Practical experience and industry connections: these are some of the reasons why Dyanna Rivera chose to pursue a Bachelor in Computer Science and Artificial Intelligence at IE University.

Born and raised in Seattle, Dyanna has had quite a journey. Aspiring to apply machine learning to the medical field and fintech after graduation, her entry into the tech world didn’t start with coding Bootcamps or modifying Minecraft. Instead, coming from a visual arts background yet fascinated by data, it was a high-school friend who encouraged Dyanna to teach herself coding essentials and research technology for climate change. In the end, she enrolled at IE University in love with data analysis

In a recent episode of the #GoingtoIEU podcast, Dyanna told us about these changes of direction and geographies and offered her insights into what the future might look like. She describes herself as a “jack of all trades,” and is clearly enthused by her passion. “The massive exchange of information gets me really excited,” she explained. Some of her other hobbies include photography, running to de-stress for exams, in addition to writing nonfiction essays about technology, culture, and underrepresented minorities throughout history.  

Beginning her course in Segovia and currently studying in Madrid, Dyanna’s journey has given her the best of both worlds. “I got a nice blend of that really small-town vibe, as well as the really big metropolitan vibe that you get from Madrid—it’s been pretty nice.” 

Dyanna chose IE University based on its practical approach paired with its theoretical rigor. Exemplifying this was the Pier 17 project, the Advanced Incubator developed as part of her program, which is modeled on NASA’s Technology Readiness Levels (TRL) phases. In applying these standards to developing new technologies, Dyanna used the same protocols that experts use for ideation, research and development, as well as implementation. 

Setting the bar high 

The Pier 17 project meant Dyanna was thinking about what would ultimately become her Capstone Project right from the start of her studies. One of the initiative’s objectives is that, before the fourth year ends, students have a prototype product ready to market in the real world. This ambitious target was something that immediately caught her attention. 

“For me, that was one of the biggest selling points when I came to IE University,” said Dyanna about eventually graduating with a marketable prototype. “So far, it’s been a wild ride.” 

Following an unsuccessful TRL phase in year one, Dyanna had to change her proposal as a second-year. So she decided to study the technological transformation of the medical sector. Intrigued by neurology and Alzheimer’s research, she wanted to examine how artificial intelligence is streamlining the diagnostics process. So Dyanna began developing a tool to detect early-stage cognitive abnormalities from samples of a person’s speech. Based on the manner of speaking, the artificial intelligence will alert doctors in case of a potential complication. 

“The idea is this algorithm would pick up on small details that the human ear might not detect right away, and these details could indicate abnormalities that could apply to a range of different diseases or disorders,” explained Dyanna. “So, it doesn’t necessarily have to be Alzheimer’s—it could be Parkinson’s, it could be depression, anxiety, a speech impediment, but the idea is that it gives doctors an insight early on.” 

Although Dyanna was highly successful academically in high school, she said that everything she learned during her first year at IE University positively challenged her foundation. And she had some candid advice for future students at IE University: “This is going to sound like tough love, because it is, but if you think you know how to study, you probably don’t.”  

In the end, she realized that her demanding high-school courses didn’t actually prepare her for university studies. Instead, she learned that effective study ultimately depends on the subject; Dyanna advises students to adapt their routines to the respective course. 

Challenging stereotypes in the STEM field 

Dyanna says that being a woman in STEM does present some challenges. Even in high school, she was one of only three young women in the computer science class. At IE University, the diversity of the community means she doesn’t feel she’s treated any differently. However, while at high school, she admits occasionally having felt intimidated being in a room full of male developers who had been coding since childhood. But she strongly believes that women, or any other minority, should be treated no differently in STEM. 

Moreover, in her opinion, low female representation in STEM fields is due to a lack of marketing rather than a lack of interest. “I think the way to get increased diversity in the field is to just allow those doors to be opened and remove your own bias,” she asserted. 

If the subject she’s so passionate about is sometimes regarded as niche, Dyanna believes that machine learning and artificial intelligence could heighten consumer trust in everyday life. Central to this is the potential for technology to improve privacy when sharing data, an issue Dyanna regards as particularly relevant in fintech and the medical sector. She believes that more people will begin to embrace these new forms of technology as soon as they become comfortable with both using new tech and in turn sharing sensitive data. 

Before this happens, normal consumers will need a basic understanding of how encryptions help protect private information. Dyanna asserts that developers need to be effective communicators when explaining complex ideas to others. In her opinion, the ability to make information about technology easily digestible and trustworthy ultimately boils down to expertise, professionalism and transparency. 

Asking the big questions 

“If you’re truly an expert and knowledgeable on a certain subject or program, and you know it like the back of your hand, and you can communicate well, that’s such a powerful combination. Because then you can start to remedy that ‘black box’ issue and be able to explain to people what exactly is happening,”

For the industry to accomplish this, Dyanna emphasizes that developers must be methodical in the conceptual stages. This means asking the big questions first, such as what do we need to make this program function? What is the problem we’re trying to solve? Only from there should the mapping out of every possible scenario begin.  

“You don’t just go straight to coding—don’t ever do that,” insisted Dyanna, before offering some advice for coders. “What you actually do is you look at the problem that you have at hand, and you start writing down the steps of the solution.” Fine advice, which no doubt can be applied much more broadly than in STEM! 

To listen to Dyanna’s story and her insights on life at IE University in full, check out the podcast