Tag: Interdisciplinary Research

How AI Is Rewriting the Rules of Art Conservation

“It opens up all these new horizons for art history, for connoisseurship, and for how the discipline is going to continue to form.”
— Professor Marc Walton

You’ve heard of using AI to make art, but an interdisciplinary team of researchers at the University of Hong Kong is now tackling a far more complex problem: applying AI to the field of art conservation. Their work could have outsize ramifications for the world’s art institutions, expanding access to cutting-edge art conservation tools, cutting the time needed for materials analysis and allowing even small museums to protect and preserve their collections.

A Hands-On Approach

Tucked away in a corner of the Hong Kong University Museum and Art Gallery is the only on-site, university museum research lab in Asia. There, a team of chemists, conservation scientists, and students under Professor Marc Walton of HKU’s Museum Studies programme and the Department of Chemistry’s Dr Kenneth Ng are developing, building, and experimenting with new instrumentation that could radically lower the barriers to characterising the materials comprising objects of art and archaeology.

Looking around the lab, it’s hard to imagine that, a little over a year ago, almost none of this infrastructure existed. Before Walton, who was previously Head of Conservation and Research at M+, joined HKU in 2024, he had never met Dr Ng. They were brought together by another new arrival to the university, Chemistry Professor Jay Siegel, who recognised the pair’s shared interest in both chemistry and mechanical tinkering.

For Professor Siegel, the collaboration offered a solution to two longstanding issues in university education: how to break down barriers between disciplines and give students hands-on experience with real-world applications.

“(The students) are very well trained, they know their theories, but they’ve never touched an artefact before.”
— Dr Kenneth Ng

Soon, what began as a series of informal conversations morphed into something very real, with Walton joining HKU, then teaming up with Ng and the Chemistry department to bring their vision to life. “Jay recognised that Kenneth and I were thinking along the same lines,” says Walton. “He couldn’t have been more correct. This is the type of cross-fertilisation you normally wouldn’t think about: bringing a chemist together with someone from the humanities.”

Bridging Art and Science

Viewed from the front, the UMAG lab’s “Franken-camera” doesn’t look particularly unusual. It’s only when you circle around back that the moniker’s logic starts to come into focus, revealing uncovered wires and chips that have been grafted on by the team to improve performance.

The Franken-camera is far from the only curious-looking tool in the lab. The team uses a wide variety of analysis instruments, from off-the-shelf items like the handheld XRF spectrometer – designed to mimic the look of a Star Trek phaser – and modified microscopes. Their commitment to home-brewed tech isn’t just about performance or customisability; by opting to custom build tools, they give students more opportunities to practice a variety of skills that could be useful for their futures, such as the integration of code with hardware.

“The one big hurdle that we face in teaching is that ‘fancy’ microscopes are usually very, very expensive and ‘thou shalt not touch it.’”

— Dr Kenneth Ng

One of the biggest new tools in their toolkit is artificial intelligence. While the underlying machine-learning tech has been around for decades, the proliferation of LLMs and AI applications is drastically shortening the time spent on materials analysis – a key step in the process of understanding and conserving a piece of art.

As an example, Walton points to the traditionally time-consuming task of point analysis of artefacts. AI is allowing the team to take a handful of data points – some with detailed spectroscopic information, others that cover a larger portion of the artefact and show spatial details – and merge the sets to produce an image cheaply and quickly.

Expanding Access Through AI

“AI allowed us to do that. Before, it was really difficult to be able to fuse these different things together, to be able to create something that combines the best of both worlds.”
— Professor Marc Walton

Both Walton and Ng spotlight AI’s impact in expanding access to art conservation. Traditional conservation characterisation methods are expensive and difficult to use, leaving institutions across the globe struggling to balance their desire to augment the value of their collections through study and treatment with the associated costs of bringing science into the museum. 

If the cost of conservation tools and processes could be brought down, the thinking goes, then many of these problems could be solved with some basic technical knowledge and a little ingenuity. 

“These are things that any museum around the world, any person that’s interested in duplicating our work, could conceivably be able to do it without spending a whole lot of money,” says Walton.

The Future as Blank Canvas 

The team cautions that AI isn’t a cure-all, and that many of the classical methods of conservation continue to work well. Rather than completely overturning the field’s received knowledge, they’re focused on teaching students how to develop and use new tools while maintaining a critical mindset.

“It’s very important for students to know the nuts and bolts of AI rather than just using it as a black box,” says Ng, using a common metaphor for the opacity of AI algorithms. “Especially as a scientist, you really need to put on that scientist hat and differentiate whether it’s hallucinating, or whether it’s giving you the right answers.”

Still, they remain excited about the tech’s potential for conservation, with Walton pointing to possibilities, not just for the museum world, but also for lowering the barriers to connoisseurship and changing the field of art history.

That’s not all: Asked whether AI can bridge the gap between objective and subjective analysis, Walton pauses for a moment before turning philosophical. “I always think the objective and subjective come together, because the agency of the artist is in the materials, which we can be objective about” he says. “But really, what we want to understand is the subjective part of it. So, we’re using science as a tool to assess the subjective.”

To learn more about how Professor Walton and Dr Ng are using AI to rewrite the rules of art conservation, watch the video below:

January 30, 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
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