Dr. Hai Ah Nam, a computational nuclear physicist, works with more computers than you do. About 299,000 more.
She is one of the 4,480 scientists and engineers at the Oak Ridge National Laboratory, the largest science and energy laboratory in the Department of Energy system. The laboratory was created in 1943 to separate uranium for the Manhattan Project (which developed the world’s first atomic bomb). [For more information on the Manhattan Project click here.] Oak Ridge came under the auspices of the Department of Energy in the 1970s.
Hai Ah tells us about the nation’s most powerful supercomputer for open science, and
how she seeks to unlock mysteries that impact – and benefit – humanity.
Since 2008, Hai Ah Nam has been a computational nuclear physicist at Oak Ridge National Laboratory with the Scientific Computing Group in the National Center for Computational Sciences (NCCS) Division in the Oak Ridge Leadership Computing Facility (OLCF). Her areas of research interests include theoretical low-energy nuclear physics, many-body methods, and high performance computing. Her current work involves developing and using several theoretical many-body nuclear methods, including coupled-cluster, quantum monte carlo, no-core shell model, and density functional theory to perform frontier scientific calculations on the leadership class supercomputers at ORNL.
Hai Ah received her PhD in Computational Science from the joint doctoral program at San Diego State University and Claremont Graduate University in 2010. Prior to joining ORNL in 2008, she worked at Lawrence Livermore National Laboratory in conjunction with her doctoral work focusing on studying the structure of atomic nuclei using the ab initio no-core shell model. (from her website: http://users.nccs.gov/~haiahnam/)
Geek Puff’s co-founder, Rebecca Jasmine, interviewed Hai Ah via Skype, about her work.
GP: Let’s start off with some personal things. Why don’t you tell us your name and your profession?
Nam: My name is Hai Ah Nam, and I work at the Oak Ridge National Lab as a computational nuclear physicist. So yes, it’s a mouthful. Do you want me to explain what it is?
GP: [laughs] Please!
Nam: [laughs] Okay. Well, any time you work at a national lab, even though you have a title, you generally wear more than one hat. So I wanted to explain a little bit more than just one of the hats–I wanted to give you an idea of two of the hats that I wear because they’re both very important.
The one is to do computational nuclear physics. What that means in a nutshell is that I get to use cutting edge, high performance computing systems like Titan. Titan is the world’s second fastest supercomputer — and I get to use that to solve large-scale problems in theoretical low energy nuclear physics.
What is theoretical low energy nuclear physics? What we’re trying to do is study. When you think about the atom (we’ve all learned about the atom in school), we always think of this core with these electrons flying around. And what I study is that core component. So there are actually protons and neutrons — it’s not just a circle — they’re actually particles that comprise the core of the atom. We’re trying to answer questions like how many could you add together before everything starts to fall apart? And you think, ok well, let me try to bring that down to a more every day level.
Like carbon, right? Carbon is one of the foremost abundant elements in the universe. And we take it for granted. We call it carbon, but actually to a nuclear physicist, there are 15 known isotopes of carbon. So when I say carbon to a nuclear physicist, they are going to say “which carbon?” Because the matter to which we consider carbon, can have a lot of different behaviors depending on how many neutrons are inside of that nucleus.
So carbon, the element, is dictated by how many protons it has. But you can add a bunch of neutrons to it, and still have carbon. But each one behaves very differently. We want to understand this very essence of matter. These isotopes are how you build all the rest of the matter around you. So we want to understand the theory as to why one isotope of carbon behaves differently than another. Carbon 12 and carbon 13 are stable, they are not decaying and you see carbon 12 abundantly everywhere, but there’s carbon 14. You’ve heard of carbon 14.
GP: I haven’t.
Nam: Ok, we use it actually, in radioactive dating. You know the Shroud of Turin — there are a lot of artifacts we date by using carbon 14. You don’t use carbon 12, you have to use carbon 14 because it has very special properties that allow it to be used for carbon dating.
