Category: Applied A.I.

On Red Tides: How Wang Mengqiu Is Tracking Algal Blooms With AI

Forests aren’t the only fragile ecosystems at risk from climate change. In the right conditions, Sargassum, a kind of macroalgae, can trap vast amounts of carbon and store it at the bottom of the ocean. But what happens when those conditions change?

The University of Hong Kong’s Professor Wang Mengqiu doesn’t want to wait to find out. Working with satellite imagery and advanced AI algorithms, she’s tracking oceanic conditions, monitoring blooms, and trying to answer an all-important question: How much of a given Sargassum bloom will become a carbon sink – and how much will wind up a carbon source?

Across the sea

The scale of Sargassum blooms can be hard to comprehend. To demonstrate what the planet is facing, Professor Wang pulls up a map showing the Great Atlantic Sargassum Belt, which stretches from the western coast of Africa across the ocean to the Caribbean and Mexico.

A recurring natural phenomenon, the Belt was observed as early as the late 15th century. “The ocean can benefit from Sargassum,” Professor Wang says, adding that they are “very big players in the ocean carbon cycle.”

Yet the Belt’s recent growth has worried researchers, as blooms set historical highs and landfall events in the Western Atlantic smother beaches and coastal wildlife.

“Satellites are an ‘eye in the sky,’ giving us a synoptic view of what’s happening below.”

– Professor Wang Mengqiu

“In the open ocean, Sargassum acts as a carbon sink,” Professor Wang explains. “But if it reaches the coast, it releases hydrogen sulfide (a compound linked to asthma) and endangers coastal vegetation, including corals and seagrasses.”

Step one in addressing the problem is figuring out just how large the Belt has become – and where and when landfall events are likely to occur. But how do you measure something that spans an entire ocean?

Professor Wang shows the scale of the Great Atlantic Sargassum Belt

AI in the sky

Typically, tracking Sargassum blooms might involve observations from a boat.

Both of these methods are expensive and time-consuming. So researchers like Professor Wang are turning to satellite and remote sensing technology, leveraging tools like AI and big data to quickly and efficiently identify Sargassum patches and map their locations.

Using satellites designed to monitor colour changes in the ocean, scientists can get a much better idea of how big the Belt – actually a thousands-of-miles-long stretch of discrete blooms – really is.

Although the underlying technology has been around since the 1980s, the field has benefitted in recent years from new hyperspectral sensors, higher-resolution images, and the shift from machine learning to deep learning.

“We don’t want to just react, we need to be able to make predictions.”

– Professor Wang Mengqiu

The launch of NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Satellite in 2024 was particularly exciting, Professor Wang says. “The satellite is a game changer because it’s hyperspectral,” she explains. “It will give us a much finer, more detailed knowledge of global changes.”

Previously, that level of detail would have been hard to analyse. But new deep learning techniques are making even hyperspectral imaging analysis possible, Professor Wang says. Her own work is one reason why: She just published a paper showing how to automatically de-noise images to better monitor algal blooms.

These advances will ultimately make it easier for scientists to predict the size and impact of future blooms, allowing for better management and mitigation.

Professor Wang explains her work using AI to track algal blooms

The search for solutions

Professor Wang’s next project is to develop algal bloom distribution maps that can be used to guide policymaking.

The Atlantic isn’t the only ocean facing growing problems related to Sargassum. In the Yellow Sea and East China Sea, for instance, blooms have caused havoc in recent years.

Professor Wang hopes to work together with the Hong Kong government to understand public needs and design real-world applications for researchers’ growing trove of remote sensing data, including alarm systems for Sargassum landfall.

“The goal is to convert Sargassum into something useful: energy, building materials, or other applications.”

– Professor Wang Mengqiu

While these ideas are still a long way off, she believes they could one day help mitigate or even eliminate the damage done by Sargassum to coastal ecosystems like Hong Kong’s – and even help vulnerable populations like the city’s fishers.

“I want to convert satellite products into something broadly useful,” she says. “As scientists, we have to serve the community.”

Professor Wang poses on the HKU campus

January 9, 2026
Jin Wu Wants the World to See the Forest for the Trees

When most people look at a forest, they see an example of nature at its best, a planetary lung that is one of our most reliable defences against climate change. But University of Hong Kong Professor Jin Wu sees something else: a delicately balanced ecosystem that, if not properly managed, could play havoc with earth’s future.

That’s because not all forests are created equal. The Amazon, for example, was traditionally dominated by evergreen trees. More recently, however, a mix of environmental changes, drought, and human encroachment have led many of these to die off and be replaced by deciduous variants.

While it’s too early to say how this will impact the climate, the end result could be a vicious cycle, Professor Wu says. As temperatures rise, trees need more water to sustain themselves. If the water runs low, more evergreens die and are replaced by deciduous trees, which do not transmit water from the soil to the air as efficiently. That means less rainfall and even more evergreen loss, releasing their stored carbon into the atmosphere as part of a process scientists call “Amazon dieback.”

