Among the advantages: low cost of maintenance, reliability, simplicity of operation, and lower cost of operation. The disadvantages: dependence on charging stations, limited range on a single charge, and lower speeds. More than a century later, his list remains remarkably pertinent.

Steinmetz could often be seen decked out in a suit and top hat, smoking his trademark BlackStone panatela cigar while riding around Schenectady, N.Y., in his 1914 Detroit Electric sedan. According to John Spinelli, emeritus professor of electrical and computer engineering at Union College, in Schenectady, sometimes both Steinmetz and his chauffeur sat in the backseat—you could control the car from both the front and the rear—so that it would appear to be a driverless car. With a top speed of 40 kilometers per hour (25 miles per hour), the car ran on 14 six-volt batteries and could go about 48 km between charges.

Steinmetz’s 1914 Detroit Electric car is now at Union College in Schenectady, N.Y., where Steinmetz had founded, chaired, and taught in the department of electrical engineering.Paul Buckowski/Union College

In 1971, the car was purchased by Union College, where Steinmetz had founded, chaired, and taught in the department of electrical engineering. The car had been discovered rotting in a field, so it needed some work. Over the next decade, faculty and engineering students restored it to its former glory. Still in running condition, it’s now on permanent display at the college.

Steinmetz’s Contributions to Electrical Engineering

Karl August Rudolf Steinmetz was born in 1865 in Breslau, Prussia (now known as Wrocław, Poland). He studied mathematics, physics, and the burgeoning field of electricity at the University of Breslau. He also joined a student socialist club and edited the party newspaper, The People’s Voice. He completed his doctoral studies, but before receiving his degree, Steinmetz fled to Switzerland in 1888, after his socialist writings came under the scrutiny of the Bismarck government.

Steinmetz immigrated to New York the following year, anglicized his first name, dropped his two middle names, and added Proteus, a nickname he had picked up at university (after the shape-shifting sea god of Greek mythology). Eventually, he became a U.S. citizen.

Charles Proteus Steinmetz solved a number of important problems that helped the power grid expand.Bettmann/Getty Images

In January 1892, Steinmetz burst onto the engineering scene when he read his paper “On the Law of Hysteresis” before the American Institute of Electrical Engineers, a forerunner of today’s IEEE. I can’t quite imagine sitting through the delivery of its 62 pages, but those assembled recognized its groundbreaking nature. The ideas Steinmetz outlined allowed engineers to calculate power losses in the magnetic components of electrical machinery during the design phase. Prior to this, the design process for transformers and electric motors was largely trial and error, and power losses could be measured only after the machine was built, which greatly added to the cost.

Steinmetz was not just an equations and theory guy, though. He loved working in the lab and building things. In 1893, General Electric acquired the small manufacturing firm of Eickemeyer & Osterheld, in Yonkers, N.Y., where Steinmetz had worked since shortly after his arrival in the United States. So Steinmetz began his new life as a corporate engineer, an interesting turn for the socialist. During his first few years with GE, he mostly designed generators and transformers. But he also created an informal position for himself as a consultant, giving expert opinions on various problems across divisions. He eventually formalized this role, becoming GE’s chief consulting engineer, and he maintained a relationship with the company for the rest of his life, even after joining the faculty of Union College in 1902.

By the time Steinmetz died in 1923 at the age of 58, he had been granted more than 200 patents and had made major contributions to various subfields in electrical engineering, including phasors and complex numbers (for steady-state AC analysis); electrical transients, switching surges, and surge protection (based on his research on lightning); industrial research (including how to run a corporate lab); and engineering methods (by writing textbooks that standardized practice).

Why Steinmetz Believed in Electric Cars

By 1914, Steinmetz was convinced that the future of transportation was electric. In June, he addressed the National Electric Light Association convention in Philadelphia with a bold prediction: “I have no doubt that in 10 years, more or less—rather less than more—we will see the field of the pleasure and business vehicle covered by such an electric car in large numbers. And I believe I underestimate when I say that 1,000,000 or more will be used.”

As we now know, Steinmetz was overly optimistic. At the time, there were about 1.2 million gasoline-powered cars in use in the United States, and only about 35,000 EVs. It would take until 2018 for the number of EVs (including plug-in hybrids) on U.S. roads to surpass a million. Worldwide, there are now about 60 million electric vehicles in use.

But Steinmetz had his reasons. He firmly believed that electric vehicles would flourish in urban areas, where most rides involved short distances at low speed. He also thought EVs would be a boon for power companies, which were eager to drum up more business, especially at night. With 1 million electric cars being charged about 5 kilowatt-hours on most nights, and at a rate of 5 cents per kilowatt-hour, Steinmetz predicted US $75 million (about $2.5 billion today) of new business for central power stations each year.

In 1971, Union College purchased Steinmetz’s car, which had been found rotting in a field, and faculty and students restored it to working condition.Special Collections & Archives/Schaffer Library/Union College

Steinmetz went to work to improve the electric car. He developed a double-rotor motor that was integrated into the rear axle, which did away with the need for a mechanical differential or drive shaft and drastically reduced the overall weight, which improved the mileage. Dey Electric Corp. incorporated Steinmetz’s design into its electric roadster and priced it under $1,000. Unfortunately, an internal combustion engine Ford Model T cost about half as much, and the Dey roadster flopped, ending production within a year.

Undeterred, Steinmetz formed the Steinmetz Electric Motor Car Corp. in 1920 with the initial goal of bringing to market an electric truck for deliveries and light industrial use. The first truck debuted on a cold February day in 1922 with a publicity stunt of climbing the steep Miller Avenue hill in Brooklyn, N.Y. According to a report in The New York Times, the vehicle went up the 14.5 percent grade between Jamaica Avenue and Highland Boulevard in 51 seconds. During a second climb, it stopped a number of times to show how easily it restarted. The truck had a range of 84 km (52 miles).