GP: Fascinating. So how does your computational…
Nam: Computational nuclear physics…
GP: Right, and your use of Titan, how does that have to do with what you’re talking about, the particular carbons?
Nam: What we’re trying to do is understand or create a comprehensive theory so that you can use it predictably and say, “Ok, if I take this isotope, I know exactly what is it going to do, how it’s going to decay and turn into something else, how it will behave under certain conditions.” And considering how much testing we do with nuclear matter, there still isn’t a comprehensive theory that you can use to say “I know exactly what will happen.”
GP: Isn’t that interesting. So this is a long arduous process.
Nam: Right. It’s not just me, there’s a large group, a lot of people working on this problem, trying to come up with the theory. And we use the supercomputer as our experimental testing ground. We come up with large codes that simulate what a nucleus of the atom, how it behaves, and then we try out different theories on there. And then we can compare it to experimental results to say whether or not we were on track. Also, ideally what we’d like to do is to be able to say to the experimentalists, “Our theories will predict that you can have this particle. This particle should exist so please experiment. And look for it.”
GP: So you’re not necessarily competing against the kind of traditional experiments. You’re coming up with a whole new way of researching it and looking at it.
Nam: Right. We all work together actually. We work very closely together — the experimentalist, the theoreticians, the computational theorists. It’s a big collaborative effort.
GP: How did you choose the computer side of it?
Nam: That was layers upon layers of internships that started out when I was an undergrad. My first internship project was when a professor said to me, “Here, learn Fortran. Here’s a book, and here’s your project for the summer.” And I was like, “Uh, ok…” But every year I learned a little bit more about computing. And soon you became the resident expert in your group. I really started to enjoy the computing, not only because I like the program but I like the immediacy of having results back. You know, I don’t have to wait for experimental being time, or anything like that. I could try out all my theories, all my crazy theories, I could try out on the supercomputer. But then also, it really gives you huge amount of flexibility.
When I had to choose computational nuclear physics, I was a single mother at the time.
And there was no way I was going to be able to go in the middle of the night to check my experiment. As a computational physicist, I could do all of my work from home. My daughter could be playing, and I could be multi-tasking. It just worked out really well. I’ve always enjoyed the flexibility of the work. I can take it anywhere, with me.
GP: As opposed to what you were saying earlier, about if you were doing an experiment, you would have to wait all the way through the end, right? As opposed to with the computer, you can tweak as you go along? And try different things? You know, you wake up with an idea or something that’s nagging you, you can address it right then and there?
GP: And the fact that you used your personal life to shape your professional life. That you’re doing things for you because it works for you as a family.
GP: Do you feel that that is something that women in STEAM take into consideration because most have families, or at least extended families?
Nam: Right. A lot of the time, what I see women doing is letting it stop. I’ve had a lot of colleagues that their family demands stop their career interests. But there are, maybe not obvious ways, to make them work together. With the computational, I’m surprised that more women are doing computational work, some kind of computing work because it really gives you that flexibility to kind of have it all. Where you can still have your family but still have a very active career.
GP: Well, I think we’ve been seeing there are few women going into computers overall, in programming, developing, all that in the last handful of years. I think there is a big push right now to try to get women re-interested in it. There all kinds of ideas and theories of why women are dropping out and it will be interesting to see in five years if we’ll have more women in computers.
Nam: Definitely. We track it very closely here. We know that the incoming post-masters, post-docs, post-bachelors interns that we have at the lab. There’s definitely far more women who are interested in the computing than there are actually are on site staff. We’re hoping that will trickle in and we’ll have more women.
GP: …that it’s starting…
Nam: Right, right.
GP: So is there anything more you’d like to say about your work?
Nam: Actually, yeah, I wanted to tell you a little bit more. So, not only do I do the computational nuclear physics part, but I also act as a liaison. I work for Oak Ridge National Lab, but I also work specifically in a place called the Oak Ridge Leadership Computing facility where we house the super computer.
