When World-Class Professors Start Learning Scrum:Scientific Research Has Entered the “Age of Agility”

Recently, two heavyweight scholars showed up in my CSM classroom—and their presence said a lot about where scientific research is headed. The first was Professor Hsing-Chung Chen, Director of the Cybersecurity Center at Asia University. He has been named one of the world’s top scientists for five consecutive years and is a globally recognized figure in cybersecurity, AI, and frontier technologies. The second was Professor Hui-Yu Tsai, Director of the Institute of Nuclear Engineering and Science at National Tsing Hua University—a cross-disciplinary leader whose work spans nuclear engineering, advanced materials, and national-defense technologies.

What struck me most was this: Professor Tsai already holds a PMP certification and has a solid grasp of Agile concepts, yet he still chose to walk back into a Scrum classroom. That tells you he wasn’t looking for “more knowledge”—he was looking for a better research rhythm.

And that’s the common thread between them. Both professors see the same shift: scientific work is accelerating, complexity is rising, and traditional linear methods simply can’t support the next decade of research demands. Scrum might just be the missing key.

The Five Major Challenges Facing Today’s Scientific Research Landscape

Both professors pointed out that today’s scientific research increasingly resembles a high-intensity innovation project, and the challenges are becoming harder to ignore. Among the most pressing issues:

• Research projects often stall for months—sometimes more than half a year—with little transparency into real progress.
• The cycle for validating or correcting a hypothesis is too long, and teams often discover they’re heading in the wrong direction only after six to twelve months.
• Cross-disciplinary collaboration creates significant information gaps that slow down decision-making.
• Student teams rely on their own habits and personal work styles, leading to inconsistent collaboration practices.
• With declining student populations, professors have fewer hands to support research—yet paper output and international-conference KPIs continue to rise.

Amid these challenges, both professors noticed a crucial pattern: the way scientific research actually operates is fundamentally aligned with the iterative logic of Scrum.

• Scrum’s rhythm naturally mirrors the research cycle: Backlog → Sprint → Review → Retrospective

The parallels are striking:

• Scientific Method: Hypothesis → Experiment → Observation → Adjustment

They also realized that Scrum isn’t just a management tool—it’s a way of working that brings scientific research closer to the real rhythm of the world.

Why Is Scrum a Good Fit for Scientific Research?

Professor Tsai shared that she had previously tried introducing some Agile practices in her lab. But because the team only copied the surface-level processes—without a clear research vision or value-based prioritization—her students lost motivation within two weeks. One even told her, “Professor, I don’t want to do this anymore.”
That experience taught her an important lesson: the problem wasn’t the method—it was that the core mindset hadn’t been fully understood.

Professor Chen added that scientific research is never linear. No matter how perfect a Gantt chart looks, it can’t keep up with the pace of real innovation. Scrum gave him confidence that starting with the most tractable problems—while making progress highly visible—creates the momentum a research team needs to keep moving forward.

Taken together, their insights reveal several reasons why Scrum can significantly improve scientific research:

• Establishes a shared rhythm that reduces confusion among students
• Accelerates hypothesis testing so teams don’t spend six months heading in the wrong direction
• Makes progress and data transparent, exposing bottlenecks early
• Reduces miscommunication across labs, departments, or disciplines
• Aligns everyone around a unified Research Vision—a clear understanding of “What question are we actually trying to answer?”

These are precisely the structures research teams need most—yet are often missing in academic environments.

How Can Scrum Be Applied in Research?

After experiencing Scrum firsthand in class, both professors noticed a strong, almost intuitive mapping between Scrum elements and the day-to-day activities of scientific research:

• Product Backlog = Research questions, sub-questions, hypotheses to validate, required data
• Sprint = Experiment cycle, analysis cycle, or model-building cycle
• Daily Scrum = Students syncing daily on progress, blockers, and support needed
• Sprint Review = Presenting new data, models, or preliminary findings to the professor or research partners
• Sprint Retrospective = Reflecting on experiment workflow, collaboration patterns, and sources of errors
• Definition of Done (DoD) = Clear criteria for “when the data is usable” and “under what conditions a research model is ready for submission”

These structured rhythms that Scrum provides enable research teams to:

• Stop moving forward purely by intuition
• Avoid being slowed down by hidden bottlenecks
• Reduce rework caused by unclear or opaque information
• Drive progress based on evidence, not wishful expectations
  

Reflections from Top-Tier Professors After Completing the CSM Course

Professor Hsing-Chung Chen, Director of the Cybersecurity Center at Asia University — Reflections After the CSM Course

I want to extend my sincere thanks to Roger for giving me this rare opportunity—to show up with genuine curiosity and a learner’s mindset. After two full days, I can truly say I’m walking away with far more than I expected.