The company planned to manufacture 1,000 trucks per year and 300 lightweight delivery cars, plus a five-passenger coupe, but it made a total of only 48 vehicles. After Steinmetz died in 1923, the company soon ceased operation.

Steinmetz wasn’t only bullish on the electric car, but on electricity in general. A New York Times article recorded his belief that by 2023, we would work no more than 4 hours a day, 200 days a year because electricity would have eliminated the drudgery and unpleasantness of labor. He also predicted that electricity would bring about an end to urban pollution: “Every city would be a spotless town.” With an expansion of leisure time, people would be healthier, engaging in gardening (especially growing their own food) and pursuing educational interests to become “much more intelligent and self-expressive creature[s].”

Steinmetz’s Chosen Family

I decided to write about Steinmetz last year, after IEEE Spectrum published an essay I wrote about why engineering needs the humanities. The article contains this line: “In 1909, none other than Charles Proteus Steinmetz advocated for including the classics in engineering education.” I had been impressed to learn of Steinmetz’s recognition of the value of a liberal arts education. But my copy editor didn’t know who Steinmetz was or why he merited the qualifier “none other.” More people should know about this remarkable man, I decided. And so I went looking for a museum object associated with him, so I could include him in a Past Forward column.

Steinmetz [left] was easily the intellectual equal of Thomas Edison [right], whom he considered a friend.Corbis/Getty Images

The electric car is only one avenue into Steinmetz’s life. I could instead have looked into Steinmetz solids (the geometric shapes that form when two or three identical cylinders intersect at right angles), Steinmetz curves (the edges of a Steinmetz solid), or the Steinmetz equivalent circuit (a mathematical model that describes a transformer using resistors and inductors). But none of those concepts could be easily captured in a picture-worthy object. His love of his electric car, on the other hand, was a fun and fitting entry point for this most unusual engineer.

I also saw an opportunity to highlight how Steinmetz became a family man. Steinmetz had dwarfism—he stood just 122 centimeters tall—as well as kyphosis, a severe curvature of the spine, as did his father and grandfather. He didn’t wish to pass along those traits, and so he never married or had children of his own. But that didn’t mean he didn’t want a family.

In 1903, Steinmetz’s favorite lab assistant, Joseph LeRoy Hayden, told his boss that he was getting married. Steinmetz invited the couple to dinner, and then invited them to live in his large home. They agreed to this unusual living arrangement, with Corinne Rost Hayden running the household and cooking for her husband and Steinmetz. She forced the men to set aside their work for regular family meals.

Eventually, the Hayden family expanded, welcoming Joe, Midge, and Billy. Steinmetz legally adopted the elder Hayden, thereby gaining three grandchildren as well. Steinmetz, whom The New York Times had named a “modern Jove” who “hurls thunderbolts at will” (from a high-voltage lightning generator), delighted at entertaining the grandkids with wondrous tricks of electricity and chemistry.

In writing about the history of electrical engineering, I sometimes fall into the trap of focusing too much on the technology. But it’s just as important to recognize the people behind the technology—their personalities, their frailties, their feelings, their challenges. Steinmetz faced adversity for his political beliefs, for being an immigrant, and for his physical stature, yet none of that ever stopped him. In word and deed, he showed that he had a generous heart as mighty as his intellect.

Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.

An abridged version of this article appears in the March 2026 print issue as “Charles Proteus Steinmetz Loved His Electric Car.”

References

IEEE Power & Energy Magazine published Steinmetz’s pro/con list comparing electric cars to those with internal combustion engines in the September/October 2005 issue, along with a good biographical overview of Steinmetz by Carl Sulzberger.

Union College published a nice story about the restoration of Steinmetz’s electric car in 2014, when it received its permanent home on campus.

There are many biographies of Steinmetz, one published as early as 1924, but I am particularly fond of Steinmetz: Engineer and Socialist by Ronald Kline (Johns Hopkins University Press, 1992).

Gilbert King’s 2011 article “Charles Proteus Steinmetz, the Wizard of Schenectady” for Smithsonian magazine describes Steinmetz’s chosen family and includes several fun anecdotes not mentioned above.

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Google quantum-proofs HTTPS by squeezing 2.5kB of data into 64-byte space

Google on Friday unveiled its plan for its Chrome browser to secure HTTPS certificates against quantum computer attacks without breaking the Internet.

The objective is a tall order. The quantum-resistant cryptographic data needed to transparently publish TLS certificates is roughly 40 times bigger than the classical cryptographic material used today. Today’s X.509 certificates are about 64 bytes in size, and comprise six elliptic curve signatures and two EC public keys. This material can be cracked through the quantum-enabled Shor’s algorithm. Certificates containing the equivalent quantum-resistant cryptographic material are roughly 2.5 kilobytes. All this data must be transmitted when a browser connects to a site.

The bigger they come, the slower they move

“The bigger you make the certificate, the slower the handshake and the more people you leave behind,” said Bas Westerbaan, principal research engineer at Cloudflare, which is partnering with Google on the transition. “Our problem is we don’t want to leave people behind in this transition.” Speaking to Ars, he said that people will likely disable the new encryption if it slows their browsing. He added that the massive size increase can also degrade “middle boxes,” which sit between browsers and the final site.

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Video Friday: Robot Dogs Haul Produce From the Field

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.

ICRA 2026: 1–5 June 2026, VIENNA

Enjoy today’s videos!

Our robots Lynx M20 help transport harvested crops in mountainous farmland—tackling the rural “last mile” logistics challenge.