“People think of forests as a way to fight climate change, but they can also facilitate climate change.”

– Professor Jin Wu

All is not lost, however. Professor Wu and his team are among the many scientists working to understand and track changes underway in the Amazon, in his case, through satellite data and remote sensing. “Remote sensing lets us scale up the knowledge we have of individual leaves and plants to a global level,” Professor Wu says with a characteristic smile. “Then we can turn that global knowledge into science-based decision-making.”

Professor Wu poses with his students in his lab

From tree to forest

The field of remote sensing is developing quickly, thanks in large part to new tools like machine learning and AI algorithms, Professor Wu says. One of the most exciting developments his team is working on is using satellite data to track the chemical fingerprint of individual plants, offering an unprecedented window into forest composition.

The idea comes from a 2009 study by ecologist Dr Greg Asner, who figured out how to identify individual plant species based on the chemical compositions of nitrogen and phosphorus. Because these plant chemical traits interact with light in different ways, satellite imagery could theoretically allow scientists to identify the functional composition of an entire forest.

“Plants all have unique chemical fingerprints, and remote sensing plus AI can help us identify them.”

– Professor Jin Wu

The key hurdle is data. Commonly used hyperspectral imaging techniques are prohibitively expensive and time-consuming. A few years ago, generating a hyperspectral image for a 300-by-300-meter plot cost around HK$20,000.

In search of an alternative, Professor Wu’s team turned to a multispectral approach. By looking at 10 criteria across dense image time-series within a year – as opposed to the over 100 needed for hyperspectral imaging – they believe they can develop a complete picture of plant coverage and the underlying high-dimensional chemical compositions around the world at a fraction of the cost.

If it works, it could allow scientists to track how plant species are changing across both space and time. And that knowledge, Professor Wu says, might prove vital in the fight against climate change.

Professor Jin Wu shows off some of the equipment he uses to monitor changes in forests.

Shrubs and CPUs

Professor Wu and his team aren’t just looking at the Amazon. They’ve also trained a machine learning model to identify shrubs in the Inner Mongolian grassland. Long-term, they plan to map vegetation across the steppe, keeping an eye on this important ecosystem.

Crucially, because their data is compatible with long-running databases, they can also look backward in time, allowing them to study the changes that have already taken place over the past 30 years.

“The field is evolving very quickly, and a lot of things we’ve never even dreamed of will soon be possible.”

– Professor Jin Wu

AI has helped make this possible, Professor Wu says, but the computational power needed represents a strain on the resources of smaller labs. Ideally, AI resources could be centralised so they benefit multiple teams at once – what he calls a “centralise and service” approach.

Science with a smile

Despite the challenges, Professor Wu remains optimistic about the future of remote sensing.

His sunny attitude toward the sometimes slow speed of research progress is reflected not just in his near constant smile, but also in the list of advice for new PhD students on his office whiteboard, which includes both old saws like “be patient” and reminders to “be generous to yourself” and to not think of yourself as purely a helper.

Professor Wu almost always has a smile on his face, even when explaining complex science.

There’s also an item on there about the importance of the spirit of “discovery,” rather than just safely trying to refine existing methods. It’s advice he appears to have taken to heart, as even the mere mention of remote sensing’s prospects in the coming decades causes him to wax excitedly about new mechanisms and techniques that could revolutionise our understanding of the planet.

“This is an important field for the 21st century, and we’re well placed to connect science and policy,” he says. “I just hope the University of Hong Kong can leverage this curve and pioneer novel research.”

January 6, 2026
Can Satellites Point the Way to More Liveable, Sustainable Cities?
In just 40 years, China went from a predominantly rural society to a highly urbanised one. What happened? And what lessons can other urbanising regions around the globe draw from that experience?

University of Hong Kong Professor of landscape architecture Bin Chen believes we can find the answers in satellite data. An expert in remote sensing and Director of HKU’s Future Urbanity and Sustainable Environment (FUSE) lab, he’s using satellite imagery to track everything from the historical growth of cities to emerging problem areas like urban heat, air pollution, and green/blue space loss.

It’s a cutting-edge field – and one with implications far beyond China. Done correctly, it could provide one of the first-ever windows into how cities grow and evolve in the real world. This emerging “urban intelligence” will in turn have major ramifications for rapidly urbanising countries across the Global South, allowing them to avoid mistakes made by other cities while empowering them to create more equitable built environments for all residents.

We asked Professor Chen to share his thoughts on the nature of his work, the importance of remote sensing and AI to urban planning, and how the world can build more equitable cities.

Professor Bin Chen points to some of his research on satellites and urban intelligence

The “intangibles” of sustainability

Professor Chen characterises his work as looking at the “tangibles and intangibles” of the built environment.

The tangibles are relatively straightforward. But the intangibles – air, heat, sunlight, light pollution, shade, and noise – play just as important a role in our lives and health, while being much harder to quantify.

