Collegify
Student case study
Tanay already knew he wanted CS - what changed was the level of precision behind that choice. Work across public infrastructure, AI and energy systems helped turn a broad interest in computing a sharper academic direction around optimization, reliability and resource-efficient systems and gave Collegify a much stronger question around which to shape the university strategy.
By Collegify · Student journey
Tanay had known for years that he wanted to study Computer Science.
The uncertainty wasn't about the subject, it was about the direction inside it.
Programming, mathematics, AI, energy systems and real-world infrastructure all interested him. What he needed was not another technical activity, but a way to understand which of those interests were beginning to form a serious academic focus.
That became the work with Collegify.
A student’s path is individual. Explore how we work and what support could look like for your own application.
Tanay’s profile already showed strong mathematical ability, long-standing exposure to programming and a natural comfort with structured problem-solving. But one pattern kept appearing across otherwise different experiences: Efficiency.
A school project using sensors to reduce unnecessary electricity use. An interest in optimization and mathematical reasoning. A growing curiosity about how technology behaves inside larger systems rather than only inside a piece of software. Collegify’s role at this stage was not to invent a new theme around him. It was to recognize the one already forming and test whether it could withstand deeper work.
That test became more meaningful when Tanay worked with the Adar Poonawalla Clean City Initiative’s Water ATM programme.
The systems were cloud-connected and designed to support access to safe drinking water. Tanay examined monitoring data, understood how the smart-card and operational systems worked, and saw the technology functioning in the communities using it. That exposure changed the stakes - A faulty system was no longer simply a technical problem. It could affect the reliability of an essential service.
For Tanay, Computer Science began shifting from an interest in what code could build to a deeper interest in how dependable systems are designed and maintained.
Collegify then used that experience as a bridge rather than as a standalone internship story. The question became: what other systems depend on computation becoming more efficient, more reliable and more intelligently designed?
That led Tanay towards research on Artificial Intelligence and energy conservation.
At first glance, the argument seemed straightforward. AI can help reduce energy use across buildings, transportation networks and infrastructure. But the deeper he went, the more complicated the question became. The technology being used to improve efficiency also consumes significant energy itself. That contradiction became the center of the work.
Tanay explored approaches such as model compression, specialized AI architectures and the computational cost of intelligent systems. The research was subsequently published. This was an important academic pivot.
The profile was no longer simply saying: Computer Science + AI.
It was beginning to say something more specific: Computing + Mathematics + Optimization + Resource-efficient systems.
That distinction gave Collegify a much stronger basis for shaping the next stage of the journey.
Once the direction became clearer, the objective was not to keep adding, it was to become more selective.
Advanced study was chosen to strengthen the mathematical and computational foundation behind the emerging interest.
Through The Science Sage, Tanay also worked on explaining computing and programming to younger students, which added a different kind of depth: the ability to reduce technical ideas to first principles and communicate them clearly.
Collegify used each experience against the same question: Does this deepen the academic direction, or merely make the résumé longer?
That filter mattered. It allowed the profile to become more coherent without becoming artificially narrow.
The same thinking carried into programme research. Rather than treating university selection as a ranking exercise, Collegify helped Tanay evaluate where the curriculum could support the direction his work had uncovered. Greater attention went to programmes offering serious depth across algorithms, mathematics, systems, optimisation, AI and interdisciplinary computation.
In some cases, combinations such as Mathematics and Computer Science became more relevant precisely because they matched the way Tanay’s interests were evolving.
The shortlist was no longer simply answering: Where is Computer Science strongest?
It was answering: Where can Tanay study the kind of Computer Science he is actually moving towards?
Tanay did not emerge from the process with a different subject. He emerged with a more precise reason for studying the same one. He had moved beyond: “I want to study Computer Science.”
Towards a more credible academic proposition: How can computational systems become more efficient, more reliable and more responsible in the resources they use?
That's the point where a strong student profile becomes something more useful. Tanay brought the academic strength, technical curiosity and willingness to test difficult questions. Collegify helped recognize the pattern, build around it deliberately and translate it into a clearer academic and university direction.
The result was not simply a better application story. It was a stronger understanding of what Tanay wanted the next stage of his education to be about.
This case study describes one student’s experience, published with supporting evidence and permission. Individual outcomes are not typical or guaranteed; admissions decisions rest with each university.
