Building an Interdisciplinary Research Career: What 15 Years Taught Me
From IT system architect to cloud computing researcher to AI healthcare researcher — with detours through linguistics, education science, and urban planning. An honest account of what interdisciplinary research looks like from the inside: the advantages, the costs, and whether it was worth it.
The Career That Refused to Stay in One Lane
When I tell people about my academic background, I usually watch a particular expression cross their faces: a slight frown, a pause, then something between admiration and concern. A PhD in Information Systems. A second PhD in progress in Education Administration. Master's degrees in Information Systems, Educational Science, Chinese Language Teaching, Smart City and Innovation, and TESOL. Bachelor's degrees in IT, Chinese Language, English Linguistics, and Business Law.
The reaction is understandable. In contemporary academia, specialization is the dominant value. Departments are organized around disciplines. Grant funding flows to well-defined research tracks. Journals have scope statements that guard their territories carefully. Hiring committees look for candidates who can be described in a sentence.
An interdisciplinary researcher can be described in a sentence — but the sentence keeps getting longer.
This essay is not a manifesto for interdisciplinary research. It is an honest account of what this kind of career actually looks like from the inside: the unexpected advantages, the real costs, the specific skills it requires, and the questions I still do not have answers to.
How It Started: The Architecture of Accident
I did not set out to build an interdisciplinary career. I set out to be an IT system architect — and was reasonably good at it.
The turn toward academia happened gradually. Working on enterprise systems in the early 2010s, I kept running into the same problem: the systems we built were technically sound but organizationally fragile. They failed not because of bugs or performance issues, but because they did not fit the human systems they were supposed to support. People used them in ways the designers had not anticipated. Organizations resisted changes the systems demanded.
This pulled me toward management information systems, then toward organizational behavior, then toward education (how do you train people to use complex systems?), then toward linguistics (how do people communicate about technical systems?). Each question led to an adjacent field, and each adjacent field opened new questions I had not thought to ask before.
The Master's in Information Systems came first — a natural extension of my professional background. The Master's in Educational Science came because I started teaching and discovered I had no formal understanding of how learning actually works. The Master's in Chinese Language Teaching came because I was already learning Mandarin and realized the pedagogy of language learning was a field unto itself. The PhD in Information Systems was the moment I committed to research as a primary identity rather than a parallel track.
Looking back, none of this was planned. It was a sequence of genuine curiosities, each pulling me into the next territory.
What Interdisciplinary Research Actually Looks Like
There is a popular image of interdisciplinary research as intellectually glamorous: brilliant minds synthesizing insights across fields, connecting dots that specialists could not see. This happens. But the daily reality is less cinematic.
The Translation Problem
Every discipline has its own language — not just jargon, but deeper assumptions about what counts as evidence, what questions are worth asking, how results should be reported. When you work across fields, you spend an enormous amount of time translating: not just terminology, but epistemology.
Early in my research on cloud computing efficiency, I began incorporating insights from organizational management literature. The engineers on my team had no patience for what they saw as vague, unfalsifiable claims. The management researchers I approached found the engineering papers impossibly narrow. Getting both communities to recognize that the other was doing real and relevant work took years of active bridge-building.
This translation work is largely invisible in publications. Papers present clean, synthesized arguments. They do not show the months of miscommunication, the rejected collaboration proposals, the reviewers who complained that the paper did not fit the journal's scope, the seminar talks where half the audience was baffled and the other half was frustrated.
The Credibility Problem
In any individual discipline, credibility is established through a recognizable set of signals: publications in prestigious journals, citations, committee memberships, successful grant applications. An interdisciplinary researcher has a fragmented credibility profile.
Within information systems, I am recognized as a researcher who brings unusual perspectives from education and linguistics. Within education science, I am recognized as someone who brings technical expertise. Within linguistics, I am a practitioner-researcher with domain applications.
But I am not, in any of these fields, the kind of deeply specialized expert who sits at the top of a single disciplinary hierarchy. Interdisciplinary researchers often find themselves evaluated as "not quite" by specialists in each field they touch.
The practical consequence: grant applications must be tailored carefully to the identity of each funding body. Publications require more explicit justification for their methodological choices. Hiring committees sometimes do not know quite what to do with a CV that looks like mine.
The Depth-Breadth Tradeoff
The honest version of the depth-breadth tradeoff is more complicated than it sounds in academic career advice.
Breadth is genuinely valuable. Working across fields gives you pattern recognition that specialists often lack — you can see when a problem that seems novel in one field has been studied extensively in another. You can import methodologies, theories, and empirical findings across disciplinary boundaries. You can identify collaboration opportunities that disciplinary specialists would not see.
But depth matters in ways that are easy to underestimate. When I began applying machine learning to smart city research, I had to develop genuine competence in the relevant ML methods — not just a surface familiarity sufficient to misapply them. When I moved into medical AI research, I spent two years reading medical literature intensively before I felt confident designing studies with clinical partners.
The breadth is real, but it takes significant investment to avoid the failure mode of shallow generalism: applying the vocabulary of multiple fields without the depth in any of them to do rigorous work.
The Specific Skills That Made It Work
Looking back, a few capabilities proved consistently important across all my interdisciplinary projects.
Genuine Curiosity About Other People's Problems
This sounds obvious but is actually not universal among researchers. Many academics are primarily interested in their own research questions and treat adjacent fields as potential sources of methods or data. Genuinely interesting interdisciplinary work requires actual curiosity about the problems and concerns of neighboring disciplines — not just their tools.