That’s where satellites can play a role, Professor Chen says, as new data and methods allow researchers to look at cities on a granular level, identifying problematic heat islands, light pollution, and air quality.

“It’s not just about climate. It’s also about liveability.”

– Professor Bin Chen

Importantly, these are also problems of equity and ESG, with poorer residents often having to go without access to parks, shade, and clean air.

“Take shade, for example,” Professor Chen says. “In a city of high-rises, where you have people living in subdivided units with no access to sunlight year-round, shade is a social issue.”

Even if every building in a given district meets regulations, it can be hard to tell what the overall outcome will be – a problem satellite imagery and AI modelling can help solve.

While Professor Chen’s team can identify the issue, he hopes work on the solutions will be an interdisciplinary affair. “We need to bring experts together,” he says. “We need knowledge from social economics, environmental studies, data science, and urban planning.”

Professor Bin Chen poses with a sign for the Department of Landscape Architecture and HKU

Urban intelligence

The long-term goal is to develop what Professor Chen calls “urban intelligence” – a deeper understanding how people, cars, buildings, and the environment interact – then apply that knowledge to developing regions around the world.

Perhaps surprisingly, given their importance in modern life, the actual mechanisms by which cities grow are not always well understood. It wasn’t until 2008, when satellite data became more widely accessible, and 2015, when large-scale cloud computing and machine learning caught up, that researchers could closely examine the growth of modern cities.

“AI makes everything more efficient.”

– Professor Bin Chen

Professor Chen points to Shanghai’s Pudong New Area as a classic example, noting that while policy documents offer a window into its growth, remote sensing technology allows researchers a seamless, transparent view of how the district grew into a global financial capital.

Leading the way

But taking advantage of these advances will require more – and more open – data.

Still, researchers can make significant progress via virtual collaborations. A few years ago, Professor Chen worked with HKU Professor Peng Gong and partners from 23 universities and institutes across China on a database of Chinese urban land use.

By mixing satellite data with on-the-ground verification, they were able to create the country’s first nationwide parcel-level essential urban land use categories map, allowing researchers to easily compare cities around China, from Beijing to Shenzhen and Wuhan.

“Remote sensing is becoming more and more powerful. We used to have to focus on individual cities, but now we can look at entire countries, even the whole globe.”

– Professor Bin Chen

That empowered numerous follow-up studies, and the team hopes to expand their map to the rest of the world in the coming years.

“We’re entering a new stage, going beyond remote sensing with multimodal data,” says Professor Chen. “But pushing the field forward will require more data sharing.”
Professor Bin Chen, an expert on satellites, remote sensing, and urban intelligence, speaks to a student
Professor Bin Chen, an expert on satellites, remote sensing, and urban intelligence, poses in front of a sign for his Future Urbanity and Sustainable Environment Lab
Professor Bin Chen, an expert on satellites and urban intelligence, poses with some of his awards
December 19, 2025
Shunlin Liang Wants to Feed the World, One Orbit at a Time
Could satellites one day help us solve pressing socioeconomic problems like food insecurity? What if they could directly tell farmers when to fertilise their crops? Or how much water they should use to irrigate?

It sounds like science fiction, but according to University of Hong Kong Chair Professor of Remote Sensing Shunlin Liang, it’s not as far-fetched as you might think.

Professor Liang, who is also the Director of HKU’s Jockey Club STEM Laboratory of Quantitative Remote Sensing, has spent much of his thirty-year career trying to maximise the social benefits of remote sensing and satellite imaging technology. Now he and his team have developed AgriFM, a new artificial intelligence foundation model that could revolutionise how farmers in food-insecure regions manage their crops.

We asked him to share his thoughts on his new model, how remote sensing can improve lives around the world, and why it’s so important for researchers to have access to open-source tools.

Laying the foundation

Roughly half of the people dealing with food insecurity worldwide live in Asia. When Professor Liang saw photos of their plight, it motivated him to take action, and the result of that work is AgriFM, an open-source foundation model that offers superior crop-mapping performance over conventional deep learning methods.

While smallholder farmers generally know every inch of their land, managers on larger farms struggle to track crop growth and needs across a wide area. Remote sensing and AI technologies can help, Professor Liang says, allowing them to quickly identify when and where additional water, pesticides, or fertilisers are needed. That, in turn, will save farmers money while boosting yields.

“We need to maximise the benefits from minimal inputs.”

— Professor Shunlin Liang

That’s the short-term goal. Long term, Professor Liang says, the technology could prove even more transformative, predicting crop yields months in advance, helping governments and NGOs identify problem spots before they lead to hunger.

Open source, open access

In some ways, AgriFM is a natural continuation of Professor Liang’s decades-long interest in developing open-source tools and making satellite data more accessible.

In 2013, Professor Liang developed the open-source Global LAnd Surface Satellite (GLASS) suite, allowing researchers anywhere in the world to work with satellite data from all the leading public databases.