[ DEEP Robotics ]

Once again, I would point out that now that we are reaching peak humanoid robots doing humanoid things, we are inevitably about to see humanoid robots doing non-humanoid things.

[ Unitree ]

In a study, a team of researchers from the Max Planck Institute for Intelligent Systems, the University of Michigan, and Cornell University show that groups of magnetic microrobots can generate fluidic forces strong enough to rotate objects in different directions without touching them. These microrobot swarms can turn gear systems, rotate objects much larger than the robots themselves, assemble structures on their own, and even pull in or push away many small objects.

[ Science ] via [ Max Planck Institute ]

Bipedal—or two-legged—autonomous robots can be quite agile. This makes them useful for performing tasks on uneven terrain, such as carrying equipment through outdoor environments or performing maintenance on an ocean-going ship. However, unstable or unpredictable conditions also increase the possibility of a robot wipeout. Until now, there’s been a significant lack of research into how a robot recovers when its direction shifts—for example, a robot losing balance when a truck makes a quick turn. The team aims to fix this research gap.

[ Georgia Tech ]

Robotics is about controlling energy, motion, and uncertainty in the real world.

[ Carnegie Mellon University ]

Delicious dinner cooked by our robot Robody. We’ve asked our investors to speak about why they’re along for the ride.

[ Devanthro ]

Tilt-rotor aerial robots enable omnidirectional maneuvering through thrust vectoring, but introduce significant control challenges due to the strong coupling between joint and rotor dynamics. This work investigates reinforcement learning for omnidirectional aerial motion control on over-actuated tiltable quadrotors that prioritizes robustness and agility.

[ DRAGON Lab ]

At the CMU Robotic Innovation Center’s 75,000-gallon water tank, members of the TartanAUV student group worked to further develop their autonomous underwater vehicle (AUV) called Osprey. The team, which takes part in the annual RoboSub competition sponsored by the U.S. Office of Naval Research, is comprised primarily of undergraduate engineering and robotics students.

[ Carnegie Mellon University ]

Sure seems like the only person who would want a robot dog is a person who does not in fact want a dog.

Compact size, industrial capability. Maximum torque of 90N·m, over 4 hours of no-load runtime, IP54 rainproof design. With a 15 kg payload, range exceeds 13 km. Open secondary development, empowering industry applications.

[ Unitree ]

If your robot video includes tasty baked goods it WILL be included in Video Friday.

[ QB Robotics ]

Astorino is a 6-axis educational robot created for practical and affordable teaching of robotics in schools and beyond. It has been created with 3D printing, so it allows for experimentation and the possible addition of parts. With its design and programming, it replicates the actions of industrial robots giving students the necessary skills for future work.

[ Astorino by Kawasaki ]

We need more autonomous driving datasets that accurately reflect how sucky driving can be a lot of the time.

[ ASRL ]

This Carnegie Mellon University Robotics Institute Seminar is by CMU’s own Victoria Webster-Wood, on “Robots as Models for Biology and Biology and Materials for Robots.”

In the last century, it was common to envision robots as shining metal structures with rigid and halting motion. This imagery is in contrast to the fluid and organic motion of living organisms that inhabit our natural world. The adaptability, complex control, and advanced learning capabilities observed in animals are not yet fully understood, and therefore have not been fully captured by current robotic systems. Furthermore, many of the mechanical properties and control capabilities seen in animals have yet to be achieved in robotic platforms. In this talk, I will share an interdisciplinary research vision for robots as models for neuroscience and biology as materials for robots.

[ CMU RI ]

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Bond Strength, Biocompatibility, and Beyond

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New AirSnitch attack breaks Wi-Fi encryption in homes, offices, and enterprises

It’s hard to overstate the role that Wi-Fi plays in virtually every facet of life. The organization that shepherds the wireless protocol says that more than 48 billion Wi-Fi-enabled devices have shipped since it debuted in the late 1990s. One estimate pegs the number of individual users at 6 billion, roughly 70 percent of the world’s population.

Despite the dependence and the immeasurable amount of sensitive data flowing through Wi-Fi transmissions, the history of the protocol has been littered with security landmines stemming both from the inherited confidentiality weaknesses of its networking predecessor, Ethernet (it was once possible for anyone on a network to read and modify the traffic sent to anyone else), and the ability for anyone nearby to receive the radio signals Wi-Fi relies on.

Ghost in the machine

In the early days, public Wi-Fi networks often resembled the Wild West, where ARP spoofing attacks that allowed renegade users to read other users' traffic were common. The solution was to build cryptographic protections that prevented nearby parties—whether an authorized user on the network or someone near the AP (access point)—from reading or tampering with the traffic of any other user.

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How Stupid Would It Be to Put Data Centers in Space?

What’s the difference between a stupid idea and a brilliant one? Sometimes, it just comes down to resources. Practically unlimited funds, like limitless thrust, can get even a mad idea off the ground.

And so it might be for the concept of putting AI data centers in orbit. In a rare moment of unalloyed agreement, some of the richest and most powerful men in technology are staunchly backing the idea. The group includes Elon Musk, Jeff Bezos, Jensen Huang, Sam Altman, and Google CEO Sundar Pichai. In all likelihood, hundreds of people are now working on the concept of space data centers at the firms directly or indirectly controlled by these men—SpaceX, Starlink, Tesla, Amazon, Blue Origin, Nvidia, OpenAI, and Google, among others.

