Alumni Connections: Aiden Chang ’23 on graduate school and machine learning conferences

“What are your plans after you graduate?”

This seemingly innocuous question is often an unfortunate blight upon any Carl’s day — a trapdoor just waiting for an unlucky student to walk across it (bonus points if they happen to be a senior), sending them into a cognitive tailspin trying to restrain their boundless curiosity into a single path while justifying their academic career.

Planning for life after Carleton can be terrifying, especially when it comes to graduate school, research-oriented careers, or funding opportunities. That’s why we asked two recent Carleton alumni about their experiences after they graduated, encouraging them to give advice to current Carls who are worrying about what comes next. 

This article will focus on Aiden Chang ’23, a computer science major now working at Nvidia who recently published at one of the top conferences in his field.

Chang’s first experience with research happened at Carleton with James Ryan, professor of computer science.

“He was working on improving some of his thesis on simulating a town interacting with itself,” Chang said. “I talked to him about it after class all the time. Then, I got to spend four to six months doing research with him. It was mostly reading some papers, running some experiments, and just getting the feel of how research works.”

Chang noted that since Carleton doesn’t have any graduate students, it’s more accessible for undergraduates to get experience with research — something that would benefit him later.

Despite this research experience, Chang didn’t originally plan on that being his next step. 

“After graduation, I thought I was going to work a job,” he said. “Boeing had an offer, so I thought that was going to be my future.”

But then halfway through his senior year, Chang decided to pursue graduate school. One of the most helpful tools he had to navigate through that was Carleton’s alumni network.

“I just reached out to a bunch of people in the alumni directory that graduated with computer science [from Carleton] or did their higher education in computer science,” Chang said. “A lot of them were actually responsive, which I think is one of the best things about Carleton. If you want a referral or something, just be straight up. Everyone is in the same Carleton family, so just be real with them [about] what kind of advice you’re seeking. If they can’t help you, they’ll refer you to someone else.”

Chang eventually enrolled at the University of Southern California for his master’s degree, where he discovered research was the future he wanted to pursue. While he was originally anxious about his success there coming from a liberal arts background, he found that his Carleton education prepared him well.

“When I actually got there, I didn’t feel that I was too out of my depth,” said Chang. “The courses at Carleton were more than enough to carry that knowledge into grad school and help me succeed. The grades themselves aren’t anywhere as hard as Carleton. It’s not hard to get an A, but it’s extremely hard to exceed expectations in your projects at school and internships, which is going to be what gets you that PhD or the job.”

Perhaps the most impactful aid to Chang’s success, though, was the relationship he built with David Abel ’13, a fellow Carl alum and senior researcher at DeepMind.

“Whenever I’d have problems,” Chang said, “I’d go and ask him and he’d give me some great advice. I’ve been talking to him for over two and a half years now. It’s helped me a lot. I didn’t have a specific adviser at USC. If I had to say anyone was my adviser during grad school, it was David.”

At USC, one particular class stood out to Chang: Innovation of Defense. Focusing on how researchers could work with the Department of Defense under the Biden administration, make products, interact with consumers, and more, it was there that he discovered the Army Research Laboratory Fellowship, an excellent opportunity to pursue his research while getting funding for grad school.

“I got in contact with the head of the Army Research Laboratory in Los Angeles,” Chang said. “He connected me with two people who worked there, and it seemed like a good fit.”

From his experience getting the Army Research Lab Fellowship, Chang has advice for any student seeking funding for graduate school:

“Before going to grad school,” he explained, “do a little research on what the professors you want to work with are doing, so when you reach out to them, you will have more context about what they do. Then reach out. Worst case scenario, they ignore you or say no. But best case scenario, you have a master’s program that could be funded and you could do top-quality research. Never fall into that imposter syndrome of, ‘I don’t have enough experience’ or ‘I don’t know enough.’”

During his time with the fellowship, Chang finished a huge research project on a video processing model.

“​​The model learns how to understand what the important parts are in any long-term video,” Chang said. “An example would be if you watch a soccer game and I ask you what the important parts are, it could be when the offense made a critical pass or the defense blocked the ball… The model is able to understand these important moments in an infinite-length video. There was a two-hour NASA stream of docking the Soyuz last year, and the model watched it through and told me the exact moments that are important. This could be used in multiple different ways, I think mostly for autonomous vehicles or robots.”

The project was originally spurred by the 2025 California wildfires. Chang explained that this model was created for search-and-rescue robots, who needed to be able to process infinite-length video to continually scan for humans or fire. Rescue workers were spread thin, and autonomous systems didn’t have enough onboard computers to continuously process long video streams while operating in the wildfire environment.

Chang eventually got this research accepted to NeurIPS, a top machine learning academic conference.

“This is my first official research paper that’s out there,” Chang said. “NeurIPS is one of the best machine learning conferences in the world, so I have a lot more respect, and confidence, as a researcher now. When people are trying to create some new research, they have to go through old research, and they usually start with the top conferences. Researchers have vetted my research paper and they think it’s worth being up there. Also, people will be using it to improve upon my research in the future.”

Now Chang works at Nvidia, part of a new college graduate program where students rotate between five teams in areas of interest before choosing to join the group or subject they’re most drawn to.

“I have a lot of experience in computer vision with machine learning and robotics,” Chang said, “so those are the two main things that I’m doing right now. If you see full-body humanoids walking around, those would be the things I’m working on. I’m enjoying the work I’m doing at Nvidia. I believe robotics is the future, and I’ve been having a lot of fun working with the people around here and doing research with the robots. So for the [next] year, I think I’m going to stick with Nvidia.”

Chang is open to the possibility of going to grad school and doing more research, though, emphasizing that his path here was unpredictable, and the future might be too.

Finally, Chang wanted to share another Carleton connection that helped him through the process of relocating to a new area outside of the Minnesota Carleton community.

“One of my best friends from Carleton, roommate, and football teammate Ben Pham ’23 ended up living five minutes away from me,” Chang said. “That helped a lot. Those relationships do last for a lifetime.”

Pham also happened to publish research the same year that Chang did, a happy coincidence for both of them.

More on Pham, his research, and experiences will come in the next edition of Alumni Connections!