While organisations like NASA and ESA release satellite data to the public, they do not make it cross-compatible. That’s where GLASS comes in, making it possible to achieve much wider coverage by drawing from and unifying data from across various space agencies in one-easy-to-use package.

“In the future, foundation models mean we’ll need fewer people working on methodological problems, freeing them to focus on real-world applications.”

— Professor Shunlin Liang

More data means more potential new models like AgriFM, which Professor Liang believes could dramatically change the remote sensing field in the next five to 10 years. Already, he’s working on upgrades to GLASS like High GLASS, which offers greater resolution.

“Foundation models will change everything,” he says.

A computing challenge

That future is still some ways off, however, in large part due to data storage constraints.

Thanks to support from the Jockey Club and HKU, his team currently has 10 petabytes of data storage, but Professor Liang has set an ambitious goal of growing that to 50 petabytes in the next few years.

“Launching satellites and collecting data is expensive. I don’t want that (data) to be limited to a small community.”

— Professor Shunlin Liang

The idea is to expand the GLASS suite’s impact. To that end, he’s hoping to partner with data vendors and donors to commercialise some products while making others free for use by “millions” of people around the world.

“I want to make the data open to everyone,” Professor Liang says. “As a university professor, this is what I should do.”
Professor Liang is a global leader in remote sensing. Now he wants to use satellites to solve food insecurity.
Professor Liang is a global leader in remote sensing. Now he wants to use satellites to solve food insecurity.
Professor Liang is a global leader in remote sensing. Now he wants to use satellites to solve food insecurity.
Professor Liang is a global leader in remote sensing. Now he wants to use satellites to solve food insecurity.
December 16, 2025
To Chu Zhiqin, Diamonds Are Forever – and for Everything

“One of the biggest limitations in fields like AI is power consumption, and the key bottleneck is that their semiconductors are based on silicon.”
— Professor Chu Zhiqin

Most people know diamonds as age-old symbols of eternal love, but to the University of Hong Kong’s Professor Chu Zhiqin, they’re the key to our technological future.

It’s no exaggeration to say that that we’re living in the age of silicon. The element has become the building block of modernity, powering the semiconductors in everything from precision medical equipment to our phones. But silicon is far from an ideal conductor, and scientists have long dreamed of fashioning superior alternatives out of more suitable materials like diamonds.

The challenge lies in the very attributes that make diamonds so desirable: their durability and toughness. How can you grow diamonds that aren’t just better conductors than silicon, but also have the flexibility needed for the next generation of computing devices, including both power-intensive uses like AI and difficult-to-manufacture wearables?

That’s where Professor Chu comes in. His research – for which he was just named a finalist in the prestigious Falling Walls Science Breakthrough awards – is revolutionising the field of diamond-based semiconductors, heralding the future arrival of flexible yet durable chips with more than 10 times the thermal conductivity of their silicon counterparts.

The Future of Semiconductors

In theory, diamonds are almost the perfect vehicles for a future “fourth generation” of semiconductors. They are relatively easy to grow – all you need is hydrogen, methane, and electricity – strong, and can handle far higher wattages and more heat than silicon.

The trick is in manufacturing. While the basic process is well established, researchers have long struggled to produce diamond structures suitable for next-generation devices like wearables: ones that are flexible without sacrificing durability.

Cracking the code took Professor Chu and his team over a year of hard work. But they finally developed a trade secret process that allows them to reliably produce high-quality, micrometre-thin diamond semiconductors that can be rolled and flexed like sheets of paper.

“That’s the beauty of being a scientist: You know more about your field than anyone else in the world. If you dig long enough and hard enough, no one will be better at it than you.”
— Professor Chu Zhiqin

The Next Generation of Computing

The barriers to replication are steep: Although the team’s work has already spawned imitators, the competing diamonds tend to crack quickly, Professor Chu says, making them unsuitable for industrial uses.

The next challenge is commercialisation. Professor Chu is trying to interest manufacturers – always difficult with any new material – and is focusing his energies on high-upside, future-centric sectors like heat spreaders for GPUs and electric vehicles. If they succeed, they could radically increase the efficiency of some of the world’s most important products.

Among the developments Professor Chu is keeping his eye on are new AI-based measuring tools. Although accumulating training data remains difficult, AI offers more efficient, dynamic measurements. “Everything can be analysed by AI,” Professor Chu says. “It tells us what the best measurement parameters are.”

“Basically, the way we’re doing measurements now is the Stone Age, but with AI, we can move forward.”
— Professor Chu Zhiqin

A Scientific Spirit

But Professor Chu isn’t solely fixated on cutting-edge technologies. As the interview winds down, he muses on another significant challenge facing the field: cultivating the next generation of scientists.

Many aspiring scientists find the process discouraging and gradually drift away from the field. “They think learning math is hard, and learning physics is boring,” he says, adding that they can’t see how they’re supposed to master everything.