So it takes a village to raise a supercomputer, and to keep it running. And I’m part of that village.
GP: How big is that village?
Nam: I’d say about 100 — a little over one hundred people are required to keep the village operating. Some people do the systems administration, keeping the machine up and functioning. Cybersecurity is a big issue, of course, with a resource like ours. We also have user assistance because we are a national user facility, provided by the government. We have a lot of different scientists, from the United States and from around the world, who want access to the Titan supercomputer. And so we have a user assistance to help them use it.
And then my group is called Scientific Computing. It’s not like you wake up every day and say, “I’m going to turn on my PC and start working on Microsoft Word,” because to use a supercomputer actually takes quite a bit of understanding about large scale architecture. There are 299,008 processing cores on Titan. So getting a lot of processors working in concert takes a lot of doing.
Not only am I user of the system, I’m also helping others to understand how to use the system. How do you really think about the computing architecture in order to make your program run efficiently? It’s a multi-million dollar resource that the government has made available to advance science for the nation.
GP: I can’t even begin to imagine what that looks like. Can you describe what Titan looks like?
Nam: [laughs] I guess I see it every day. But it’s rack after rack, you see rows of computers all stacked together.
GP: In a huge room?
GP: Super cooled?
Nam: It’s cooled. If you go in there, it is very noisy so you have to put on earplugs. The OSHA standards are such that you know you can ruin your hearing if you stay in there too long. It’s immense in how much heat it produces every day, and how much noise it produces, but nobody really thinks about it because you log into from your own laptop or desktop machine. But it’s this enormous backend powerful resource that you can use to do huge computations.
GP: And of those hundred of people in that village, how many of those are in charge of taking care or the running of the computers if a glitch happened?
Nam: There are probably about 20. There’s a group called HPC Operations and there are 20 people in there, roughly. There are different aspects of a computing system — not only the actual compute node itself, but the file system is a huge resource that when you have hundreds of users sharing the same file system, it can cause a lot of… interesting… nights. [laughs] We’ve all been up late nights. That’s the problem with having something you can do at anytime.
GP: [laughing] It’s a blessing and a curse!
Nam: Yes, you take it with you everywhere. We also have a group called Technology Integration because it’s not like we can buy everything off the shelf. We try to as much as possible from mainstream technology but what we can’t find, we have a group in-house that’s here to develop and to work with vendors out there in the computing industry, to bring in, to create something we can use.
GP: How about in your personal group, scientific computing?
Nam: There’s also about 20. We have post-docs, and my group is a band of misfits because everyone does something different. I work with nuclear physicists, astrophysicists, people who do molecular dynamics, chemists. You name it, there is an expert in my group. But the thing that connects us all is that we really think very closely about the supercomputer, and how to use it — how do we get science out of the supercomputer.
GP: That’s fascinating and I think I could listen to you talk about this forever, but I want to move on so we learn more about you. You mentioned that you wear two hats. Anything more you want to say about that, or to take it down a different path?
Nam: Well, maybe I’m thinking too far into the future, but there are a lot of really neat changes happening in the high performance computing world. And right now we’re at a crucial time where we’re thinking about something called an exascale machine. Have your ever heard of an exascale?
GP: Maybe, but I may be thinking of something else. Tell us.
Nam: Huge numbers of computations. They talk about it in terms of FLOPS (floating point operations.) Right now we’re in the era of heta-scale, and you’re trying to take a thousand more than that going to the next era. We’re in the process of thinking of how do we create the next machine. There is such a demand for supercomputing now, but guess what? It takes a lot of energy to run these machines – a lot of power consumption. So in order to do that, there’s huge shifts in technology. If you hear about the GPGPU in the computing world, you know using GPUs in the computing world has been a big topic. Those are all things I had to think about when we work on building our codes.
What’s a GPU or GPGPU?
