2026-02-25 03:00:03
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.
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.”
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.”
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.
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.
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.
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.”
2026-02-24 23:00:03
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.
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.
2026-02-23 21:00:03
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 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.”
2026-02-23 19:00:02
AI is revolutionizing the cybersecurity landscape. From accelerating threat detection to enabling real-time automated responses, artificial intelligence is reshaping how organizations defend against increasingly sophisticated attacks.But with these advancements come new and complex risks—AI systems themselves can be exploited, manipulated, or biased, creating fresh vulnerabilities.
In this session, we’ll explore how AI is being applied in real-world cybersecurity scenarios—from anomaly detection and behavioral analytics to predictive threat modeling. We’ll also confront the challenges that come with it, including adversarial AI, data bias, and the ethical dilemmas of autonomous decision-making.
Looking ahead, we’ll examine the future of intelligent cyber defense and what it takes to stay ahead of evolving threats. Join us to learn how to harness AI responsibly and effectively—balancing innovation with security, and automation with accountability.
2026-02-23 17:00:03
Social media is going the way of alcohol, gambling, and other social sins: Societies are deciding it’s no longer kid stuff. Lawmakers point to compulsive use, exposure to harmful content, and mounting concerns about adolescent mental health. So, many propose to set a minimum age, usually 13 or 16.
In cases when regulators demand real enforcement rather than symbolic rules, platforms run into a basic technical problem. The only way to prove that someone is old enough to use a site is to collect personal data about who they are. And the only way to prove that you checked is to keep the data indefinitely. Age-restriction laws push platforms toward intrusive verification systems that often directly conflict with modern data-privacy law.
This is the age-verification trap. Strong enforcement of age rules undermines data privacy.
Most age-restriction laws follow a familiar pattern. They set a minimum age and require platforms to take “reasonable steps” or “effective measures” to prevent underage access. What these laws rarely spell out is how platforms are supposed to tell who is actually over the line. At the technical level, companies have only two tools.
The first is identity-based verification. Companies ask users to upload a government ID, link a digital identity, or provide documents that prove their age. Yet in many jurisdictions, 16-year-olds do not have IDs. In others, IDs exist but are not digital, not widely held, or not trustworthy. Storing copies of identity documents also creates security and misuse risks.
The second option is inference. Platforms try to guess age based on behavior, device signals, or biometric analysis, most commonly facial age estimation from selfies or videos. This avoids formal ID collection, but it replaces certainty with probability and error.
In practice, companies combine both. Self-declared ages are backed by inference systems. When confidence drops, or regulators ask for proof of effort, inference escalates to ID checks. What starts as a light-touch checkpoint turns into layered verification that follows users over time.
This pattern is already visible on major platforms.
Meta has deployed facial age estimation on Instagram in multiple markets, using video-selfie checks through third-party partners. When the system flags users as possibly underaged, it prompts them to record a short selfie video. An AI system estimates their age and, if it decides they are under the threshold, restricts or locks the account. Appeals often trigger additional checks, and misclassifications are common.
TikTok has confirmed that it also scans public videos to infer users’ ages. Google and YouTube rely heavily on behavioral signals tied to viewing history and account activity to infer age, then ask for government ID or a credit card when the system is unsure. A credit card functions as a proxy for adulthood, even though it says nothing about who is actually using the account. The Roblox games site, which recently launched a new age-estimate system, is already suffering from users selling child-aged accounts to adult predators seeking entry to age-restricted areas, Wired reports.
For a typical user, age is no longer a one-time declaration. It becomes a recurring test. A new phone, a change in behavior, or a false signal can trigger another check. Passing once does not end the process.
These systems fail in predictable ways.
False positives are common. Platforms identify as minors adults with youthful faces, or adults who are sharing family devices, or have otherwise unusual usage. They lock accounts, sometimes for days. False negatives also persist. Teenagers learn quickly how to evade checks by borrowing IDs, cycling accounts, or using VPNs.