Likely costs to design, build, and launch a 1-GW orbital datacenter, based on a network of some 4,400 satellites and including operating costs over a five-year period, would exceed US $50 billion. That’s about three times the cost of a 1-GW data center on Earth, including five years of operation.John MacNeill

So how much would it cost to start training large language models in space? Probably the best accounting is one created by aerospace engineer Andrew McCalip. McCalip’s exhaustive, detailed analysis includes interactive sliders that let you compare costs for space-based and terrestrial data centers in the range of 1 to 100 gigawatts. One-gigawatt data centers are being built now on terra firma, and Meta has announced plans for a 5-GW facility, with anticipated completion some time after 2030.

In an interview, McCalip says his initial rough calculations a few years ago suggested that data centers in space would cost in the range of 7 to 10 times more, per gigawatt of capacity, than their terrestrial counterparts. “It just wasn’t practical,” he says. “Not even close.” But when Elon Musk began publicly backing the idea, McCalip revisited the numbers using publicly available information about Starlink’s and Tesla’s technologies and capabilities.

That changed the picture substantially. The figures in his online analysis assume an orbital network of data-center satellites that borrows heavily from Musk’s tech treasure chest—“essentially…you just start putting some radiation-resistant ASIC chips on the Starlink fleet and you start growing edge capacity organically on the Starlink fleet,” McCalip says. The network would rely on the kind of watt-efficient GPU architecture used in Teslas for self-driving, he adds. “You start dropping those onto the backs of Starlinks. You can slowly grow this out, and this would be approximately the performance that you would get.”

Bottom line, with some solid but not necessarily heroic engineering, the cost of an orbital data center could be as low as three times that of the comparable terrestrial one. That differential, while still high, at least nudges the concept out of the instantly dismissible category. “I have my particular views, but I want the data to speak for itself,” McCalip says.

For this illustration, we picked a configuration with an aggregate 1 GW of capacity. The network would consist of some 4,300 satellites, each of which would be outfitted with a 1-square-kilometer solar array that generates 250 kilowatts. The data center on that satellite, powered by the array, might have at least 175 GPUs; McCalip notes that a popular GPU rack, Nvidia’s NVL72, has 72 GPUs and requires 120 to 140 kW.

The total cost of the satellite network would be around US $51 billion, including launch and five years of operational expenses; a comparable terrestrial system would cost about $16 billion over the same period.

Stupid? Not stupid? You decide.

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Achieving Micron-Level Tolerances: CAD Optimization for Sub-10µm 3D Printing

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How to Thrive as a Remote Worker

This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!

Standing Out as a Remote Worker Takes a Different Strategy

My first experience as a remote worker was a disaster.

Before I joined a San Francisco-based team with a lead developer in Connecticut, I had worked in person, five days a week. I thought success was simple: write good code, solve hard problems, deliver results. So I put my head down and worked harder than ever.

Twelve-hour days became normal as the boundary between work and personal life disappeared. My kitchen table became my office.

I rarely asked for help because I didn’t want to seem incompetent. I stayed quiet in team Slack channels because I wasn’t sure what to say.

Despite working some of the longest hours of my career, I made the slowest progress. I felt disconnected from the team. I had no idea if my work mattered or if anyone noticed what I was doing. I was burning out.

Eventually, I realized the real problem: I was invisible.

The Office Advantage You Lose When Remote

In an office, visibility happens naturally. Colleagues see you arrive early or stay late. They notice when you are stuck on a problem. They hear about your work in hallway conversations and over lunch. Physical presence creates recognition with almost no effort.

Remote work removes those signals. Your manager cannot see you at your desk. Your teammates don’t know you’ve hit a roadblock unless you say so. You can work long days and still appear less engaged than someone in the office.

That is the shift many people miss: Remote work requires execution plus deliberate communication.

What Actually Works

By my second remote role, I knew I had to change to protect my sanity and still succeed.

Here are five things I did that made a real difference.

1. Over-communicating

I began sharing updates in team channels regularly, not just when asked. “Working on the payment integration today; ready for review tomorrow.” “Hit a blocker with API rate limits; investigating options.” These took seconds but made my work visible and invited help sooner.

2. Setting limits

When your home is also your office, overwork becomes the default. I started ending most days at 5 p.m. and transitioning out of work mode with a walk or gym session. That ritual helped prevent burnout.

3. Volunteering for presentations

Presenting remotely felt less intimidating than standing in front of a room. I started volunteering for demos and lunch-and-learns. This increased my visibility beyond my immediate team and improved my communication skills.

4. Promoting others publicly

When someone helped me, I thanked them in a public channel. When a teammate shipped something impressive, I called it out. This builds goodwill and signals collaboration. In remote environments, gratitude is visible and memorable.

5. Building relationships deliberately

In an office, relationships form naturally. Remotely, you have to create those moments. I started an engineering book club that met every other week to discuss a technical book. It became a low-pressure way to connect with people across the organization.

The Counterintuitive Reality

With these habits, I got promoted faster in this remote job than I ever did in an office. I moved from senior engineer to engineering manager in under two years, while maintaining a better work-life balance.

Remote work offers flexibility and freedom, but it comes with a tax. You are easier to overlook and more likely to burn out unless you are intentional in your actions.

So, succeeding remotely takes deliberate effort in communication, relationships, and boundaries. If you do that well, remote work can unlock more opportunities than you might expect.

—Brian

This Former Physicist Helps Keep the Internet Secure

Despite its critical role in maintaining a secure network, authentication software often goes unnoticed by users. Alan DeKok now runs one of the most widely used remote authentication servers in the world—but he didn’t initially set out to work in cybersecurity. DeKok studied nuclear physics before starting the side project that eventually turned into a three-decade-long career.

Read more here.

More Than 30,000 Tech Employees Laid Off in 2026

We’re just two months into 2026, and layoffs in the tech industry are already ramping up. According to data compiled by RationalFX, more than half of the 30,700 layoffs this year have come from Amazon, which announced that it would be cutting the roles of 16,000 employees in late January. Will the trend continue through 2026?