Geek Puff Glossary: On a regular old computer, there’s the CPU (central processing unit) which is the main brain, it’s the ‘intel inside’ part of the computer, aka the ‘chip’. Also inside a computer is a graphics chip that does the graphics. The CPU says, “put this picture on the screen,” and the graphics chip does the work of putting the picture on the screen. Doing graphics is a very tough job, and that is why they have a separate chip to do that work.
Well, graphic chips started to get really powerful. The technology is specialized and it became quite good at doing math. It’s good at doing math because displaying the graphics in games means doing alot of math, and doing it really really fast. So the GPU (graphics processing unit) became a little math powerhouse chip.
People noticed that the graphics chips could do math better than the CPU, and started pushing not only the graphics to the graphics chips, but also the hard math problems. So inside the computer, the CPU told the graphics chip to put the picture on the screen and also to do some math problems for it. Now in supercomputing, which basically means “do a lot of math as fast as you can,” they have been using the graphics chips to not do graphics, but to do the math.
GPGPU means “general purpose computing on graphics chips”. They are taking the graphics chips and using them as the main part of the computer. Put a million of these chips in a machine and you can do a lot of math in a hurry.
GP: Yeah, because you think about technology and how quickly it evolves and changes. So it is amplified for the work that you do because it’s just not replacing… one.
GP: It’s replacing 299,000. Is that right?
Nam: Right, but more than that. So next time you’re trying to actually do even more. Well, we don’t even think about computers anymore. We start to think about xxxx. How many per person do you get to do per computation. You start thinking about billions and billions of simultaneous xxx working for you. Just orchestrating that, getting your head around it is daunting sometimes.
GP: With you talking about the future, you’re not getting ahead of yourself because it’s always staying one step behind it because it’s always evolving. I heard an interview that you did on YouTube, that you are involved with a student cluster competition? Are you still involved in that?
Nam: I am. I was the chair of it in 2010 and have been involved as an advisor throughout the years. The thing about the student cluster competition that’s really neat is that it started at the Supercomputing Conference in 2007 and the gentleman who hired me here at the lab was the chair at the time. I met him through that competition because I was helping the teams understand an application, a particular code. By 2010 I was hired on here and became the chair of that competition.
GP: [laughing] Did you ask to be chair or was it handed to you?
Nam: [laughing] No, no, he handed it to me.
GP: Good for you.
Nam: The thing that is really exciting about this competition is that the technology that the kids come with on the floor. They have one tiny rack that they can put in a 10 by 10 booth and it is faster than technology we saw a decade ago. I mean it’s amazing. I really enjoy working on that competition with the students — they are all undergraduates and eager to learn so much about high performance computing and these applications.
GP: Yeah, I can only imagine that you’ll run across them in the years to come. They are interested now and at the level where they are competing right now, those kids will probably be quite successful in their careers once they get out of college.
Nam: Definitely, actually we try to scoop them up at the lab as fast as we can because they are so talented.
GP: Well that’s great. Let me ask you another question. So we’ve talked about your work and what you do. So you are in the STEM/STEAM field in a couple different ways. What inspired you to go into the field that you are in?
Nam: Right. I am an unlikely candidate to be in STEAM. I don’t think I ever take the direct route for anything, in all of my life. I am Korean-American and my parents were immigrants who worked very hard to ensure that we got a good education.
GP: Where did you grow up?
Nam: Southern California, in Orange County. And of course we were a minority at the time — there were very few Asians there. But of course, you’re expected to excel, my parents had the expectation that I would do good in everything, right? For some reason, they put a lot of pressure on my sister and brother to go into medical school.
GP: Are they older?
Nam: They are older and I’m the youngest of three. But for me, they basically said “Do something important.” You know, that’s a lot of leeway. There was no pressure to go to medical school. I didn’t have any direction, so I did exactly what I’ve been doing all of my life, which is: I chose an area based on the people that I like. I’ve always gone to mentors. If there is someone I like and they are doing something interesting, then that makes me think that maybe I’ll like it too. They’ve seemed to make a career out of it. The person I liked best by the end of my senior year of high school was my French teacher. So I thought, “Ok, I’ll be a French major…”
GP: How did your parents feel about that?
