Tag: AI & Machine Learning

This category features stories, research, and insights that explore how artificial intelligence and machine learning are reshaping technology, science, and society. Posts here highlight HKU CDS projects that teach machines to learn from data, uncover patterns, and make intelligent decisions — from deep-learning breakthroughs and natural-language models to real-world applications in healthcare, education, and sustainability. The goal is to reveal both the science behind these systems and their impact on people’s lives.

  • Stepping Into the Past With Professor Chen Yuqi

    Stepping Into the Past With Professor Chen Yuqi

    In Isaac Asimov’s 1951 novel Foundation, the mathematician Hari Seldon coins the term “psychohistory” to describe his use of mathematics and statistical analysis to accurately predict future trends. 

    It’s an idea fit for science fiction – but it’s also been a consistent source of inspiration for University of Hong Kong Professor Chen Yuqi, a member of the Faculty of Arts and the Centre for Quantitative History, who’s using artificial intelligence (AI) tools to unlock the past. 

    Professor Chen, who graduated with a BA and PhD from Peking University before joining HKU in 2025, is at the forefront of history’s AI revolution. 

    During her PhD, she collected and built a database of 20,000 bronzes with inscriptions, which she used to reconstruct social networks in early Chinese history. While on a yearlong exchange at Harvard University in the United States, she worked with an interdisciplinary team collecting and organising a database of Chinese biographies. In turn, that sparked an interest in the historic roots of different psychologies across cultures – think collectivist versus individualistic – and historical psychology more broadly.

    Those experiences – plus a formative internship at Tencent – gave Professor Chen valuable experience in coding. It also set her on a different path from many of her colleagues, as she immersed herself in the field of digital humanities, just as the advent of new AI tools opened up exciting possibilities for the study of history. 

    Now, she’s exploring these possibilities at HKU: everything from the psychology of ancient Chinese scholars to reconstructing the rise and fall of clans from their own histories. And on top of it all, she’s found time to release an AI-native game that builds on ideas pioneered at Stanford to immerse players in the world of the late Northern Song dynasty (960–1127 CE). 

    “I saw people experimenting with how AI communicates with AI,” Professor Chen says. “And as a historian, I thought: What would happen if we used different AIs to roleplay different historical figures?”

    From psychohistory to historical psychology

    Let’s say you want to survey a group to learn its views on a given topic – academics on individualism and collectivism, for example, or parents and children on filial piety and family values. How would you go about it?

    The simplest answer is just to design a questionnaire and ask them. But what if you wanted to do the same thing for a group of people who have all been dead for hundreds of years, if not longer?

    That’s where AI comes in, Professor Chen says. Using AI models finetuned on historical texts, she can trace psychological and ideological trends across millennia of history.

    What would happen if we used different AIs to roleplay different historical figures?

    – Professor Chen

    The key – and the primary challenge – is verification. The AI models learn how ancient writers think, then she tests their training by looking at verifiable historical events such as the controversial Wang Anshi reforms that divided the Northern Song court almost until its collapse. 

    Almost every contemporary writer staked out an opinion on Wang’s policies, meaning it offers a perfect litmus test for the AI’s understanding of their beliefs. If the model returns correct predictions, then Professor Chen can use it to study how other attitudes may have changed over time. 

    For instance, Professor Chen’s models offer insight into how conservative/reformist tendencies predict scholar attitudes toward historical events, and how their relative power waxed and waned over time. They also let her see how core values, such as filial piety, are shaped by political and economic upheaval. 

    “In the past we couldn’t get a psychological values variable, because it’s very hard to measure from historical texts,” Professor Chen explains. “We can’t ask dead people to come to the lab and fill out questionnaires.”

    Now she doesn’t have to.

    Game theory

    More recently, Professor Chen has been putting her time into a very different kind of project: an interactive historical simulation that lets students experience the past for themselves.