Read more here.

IEEE Online Mini-MBA Aims to Fill Leadership Skills Gaps in AI

Recent research suggests that a majority of organizations have a significant gap when it comes to AI skills among leadership. To help fill the gap, IEEE has partnered with the Rutgers Business School to offer an online “mini-MBA” program, combining business strategy and deep AI literacy. The program spans 12 weeks and 10 modules that teach students how to implement AI strategies in their own organizations.

Read more here.

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Jimi Hendrix Was a Systems Engineer

3 February 1967 is a day that belongs in the annals of music history. It’s the day that Jimi Hendrix entered London’s Olympic Studios to record a song using a new component. The song was “Purple Haze,” and the component was the Octavia guitar pedal, created for Hendrix by sound engineer Roger Mayer. The pedal was a key element of a complex chain of analog elements responsible for the final sound, including the acoustics of the studio room itself. When they sent the tapes for remastering in the United States, the sounds on it were so novel that they included an accompanying note explaining that the distortion at the end was not malfunction but intention. A few months later, Hendrix would deliver his legendary electric guitar performance at the Monterey International Pop Festival.

“Purple Haze” firmly established that an electric guitar can be used not just as a stringed instrument with built-in pickups for convenient sound amplification, but also as a full-blown wave synthesizer whose output can be manipulated at will. Modern guitarists can reproduce Hendrix’s chain using separate plug-ins in digital audio workstation software, but the magic often disappears when everything is buffered and quantized. I wanted to find out if a more systematic approach could do a better job and provide insights into how Hendrix created his groundbreaking sound.

My fascination with Hendrix’s Olympic Studios’ performance arose because there is a “Hendrix was an alien” narrative surrounding his musical innovation—that his music appeared more or less out of nowhere. I wanted to replace that narrative with an engineering-driven account that’s inspectable and reproducible—plots, models, and a signal chain from the guitar through the pedals that you can probe stage by stage.

Each effects pedal in Hendrix’s chain contributed to enhancing the electric guitar beyond its intrinsic limits. A selection of plots from the full-circuit analysis shows how the Fuzz Face turns a sinusoid signal from a string into an almost square wave; how the Octavia pedal inverts half the input waveform to double its frequency; how the wah-wah pedal acts as band-pass filter; and how the Uni-Vibe pedal introduces selective phase shifts to color the sound.James Provost/ Rohan S. Puranik

Although I work mostly in the digital domain as an edge-computing architect in my day job, I knew that analog circuit simulations would be the key to going deeper.

My first step was to look at the challenges Hendrix was trying to address. Before the 1930s, guitars were too quiet for large ensembles. Electromagnetic pickups—coils of wire wrapped around magnets that detect the vibrations of metal strings—fixed the loudness problem. But they left a new one: the envelope, which specifies how the amplitude of a note varies as it’s played on an instrument, starting with a rising initial attack, followed by a falling decay, and then any sustain of the note after that. Electric guitars attack hard, decay fast, and don’t sustain like bowed strings or organs. Early manufacturers tried to modify the electric guitar’s characteristics by using hollow bodies fitted with magnetic pickups, but the instrument still barked more than it sang.

Hendrix’s mission was to reshape both the electric guitar’s envelope and its tone until it could feel like a human voice. He tackled the guitar’s constraints by augmenting it. His solution was essentially a modular analog signal chain driven not by knobs but by hands, feet, gain staging, and physical movement in a feedback field.

Hendrix’s setups are well documented: Set lists, studio logs, and interviews with Mayer and Eddie Kramer, then the lead engineer at Olympic Studios, fill in the details. The signal chain for “Purple Haze” consisted of a set of pedals—a Fuzz Face, the Octavia, and a wah-wah—plus a Marshall 100-watt amplifier stack, with the guitar and room acoustics closing a feedback loop that Hendrix tuned with his own body. Later, Hendrix would also incorporate a Uni-Vibe pedal for many of his tracks. All the pedals were commercial models except for the Octavia, which Mayer built to produce a distorted signal an octave higher than its input.

Hendrix didn’t speak in decibels and ohm values, but he collaborated with engineers who did.

I obtained the schematics for each of these elements and their accepted parameter ranges, and converted them into netlists that ngspice can process (ngpsice is an open source implementation of the Spice circuit analyzer). The Fuzz Face pedal came in two variants, using germanium or silicon transistors, so I created models for both. In my models, Hendrix’s guitar pickups had a resistance of 6 kiloohms and an inductance of 2.5 henrys with a realistic cable capacitance.

I chained the circuit simulations together using a script, and I produced data-plot and sample sound outputs with Python scripts. All of the ngspice files and other scripts are available in my GitHub repository at github.com/nahorov/Hendrix-Systems-Lab, with instructions on how to reproduce my simulations.

What Does The Analysis of Hendrix’s Signal Chain Tell Us?

Plotting the signal at different points in the chain with different parameters reveals how Hendrix configured and manipulated the nonlinear complexities of the system as a whole to reach his expressive goals.

A few highlights: First, the Fuzz Face is a two-transistor feedback amplifier that turns a gentle sinusoid signal into an almost binary “fuzzy” output. The interesting behavior emerges when the guitar’s volume is reduced. Because the pedal’s input impedance is very low (about 20 kΩ), the pickups interact directly with the pedal circuit. Reducing amplitude restores a sinusoidal shape—producing the famous “cleanup effect” that was a hallmark of Hendrix’s sound, where the fuzz drops in and out as desired while he played.