Nam: …and I’ll go into international business… The one direction my father made when I was growing up was that I should be like Connie Chung. I was like, ok, maybe I could go into law. I don’t know. The thing about high school was that I could kind of do anything pretty well. But I just didn’t have a propensity for any one particular direction. I actually really loved art. I love math. I love English. I did better in all my English classes than I did in my math classes. So I figured, you know, international business could be a reasonable path. But when I went into college, I took my first physics course — I took it with astronomy. The reason I took it was because everyone said it was hard. And I said, “No, never. It might be hard for you, but it won’t be hard for me.” [laughs] Because, you know, I did pretty well in high school.
Nam: And it was hard. It was very hard. I stayed up late nights studying. I cried when I got my first B. It was challenging. But the thing was, it was exciting as well. I was taking astronomy at the time and that just really opened my mind to how little we know about the world around us.
I thought wow, if I can make a contribution to understanding even a small fraction of this amazing world, I think I’d be satisfied.
And so I went into physics — I changed my major to physics that year.
GP: Wow. And that was the start of it.
Nam: That was the start of it.
GP: Fascinating. And where did you go to undergrad?
Nam: I have a Bachelors in Art from Scripps College in southern California.
GP: No kidding.
Nam: Yeah, that was interesting because that’s more of an artsy school. But actually I feel like I got a really great physics education there because… well, if you don’t know about the Scripps Colleges, they are one of the Claremont Colleges and there are five different colleges on that campus. So if you can’t find a course at Scripps, you can go to any one of the other colleges and take classes. I was able to take classes at Harvey Mudd College which is an engineering school, and Pomona College which catered to a lot of pre-meds. Claremont McKenna College. I feel like I got a really great physics background there.
GP: It sounds like it was really well rounded.
Nam: Yes, it was. And I really enjoyed the fact that I got a really great arts background too, because I could take courses like Roman Decadence [laughs] and turn of the 19th century literature. That kind of shows you the other side of me. I loved reading and arts as well.
GP: And don’t you think that now, with the introduction of the “A” in STEAM that your background in art is actually helping you with your communication, your writing, your presentations in a field that’s not heavy in that?
Nam: Definitely. I think there needs to be more emphasis on that. I think it makes you a better-rounded person, it makes you think, and to add a different perspective to the problem as well as to the presentation of your ideas to others. Definitely. I’m glad they added the “A.”
GP: I think that unknowingly, you probably chose a really perfect path. You were able to study things that were interesting to you in both worlds. And now in your career, you’re able to combine them. So let’s talk about what you do on a daily basis. What are your top 5 things you do on a daily basis?
Nam: Wow, so again, I wear a lot of hats. But in general,
the primary things that I do is solve problems.
And we solve problems by thinking — so I get time to think about how to put things together, to read and think about the physical world, also solving problems through collaboration. I work a lot with others. Sometimes a little bit too much, I don’t really want to be in that many meetings. The collaboration I think can be toned down. [laughs] I do a lot of programming. I write programs in Fortran. It’s funny because a lot of people say “Fortran? That’s an old person’s language” but it’s because I know Fortran that I can stay employed very well because not a lot of people know Fortran any more. But I would say about 50 percent of the codes that run on supercomputers are still written in Fortran.
Geek Puff Glossary: Fortran is a programming language. For more on Fortran follow this link.
Nam: So, I write programs, I run programs because you’re basically running an experiment. And I analyze the results. And then afterwards, I do a lot of reading and writing of papers. You have to read the papers then write one yourself to contribute to the scientific community. And then also proposals. We do a lot reading and writing of proposals — I’m a reviewer for different proposals, and also write them because you have to convince others that your work is pertinent and worthy to be funded.