I am now even more convinced that Scrum is an exceptional framework, especially for research and innovative product development. In the past, our scientific projects relied heavily on “waterfall-style management,” complete with carefully drawn Gantt charts and neatly arranged stages. But the path of innovation has never been linear.

Several years ago, I began experimenting with different ways to drive projects, and the results were surprisingly positive. This CSM course reinforced my belief: the spirit of Scrum aligns perfectly with the real challenges of scientific research. When we are tackling problems the world has not yet solved, the most practical approach is to break the colossal challenge into a hundred smaller questions—starting with the quickest wins that build confidence, momentum, and visible progress.

This is exactly the essence Roger emphasized in class: simplify the complex, create structure, and help teams move forward steadily while learning continuously. I wholeheartedly agree, and I’ve seen these principles work in practice.

Finally, I want to thank Roger once again. His passion and professionalism were evident in every moment. He seamlessly blended theory, hands-on exercises, and game-based learning, turning the course into not just a transfer of knowledge but a true shift in mindset. It’s clear that behind this is decades of teaching and real-world experience—refined into something that can deliver deep impact in just two days.

Thank you, Roger. I hope every participant continues using Scrum to improve processes, increase efficiency, and deliver greater value—wherever we each apply our craft.

My sincere gratitude once again. I look forward to staying connected and continuing to grow together.

Professor Hui-Yu Tsai, Director of the Institute of Nuclear Engineering and Science at National Tsing Hua University — Reflections After the CSM Course

I truly enjoyed learning alongside everyone over the past two days, and I’m deeply grateful to Roger for his clear explanations and thoughtful guidance throughout the hands-on activities. During the APP-design exercise, I was genuinely excited—because it was the first time I had ever completed an entire Scrum process from start to finish. That experience alone gave me a much deeper understanding.

Before this course, I had actually tried introducing Agile practices in my own lab. But looking back, the attempt didn’t go very well. The real issue was not the method—it was that our goals and value priorities were never clearly defined. Every action that follows should logically stem from those early decisions, and that was exactly what I had overlooked. At the time, we were simply following the motions. After two weeks, one of my students even told me, “Professor, I don’t want to participate anymore.” In hindsight, that happened because we were practicing only the form, not the essence.

Roger mentioned that not many people in academia use Scrum—and that made me think, “Then maybe I should try it and see if it can boost our lab’s productivity.” Our KPIs are straightforward: publish papers and attend conferences. So if Scrum can help us work more efficiently, I’m very eager to explore it.

Previously, what I learned felt more like the “shape” of Scrum. I knew the steps, I knew the processes, and I knew how to pass the exams. I even read the Scrum Guide and understood the words. But I had never truly done it. Over the past two days, I finally realized something important: every word in the Scrum Guide carries depth and intention. It may look concise, but behind each line is a meaningful principle. It’s very similar to scientific research—we must learn to read the value behind the text.

Looking back, I regret not approaching it this way earlier. But moving forward, I will shift from practicing Scrum as a “procedure” to practicing it with real understanding.

Thank you, Roger, and thank you to everyone in the class. I gained far more than I expected, and I look forward to applying what I learned.

The Future of Agile Science

They believe that scientific research may evolve in several important ways:

• Research output will become more consistent — and significantly faster
• Students will show greater ownership, initiative, and accountability
• International collaborations will become more efficient and less time-consuming
• Cross-disciplinary integration will flow more smoothly
• Agile will become a core competency for the next generation of researchers
• Labs will be able to identify breakthroughs every two weeks instead of every few months

These emerging trends will accelerate the rise of Agile Science as a new methodology for scientific research.

Conclusion

Scrum has never belonged solely to software development or corporate product teams—it is a framework for navigating complexity and the unknown. When scholars on the front lines of scientific research begin adopting it, experimenting with it, and believing in it, research transforms from “slow and meticulous craftsmanship” into a fast-iterating, high-quality learning cycle.

The wave of Agile Science in Taiwan has already begun. And the way research labs operate in the next decade may look entirely different from what we see today.