That’s an unfortunate misconception, Professor Chu believes. “Scientists actually are highly specialised,” he says. “It’s not that scientists can solve everything, but we can solve a few, very specific things.”

This preoccupation is reflected in his lab’s work. When asked what his favourite device is, he doesn’t mention the powerful Zeiss microscope, but a miniature quantum mechanics learning kit developed by one of his graduate students, Madhav. The box allows teachers to show young students quantum mechanics in action, rather than theory, letting them experience for themselves what it’s like to be a scientist.

It may seem small – or an unusual project for a lab of this stature – but it’s just one more way Professor Chu and his team are shaping the future of science.

October 31, 2025
Teaching Machines to Think Quantumly: Qi Zhao on the Frontier of AI-Driven Computing

“Quantum computers don’t just calculate. They learn from the rules of nature itself.” — Prof. Qi Zhao

Artificial intelligence is everywhere — in our phones, our cities, and the tools we use to think.

But for Professor Qi Zhao at The University of Hong Kong’s School of Computing and Data Science (CDS), the next leap in AI may come from a place far smaller than any silicon chip.

His research explores how quantum physics and machine learning can work together to create a new kind of intelligence — one that learns the way the universe learns.

From Theory to Computation

Zhao trained as a quantum information theorist, studying how data behaves when stored in particles rather than bits.
At HKU CDS, he leads a group that builds hybrid computing models combining classical algorithms with quantum processors.

His goal is simple to state but hard to achieve: use quantum systems to make AI faster, smarter, and more energy-efficient.

“Classical computers follow fixed paths,” he explains. “Quantum computers can explore many paths at once. That difference changes how learning works.”

Reimagining Computation

Traditional AI trains neural networks through repetition — adjusting parameters until patterns emerge.
Quantum computers take a different approach.

They rely on variational quantum algorithms, where a small quantum circuit learns by tuning itself with help from a classical controller.

Think of it as teamwork: the quantum part handles exploration; the classical part handles evaluation. Together, they solve problems that would take ordinary machines far longer to compute. Zhao’s team studies how this cooperation could transform optimization tasks, from image recognition to material design.

Quantum Machine Learning in Action

Inside his lab, AI helps control fragile quantum hardware.

Algorithms adjust pulse shapes, timing, and temperature to keep qubits stable. The system learns which conditions produce reliable results and adapts automatically when the environment changes. “It’s feedback learning in the truest sense,” Zhao says. “The machine is teaching itself how to stay coherent.”

These experiments do more than improve performance. They show how AI and quantum physics can enhance each other.
AI stabilizes quantum devices; quantum mechanics gives AI new mathematical tools for creativity and pattern discovery.

Learning from Quantum Data

Zhao believes that the next revolution will come when AI no longer just analyzes quantum data — it learns inside quantum data.

His group explores models where quantum systems perform the learning directly, finding relationships hidden from classical logic.
Such systems might recognize molecular structures or financial correlations beyond human intuition.

“This is where AI stops imitating intelligence,” he explains. “It begins to share it.”

Mentorship and Collaboration at CDS

As a mentor, Zhao encourages students to cross boundaries between physics and computer science.
He collaborates closely with Prof. Giulio Chiribella, Prof. Yuxiang Yang, and Prof. Ravi Ramanathan, creating a bridge between theory, experiment, and data science.

In class, he simplifies complex formulas into visual intuition. His students learn not only to code algorithms but also to think about why an algorithm works. “The most exciting discoveries,” he says, “often happen when we try to explain them simply.”

Looking Ahead: The Shape of Quantum Intelligence

Zhao imagines a future where AI systems powered by quantum hardware design drugs, manage energy grids, or simulate ecosystems in real time.

These machines will not replace human reasoning; they will extend it.

“Intelligence isn’t just logic,” he says. “It’s the ability to learn from limited information. That’s what quantum mechanics has been doing for billions of years.”

In his view, teaching machines to think quantumly is not just about computation — it’s about understanding learning itself.
And at HKU CDS, that journey has already begun.

August 22, 2025
When Randomness Becomes Intelligence: Ravi Ramanathan on Quantum Security and the Limits of AI

“In both physics and life, uncertainty isn’t a flaw — it’s what keeps everything interesting.” — Prof. Ravi Ramanathan

In a world driven by algorithms, certainty feels powerful. Yet for Professor Ravi Ramanathan of The University of Hong Kong’s School of Computing and Data Science (CDS), the opposite may be true.
He studies how randomness and trust shape the future of both quantum security and artificial intelligence — two fields that depend on data, but also on doubt.

From Theoretical Curiosity to Digital Trust

Ramanathan’s path began in theoretical physics. He was fascinated by the strange mix of order and unpredictability inside quantum systems. Over time, that curiosity evolved into a question that now defines his research: Can uncertainty itself protect information?