GP: Right, that makes sense. Of all that you just talked about, what do you enjoy most about your work?
Nam: I guess the thing that makes my work the most fulfilling is that it has a broad impact. It’s hard to say to people exactly what the applications are because I do a basic science. But I do know that someday, another researcher will read my paper and say “wow this is going to help them unlock yet another mystery.” And that could be something that impacts – and benefits – humanity. That’s why the national labs exist, so that we can do research that will inevitability have impact for the nation.
GP: Right. That’s pretty big.
Yeah, I like knowing that I’m doing something good for others and that it will have a long-lasting impact.
GP: Yeah. That is great. So tell me, is there a particular one project that you can talk about that you’re currently working on?
Nam: Sure. I’ll tell you about the collaboration that I’m in. I work with a large group of scientists in a project called Nuclei. Nuclei is plural for the nucleus, so when you talk about many nucleus’, they’re called nuclei… But it actually stands for Nuclear Computational Low Energy Initiative. It’s a band of scientists from 17 different institutions. Everyone has different levels of expertise in computing, in nuclear physics, and we’re all working together to come up with that comprehensive theory to describe a large number of nuclei, basically. We’re funded by the Department of Energy under something called the Scientific Discovery Through Advanced Computing Initiative — so it’s called SciDAC. That’s my current collaboration, and through that collaboration, I’m working on taking a particular application that studies a size of nucleus – a medium mass nuclei – and to get it to run on the Titan system. Titan is interesting because it has GPUs (general purpose computing on graphics chips); it’s not the obvious, not the easiest platform to write an application for. It’s not easy. You just don’t wake up in the morning and just program on it. It really does take a certain level of education. It really takes some thought to run on the GPU properly.
GP: And how long would it take you, for that particular project, from start to completion?
Nam: So this collaboration is banded together for five years. And this particular project, after we finish getting it to work efficiently on the GPU, then we use it for calculations. So during the course of those five years, it’s not like you’re just done with it. You’re actually trying to develop it to calculate something of interest. So with that particular code, we’re looking at understanding more about the oxygen isotopes. Or tin, or nickel. Trying to create these tools that we can use for many years to continue to study different behaviors of different nuclei.
GP: So anything else about that that you’d like to talk about? I know you talked about the implications of your work before in broader terms. Is there anything else that you’d like to say about that?
GP: Maybe we’ve talked about that. Let’s just wrap it up and talk about some simple questions then.
Nam: Ok, I thought those were simple questions! [laughs]
GP: Simple for you maybe! [laughs] Tell me, if you were to choose again, would you still choose to be in a STEAM field?
Nam: Definitely. I would definitely choose a STEAM field for multiple reasons. One is that
when you have a STEAM background, you have the tools to shape the world around you.
You don’t ever feel like “what do I do if a problem exists.” You’re actually building this toolset of affecting a problem, and that I enjoy. I also really like – as a practical person – I like that it is financially a really great choice. You know, when I was younger, it didn’t really matter as much, but now that I’m a mother of two and wanting to make sure that my kids have every opportunity that I’d like for them to have. And also to be contributing back to the community. And impacting the world in other ways, it’s very nice that a STEAM background gives you that financial security.
GP: Right, absolutely. Are you encouraging your children to go into anything in STEAM?
Nam: I would love to. I would love for her to. I have an eleven year old, and I have a 6-1/2 month old. But my eleven year old, she says she doesn’t like science, but I suspect it’s mostly because she hasn’t found her area of it yet. She actually had a really great science teacher this year that sparked an interest, so I’m very pleased that that occurred. You know, she sees me working a lot, and she doesn’t understand that it’s a labor of love. Instead she thinks, “wow, you work a lot. I might not want to do this.” So I need to find a way to show her how to understand that you put a lot of time into things you love.
