On the Search for What’s Next

Ally Flinn ’25 knows it sounds cheesy or made up, but she swears it’s true: Her career path was set at 10 years old. She just didn’t know it then. 

Growing up, Flinn was interested in becoming a doctor, but her focus shifted a bit one day when her mother shared an article on brain-computer interfaces. The futuristic technology that promised to one day link the brain to machines—potentially restoring function to people disabled by neuromuscular disorders—had Flinn dreaming of the impossible through bioengineering.

“I loved the anatomical element as well as the creativity involved,” Flinn recalls. “How it integrated not only the physics and math you traditionally associate with engineering but also biology and chemistry. There were so many different directions you could go.”

In the years that followed, Flinn considered other paths. As a high schooler, she was a three-time “Investor of the Year” in a local competition, consistently unearthing the standout stocks of tomorrow. Numbers made sense to her, so she considered finance and economics before giving medicine another look when she chose the pre-med track at Santa Clara.

But the more she considered medical school, the more it felt too confining.

“I didn’t want to do things the way a textbook said or the way things have been done for the past 10 years for the rest of my life. I wanted to write the textbook, if that makes sense,” Flinn says. “I wanted to create the rules and have the freedom to be creative.

“Looking back, I think biotech was always my plan,” she adds

After switching from a pre-med to biomolecular focus, Flinn found her purpose in a coding class junior year. She joined a research project led by Professor Hamed Akbari that used machine learning to identify patterns in EEG scans that could hopefully diagnose and assess the severity of alcoholism. 

The implications of the research were wide-ranging. Alcoholism is a progressive disease but is rarely diagnosed until the liver begins to fail. If low-cost tests like EEGs could find patterns in brain scans, doctors could offer earlier diagnoses and determine severity, which could open the door for more personalized care. 

Following the study on alcoholism, Flinn continued working with Akbari for her Senior Design project, where she used deep learning to analyze EEGs of schizophrenia patients.

Today, Flinn is focused on raising the bar in healthcare through artificial intelligence. In addition to taking 23 credit hours at Santa Clara this quarter, she works 20 hours a week at Nvidia on the company’s artificial intelligence projects. Following graduation, her part-time role as an auditor will become full-time. 

Flinn recently sat down to talk about the impact of AI on healthcare, managing ethical implications, and what it’s like working in an emerging industry. 

You’re originally from Texas. Why did you decide to come to Santa Clara?

I remember when I visited Santa Clara, the Sobrato Campus for Discovery and Innovation had just been built. I had read all this stuff about student research opportunities and networking at Santa Clara and I just realized I wanted to be in Silicon Valley. It’s funny because now that I’m here, it’s lived up to expectations. I can’t tell you how many times I’ve gone to the dog park and ended up having a conversation with someone who works at a medical device company. I feel like that doesn’t happen anywhere else.

What gets you excited about the potential of artificial intelligence in healthcare?

For me, it’s the potential for earlier diagnosis and personalized treatment. The research I did with Dr. Akbari would allow doctors to use this quick and inexpensive test—one that’s available at any hospital—to identify visual or data patterns that are associated with certain disease states, symptoms, or presentations of any cognitive disease. We analyzed alcohol use disorder and schizophrenia, but the goal is to generalize it across the board. So, hopefully, we’d be able to identify anything from glioblastoma to neurodegenerative diseases or psychiatric disorders. The earlier we have a diagnosis, the faster and more personalized the treatment. That’s important because there’s no one-size-fits-all solution in healthcare, especially when it comes to our brains. 

What would personalized care look like for a schizophrenia patient, for example?

I chose to study schizophrenia because it is one of the most complex and least understood psychiatric disorders. It also has one of the highest suicide rates. Currently, schizophrenia is diagnosed with DSM 5, which only determines whether you are schizophrenic or not. It doesn’t distinguish subtypes. But when you look at schizophrenia patients, there is a wide range of symptoms. We hope our research will help us better understand what’s going on in the brain pathologically and assign severity scores that could, for example, predict how someone might react to medications or dosages. 