At HKU CDS, his group designs quantum cryptographic protocols that don’t rely on trusting the devices used to send or receive data. Instead, they use the laws of physics — not human assurances — to guarantee security.
“It’s like replacing a lock built by people with one built by nature,” he says.

Building Unhackable Systems

Traditional encryption depends on mathematical puzzles that powerful computers might one day solve. Quantum cryptography flips that logic.
It uses quantum particles, whose behavior changes when observed, to detect any eavesdropper immediately.

Ramanathan’s work focuses on device-independent security — a method where users don’t even need to know how their devices are built. As long as the results obey specific quantum correlations, the communication is secure.

This idea has major implications for AI as well. “AI systems make decisions based on patterns in data,” he explains. “If those data streams are ever compromised, the intelligence that depends on them becomes fragile. Quantum security keeps the foundation solid.”

The AI Connection: Intelligence Without Certainty

While most AI operates deterministically — producing the same output for the same input — the quantum world thrives on probabilities.
Ramanathan believes that future forms of quantum AI may combine these two views of intelligence: the structured logic of algorithms and the creative randomness of quantum mechanics.

“Learning,” he says, “might require a balance between prediction and surprise.”
By introducing controlled randomness, quantum systems could explore possibilities that classical AI would never consider. The result might be machines that don’t just calculate outcomes — they imagine them.

The Ethics of Uncertainty

As AI becomes more autonomous, questions of control and trust follow closely behind. Ramanathan’s research in quantum randomness adds a unique ethical layer: unpredictability can protect privacy.
“In cryptography, unpredictability is freedom,” he says. “If every decision were predictable, there would be no security — and no choice.”

He often compares the challenge of securing algorithms to the challenge of keeping human decision-making free from bias. Both require space for uncertainty. Both depend on humility before complexity.

Collaboration and Teaching at CDS

At CDS, Ramanathan collaborates with Prof. Giulio Chiribella, Prof. Yuxiang Yang, and Prof. Qi Zhao to connect foundational theory with practical technology.
He teaches courses that blend mathematics, physics, and computer science, encouraging students to question what it really means to “know” something in a computational world.

His mentoring style mirrors his research: open-ended, curious, and slightly unpredictable. “Students learn best when they discover answers for themselves,” he notes.

Looking Forward: Trusting Uncertainty

Ramanathan sees a future where quantum communication and AI-driven reasoning merge into systems that are both intelligent and secure.
These technologies may protect digital infrastructures, power next-generation networks, and even redefine how machines reason about risk.

“Randomness is often seen as noise,” he says. “But in nature — and in intelligence — it’s also creativity. Embracing it might be our best safeguard in an age of perfect prediction.”

August 15, 2025
How Quantum Sensors Learn: Prof. Yuxiang Yang on Intelligence Beyond Silicon

“To make better sensors, we need to teach them how to learn from the world around them.” — Prof. Yuxiang Yang

When most people think about intelligence, they picture circuits and code — the realm of artificial intelligence. But for Professor Yuxiang Yang of the School of Computing and Data Science (CDS) at The University of Hong Kong, true intelligence may start not in software, but in how we sense reality itself.

Yang’s research bridges the boundary between quantum physics and machine learning, using AI-driven methods to make quantum sensors more precise, adaptive, and even self-correcting. His work explores what happens when perception — human, mechanical, or quantum — becomes an intelligent process.

A Physicist Who Thinks Like an Engineer

Before arriving in Hong Kong, Yang trained as an optical physicist fascinated by the limits of measurement. “Every experiment,” he says, “is a conversation with nature. The better you listen, the more you understand.”

Now at HKU CDS, he leads projects that bring AI algorithms into quantum laboratories, transforming how sensors detect the tiniest fluctuations in magnetic fields, light, or vibration. Where most people see noisy data, Yang sees a new kind of language — one that can be decoded through learning.

The Science of Quantum Perception

Quantum sensors exploit delicate quantum states to measure the world with extreme accuracy — detecting signals a billion times smaller than what conventional devices can capture. The challenge, however, is noise: every vibration, temperature shift, or stray photon can overwhelm the signal.

Yang’s team tackles this by training AI models to separate meaningful information from random interference. Instead of manually tuning every parameter, algorithms learn to predict, filter, and adapt in real time. In effect, the sensor begins to “think” about its own environment.

When Sensors Get Smarter

The concept is revolutionary: a sensor that improves through experience. By combining reinforcement learning with quantum-optical hardware, Yang’s group designs systems that adjust their measurement strategies automatically.

In one project, machine-learning routines identify patterns in experimental noise and recommend control settings that boost sensitivity. In another, neural-network architectures inspired by human vision help interpret complex interference patterns in light-based sensors.

The results are striking — faster calibration, higher precision, and lower error rates. What used to take days of manual adjustment can now happen in minutes. “It’s like giving intuition to an instrument,” Yang says.

