Skip to content

Intern Spotlight: Marc Garcia (Student Career)

Tags: internships

This summer we’re introducing interns from Student Career, RESESS, and Geo-Launchpad programs to highlight their research projects and how EarthScope programs further their career goals.

Marc Garcia (he/him) is EarthScope’s 2024 Summer Cloud On-Ramp Career Intern working with Tammy Bravo and Tim Dittmann. He’s currently a third year Ph.D. student at the University of Texas at El Paso working on geoscience and seismology. He also went to University of Texas at El Paso for his undergraduate bachelors in geology and masters in geophysics. His current research focuses on Latin American tectonics—specifically southern Mexico—applying machine learning methods into studying the structure within the subduction zone area. 

Marc has always enjoyed science, but never thought he’d find himself in Earth science. It actually happened in undergraduate one year through an advising accident where he didn’t register for an advising appointment, so he had trouble registering for courses that semester as a result. The geology advisor at the time was able to help him out and introduce him to geology, which he had never really considered as an avenue he would go down until then. After taking a couple of classes he found himself loving it and made it his major.

Right now, his research is focused on the 2017 magnitude 8.2 Tehuantepec earthquake and related to the damage in Chiapas, Oaxaca, and Mexico City as a result. It was the largest intraplate normal faulting earthquake that Mexico has ever recorded, and there has been a huge effort between Marc’s university and some universities in Mexico to study and understand what happened. This area is really seismically dense and complex, and it has taken a lot of effort and robust tools to investigate what really happened. They deployed temporary stations on land to monitor the area of this earthquake in an attempt to understand the nature of the earthquake, as the station coverage was not the best previously. They now have big data from lots of seismic signals, and that leads to a lot of things to do and process. The machine learning method they use has been found to be at least as precise and as reliable as what a human is capable of doing for cataloging earthquakes.


What are you doing this summer, and how does it relate or forward your research?

I work with that big data in my research. EarthScope itself is moving to cloud systems, which benefits a lot of things, especially my area of work. In terms of machine learning, training these big models deals with a large amount of memory, so downloading, writing files, training—all that takes tons and tons of memory. Having a cloud system where you have practically unlimited access to data is very interesting to see, especially on the machine learning side. Of course, with any other problem, it benefits not having to download any data. So for me right now, understanding this new wave of cloud architecture—I’m bringing my perspective of working with big data, creating new tools to train new models or deploying these tools around the world. The possibilities are endless, which is really exciting. There’s a lot of overlap with what I do, even just as basic as improving my technical skills and understanding of systems. So pretty exciting stuff.

What does your day to day look like for the internship?

The internship itself is a virtual internship, so I get to work from home. Day to day it’s packed with meetings from time to time. It starts with a sit in with my team to talk about updates, and then I will either move on to staff meetings or team meetings where we talk about the cloud. I’m in an interesting position where I’m on both sides between Engagement and Data Services, so they’re trying to figure out both how the user is going to use this system and what the framework is that we need to build for the user to use the system. Aside from that, I work on my computer building notebooks. That’s all part of the process, which is really fun.

How did you find this internship position, and what attracted you to it when you did it?

I actually found this on an Instagram post. I follow EarthScope’s Instagram, and I’m always liking their stuff. I think I found out pretty late in the application period—when I found out there was maybe two weeks left until the deadline. I didn’t really think it was something that I was qualified to do at first, but now that I’m in kind of the system, this work is perfect for me. Moving towards a career is just pretty nice. I’d heard of EarthScope before. I did their Seismic Skill Building Workshop three or four years ago now, so I have been kind of along the EarthScope journey. I’ve talked with them at several conferences—AGU, GSA, SACNAS. I’ve stopped at their booths before, so I’ve always kept them in mind because they’ve always done some really good stuff, especially on the engagement side. So after seeing this internship, it was kind of a no brainer after thinking about it. I thought it would benefit me greatly, which it has.

Have you learned any new techniques or even any research areas that have piqued your interest while you’ve been doing it? Or is it fitting in with what you’ve already been doing?

