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Welcome to Engineering Innovations, the official podcast of Purdue University's Elmore Family

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School of Electrical and Computer Engineering.

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I'm your host, Kristen Malavenda.

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I'm the Communications Director for the school.

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In each episode of Engineering Innovations, I'll sit down with faculty from Purdue ECE

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to talk about their path to becoming an engineer, the focus of their research, the future of

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the field, and more.

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And joining us today is Millan Kulkarni.

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He's a professor, but also the interim head of Purdue ECE.

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Thanks for joining us today, Millan.

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Really glad to be here.

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So let's start with that first thing I mentioned, the path to becoming an engineer.

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What's the earliest you remember doing things that would later lead you down the path to

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being an engineer?

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So I have, I think what from the outside looks like an incredibly stereotypical story of

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becoming an engineer.

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Both my parents are in technical fields.

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In fact, my dad was a mechanical engineer as an undergrad and industrial engineering and

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operations research for his PhD and actually so did my mom.

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So engineering kind of runs in the family.

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It's in the blood, so to speak.

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And so from an early age, of course, when instead of getting toys that, you know, balls

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and bats and stuff like that, we were not an athletic family.

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Instead, we would get, you know, erector sets and things and building toys of various sorts.

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I remember getting this really cool little plastic gear system called Capsula where I

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could build little boats that had propellers that could like run through the bathtub.

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So from a very early age, I was always about building things and taking things apart.

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The particular kind of engineer that I am is a computer engineer.

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And I'm from that generation where it was actually not that common when you were, you

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know, when we were young to have computers at home.

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But now, of course, everybody has computers and we got our first computer when I was seven

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years old.

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And was that unusual for the time?

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It was right around when that was starting to happen.

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If your parents were in a technical field, maybe it was more likely for you to have a

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computer, but most people maybe didn't have them.

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They maybe had them at school at best.

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So I was seven years old and my dad, because he's an engineer, taught me how to program

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basically from the beginning.

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This was a very old computer.

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There wasn't that much you could do with it, but you could write computer programs.

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And so I learned to program when I was quite young.

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And so like I said, stereotypical story.

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Learn to program when I was young was always into computers, always into taking them apart,

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rebuilding them, building my own computers.

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So when I got to undergrad, it was a natural choice to become a computer engineer.

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And it kind of progressed from there.

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So when you finished your undergrad, decided to go undergrad school, what turned you towards

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academics as opposed to industry?

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Yeah.

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So this is a, again, this is one of those stories where if you maybe look from the outside,

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it actually seems obvious that I would go into academia.

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There's this kind of common statistic that goes around that a huge fraction of people

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who are in academia have parents who are in academia.

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And that's true for me.

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My dad's a university professor.

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So you might look at that and say, well, obviously, Miland was going to become a university professor.

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But that actually wasn't obvious.

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Even when I was an undergrad, it wasn't clear that I was going to go to graduate school.

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Certainly that was the wish that my parents had for me as people that had gone to graduate

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school.

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But I was an undergrad during the height of the dot com bubble.

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So why would you go to grad school when you could join a dot com startup and making millions

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of dollars?

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Right.

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So maybe luckily for me, but maybe not the dot com bubble burst right before I graduated.

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So I graduated in 2002.

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The dot com bubble had just burst a year or two ago.

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And so grad school kind of seemed like an obvious option.

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So I went to graduate school.

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I was getting my PhD in computer science.

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And even then it wasn't actually clear that I wanted to go into academia.

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I sort of enjoyed what I was doing, but it wasn't, I don't know that I loved doing research

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at least in the first few years of being in graduate school.

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And so I was thinking, well, I'll get my degree and then go off to industry.

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Go to Microsoft, go to Intel.

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At the time there was this up and coming startup company that a lot of people were going to

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called Google.

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Maybe I wouldn't be doing this podcast if I had done that.

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I'd be on an island somewhere instead.

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But I, by about year four of graduate school, I realized that research was actually something

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that I really enjoyed doing and teaching was something that I really enjoyed doing.

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And so at that point academia became the obvious destination.

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And speaking of research, like where did you start with your research and where are you

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today and what research you're doing?

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Yeah.

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So this is, I'll back up to grad school again.

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When I was getting into graduate school, I did what I would actually tell students now

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to never do, which is that I wrote my statement of purpose saying, you know what, I actually,

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I like a lot of different fields in computer science.

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And so I don't really know what I want to do.

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I don't know if I want to do computer architecture, which is basically, you know, hardware, right?

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Or artificial intelligence.

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Like those were my two choices, kind of on the two opposite ends of computer science.

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And that's how I wrote my statement of purpose.

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And I'm honestly just lucky that somebody said, despite that terrible statement of purpose,

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that I could, you know, join grad school.

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But when I was in graduate school, I took a class in compilers from a professor, Keisha

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Fengali.

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And I loved the class.

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It was the first time that I had seen a class where deep mathematical concepts could be

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applied to real world engineering problems.

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And it fascinated me, right?

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This idea that I could take these mathematical concepts that seem completely unrelated to

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the problem of compiling.

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And for those of you that don't know, compiling is basically the problem of translating a

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computer program that you might write into what the computer understands so that it can

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actually execute.

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Got you.

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But the idea that you could do this, that you could take these deep mathematical ideas

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and use them to drive how you translate a computer program into machine code, blew my

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mind.

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And it didn't help that that that professor, Keisha, was was a fantastic teacher.

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So that first year I asked him whether I could maybe do research in compilers, which

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is something I had never considered before.

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And that's kind of how I got into compiler research.

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When I was in graduate school, I did a lot of work on automatic parallelization, the idea

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of finding opportunities to run different parts of a program simultaneously so that

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everything runs faster.