Finding the right medication for schizophrenia can be difficult. Unfortunately, a medication that has helped a hundred other patients could make symptoms worse for one patient because their situation or genetics are a little different. As a result, non-compliance with taking medication is high. So, if doctors could predict what medication best suits a patient, it could be life-saving. I think it would also offer a lot of hope for schizophrenia patients and their families, who face an incredibly difficult path. Having a better understanding of what they’re facing could change how we as a society see people who are experiencing these illnesses.

Professor Akbari was your advisor for both of your research projects. How important was he to your academic experience?

Coming from Texas to an area without a family member or friend within 500 miles was incredibly difficult at first. I doubted my abilities and lacked confidence. I felt disconnected from the life I had built over my first 18 years. Dr. Akbari’s class helped me rediscover my love for learning. He saw potential in me I couldn’t see yet. His unwavering mentorship, kindness, and encouragement created a space for me to dream big. And I am far from the only student who feels this way. We all speak so highly of his dedication to students and caring classroom environment. Dr. Akbari is, without a doubt, the most extraordinary educator I have ever had. I cannot thank him enough. 

As you mentioned, AI can be a life-saving force in healthcare. But how do you manage the ethical considerations of AI?

When you look at it, AI comparatively has a low barrier to entry. You just need a computer. That means as the industry grows, there are people who will use it for good and people who will use it in an unethical way. It would be naive to think otherwise. So I think we need to learn as much as we can to understand the implications and get ahead of them.

For me, a big way to mitigate ethical concerns is to engage with people who are not like-minded. For example, I can believe something is ethical or that it doesn’t negatively impact a certain population, but someone with a different perspective could explain how it does. So I think listening to different perspectives and putting those puzzle pieces together is important because I don’t think any one person knows what’s best for everyone. 

Of course, this isn’t the first time humans have dealt with ethical discussions around innovation. The technology we use today was science fiction 100 years ago. Thirty years ago, the Internet was exponential in the same way AI is exponential today. So it’s difficult to say what the world or technology is gonna look like in 15-20 years. However, I will say I like being at the forefront of that in my industry and being a part of this conversation.

Do you think Santa Clara helped prepare you to engage in those ethical conversations?

I do. A lot of my classes had ethics units. I took a two-quarter class called “Technology and the Common Good.” So I spent two quarters focusing on the ethics of technology. My religion classes have also been helpful. Right now, I’m taking a class called “Good vs. Evil, but Mostly Evil” which analyzes classical literature in a modern context. We did a unit on the Greek play Medea and asked, “OK, who’s the bad guy in this story?” “Why did they do what they did?” I think it’s important to examine motivations. If we look at actions in a vacuum, it’s easy to assign labels, but there’s value in considering motivation or a person’s thought process. I credit Santa Clara for confronting me with these ideas because I don’t think I spent much time thinking about it before coming here.

What’s your role at Nvidia?

Currently, I work in quality and annotation for generative AI with our India team. Essentially, we are a data factory that trains the model. I’m in charge of overseeing data production at a more technical level. I work on a range of projects. We have team members who are specialists in math and chemistry. There are finance and business divisions as well. My domain is a bit of an umbrella over all of those things. The education I received at Santa Clara was really well-rounded. I’ve taken high-level math classes. I’ve taken advanced bio classes. I’ve taken organic chemistry. I was almost an economics major. So my ability to flex between domains and connect dots has been helpful in a real-world setting. It took some getting used to working with the time difference in India. Obviously, you can’t go in person and have a conversation with someone when you need to, but it’s taught me to think ahead and anticipate questions. I really love it though. Everyone is awesome and very intelligent. I value that a lot. 

Where do you see your career headed?

I want to do something that both ignites my passions and creates an impact. I could see myself working in pharmaceutical development, molecule prediction development, AI medical imaging, and even the cross-section of robotic surgery and AI. Like I said, finding something I find exciting is important, but I also want existential satisfaction. Creating something that improves the lives of people is the goal.