The Jimi Hendrix Experience, (left to right) Mitch Mitchel, Jimi Hendrix, Noel ReddingFred W. McDarrah/Getty Images

Second, the Octavio pedal used a rectifier, which normally converts alternating to direct current. Mayer realized that a rectifier effectively flips each trough of a waveform into a peak, doubling the number of peaks per second. The result is an apparent doubling of frequency—a bloom of second-harmonic content that the ear hears a bright octave above the fundamental.

Third, the wah-wah pedal is a band-pass filter: Frequency plots show the center frequency sweeping from roughly 300 hertz to 2 kilohertz. Hendrix used it to make the guitar “talk” with vowel sounds, most iconically on “Voodoo Child (Slight Return).”

Fourth, the Uni-Vibe cascades four phase-shift sections controlled by photoresistors. In circuit terms, it’s a low-frequency oscillator modulating a variable-phase network; in musical terms it’s motion and air.

Finally, the whole chain became a closed loop by driving the Marshall amplifier near saturation, which among other things extends the sustain. In a reflective room, the guitar strings couple acoustically to the speakers—move a few centimeters and you shift from one stable feedback mode to another. To an engineer, this is a gain-controlled acoustic feedback system. To Hendrix, it was part of the instrument. He learned to tune oscillation with distance and angle, shaping sirens, bombs, and harmonics by walking the edge of instability.

Hendrix didn’t speak in decibels and ohm values, but he collaborated with engineers who did—Mayer and Kramer—and iterated fast as a systems engineer. Reframing Hendrix as an engineer doesn’t diminish the art. It explains how one person, in under four years as a bandleader, could pull the electric guitar toward its full potential by systematically augmenting the instrument’s shortcomings for maximum expression.

This article appears in the March 2026 print issue as “Jimi Hendrix, Systems Engineer.”

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This Physics Professor Credits Collaboration for Her Success

For Cinzia DaVià, collaboration isn’t just a buzzword. It’s the approach she applies to all her professional endeavors.

From her contributions to the development of a silicon sensor used in CERN (European Organization for Nuclear Research) particle accelerator experiments to her current research on portable energy generation solutions, there’s a common thread.

Cinzia DaVià

Employers

University of Manchester, England;

Stony Brook University, in New York

Job titles

Professor of physics; research professor

Member grade

Senior member

Alma maters

University of Bologna, Italy; University of Glasgow

As a professor of physics at the University of Manchester, in England, and a research professor at Stony Brook University, in New York, she has built strong connections across academic disciplines. Her continued involvement at CERN connects her with a broad array of professionals.

DaVià, an IEEE senior member, says she leverages her expertise and her network of collaborators to solve problems and build solutions. Her efforts include advancing high-energy particle experiments, improving cancer treatments, and mitigating the effects of climate change.

Collaboration is the foundation for any project’s success, she says. She credits IEEE for making many of her professional connections possible.

Even though she is the driving force behind building her alliances, she prefers to shine the spotlight on others, she says. For her, focusing on teamwork is more important than identifying individual contributions.

“The people involved in any project are really the ones to be celebrated,” she says. “The focus should be on them, not me.”

A career influenced by Italian television

As a young child growing up in the Italian Dolomites, her passion for physics was sparked by a popular documentary series, “Astronomia,” an Italian version of Carl Sagan’s renowned “Cosmos” series. The show was DaVià’s introduction to the world of astrophysics. She enrolled at Italy’s Alma Mater Studiorum/University of Bologna, confident she would pursue a degree in astronomy and astrophysics.

A summer internship at CERN in Geneva changed her career trajectory. She helped construct experiments for the Large Electron-Positron collider there. The LEP remains the largest electron-positron accelerator ever. An underground tunnel wide enough to accommodate the LEP’s 27-kilometer circumference was built on the CERN campus. It was Europe’s biggest civil engineering project at the time.

The LEP was designed to validate the standard model of physics, which until then was a theoretical framework that attempted to explain the universe’s building blocks. The experiments—which performed precision measurements of W and Z bosons, the positive and neutral bits central to particle physics—confirmed the standard model.

The LEP also paved the way, figuratively and literally, for CERN’s Large Hadron Collider. Following the LEP’s decommissioning in 2000, it was dismantled to make way for the LHC in the same underground testing tunnel.

As DaVià’s summer internship work on LEP experiments progressed, her professional focus shifted. Her plans to work in astrophysics gradually transitioned to a focus on radiation instrumentation.

After graduating in 1989 with a physics degree, she returned to CERN for a one-year assignment. As she got more involved in research and development for the large collider experiments, her one year turned into 10.

She received a CERN fellowship to help her finish her Ph.D. in physics at the University of Glasgow—which she received in 1997. Her work focused on radiation detectors and their applications in medicine.

“Nothing was programmed,” she says of her career trajectory. “It was always an opportunity that came after another opportunity, and things evolved along the way.”

A fusion of research and results

During her decade at CERN from 1989 to 1999, she contributed to several groundbreaking discoveries. One involved the radiation hardness of silicon sensors at cryogenic temperatures, referred to in physics as the Lazarus effect.

In the world of collider experiments, the silicon sensors function as eyes that capture the first moments of particle creation. The sensors are part of a larger detector unit that takes millions of images per second, helping scientists better understand particle creation.

In large collider experiments, the silicon sensors suffer significant damage from the radiation generated. After repeated exposure, the sensors eventually become nonfunctional.

DaVià’s contributions helped develop the process of reviving the dead detectors by cooling them down to temperatures below -143° C.

Her proudest professional accomplishment, she says, was a different discovery at CERN: Her research helped usher in a new era of large collider experiments.

For many years, researchers there used planar silicon sensors in collider experiments. But as the large colliders grew more sophisticated and capable, the traditional planar silicon design couldn’t withstand the extreme radiation present at the epicenter of collider collisions.

