Vikas Thammanna Gowda

I am an Assistant Professor of Computer Science at Champlain College, Burlington, VT. My research interests include privacy-preserving data publishing, data anonymization algorithms, trustworthy AI, and fairness and bias in AI systems.

Before joining Champlain, I completed my PhD in Electrical Engineering and Computer Science at Wichita State University, where my dissertation focused on methods to achieve t-closeness for privacy-preserving data publishing.

I am looking for motivated students interested in data privacy, trustworthy AI, and machine learning. Feel free to reach out if you’d like to discuss potential collaborations or research opportunities.

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Teaching Philosophy and Practice

Teaching is a deeply personal vocation for me. I grew up watching my father, a retired college dean in India, who demonstrated what it means to treat education as a lifelong commitment rather than a profession. That early influence shaped not only my path into academia but my conviction that a good teacher does not simply transfer knowledge; they shape how a student thinks, questions, and grows. As a second-generation educator, I carry that responsibility seriously.

The courses I most enjoy teaching span the breadth of computing: programming, data structures, databases, parallel computing, data analytics, machine learning, and agentic AI. What connects them is not just technical content but the opportunity each provides to develop rigorous, analytical habits of mind.

What I emphasise in the classroom is process over outcome. Rather than guiding students toward a single correct answer, I encourage them to apply multiple methodologies to the same problem, compare the results, and reason carefully about the differences. This approach reflects how research actually works — messy, comparative, and iterative — and prepares students to think independently rather than follow prescribed steps.

Wherever possible, I bring my own research directly into the curriculum. In machine learning courses, students analyse privacy-utility tradeoffs by comparing classification metrics on original data against multiple anonymized versions, each representing a different point on the privacy-utility spectrum. In computer vision projects, they evaluate how sensor quality and image capture conditions affect model accuracy using a traffic sign dataset I have collected specifically for this purpose. These are not abstract exercises — they are live research questions, and students engage with them as such.

My teachers shaped my path in ways I am still discovering. I hope to do the same for my students.

AI as General Education

Artificial intelligence is no longer a technology of the future — it is the defining infrastructure of the present. It shapes how decisions are made in healthcare, law, finance, education, and public policy. Yet the vast majority of university students graduate without ever being asked to think critically about the systems that will govern their professional lives. That gap is not acceptable, and it is not inevitable.

My work is grounded in a straightforward conviction: AI literacy is not a privilege for computer scientists. It is a right for every educated person. A nursing student who cannot question a diagnostic algorithm, a social worker who cannot recognize bias in a risk assessment tool, a business graduate who cannot interrogate the assumptions embedded in a hiring model — these are not niche failures. They are systemic ones. And they are the direct consequence of a higher education system that has not yet caught up with the world it is preparing students to enter.

This is the driving question behind my research and curriculum work: what does it look like to build AI literacy into the foundation of a university education, across every discipline, at every institution? Not as an elective. Not as a technical add-on. But as a core general education requirement — as fundamental as writing, quantitative reasoning, or ethical inquiry.

Toward that end, I am developing a three-course certificate sequence designed to be discipline-agnostic, stackable, and accessible to all students regardless of major or technical background. The sequence moves from foundational AI literacy — the history, mechanics, ethics, and societal implications of AI systems — through applied competency in the fields where AI’s consequences are most immediate and most consequential. The goal is not to produce programmers. It is to produce graduates who can think, who can question, and who can lead in a world increasingly shaped by algorithmic decision-making. The question facing higher education is not whether AI will change everything. It already has. The question is whether we will prepare every student to meet that change with understanding, critical judgment, and the confidence to engage — or whether we will leave that capacity concentrated in the hands of the few who happened to choose the right major.

I believe in the latter. I am building toward the former.