Today we have a very special guest for this week’s blog! Geoffrey Lentner is a longtime friend of mine and fellow Purdue University graduate; we actually first met as physics undergrads at Purdue working in an astrophysics lab as research assistants! Since then, we’ve shared many of the same experiences both in terms of our educational and career paths and, of course, our love for physics (especially astrophysics). Geoffrey now works at Purdue as a data scientist and plays a critical role in assisting research groups across campus with their computational needs. Recently I was able to sit down with Geoffrey to find out a little more about his work and his take on the world of data science, computer science education, and what the future holds for computer technology.
So what exactly do you do as a data scientist? What sort of educational path brought you to this career?
My current job title is “Lead Research Data Scientist.” I work at the Rosen Center for Advanced Computing at Purdue University. Our group builds and operates Top500 supercomputers right here on campus. I have a support role in what we typically call “facilitation.” I’m an expert in high-performance computing applications and writing scientific software for big, distributed computing systems which behave differently than your laptop. My academic background is in astrophysics. Most of my colleagues in similar roles also have a background in one of the domain sciences.
What is your day-to-day experience like as a data scientist? What do you like most about it?
Day to day I find myself doing a few different things. We have a ticketing system where folks submit questions about things they are struggling with. I also help with our office hours where we provide support to researchers struggling with some issue. I also build relationships with research groups on campus to understand what they are doing and act both as an advocate for them and their needs, as well as consult on their projects. Sometimes, I work under contract to help build software for groups. I collaborate closely with a group of astronomers in the Physics department this way. I like that I’m always on the cutting edge of all things scientific computing. Specifically for the role I’m in now at Purdue, I really enjoy all the different things I get to learn and work on. If I worked in a traditional academic role I would focus on a single area of research, but in my group I consult with folks in every corner of the university.
Why do you think it’s important for young people to learn how to code?
Today, every room you walk into has several computers in it, even where you might not expect. Our entire world is digitized in ways most folks don’t even consider. Your daily life, your transportation, your work, your entertainment, everything is run on software. The number of professional jobs in the world that, if not directly requiring coding, work with people who do, is only going up. Learning to code might be the single biggest thing you can do for yourself in this century.
What advice would you give to young people wanting to pursue a career in computer programming/computer science?
In 2022, you really need to evaluate more specifically where your interest is. Are you interested in building websites, desktop apps, scientific software, data processing, systems, embedded devices, etc.? There is programming everywhere. A CS degree is still a great choice for many careers, even if just to get a good foundation for what you’re ultimately interested in. But not necessarily in all cases. I don’t necessarily recommend a “bootcamp,” but there are other programs out there to teach you software development. My advice is to understand first what the right path is for the work you are interested in, and second, to realize that you can always make a change. The technology space is probably the most fluid of any and switching to something mid-career is the rule, not the exception.
In your opinion, what does the future of computer science look like?
I think the future will be one in which more and more non-professionals will be able to easily compose their own systems from community components at extremely low cost — think Raspberry Pi pieces that snap together and program themselves. At the high end, we’ll have new and different technologies. Not just GPUs (graphics processing units), but quantum chips, photonics, and who knows what else. Some people think the idea of the “cloud” will evolve to not just be data centers, but all the small devices around us operating in a logical swarm (in a good way). Regarding AI, we’ve seen an uptick in very impressive models doing extraordinary things.
If you’re interested in hands-on, project-based learning with a focus on computer science and programming, be sure to check out our Build Smart and micro:bit kits and curriculum! Until next time.
– Dr. Jake Roark