In many scientific careers, there comes a point when a researcher stops chasing answers and starts caring more about the quality of the questions. For Prof. Victor Chang, that moment arrived not in a laboratory or a lecture hall, but a far more everyday setting: watching organisations drown in data they simply didn’t know what to do with. Manufacturers, hospitals, and banks were all generating more information than they could process, and most of them shrugged and moved on. Prof. Victor looked at that same problem and thought: “this is exactly where I should be.”
That instinct has defined everything since. Today, he holds a chair as Professor of Business Analytics at Aston University, ranks among the most cited researchers in his field, and carries fellowships from some of the most respected scientific and professional bodies in the country. The recognition that follows him is not something he has pursued; it is something the work has accumulated on its own. His influence does not stop at the edge of a published paper — it travels forward through every student he has shaped, every collaboration he has seeded, and every system he has helped bring into responsible, trustworthy use.
At his core, Prof. Victor holds a single conviction that runs through everything he does: intelligence, artificial or otherwise, is only worth something when it genuinely serves people. For him, that is not a tagline. It is the architecture of his career.
Rooted in Curiosity: The Early Years
Prof. Victor grew up in a household where asking questions was normal and working hard was simply what you did. Science pulled him in from an early age — not because of the glamour of breakthroughs, but because of the quieter pleasure of tracing how things connect. Why do systems fail? What happens to information when organisations cannot make use of it? Could data, handled with genuine care and intelligence, change real outcomes for real people? These were not abstract puzzles to him. They felt like the right problems to spend a life on.
His undergraduate years gave him the technical foundations he needed. The direction of his career, however, became clear during his master’s and doctoral studies. Working at the intersection of computer science and practical complexity, he arrived at a conviction that has stayed with him ever since: the data generated by people and institutions every day holds genuine potential to improve lives, but only when the systems built around it are designed to be transparent, fair, and trustworthy. That was not a research hypothesis. It was a purpose.
Finding the Gap and Making It His Work
When Prof. Victor entered the field of AI and data science, ambition was not in short supply. What was harder to find, in his assessment, was accountability. Organisations were producing vast quantities of data and deploying systems to process it, but those systems were often opaque, sometimes unfair, and rarely built with any real concern for individual privacy. This bothered him but made him self-reflect – and it clarified exactly where he needed to focus.
The guiding principle behind his research — what he describes as utility without compromise — follows directly from that observation. Artificial intelligence earns its place only when it can be trusted, and trust is only possible when systems are honest about how they work, clear about their limitations, and careful about whose data they are handling and why. This led him to federated learning: a technique that allows AI models to train across distributed, separate datasets without ever pooling the sensitive information those datasets contain. It is a practical answer to a moral question, which is exactly the kind of solution Prof. Victor finds worth building.
Aston University: Building Something That Lasts
When Prof. Victor joined Aston University, he did not frame it as starting something new. He described it, later, as arriving somewhere he had long been working towards. At Aston, he finally had the space to build with permanence — research groups with real scope, cross-disciplinary partnerships capable of operating at scale, and an institutional culture that treated responsible AI not as a footnote to the main research agenda, but as its central concern.
The body of work he has produced there reflects that sense of settled purpose. Alongside an extensive publication record, he has secured grants for long-horizon research, developed UK–Japan partnerships in federated learning, and built research clusters that have drawn talented scholars from across the field. His fellowships span the IET, the BCS, the Operations Research Society, the Institute of Physics, the Institute of Leadership, and the Royal Society for Public Health — affiliations that, taken together, trace the breadth of communities his work is designed to reach.
Recognition Earned Across Different Domains
The awards Prof. Victor has received in recent years stand out not just for their frequency, but for how varied they are. In 2024, he received the UK Inspirational Individual Award from BCS and the wider UK industry. In 2025, he was named Data Leader of the Year at the British Data Awards and recognised for Cybersecurity Initiative of the Year at the Business Awards UK. The business magazine Insightsuccess selected him as an Inspirational Icon to Watch for 2025.