The AI–Quantum Feedback Loop

For Yang, the relationship between AI and quantum science runs both ways. Quantum experiments provide unique datasets that could inspire new machine-learning architectures; conversely, AI provides tools that push quantum systems to their limits.

“Quantum systems don’t process information the way classical computers do,” he explains. “They can explore many possibilities at once. Understanding how they ‘learn’ from data may reshape how we define intelligence itself.”

This feedback loop — between artificial learning and quantum understanding — is precisely where Yang believes future breakthroughs will occur.

Mentorship and Collaboration at CDS

At HKU CDS, Yang works closely with colleagues such as Prof. Giulio Chiribella, Prof. Ravi Ramanathan, and Prof. Qi Zhao in a research environment that encourages cross-disciplinary thinking.

His students are trained not only to run experiments, but to write code, model data, and reason from first principles. Many take part in HKU’s Common Core course “The Quantum Revolution,” where Yang introduces AI-driven approaches to measurement as a window into the nature of knowledge itself.

“We’re building a new generation of researchers who speak both languages — the language of physics and the language of data,” he says.

Looking Ahead: Perception as Intelligence

As AI systems become ubiquitous, Yang believes the next frontier is not only smarter computation but smarter perception — machines that can sense the world with the nuance of quantum systems.

His vision is ambitious yet grounded: quantum sensors that monitor ecosystems, detect disease, or guide autonomous vehicles with unprecedented precision — all while learning continuously from the environment.

“In the end,” he reflects, “AI and quantum physics are asking the same question: how do we extract meaning from uncertainty? The answer may begin with how we choose to observe.”

August 15, 2025
Prof. Giulio Chiribella: Rewriting the Rules of Reality

“If we change the way events can be ordered, we may discover new ways to compute, communicate, and understand the universe.” — Prof. Giulio Chiribella

At the heart of modern physics lies a paradox: the deeper we go into the quantum world, the more reality seems to defy common sense. For Professor Giulio Chiribella, this mystery isn’t a roadblock — it’s the fuel for his life’s work. As a theoretical physicist and thought leader at HKU’s School of Computing and Data Science (CDS), Chiribella is rewriting the rules that govern how we understand time, information, and causality.

An Early Fascination with Fundamental Questions

Growing up in Italy, Chiribella was drawn not just to physics but to philosophy — asking questions about what’s real, what can be known, and how we represent the world. He earned his PhD at the University of Pavia, where his fascination with the mathematical structure of quantum theory began to crystallize. At Canada’s Perimeter Institute for Theoretical Physics, he established himself as one of the world leaders in a vibrant research community pushing the understanding of quantum foundations and the boundaries of theoretical physics. Later, he became a professor at the University of Oxford, deepening his exploration of the foundations of quantum mechanics.

Now based in Hong Kong, Chiribella sees the city as a fertile ground for developing the next generation of quantum research — a place where abstract ideas can evolve into global impact.

Foundations First: Indefinite Causality and Quantum Processes

Much of Chiribella’s research centers on quantum foundations, particularly the concept of indefinite causal order — where cause and effect can be in a quantum superposition. In this framework, event A might cause event B and vice versa, simultaneously. These ideas aren’t just theoretical mind-benders. They’re opening doors to new types of quantum communication and computation that outperform even traditional quantum protocols.

His work also explores process theories, abstract mathematical structures that describe physical systems more flexibly than conventional models. These tools help scientists test which parts of quantum theory are essential — and which might change in future theories.

A Thought Experiment Across Time

As someone who reshapes how we think about quantum theory itself, Chiribella often reflects on how far the field has come — and how much further it could go. When asked what he’d show to the pioneers of quantum physics, his answer reveals both reverence and ambition:

“If we could go back and talk to Schrödinger, Heisenberg — or even Einstein — we could tell them that quantum theory can be written in a much more general way… not just using states, but processes. That would probably surprise them — in a good way.”

Einstein, famously skeptical of the indeterminacy at the heart of quantum theory, might have found Chiribella’s process-based view provocative. Instead of smoothing over quantum strangeness, it embraces it — using tools like indefinite causality to explore new mathematical structures that go beyond classical intuition. While Einstein wanted a deeper reality beneath quantum randomness, Chiribella’s work suggests that the structure of reality itself may be fundamentally different — not deterministic, but richer and more relational than previously imagined.

For Chiribella, it’s not just about solving problems within the rules — it’s about uncovering new rules entirely.

From Thought Experiments to Technology

While his thinking starts with fundamental questions, the implications are far-reaching. Chiribella’s frameworks are being applied in quantum machine learning, quantum communication networks, and quantum cryptography. He has co-authored seminal papers on quantum supermaps, causal networks, and programmable quantum processors — many of which laid the groundwork for today’s advances in distributed quantum computing.

A Quantum Vision for Hong Kong

As the founder of the Quantum Information, Computation and Intelligence (QICI) Lab at HKU, Chiribella is helping position Hong Kong as a global hub for quantum innovation. His leadership was instrumental in bringing AQIS 2025— Asia’s largest quantum conference — to Hong Kong for the first time. The event will convene leading theorists, experimentalists, and industry players to push the boundaries of what’s next.

