Hao Feng, SEAS Masters in Scientific Computing, 2024
This summer at DataLynn, I was part of an adventure that was more than just an internship. Engaging with the future of AI education and products, I found myself both as a learner and a creator, working on innovative concepts that are set to redefine how aspiring AI professionals train and prepare for their careers.
One of the crowning achievements of our team was the development of intricate interview challenges. Utilizing models like ChatGPT and questions from tech giants such as Amazon, Google, and Meta, we sculpted scenarios that were at once challenging and profoundly reflective of the industry’s demands. The tightrope walk between complexity and clarity was an intellectual dance, where every detail, from business scenarios to column names, had to be perfectly aligned.
The data challenges, however, were a highlight that stood apart. These were not mere exercises or simulations but a comprehensive project that merged academic rigor with real-world applications. It was like orchestrating a symphony where the music was data, and the notes were challenges intricately crafted to mirror the very pulse of industry requirements.
We were building not just questions but entire course-like structures that encapsulated real-world datasets. These challenges were designed to test data analysis and ML skills in a way that mirrored actual industry tasks. The complexity of the problems, the innovative solutions they demanded, and the practical experience they offered were akin to academic projects with the excitement of real-world applicability.
It wasn’t just about creating a challenge; it was about educating, innovating, and inspiring. Every question had to be a learning journey, every solution a step towards understanding a deeper aspect of data science. Whether it was dealing with 2-3 data tables or ensuring the sufficiency of data for solving questions, the task was monumental, exciting, and deeply satisfying.
Guiding interviewees through the solution code by providing core codes as blanks was another novel experience. It was more than testing knowledge; it was an educational endeavor, creating a path that guided learners through the complexities of the problem and allowed them to reconstruct the solution. It was learning, teaching, and testing all woven into a single, fascinating process.
Receiving appreciation from investors like Zhenge Fund was a thrilling climax, a validation that our innovations were not confined to the theoretical realm but had tangible market value. It was more than just recognition; it was an affirmation that we were part of something that could significantly influence AI education.
Reflecting on my time at DataLynn, I see it as a transformative experience, a blend of academia, innovation, and real-world excitement. The challenges were puzzles, the solutions were art, and the entire process was an education in itself. It was a summer that challenged me, inspired me, and left me with an insatiable curiosity and an expanded vision of what AI can truly achieve. It was a summer where I was not just an intern but a creator, a thinker, and a part of the future.
This is part of a series of posts by recipients of the 2023 GAPSA Summer Internship Funding Program that is coordinated by Penn Career Services. We’ve asked funding recipients to reflect on their summer experiences and talk about the industries in which they spent their summer. You can read the entire series here.