Then, in early 2026, The Enterprise World named him The Most Transformational Professor Advancing AI and Data Science Education. What makes that particular title significant is not its novelty but what it actually measures: lasting influence — the kind that shapes how an entire field is understood by those who will carry it forward. That is precisely the kind of impact Prof. Victor has been working toward for years, and it is, in every sense, fitting.
“Data has enormous potential to improve lives, but only if the systems around it are transparent, fair, and responsible.”
Leadership as Direction, Not Authority
Prof. Victor does not lead from the front because he believes authority belongs to whoever holds the most impressive title. He leads because someone has to know where the group is heading and why it matters — and he takes that responsibility seriously. His approach is collaborative at its foundation: he listens carefully before deciding, and once a direction has been chosen, he commits to it with genuine conviction.
One thing he is firm about: the most corrosive mistake a leader can make is treating endless deliberation as a form of rigour. Consultation matters. Listening matters. But there comes a point when the information has been gathered, the perspectives have been heard, and the only remaining task is to decide. He would rather make a well-considered decision promptly than wait for a certainty that is never going to arrive.
Prof. Victor’s Source of Inspiration
When asked what drives him, Prof. Victor comes back to one word: impact. Not impact as a performance metric or a phrase in a grant application, but the tangible, verifiable kind — a clinician who trusts a tool he helped build, a hospital that can now collaborate across institutions without compromising its patients’ privacy. Those moments are not abstractions for him. They are the reason the work exists.
Intellectual difficulty plays an equally important role. AI and data science are genuinely hard, in the productive sense of the word: there is always more to understand, always new questions emerging behind the ones just answered, always tools developing faster than any single person can fully absorb. That kind of restlessness suits Prof. Victor. He is not someone who settles easily when the problems run dry.
Underneath all of it is a quiet, unwavering belief that the decisions being made right now about how AI is built, deployed, and governed will shape societies for decades. Contributing thoughtfully to those decisions — even in a single corner of one field — is not something he takes for granted. It is, most mornings, the reason he shows up.
“Leadership, to me, is not about authority; it is about knowing where the team is going and why that direction matters.”
What He Would Tell Someone Starting Out
Prof. Victor’s advice to aspiring leaders is not motivational. It is practical, and it comes from experience rather than theory.
- Understand what leadership actually means to you before you find yourself in a position of it. People who step into authority without having thought that through tend to discover their blind spots quickly — and so does everyone around them. Getting clear on your values, and on what kind of environment you want to build, will serve you better than almost any tactical skill.
- Invest in people before you invest in outcomes. In settings where results are what get measured, this feels counterintuitive. It is not. The leaders who build things that last almost always understand that outcomes are downstream of people. Hire carefully. Mentor without holding back. Give honest feedback, even when it is uncomfortable to give and uncomfortable to receive. Build conditions in which talented people can do their best work. The outcomes tend to take care of themselves.
- Develop a real tolerance for uncertainty. Leading in any field that is genuinely moving means making consequential decisions without ideal information, and living with the results. The leaders Prof. Victor has most admired were not those who were always right. They were those who decided well under pressure, learned quickly when things did not work, and refused to let the fear of being wrong stop them from deciding at all.
- Stay close to the work. The higher the role, the more tempting it becomes to manage from a distance — through summaries, dashboards, and other people’s interpretations. Resist that. The details matter. The expertise matters. The credibility that comes from genuinely knowing your field cannot be borrowed or faked. It has to be earned, and kept.
The Work That Remains
Prof. Victor returns, often, to a single guiding idea: build systems that serve people, not the other way around. In a field that can so easily become consumed by its own technical possibilities, that principle requires active discipline. Every model trained, every dataset used, every system deployed — each one has consequences for real people. Keeping those consequences visible, and staying genuinely accountable to them, is, in his view, the most important responsibility that comes with this kind of work.
Technology will advance. That part is settled. What remains genuinely open is whether it advances wisely — whether the people shaping it ask the right questions, build in the right protections, and remain honest about what they still do not know. The gap between technical progress and real responsibility is where Prof. Victor has chosen to spend his career. Looking at everything he has built — the research, the international partnerships, the students he has quietly prepared to carry the field forward — it is difficult to argue the choice was anything other than exactly right.