But beyond visibility, his vision is about cultivating a deep-thinking research culture — one that embraces foundational work as the seed of future technologies.

The Quantum Team Around Him

Chiribella’s activity has a positive impact throughout the whole QICI quantum team. His colleague Prof. Ravishankar Ramanathan carries forward ideas from the quantum foundations playbook into cryptographic protocols that don’t rely on trust. Prof. Yuxiang Yang complements the group with expertise in quantum sensors and applying AI to enhance their sensitivity. And Prof. Qi Zhao, a close collaborator, works at the intersection of quantum information theory and learning, investigating how quantum systems can process, compress, and extract information in ways that classical systems cannot.

Together, they form a rare cluster of talent that spans both theory and experiment, foundations and applications — a hallmark of Chiribella’s approach to research and collaboration.

Mentorship and Teaching: Nurturing Quantum Thinkers

Chiribella is also known for his commitment to mentorship. In classes and labs, he encourages students to think like scientists, challenging assumptions rather than rushing to solve problems mechanically. His students are not just learning quantum mechanics — they’re exploring what it means to investigate reality.

This mindset is embedded in HKU’s Common Core course, “The Quantum Revolution,” which Chiribella helped shape. It invites students from all disciplines to grapple with the counterintuitive features of quantum theory and reflect on its broader impact on knowledge and society.

Looking Forward: Questions Still Unanswered

As the world celebrates 100 years since the birth of quantum physics, the research in this area is far from done. Chiribella’s sights are set on exploring post-quantum theories, testing the boundaries of causality, and understanding how computation, information, and physical law interact.

“The interplay between information, space, time, and matter is one of the most exciting frontiers of contemporary physics. If we manage to understand it, it will open the doors to new physics that we haven’t even imagined yet.”

In a field defined by uncertainty, Chiribella’s work seeks clarity — not in final answers, but in how to ask the right questions. And through this work, Hong Kong is becoming a place where deep foundational questions can be explored at the cutting edge of science.

August 8, 2025
Making Cities Smarter with AI

Once sci-fi fantasy, AI now is able to predict the city’s future like a crystal ball, thanks to the visionary projects at the University of Hong Kong.

Spearheaded by Professor Chao Huang of the Computer Science Department and his innovative team, including lead researcher Zhonghang Li, they are revolutionising our understanding of urban systems.

Meet “Urban GPT” and “Open City,” two projects designed to make our cities not just smarter, but future-predictable. Buckle up and let’s see what the future of urban living looks like.

Q: What is Urban GPT?

A: Imagine a crystal ball that can tell you what’s going to happen in your city—that’s Urban GPT. This incredible large-language model forecasts everything from traffic flow and transportation demand to even crime predictions. No need for coding skills or an engineering background. Just chat with Urban GPT in natural language, give it a few basic details and instructions, and it’ll whip up predictions in a flash—under a second.

Q: What sets Urban GPT apart from other smart city AI tools?

A:

Versatility on Demand

While most smart city tools are programmed for specific tasks, Urban GPT is an AI multitasker. From traffic to crime prediction, it’s got all bases covered.

Ready When You Are

Urban GPT does not rely on years of training with long-term historical data. It’s designed to deliver high-accuracy predictions from day one. Even under zero-shot scenarios, Urban GPT still maintains high accuracy.

Q: Who will reap the rewards of Urban GPT?
A: By harnessing cutting-edge predictive analytics, city planners can seamlessly optimise traffic flow and strategically deploy law enforcement resources. The result? A future-forward approach to urban operations that ensures cities run smoother and smarter.

Q: What is Open City?

A: Open City is the driving force behind Urban GPT, helping it analyse urban data effectively. Professor Huang and his team discovered that smaller cities often lack the tech needed for large models, so they created their own urban foundation model from scratch to make sure it works for everyone.

Zhonghang Li, lead researcher of the Computer Science Department and Prof. Chao Huang’s innovative team, together they are revolutionising our understanding of urban systems.

Q: What are the common concerns about AI tools?

Scalability and Implementation: However, Professor Huang’s team is adept at deploying AI tools across varied urban landscapes, ensuring easy integration with existing systems.

Data Privacy and Security: Using advanced techniques like federated learning, they ensure your data remains private, while still fully utilising AI’s capabilities.

Q: What is the next step?

A: Professor Huang and his team are developing an AI assistant. Imaging Jarvis from the movie “Iron Man”. With the support of Urban GPT and Open City, this AI assistance can help you plan a long trip. No need to worry about flight tickets, sightseeing routes or travel plan. Your personal AI assistant will handle it all with just a snap of your fingers.

More about Prof. Huang’s other researches and initiatives: https://sites.google.com/view/chaoh

May 27, 2025