GP: Yeah. Even if you were a chef in a restaurant, those hours would be worse than probably yours now. And I think you’re right – I have eleven year old twin boys – and I see them interested in math right now, but not so much in science. And I have one who is very interested in computers so all this information you’re telling me about Titan and the computers, he’ll find fascinating. So anyway, I think that our generation, whatever we’re doing now to encourage the generation behind us, our children, I think we’ll help them find those niches when they get to start making choices when they go to school. Hopefully that will be your daughter as well.
Nam: I hope so. I hope she will think beyond the amount of work that I do and see the wonderment that is associated with it.
GP: Yeah, I think that’s a great word for it. So, a couple more questions… Can you give us an insider tip that relates to your field? Say for example, for someone who were trying to get into it. Or maybe someone who… wants to get out of it?
Nam: Oh! You’re not allowed to leave. [laughs] I think internships are a key to everything. Getting experience. I try to have a student every summer to come to the lab. All the national labs have internship programs that students can come to. Last year, I had a very unusual situation where I had a twelve year old come as my intern.
GP: No kidding.
Nam: Yes, from the local community. And he had a great time here, he learned how to do some programming. And so the national labs are a great place to get experience. We’re not expecting you to do our work for the rest of your life. We want you to get a flavor of all the kinds of things that are out there.
GP: And how long are those internships?
Nam: They are over the summer. But there are also some throughout the year, especially if you’re an undergraduate or graduate student, you can definitely come throughout the year. They are usually about 10 weeks in duration. You apply, you get paid a stipend. You get paid to learn pretty much. I think that’s a pretty good deal. So this year, I have three interns. One from Wellesley College, one from Rice, and the other from University of Delaware, so they’re from all over the United States. Students are coming to get experience working on a supercomputer, learning about all the different neat science that’s happening at the lab. Because this lab has over 4,000 people and it has a variety of different user facilities. All sorts of really cool things are happening here.
GP: It sounds fantastic. So for some kids who don’t actually know how to find and internship, would they go to your website? How would they find those internships?
Nam: Definitely. You can go to the ORNL.gov website. I think there’s a link for “careers” that will take you to a link for internships. [Link to ORNL internships at end of story.] But not only at Oak Ridge National Lab, but there’s also national labs in California, in Illinois, all over the United States. So there’s always an opportunity to learn. And that’s the best way that you’ll find out what’s the right path for you.
GP: I think that’s absolutely great advice. You know, we’re here in Missoula, Montana and there’s a university here, and there’s a lot of very bright students. But because we’re so isolated from the rest of the world, the rest of the nation, it’s a little intimidating for our students to leave Montana. But if they hear stories about like what you’re talking about, and hear how welcoming some of the companies can be, and how easy it is to get internships. And certainly about what you were talking about with the stipends, and how it’s 10 weeks, and how it’s been done before… I think that if we put that information out to our community, there would be quite a few, a handful, that would take an internship.
Nam: That would be great. We’re always looking for really great students who are eager to learn.
GP: So how about the last question, to wrap up our interview? Why don’t you tell us something about yourself that very few people know?
Nam: Something that very few people know is that Ray Bradbury, writer of Fahrenheit 451, once called me, my sister and my brother, his illegitimate children. We met him when I was in college. My first year in college, I drove down from San Francisco all the way down to Laguna Beach, California – that was like a 7 or 8 hour drive – met him with my sister and my brother down there at a bookstore called Fahrenheit 451 to meet Ray Bradbury because he was doing a signing. We took a picture with him, had him autograph all of our copies of the different books we had of his, and
as he was taking the pictures, he said “these are my illegitimate children.”
GP: That is hilarious. Did that ever make it into the newspaper or anything like that? Or is that just private for you?
Nam: No, no. That was our own personal picture and a family story.
GP: Well, Hai Ah, it was so nice talking with you. I mean I feel like it could go on for another two hours because I find it fascinating but I’m sure you probably have to get back to supercomputing.
Nam: Yes, or a small child.
GP: Oh right. You don’t know which is more important. [laughs]
Nam: Well, it is a juggling act.