The appeal process itself creates new privacy risks. Platforms must store biometric data, ID images, and verification logs long enough to defend their decisions to regulators. So if an adult who is tired of submitting selfies to verify their age finally uploads an ID, the system must now secure that stored ID. Each retained record becomes a potential breach target.
Scale that experience across millions of users, and you bake the privacy risk into how platforms work.
This is where emerging age-restriction policy collides with existing privacy law.
Modern data-protection regimes all rest on similar ideas: Collect only what you need, use it only for a defined purpose, and keep it only as long as necessary.
Age enforcement undermines all three.
To prove they are following age-verification rules, platforms must log verification attempts, retain evidence, and monitor users over time. When regulators or courts ask whether a platform took reasonable steps, “We collected less data” is rarely persuasive. For companies, defending themselves against accusations of neglecting to properly verify age supersedes defending themselves against accusations of inappropriate data collection.
It is not an explicit choice by voters or policymakers, but instead a reaction to enforcement pressure and how companies perceive their litigation risk.
Outside wealthy democracies, the trade-off is even starker.
Brazil’s Statute of Child-rearing and Adolescents (ECA in Portuguese) imposes strong child-protection duties online, while its data-protection law restricts data collection and processing. Now providers operating in Brazil must adopt effective age-verification mechanisms and can no longer rely on self-declaration alone for high-risk services. Yet they also face uneven identity infrastructure and widespread device sharing. To compensate, they rely more heavily on facial estimation and third-party verification vendors.
In Nigeria many users lack formal IDs. Digital service providers fill the gap with behavioral analysis, biometric inference, and offshore verification services, often with limited oversight. Audit logs grow, data flows expand, and the practical ability of users to understand or contest how companies infer their age shrinks accordingly. Where identity systems are weak, companies do not protect privacy. They bypass it.
The paradox is clear. In countries with less administrative capacity, age enforcement often produces more surveillance, not less, because inference fills the void of missing documents.
Some policymakers assume that vague standards preserve flexibility. In the U.K., then–Digital Secretary Michelle Donelan, argued in 2023 that requiring certain online safety outcomes without specifying the means would avoid mandating particular technologies. Experience suggests the opposite.
When disputes reach regulators or courts, the question is simple: Can minors still access the platform easily? If the answer is yes, authorities tell companies to do more. Over time, “reasonable steps” become more invasive.
Repeated facial scans, escalating ID checks, and long-term logging become the norm. Platforms that collect less data start to look reckless by comparison. Privacy-preserving designs lose out to defensible ones.
This pattern is familiar, including online sales-tax enforcement. After courts settled that large platforms had an obligation to collect and remit sales taxes, companies began continuous tracking and storage of transaction destinations and customer location signals. That tracking is not abusive, but once enforcement requires proof over time, companies build systems to log, retain, and correlate more data. Age verification is moving the same way. What begins as a one-time check becomes an ongoing evidentiary system, with pressure to monitor, retain, and justify user-level data.
None of this is an argument against protecting children online. It is an argument against pretending there is no trade-off.
Some observers present privacy-preserving age proofs involving a third party, such as the government, as a solution, but they inherit the same structural flaw: Many users who are legally old enough to use a platform do not have government ID. In countries where the minimum age for social media is lower than the age at which ID is issued, platforms face a choice between excluding lawful users and monitoring everyone. Right now, companies are making that choice quietly, after building systems and normalizing behavior that protects them from the greater legal risks. Age-restriction laws are not just about kids and screens. They are reshaping how identity, privacy, and access work on the Internet for everyone.
The age-verification trap is not a glitch. It is what you get when regulators treat age enforcement as mandatory and privacy as optional.
2026-02-22 21:00:02
The first time she tried to seduce me,
(atoms falling in a vacuum)
she asked about blackberries—
(every mass exerts some gravity)
Did I know their season, where they grow?
(galvanometers, gravimeters)
I could answer both easily—
(tools to measure small attractions)
down the dirt road in September.
(devices that report, don’t interfere)
She eagerly went there with me,
(variations in readings occur)
We ate more berries than we kept.
(electron exchange may explain this)
The sweet dark juice painted our lips.
(equilibrium then entropy)