Computing’s Top 30: Li Yang

By IEEE Computer Society Team on

Li Yang is one of our "Computing's Top 30 Early Career Professionals" for 2025. This program seeks to highlight an esteemed group of rising stars who earned this honor for their exceptional early-career achievements and role in driving advancements across the computing landscape. 

Introduction

Hello, my name is Li Yang. I am an Assistant Professor in the Faculty of Business and Information Technology at Ontario Tech University in Canada, and I also serve as an Adjunct Research Professor at Western University. My research focuses on developing trustworthy, autonomous, sustainable, and secure AI, with particular emphasis on cybersecurity, IoT environments, and future communication networks.

What inspired you to pursue a career in technology?

I was inspired to pursue technology because it offers a rare combination of creativity, rigor, and tangible impact. I have always been interested in how complex systems can be designed to solve meaningful problems, but what made computing especially exciting was its ability to turn ideas into tools that can directly shape the world around us. Whether in communications, transportation, health, or public infrastructure, technology has the power to improve both efficiency and resilience when it is designed well.

As my research interests developed, that broad interest became more focused. As AI began to play a larger role in networks and connected systems, I became increasingly aware that intelligence alone is not enough. A highly accurate system is not necessarily a trustworthy one. If it cannot withstand attacks, adapt to changing environments, or operate under real deployment constraints, then its usefulness becomes limited, especially in security-sensitive settings. That insight strongly influenced the direction of my work.

That realization led me toward trustworthy, autonomous, sustainable, and secure AI. I became interested not only in improving model performance, but also in addressing robustness, adaptability, efficiency, explainability, and security. In many cases, advanced machine learning remains difficult to adopt because of the expertise and trial and error required to build good systems. Through automation and AutoML, I saw a path toward reducing the manual burden of model development and lowering the barrier for more researchers and practitioners to use AI effectively. For me, technology is most meaningful when it combines scientific innovation with reliability, openness, and real societal value.

What do you consider your highest achievement so far? How do you plan to continue or build on that success?

If I had to identify one achievement that matters most to me, it would be the development of a clear and distinctive research program around trustworthy, autonomous, sustainable, and secure AI for cybersecurity and intelligent infrastructure. What matters most to me is not any single paper or award in isolation, but the fact that my work has helped build a coherent direction that connects foundational AI and machine learning optimization with practical security challenges in real systems.

I am grateful that this work has received strong recognition. Being selected as one of the IEEE Computer Society’s Computing Top 30 Early Career Professionals for 2025 was a particularly meaningful milestone, because it reflects both technical contributions and broader field-level impact. I was also honored to be named in Stanford and Elsevier’s World’s Top 2% Scientists list for 2024 and 2025, and to receive a Best Paper Award at the AutonomousCyber workshop at ACM CCS 2024. In addition, my work on machine learning optimization has had broad international influence, with my research in this area becoming widely cited and recognized across the field.

Looking ahead, I want to build on this success by moving further from methods to systems. That means developing AI that is not only optimized in theory, but also deployable, adaptive, and resource-aware in environments such as EV charging infrastructure, 6G network automation, space systems, and other cyber-physical settings. I also want to expand my impact through mentoring, interdisciplinary collaboration, and open-source tools that help others adopt trustworthy AI more effectively and responsibly.

How are you currently involved in the tech community aside from your job (volunteering, open-source projects, mentoring, etc.)?

My involvement in the tech community extends well beyond research and teaching, because I believe that advancing a field also requires helping to sustain its broader ecosystem. In my case, that includes editorial service, conference participation, open-source development, volunteering, and mentoring.

A substantial part of my contribution is through research service. I serve as an Associate Editor for leading IEEE transactions and review papers for a wide range of leading journals and conferences. This work is demanding, but I value it because it helps support quality, fairness, and thoughtful evaluation in areas that are evolving rapidly, particularly AI and cybersecurity. It also keeps me engaged with new ideas and emerging technical challenges across the field.

I am also active in professional societies and conference communities. I have participated in technical program committees, chaired sessions, delivered invited talks and tutorials, and supported IEEE chapter activities, including public events and seminars. These roles have allowed me to contribute not just to research exchange, but also to the cultivation of a broader professional community.

Another important part of my engagement is open source. I maintain more than a dozen publicly available repositories tied to research in machine learning optimization, AutoML, and intrusion detection, which have received more than 3,000 GitHub stars. What has been especially rewarding is hearing from researchers and practitioners who apply these tools in their own work. I also devote significant time to mentoring students and early-career researchers, helping them strengthen not only technical skills, but also writing, presentation, and research judgment. For me, community involvement is most meaningful when it helps create lasting capacity in others.

Is there any emerging technology or industry segment you find exciting or interesting?

I am especially excited by the convergence of trustworthy AI, autonomous cybersecurity, and intelligent infrastructure. One of the most important emerging directions, in my view, is trustworthy autonomous cybersecurity. As digital systems become more complex and threat environments become more dynamic, we can no longer rely on security workflows that are primarily manual, reactive, and difficult to scale. We need AI-driven security systems that can monitor continuously, adapt to new behaviors, detect drift, and support fast decision-making with much less human intervention. What makes this area especially compelling is that it brings together multiple dimensions of AI research, including automation, robustness, continual adaptation, and operational accountability.

I am also very excited by TinyML and edge intelligence for security-sensitive environments. In many real deployments, centralized AI is not always practical or desirable. Edge-based intelligence can improve latency, privacy, and resilience, especially in IoT and cyber-physical systems. Designing models that are accurate, lightweight, and secure enough for deployment on constrained devices is both technically challenging and highly consequential.

Another emerging area I find very compelling is zero-touch security for future 6G networks and intelligent infrastructure. These systems will depend on AI for orchestration, optimization, and protection, which means security and automation must evolve together. Beyond networking, I think sectors such as EV charging, radiation detection, and space systems are especially exciting because they require AI methods that balance intelligence with safety, resilience, and real operational constraints. Across all of these domains, the key challenge is the same: how to build AI systems that are not only capable, but truly trustworthy in environments where failure is costly.

What advice would you give to young professionals or recent graduates who are trying to enter your field?

My first advice would be to build strong fundamentals before specializing. AI and cybersecurity move quickly, and it is easy to be drawn toward whatever is currently popular. But long-term growth usually depends on having a solid grounding in machine learning, systems, networking, security principles, and statistics. Once that foundation is strong, it becomes much easier to move into popular research areas in a meaningful way.

I would also strongly recommend developing good research and engineering habits. Be careful with experimental design. Document your work clearly. Share code when appropriate. Reproducibility is not just an academic ideal. It improves credibility, accelerates your own learning, and helps others build on what you have done.

Most importantly, choose a direction that has meaning to you. In my own case, trustworthy and secure AI has been compelling because it combines technical challenge with real societal importance. If you focus on problems that improve safety, resilience, and trust, your motivation will be stronger, and your work is more likely to create lasting value.

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