DaVià’s research contributed to the development, together with inventor Sherwood Parker, of 3D silicon sensors that could withstand extreme radiation.

The new sensors are radiation-resistant and exceptionally fast, she says.

Scientists began replacing planar sensors in the detectors deployed closest to the center of each collision. Planar detectors are still widely used in collider experiments but farther from direct impacts.

The development of the 3D silicon sensor was groundbreaking, but DaVià says she is proud of it for a different reason. The collaborative approach of the cross-functional R&D team she built is the most noteworthy outcome, she says.

Initially, people with conservative scientific views resisted the idea of creating a new sensor technology, she says. She was able to bring together a broad coalition of scientists, researchers, and industry leaders to work together, despite the initial skepticism and competing interests. The team included two companies that were direct competitors.

That type of industry collaboration was unheard of at the time, she says.

“I was able to convince them,” she says, “that working together would be the best and fastest way forward.”

Her approach succeeded. The two companies not only worked side by side but also exchanged proprietary information. They went so far as to agree that if something halted progress for one of them, it would ship everything to the other so production could continue.

DaVià coauthored a book about the project, Radiation Sensors With 3D Electrodes.

A focus on sustainable entrepreneurship

DaVià has long been concerned about the impact of extreme weather events, especially on underserved populations. Her interest transformed into action after she attended the American Institute of Architects International and AIA Japan Osaka World Expo last year.

During the symposium, held in June, panelists shared insights about natural disasters in their regions and identified steps that could help mitigate damage and protect lives.

The topics that particularly interested DaVià, she says, were excessive glacial melt in the Himalayas and the lack of tsunami warnings on remote Indonesian islands.

One of the ideas that surfaced during a brainstorming session was that of “smart shelters” that could be deployed in remote areas to assist in recovery efforts. The shelters would provide power and a means of communication during outages.

The concept was inspired by MOVE, an IEEE-USA initiative. The MOVE program provides communities affected by natural disasters with power and communications capabilities. The services are contained within MOVE vehicles and are powered by generators. A single MOVE vehicle can charge up to 100 phones, bolstering communication capabilities for relief agencies and disaster survivors.

DaVià’s knowledge of MOVE guided the evolution of the smart shelter concept. She recognized, however, that the challenge of powering portable shelters needed to be solved. She took the lead and formed a cross-disciplinary team of IEEE members and other professionals to make headway. One result is a planned two-day conference on sustainable entrepreneurship to be held at CERN in October.

“IEEE helps bring people together who might not otherwise connect.”

The goal of the conference, she says, is to “join the dots across different disciplines by involving as many IEEE societies and external experts as possible to work toward deployable solutions that help improve life for people around the world.”

The two-day event will include a competition focusing on solutions for sustainable energy generation and storage systems, she says, adding that entrepreneurs will share their ideas on the second day.

Her commitment to developing solutions to mitigate destruction caused by extreme weather led to her involvement with the IEEE Online Forum on Climate Change Technologies. She led the way in creating the Climate Change Initiative within the IEEE Nuclear and Plasma Sciences Society (NPSS).

She was the driving force behind securing funding for two of the society’s climate-related events. One was the 2024 Climate Workshop on Nuclear and Plasma Solutions for Energy and Society. The second event, building on the success of the first, was last year’s workshop: Nuclear and Plasma Opportunities for Energy and Society, held in conjunction with the Osaka World Expo.

New paths to guide others

DaVià reduced her involvement at CERN, when she joined the faculty at the University of Manchester as a physics professor. In 2016 she joined Stony Brook University as a research professor in the physics and astronomy department. She divides her time between the two schools.

She still maintains an office at CERN, where she works with students involved with particle physics. She is also an advisory board member of its IdeaSquare, an innovation space where science, technology, and entrepreneurial minds gather to brainstorm and test ideas. The goal is to identify ways to apply innovations generated by high-energy physics experiments to solve global challenges.

DaVià is the radiation detectors and imaging editor of Frontiers in Physics and a cochair of the European Union’s ATTRACT initiative, which promotes radiation imaging research across the continent. She is an active member of the European Physical Society, and she is an IEEE liaison officer for the physics and industry working group of the International Union of Pure and Applied Physics.

She has coauthored more than 900 publications.

IEEE as the connector

DaVià’s involvement with IEEE dates back to her undergraduate years, when she was introduced to the organization at a conference sponsored by the IEEE NPSS.

As her career grew, so did her involvement with IEEE.

She remains active with the society as a distinguished lecturer. She is a member of the IEEE Society of Social Implications of Technology, the IEEE Power & Energy Society, and the IEEE Women in Engineering group. She received the 2022 WIE Outstanding Volunteer of the Year Award.

She stays involved in IEEE to help her understand the work being done within each society and identify opportunities for cross-collaboration, she says. She sees such synergies as a key benefit of membership.

“IEEE helps bring people together who might not otherwise connect,” she says. “We are stronger together with IEEE.”

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Your Watch Will One Day Track Blood Pressure

Your smartwatch can track a lot of things, but at least for now, it can’t keep an accurate eye on your blood pressure. Last week researchers from University of Texas at Austin showed a way you smartwatch someday could. They were able to discern blood pressure by reflecting radio signals off a person’s wrist, and they plan to integrate the electronics that did it into a smartwatch in a couple of years.

Beside the tried-and-true blood pressure cuff, researchers in general have found several new ways to monitor blood pressure using pasted-on ultrasound transducers, electrocardiogram sensors, bioimpedance measurements, photoplethysmography, and combinations of these measurements.