    That’s the premise of “Into the Painting,” which lets players choose from hundreds of characters featured in the Song Dynasty masterpiece “Along the River During the Qingming Festival,” then play out their lives in the waning days of the Northern Song.

    That’s actually a marked departure from Professor Chen’s original idea: Allowing users to play as great historical figures, from Qin Shihuang to Napoleon. 

    The problem, as she explains it, was that the game worked by changing history – limiting its potential as a teaching tool.

    So, she went back to the drawing board in late 2025, spending six months designing, coding, and testing “Into the Painting.” The game ducks its predecessor’s issues by instead putting players in the shoes of ordinary Chinese, allowing them to change their lots in life while still remaining subject to broader historical forces. 

    It was a monumental undertaking. To date, Professor Chen has tagged over 200 figures in the painting, giving each a name and unique backstory in line with their depiction. Then she used a cutting-edge multi-agent AI approach to realise real-time simulation and create unique branching storylines, allowing players to navigate their characters through the Northern Song’s turbulent final years. Succeed, and you can play again as another character. Fail, and your storyline ends.

    “Too much of history is about elites,” she says. “99% of individuals are absent from history. They aren’t the main characters, but they are important.”

    The start screen of Into the Painting, an AI-driven game

    AI-Driven

    Professor Chen says it was important that “Into the Painting” be not just AI-powered – referring to the use of AI tools in the creation process – but also AI-driven.

    That means the storylines are truly branching, with nearly limitless options available to players depending on their choices. In this, “Into the Painting” is a genuine step forward from previous experiments like “Stanford Town,” which saw Stanford researchers populate a town full of generative AIs to study how they would interact. 

    Professor Chen experimented with similar ideas, creating a sandbox to simulate debates among various Chinese literati. 

    99% of individuals are absent from history. They aren’t the main characters, but they are important.

    – Professor Chen

    But she harboured dreams of something bigger; even she was frustrated with the model’s limitations. “One thing about agent-based modelling is that you can’t always interact with it,” Professor Chen explains. “You can only observe.”

    Her desire to change this paradigm led her to develop FISH – a Framework for the Interactive Simulation of History. Combining her previous experience at Tencent and her research, she wanted to push the boundaries of AI-based modelling to allow players to interact with the models, not just observe them.

    “I don’t want ‘Into the Painting’ to just be a single, narrow game,” she says. “I want to develop it into a framework, creating general principles that anyone can use to develop interactive simulations of historical periods.”

    “The thing about AI-driven games is that they are truly unlimited,” she adds. “You can have hundreds of different endings. It’s all open.”

  • How AI Is Rewriting the Rules of Art Conservation

    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.” 

    Professor Marc Walton explains his approach to AI and art conservation to students

    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. 

    Dr Kenneth Ng inspects one of the lab's instruments

    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. 

    A student uses an AI-powered tool to perform analysis on a sample work of art.

    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:

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

    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
    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
    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
    Professor Wang poses on the HKU campus
  • Jin Wu Wants the World to See the Forest for the Trees

    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
    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.
    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 stands in front of a whiteboard
    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.”

    A whiteboard in Professor Wu's office with advice for his students.
  • Reimagining the Future of Work with the Centre for AI Management and Organization

    Reimagining the Future of Work with the Centre for AI Management and Organization

    “My hope is that when people think about issues like AI and organisation, they think about CAMO.” 
    — Professor Jin Li

    When Professor Jin Li launched the Centre for AI, Management, and Organization (CAMO) earlier this year, his immediate goal was filling a gap in the scholarship on artificial intelligence and its impact on the future of work.  

    But Professor Li’s long-term vision is even more ambitious: Transforming the University of Hong Kong into a global leader in AI, Management, and Organisation research, positioning it at the forefront of a revolution that will shape the next half-century of work and corporate governance. In his own words, he hopes the centre will contribute the “grand ideas” and frameworks societies need to navigate the AI transition.  