When I began working with dentists on AI diagnostic tools, I spent three months attending clinical sessions and doing nothing but asking questions. What does your actual workflow look like? Where are the friction points? What would you trust automated systems to do, and what would you never trust them with? This listening phase was not efficient. But it produced research that the clinicians found relevant — because it started from their actual problems rather than from my prior assumptions about what they needed.
Language Learning as Metacognitive Training
This is the most unusual skill on my list, and the one I am most confident matters.
Learning Mandarin Chinese as an adult — seriously, to the level of academic proficiency — taught me things about how language and thought interact that I could not have learned any other way. It forced me to hold two different grammatical structures, two different writing systems, and two different cultural contexts simultaneously in mind.
I believe this experience made me better at the translation work that interdisciplinary research requires: the ability to genuinely inhabit another conceptual framework rather than just mapping foreign concepts onto familiar ones. Whether this generalizes beyond my specific case, I cannot say with certainty.
Writing for Multiple Audiences
The ability to write about the same research in ways that are accessible and compelling to different communities — without being condescending or losing rigor — is a skill that requires deliberate practice.
I write differently for medical AI journals than for computer science conferences, differently for education policy audiences than for engineering meetings, differently for graduate students than for established researchers. This is not code-switching in the superficial sense of adjusting vocabulary. It requires genuinely understanding what each audience cares about, what arguments they find compelling, and what prior knowledge they bring.
Developing this skill took years of rejection — papers that fell between disciplinary stools, talks that left half the audience cold — before I developed a better intuition for audience alignment.
What I Would Tell My Younger Self
If I could speak to the person who was just starting this path in 2010, here is what I would say.
Find collaborators who complement rather than overlap.
The most productive research partnerships I have had were with people whose expertise was genuinely complementary to mine — clinicians who knew medicine deeply but little about AI, linguists who knew language acquisition but nothing about computational methods, urban planners who understood cities but wanted quantitative modeling tools. These partnerships produce things that neither party could have produced alone.
Partnerships with people who have exactly your profile are comfortable and efficient. They rarely produce breakthrough work.
Publish in the communities you are trying to join, not just the ones you already belong to.
Early in my career, I published almost exclusively in information systems venues — the community where I had established credibility. Entering new communities required submitting to their venues, accepting their review processes, and learning their standards through rejection.
This is uncomfortable. Rejection rates at top venues in unfamiliar fields are high, and your work is evaluated against a standard you may not fully understand yet. But it is the only way to build genuine credibility across disciplinary boundaries rather than just claiming it.
Protect some long-horizon thinking time.
Interdisciplinary research is particularly vulnerable to the short-term pressures of academic life: grant cycles, publication quotas, administrative demands. The synthesis work that gives interdisciplinary research its value — the slow accumulation of understanding across fields — requires sustained, unhurried thinking.
I did not understand this early enough. Some of my most valuable intellectual connections were made not during focused research sprints but during periods of deliberately unstructured reading and reflection.
The question of whether it was worth it is the wrong question.
People occasionally ask whether I regret the unconventional path. I understand why they ask — the costs in terms of disciplinary recognition and career legibility are real, and I have paid them.
But I think the question frames the choice incorrectly. My research is more interesting to me because of its breadth. My teaching is richer because I can draw on multiple disciplines. My understanding of complex problems — healthcare systems, city governance, language learning — is genuinely deeper because I have studied them from multiple angles.
Worth it according to what metric? The metric that matters to me is whether I am doing work that is real, that I care about, and that contributes something that would not exist without me. On that metric, the answer is yes.
A Note on the Vietnamese Academic Context
Building this kind of career in Vietnam comes with specific challenges that deserve honest acknowledgment.
Vietnam's research infrastructure is developing rapidly, but resource constraints are real. Access to high-quality datasets, computational resources, and international collaboration opportunities is more limited than in the research environments that produce most of the interdisciplinary research literature.
The pressure to publish in high-impact international journals while simultaneously meeting local administrative obligations creates time pressures that can be genuinely difficult to manage.
And the interdisciplinary vision itself — working across departments, combining methodologies from different traditions — requires institutional support that is not always present. Departments are organized around disciplines. Evaluation systems reward disciplinary productivity metrics.
None of this is insurmountable. But it requires more deliberate institutional navigation than the same career would require at a well-resourced Western research university.
The advantage, which I want to name explicitly, is that working in this context gives access to research problems that are genuinely underexplored. The challenges of applying AI to Southeast Asian healthcare systems, of developing NLP for Vietnamese language, of understanding how smart city technologies function in rapidly urbanizing developing cities — these are important problems that receive far less attention than their significance warrants.
The research gap is also an opportunity.
Closing Thought
The career I have built looks, from the outside, like a collection of disparate credentials. From the inside, it feels like a single sustained inquiry into the same question posed from multiple directions: how do complex systems — technological, social, linguistic, organizational — interact with human beings, and how can we design those interactions better?
That question is genuinely interdisciplinary. Answering it requires moving across fields. The costs are real. So are the rewards.
Dr. Lê Ngọc Hiếu (Hao Lee) · Lecturer & Researcher · Van Hien University (VHU), PTIT, HCMUTE · Ho Chi Minh City, Vietnam · occbuu@gmail.com
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