I wouldn’t say it’s fitting in perfectly with what I’m doing, and I definitely am out of my element. I don’t come from a background of computer science or understanding these big cloud architectures, so I definitely learned a lot of things, even as simple as what Docker containers and Docker images are, and understanding how the cloud system works. Even JupyterHub itself—I’ve never had experience with a JupyterHub environment. Understanding how that is shared through users has really been a learning experience. I’ve had experience coding, working with notebooks, and things like that, but not as much as this. So it’s been pretty fun to learn those workflows, which is something that’s pretty intriguing with my research right now. I’ve done a lot of effort in building, or at least training some models and building these workflows and now there is an opportunity to put something on a bigger scale on a cloud system, where a user can come in and work a same, similar workflow somewhere else. So I think the opportunities are really endless with it. You can do so much on this cloud system, which is really awesome.

What’s the next few weeks for you?

We are currently preparing for our first short course to be run on the hub itself, which is the MsPASS course. I will be a part of that course, actually, so I registered to be part of that course. I’m interested to learn that aspect too. Right now, what we’ve been working on is making sure the models run on the hub, as well as having the proper documentation for students and users to be able to have it be as simple as logging in and being able to run a Jupyter Notebook. Or at least not feel confused or worry about, “Oh, can I touch this? Can I not do that?” We are making sure those initial questions are answered and our documentation is good for that. And then, hopefully, once we have a successful run of this course, “How do we run the next set of courses?” or “How do we bring in tools that students, users, researchers, etcetera can go in and use or develop?” That’s a bit of a tall order to ask—there’s a lot of avenues this can go down. There’s really so many pathways, and really it’s endless what we can do with the power of this cloud system.

What’s been your favorite part of the program so far?

So far, at least, it’s just been interacting with the team, learning, and just being able to get that blend of engagement where we’re talking about users and how they’re going to use this, and then also understanding the data services and deploying this infrastructure. Those perspectives and understanding all that has been really cool. Meeting staff, too—I’ve been able to work with Tammy and Tim, which has been super excellent. I’ve learned so much from them. I get to practically geek out with them every other day, showing them my codes. It’s pretty fun. I also work with Scott [Johnson], who’s kind of leading a little bit of the engagement. I can’t remember all of their names, but Chad [Trabant] as well, who’s on the Data Services side and has kind of brought me in the whole infrastructure side. It’s been a lot of people that I’ve met, and it’s pretty cool to really be able to meet everyone at EarthScope.

Anything else you’d like to add about the internship or research, things like that?

When I first thought about a virtual internship, I wasn’t really excited about it. I had my doubts. I did a virtual internship during the pandemic, and I wouldn’t say it was the best experience. Mind you, it was pretty rushed—they weren’t planning for a virtual internship, so it was kind of scattered all over the place, and I didn’t really accomplish what I wanted to do. But this experience [at EarthScope] has been really, really good. I haven’t felt like I don’t fit within the company or like I am ignored. I am able to chat with just the click of a button within Slack or an email—they’re really responsive. I don’t feel confused. They’ve been really clear with their goals, and I’m able to try new things since they’re really supportive. Anybody who I’ve met has been really supportive and I’ve never felt out of place in any meeting. It can be daunting, especially if it’s a big Data Services meeting where I know nothing—it can be a little scary, but I haven’t felt that way. I’ve actually felt very comfortable, so it’s been really nice. We were kind of tossed in the fire on day one, but it wasn’t anything super crazy where you’re lost and confused and you just don’t know what to do. It was kind of like, “Okay, this is what we need to do. This is how we’re going to do it. We will build you up to what we need you to do, and then you can branch off to do what you want.” Hopefully that’s kind of where I’m going now. I think I’ve kind of gotten a feel for it a month in feeling out all the software, workflows, and what they expect for me. So yeah, all uphill from here!

Marc is in the middle of getting his Ph.D. and will continue that next semester. He is getting prepared to publish his first publication and is excited to jump over that first hurdle. Then, hopefully in the next two years he will finalize his Ph.D. and dissertation. Marc would love to stay in the field of seismology, as he finds it extremely rewarding. The wave of machine learning and AI is advancing quickly and shows no signs of slowing down, and so being in that field, researching or developing, is where he sees himself. This internship allows him to get firsthand experience in working with a company that is acclimatizing to these advancements, which he has been enjoying. Because of his academic focus and his experience with EarthScope, he would love to stay within the science community, as well, as we are just barely scratching the surface and he is excited to see where things go. He believes that seismology definitely complements a lot of what machine learning is, and learning datasets is really what seismology does. He is excited to grow as our knowledge of machine learning and Earth science both grow together.