    Drawing on the combined expertise of faculty from across the university – plus a board of advisers featuring members from the University of California, Berkeley, Columbia, the Massachusetts Institute of Technology, the University of Tokyo, and the London School of Economics – CAMO is well on its way to reaching that lofty goal. We asked Professor Li about what drew him to AI, how artificial intelligence is changing the future of work, and what’s next for CAMO.  

    The Pursuit of Happiness 

    When Professor Li was a young student growing up in Shanghai, his school emphasised that the “purpose of life is the search for excellence.” A top student at one of Shanghai’s best schools, Professor Li’s only weak subject was chemistry. The harder he worked and the more he struggled, the more he began to wonder whether there was more to life than excellence – that perhaps the real purpose of life was the search for happiness. 

    He found his answer in an introductory economics class at the California Institute of Technology (CalTech). As his professor explained that individuals maximise utility, he remembers thinking that this seemed an awful lot like maximising happiness. 

    “Now is the best time to study AI.” 
    — Professor Jin Li

    Although not directly involved in AI research at the time, a number of his friends and classmates would go on to play pivotal roles in an earlier wave of the AI revolution, and their work sparked his own interest in the field. Now, as he enters what he calls the “second curve” of life, he sees AI research as both important and a way to pursue a topic that has always interested him. 

    Out of the ‘Stone Age’

    Professor Li is fond of a famous line from Edward O. Wilson, in which the American biologist notes that we live in a world of “Palaeolithic emotions, medieval institutions… and god-like technology.” He envisions CAMO as contributing to the upgrading of those institutions to keep pace with technological change.  

    As an example, he points to the way many firms have struggled to update corporate governance and workplace norms for the AI age. Previously, the ideal firm was mid-sized: big enough to achieve economies of scale, but not so big that it becomes bogged down by bureaucracy – an example of what economists call the “U-shaped” relationship between size and efficiency. 

    “As economists, as management strategy scholars, we don’t have that much to do with technology. We cannot change human nature either. What we can do is to think about new organisations, new institutions.” 
    — Professor Jin Li

    Now, however, the AI boom has helped fuel the rise of both unicorns and tech giants like FAANG (Facebook, Apple, Amazon, Netflix, and Google). That tectonic shift has caught many companies off-guard, with CEOs and even consultants unsure of how to adapt. “Firms need to have a playbook,” says Professor Li. “They need to have a framework for how to move forward.” 

    The Future of Work 

    To help write that playbook, CAMO has already surveyed more than 100 C-suite leaders and 500 HR reps as it works on a practical guide for companies navigating the AI transition.  

    One of the centre’s current points of emphasis is identifying the jobs humans don’t want to do, helping firms to decide on automation priorities without exacerbating popular fears of AI “replacing” workers. This approach is also what sets CAMO apart. “There are very few centres that focus on organisation,” Li says. “It may sound cocky, but I don’t think there are more than three to five institutions in the world with the same calibre of people as us.” 

    In addition to laying the “intellectual foundation” for the study of AI and the future of work through his research at the centre, Professor Li is also hard at work on a new book about “The Great Compression.”  

    “I like to call incentive and knowledge the ‘yin’ and ‘yang’ of AI.” 
    — Professor Jin Li

    The access to knowledge promised by AI has spawned new incentives – to cheat, to game the system, to “shirk” – all of which managers must understand and learn to spot. Professor Li’s book will explore these two sides of AI, as well as their combination, which he identifies as “power.” In the process, he hopes to help managers better navigate both the opportunities and risks of the AI era. 

    If all that seems daunting, Professor Li would likely agree. When asked about the biggest challenges he’s facing, he replies quickly: “Time.” There are so many interesting and exciting potential projects, he explains, but the CAMO team must be selective and focused in its priorities. “It’s such an exciting time that I’m not getting enough sleep,” he says with a wry smile. “But now we’re being bombarded with so many interesting possibilities.”