Success Stories

11 Success Stories: How Students in Tech–Humanities Fields Built Standout Applications

Admissions readers evaluating interdisciplinary fields such as linguistics, computational linguistics, or language technology often look for a specific pattern: students who combine rigorous technical work with a clear human purpose. The committee previously noted that applicants in emerging fields stand out when they produce resources other people can use, connect technology with cultural questions, and demonstrate both community motivation and concrete technical output.

The following real admissions outcomes illustrate how that pattern has played out for students admitted to highly selective universities. While their projects vary widely, they show consistent signals that admissions officers tend to reward.

Pattern 1: Building Tools or Datasets That Others Can Use

One common thread among successful applicants in emerging computational fields is that they do not just build a project—they create something reusable by others. Admissions readers see this as evidence that a student understands how research and technology ecosystems actually work.

Arvin R. – Stanford (Computer Science, AI Track)

Arvin built a machine‑learning system that recognized hand signs from images. Technically, the work involved training a convolutional neural network using more than 5,000 labeled images. He then converted the model into a format that could run directly on an iPhone camera using Apple’s CoreML system.

What made the project stand out was not simply the machine learning model itself. Arvin documented the entire pipeline in a public GitHub repository and implemented a continuous integration system that automatically tested updates to his code. That meant other developers could build on his dataset, reproduce his results, and extend the project.

Admissions reviewers often interpret this kind of work as evidence of “research maturity.” The student is not just coding for a class assignment; they are contributing tools that other people can use.

For students interested in computational linguistics, the same principle frequently appears in the form of language datasets, annotation tools, benchmarks, or language-processing models that others can experiment with.

Chen J. – Carnegie Mellon (Cybersecurity)

Chen developed a blockchain-based voting protocol using zero-knowledge proofs. The system allowed voters to verify their eligibility without revealing their identities. The technical components included Solidity smart contracts and a privacy-preserving cryptographic protocol.

What strengthened the application was that Chen treated the project like a real research system. He included a “red team” report where he attempted to attack his own protocol and documented the vulnerabilities he discovered and fixed.

That approach—building a system, testing it, and sharing documentation—is exactly how real research and open technical communities operate. Admissions readers often see that as strong evidence that the student will thrive in a research-oriented environment.

In interdisciplinary areas like language technology, similar projects sometimes take the form of shared corpora, evaluation datasets, or tools that help others analyze language data.

Pattern 2: Mission‑Driven Projects That Connect Technology With Society

Another pattern the committee highlighted is that mission-driven projects—especially those connecting technology with real human issues—often resonate strongly in admissions.

Aisha B. – Harvard (Computer Science + Government)

Aisha built a system that analyzed local court data for potential algorithmic bias. She wrote scripts using Python and Beautiful Soup to collect more than 10,000 public court records. She then analyzed patterns in sentencing using statistical tools such as Pandas and R.

The technical work itself was significant, but what made the project distinctive was its purpose. Aisha presented her findings to her local city council, demonstrating that her analysis could inform real policy discussions.

This combination—technical analysis combined with civic or cultural motivation—is especially powerful in interdisciplinary majors. It signals that the student is not only capable of building technology but also thinking critically about how it affects people and institutions.

In the context of linguistics or computational linguistics, similar mission-driven work often involves language access, translation technology, linguistic preservation, or tools that help communities document or analyze language.

Pattern 3: Demonstrating Technical Depth Through Real Systems

Even when a project has a social or cultural theme, the strongest applications still show real technical execution. Admissions committees want to see evidence that a student can build complex systems, debug them, and iterate.

Liong Ma – MIT (Mechanical Engineering)

Liong designed and built a fully functional desktop CNC mill. The system integrated mechanical components, electronics, and software:

• Custom-machined aluminum structural parts
• NEMA 17 stepper motors controlled by an Arduino running GRBL firmware
• CAD/CAM toolpaths generated in Fusion 360

The most interesting part of his application portfolio was not the finished machine but the documentation of failures. Liong described how he discovered mechanical backlash in the lead screws and implemented software compensation to fix the issue.

Admissions reviewers often respond strongly to this type of documentation because it demonstrates authentic engineering thinking: testing, diagnosing problems, and improving a system through iteration.

Even though Liong’s project was mechanical rather than computational linguistics, the underlying signal is the same: building something technically sophisticated and explaining how it evolved.

Pattern 4: Research‑Style Investigation and Data Analysis

Another path that successful applicants often take is conducting structured research using real datasets.

Rishab Jain – Harvard & MIT (Biomedical Engineering)

Rishab developed a deep learning model designed to improve the targeting accuracy of pancreatic cancer radiotherapy. His model analyzed imaging data to track how organs move during breathing, which can complicate radiation treatment.

He validated the system using a dataset of hundreds of CT scans and demonstrated measurable improvements in targeting accuracy.

What admissions committees notice in projects like this is the full research cycle: identifying a problem, designing an algorithm, testing it on real data, and evaluating the results.

Students pursuing computational linguistics often follow similar paths when analyzing large language datasets or training models that process text or speech.

Pattern 5: Technology Applied to Cultural or Human Questions

The committee also highlighted that projects connecting technology with culture—especially cultural preservation—can be particularly compelling when paired with genuine technical execution.

Admissions officers tend to view these projects as evidence that a student understands both the computational side of the field and the human context behind it.

Successful applicants in this space often:

• Build datasets documenting language, culture, or social patterns
• Create tools that make cultural information easier to analyze or preserve
• Combine programming or machine learning with linguistics or social science questions

What distinguishes strong examples is that they move beyond abstract interest. Instead of simply discussing cultural or linguistic issues, the student builds a technical artifact—a model, dataset, tool, or research system—that addresses the problem in a measurable way.

Why This Pattern Matters for Interdisciplinary Fields

Across these successful applicants—from Stanford to MIT to Harvard—a consistent structure appears:

• A clearly defined problem or question
• A technically rigorous solution
• A tangible output such as a tool, dataset, or research system
• A connection to real human or societal impact

For interdisciplinary majors like computational linguistics, admissions committees often pay particular attention to students who demonstrate both sides of the field. Purely technical projects can be impressive, but the strongest applications often reveal why the technology matters in a linguistic, cultural, or human context.

Students who successfully integrate these elements tend to present applications that feel coherent and purpose-driven rather than fragmented across unrelated activities. That narrative clarity often becomes one of the defining strengths of successful interdisciplinary applicants.