“We found that existing methods all face limitations,” Yiming Han, a doctoral candidate in the lab of Yaoyao Jia told engineers at the IEEE International Solid State Circuits Conference (ISSCC) last week in San Francisco. For example, ultrasound sensing requires long-term contact with the skin. And as cool as electronic tattoos seem, they’re not as convenient or comfortable as a smartwatch. Photoplethysmography, which detects the oxygenation state of blood using light, doesn’t need direct contact, and indeed researchers in Tehran and California recently used it and a heavy dose of machine learning to monitor blood pressure. However, these sensors are thought to be sensitive to a person’s skin tone and were blamed for Black people in the United States getting inadequate treatment during the COVID-19 pandemic.

The University of Texas team sought a non-contact solution that was immune to skin-tone bias and could be integrated into a small device.

Continuous Blood Pressure Monitoring

Blood pressure measurements consist of two readings—systole, the peak pressure when the heart contracts and forces blood into arteries, and diastole, the phase in between heart contractions when pressure drops. During systole, blood vessels expand and stiffen and blood velocity increases. The opposite occurs in diastole.

All these changes alter conductivity, dielectric properties, and other tissue properties, so they should show up in reflected near-field radio waves, Jia’s colleague Deji Akinwande reasoned. Near-field waves are radiation impacting a surface that is less than one wavelength from the radiation’s source.

The researchers were able to test this idea using a common laboratory instrument called a vector network analyzer. Among its abilities, the analyzer can sense RF reflection, and the team was able to quickly correlate the radio response to blood pressure measured using standard medical equipment.

What Akinwande and Jia’s team saw was this: During systole, reflected near-field waves were more strongly out of phase with the transmitted radiation, while in diastole the reflections were weaker and closer to being in phase with the transmission.

You obviously can’t lug around a US $50,000 analyzer just to keep track of your blood pressure, so the team created a wearable system to do the job. It consists of a patch antenna strapped to a person’s wrist. The antenna connects to a device called a circulator—a kind of traffic roundabout for radio signals that steers outgoing signals to the antenna and signals coming in from the antenna to a separate circuit. A custom-designed integrated circuit feeds a 2.4 gigahertz microwave signal into one of the circulator’s on-ramps and receives, amplifies, and digitizes the much weaker reflection coming in from another branch. The whole system consumes just 3.4 milliwatts.

“Our work is the only one to provide no skin contact and no skin-tone bias,” Han said.

The next version of the device will use multiple radio frequencies to increase accuracy, says Jia, “because different people’s tissue conditions are different” and some might respond better to one or another. Like the 2.4 gigahertz used in the prototype these other frequencies will be of the sort already in common use such as 5 GHz (a Wi-Fi frequency) and 915 megahertz (a cellular frequency).

Following those experiments, Jia’s team will turn to building the device into a smartwatch form factor and testing them more broadly for possible commercialization.

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AI’s Math Tricks Don’t Work for Scientific Computing

AI has driven an explosion of new number formats—the ways in which numbers are represented digitally. Engineers are looking at every possible way to save computation time and energy, including shortening the number of bits used to represent data. But what works for AI doesn’t necessarily work for scientific computing, be it for computational physics, biology, fluid dynamics, or engineering simulations. IEEE Spectrum spoke with Laslo Hunhold, who recently joined Barcelona-based Openchip as an AI engineer, about his efforts to develop a bespoke number format for scientific computing.

LASLO HUNHOLD

Laslo Hunhold is a senior AI accelerator engineer at Barcelona-based startup Openchip. He recently completed a Ph.D. in computer science from the University of Cologne, in Germany.

What makes number formats interesting to you?

Laslo Hunhold: I don’t know another example of a field that so few are interested in but has such a high impact. If you make a number format that’s 10 percent more [energy] efficient, it can translate to all applications being 10 percent more efficient, and you can save a lot of energy.

Why are there so many new number formats?

Hunhold: For decades, computer users had it really easy. They could just buy new systems every few years, and they would have performance benefits for free. But this hasn’t been the case for the last 10 years. In computers, you have a certain number of bits used to represent a single number, and for years the default was 64 bits. And for AI, companies noticed that they don’t need 64 bits for each number. So they had a strong incentive to go down to 16, 8, or even 2 bits [to save energy]. The problem is, the dominating standard for representing numbers in 64 bits is not well designed for lower bit counts. So in the AI field, they came up with new formats which are more tailored toward AI.

Why does AI need different number formats than scientific computing?

Hunhold: Scientific computing needs high dynamic range: You need very large numbers, or very small numbers, and very high accuracy in both cases. The 64-bit standard has an excessive dynamic range, and it is many more bits than you need most of the time. It’s different with AI. The numbers usually follow a specific distribution, and you don’t need as much accuracy.

What makes a number format “good”?

Hunhold: You have infinite numbers but only finite bit representations. So you need to decide how you assign numbers. The most important part is to represent numbers that you’re actually going to use. Because if you represent a number that you don’t use, you’ve wasted a representation. The simplest thing to look at is the dynamic range. The next is distribution: How do you assign your bits to certain values? Do you have a uniform distribution, or something else? There are infinite possibilities.

What motivated you to introduce the takum number format?

Hunhold: Takums are based on posits. In posits, the numbers that get used more frequently can be represented with more density. But posits don’t work for scientific computing, and this is a huge issue. They have a high density for [numbers close to one], which is great for AI, but the density falls off sharply once you look at larger or smaller values. People have been proposing dozens of number formats in the last few years, but takums are the only number format that’s actually tailored for scientific computing. I found the dynamic range of values you use in scientific computations, if you look at all the fields, and designed takums such that when you take away bits, you don’t reduce that dynamic range

This article appears in the March 2026 print issue as “Laslo Hunhold.”

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