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When I got to Purdue, I started working on something a little bit different, but kind

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of still broadly in the same space, which was how can I take the kinds of computer programs

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that you might see in fields like computer graphics or in fields like data mining, that

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compilers historically have not done a good job of targeting and make them run faster

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by doing various kinds of compiler transformations, by basically restructuring the program so

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that it runs better on the computer hardware that you might have.

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And the reason that I really like this field of research, which is honestly with slight

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variations what I continue to do today, the reason that I like this field of research

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is it requires deep understandings of applications, of understanding the kinds of algorithms that

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people are running, what it be like, what somebody that's writing a high performance

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ray tracing engine really cares about, while also having a deep understanding of computer

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hardware.

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So what do you have to do to make a program run fast on a modern computer, on a modern

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GPU?

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So you have to understand both of these sides and kind of harkening back to graduate school

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where it was, how do I take these deep mathematical insights and make them work for compilers?

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This is about how do I take these deep algorithmic insights that a lot of people have and translate

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them into something that a computer understands well.

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Gotcha.

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And I just love that challenge.

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And I know from just interacting with you that you're a professor who really enjoys

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teaching undergrads, how do you use your research and your just experience to teach

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your undergrad classes?

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Yeah.

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So this is, I love teaching.

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I tell people that it's because of teaching that I would never leave faculty.

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You could go to industry research labs and do research, you could go to national labs

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and do research, but there's nowhere else that you could really get the experience of

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sitting in front of hundreds of students or standing in front of hundreds of students

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and teaching them something they didn't know before.

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And nothing can replace that.

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One of the, so I teach, when I teach undergrads, I teach lower level courses.

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I teach, you know, intro to computer programming.

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We call it, well, advanced C programming is any other course.

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And discrete math, which is kind of the foundational math course that computer engineers need to

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have.

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And one of the interesting challenges in both of those courses is that it can be really

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hard for students to understand how these very fundamental concepts that we're trying

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to teach them translate into what they actually need to know in the real world.

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You might look at a math problem and say, this is math.

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What does this have to do with what I'm going to do when I graduate and go work at a Google

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or Apple or a Microsoft?

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You might look at a really basic C programming assignment and say, well, this is incredibly

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basic.

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It has nothing to do with, you know, the Python code I'm going to write if I'm doing some

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deep machine learning work when I graduate.

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And so for me, a big challenge is how do you make those fundamentals interesting and applicable

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for students?

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And I've actually found my own research to be really effective there because it's very

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easy for me to draw direct lines between some of the fundamental computer programming

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stuff that I might teach in C programming to, hey, these techniques actually show up in

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these real world applications and understanding how these techniques work is how you can make

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progress.

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Using a discrete map, hey, these mathematical techniques that seem really abstracted and

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really almost obscure, they actually really show up in my research and other people's

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research.

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These things are at the cutting edge of what people care about in the field.

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And so being able to make to draw those connections is really helpful.

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It shows the students that what they're learning isn't just abstract.

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It isn't just something we're making them do.

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It really does undergird so much of what they're going to do in the future.

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So you're not too far removed from your studies, but I'm sure that what undergrads go through

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these days in engineering is different than what you did.

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What are those differences?

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So some parts of electrical computer engineering are fundamental.

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When I teach discrete math, yes, some of the applications that I might talk about today

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are different than the applications I might have talked about 20 years ago or my teachers

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back then might have talked about.

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When I teach C programming, same deal.

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Some of the applications and some of the stuff at the frontiers looks different, but a lot

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of the fundamentals are still the same.

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Circuits are circuits.

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A C program is a C program.

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Discrete math is discrete math.

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Where the differences really start to show up is when you start thinking about how those

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things are applied.

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Just as a simple example, when I was an undergrad, I took a course in artificial intelligence.

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Those are senior level electives.

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And a thing that literally never came up in this entire semester of learning about artificial

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intelligence was a neural network.

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We didn't talk about it at all.

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We focused on what in computer science we call symbolic artificial intelligence, because

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that's what people were thinking about.

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Now the idea that you could take a course in artificial intelligence and just never

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talk about neural networks or honestly not spend a huge fraction of your time on neural

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networks, it was my mind.

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Yeah, right.

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The field moves fast at the frontiers.

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The foundations stay the same and the frontiers shift quickly.

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And that actually makes for a really exciting educational experience because you're always

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thinking about how to tie those foundations to those frontiers.

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So honestly that's one of the big changes is not so much the content of your sophomore

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level courses.

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It's the content of those junior and senior level courses that has really shifted quickly.

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But honestly one of the things that I think really cool about our field is that you're

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able to do these junior and senior level courses that do start talking about things

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at the frontiers of the field and those foundations still work.

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Right?

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Right, they're still what you need to know.

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So how you mentioned that the field is just constantly changing.

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How do we as Purdue ECE keep up with that and make sure we're teaching the students

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what they need to know today?

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Yeah, so this is a great point and it's something that we think about a lot.

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So there's a few things that I would say.

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So one, I honestly believe that this is the reason that research universities work as

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well as they do.

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It means that when we're getting folks in to a university like Purdue where we're teaching

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thousands of students, the people that are teaching the thousands of students are people

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who are at the forefront of their fields.

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They know what the modern world needs in computer engineering or in power systems or in automatic

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controls.

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So they are defining that modern world and then they turn around and they get to talk

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to juniors and seniors about how the modern world works.

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So I think it's really important we have these phenomenal researchers at a university like

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Purdue in a school like ours that can then turn around and teach these students.

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I think that's one of the ways that we keep those connections.

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One of the other things that I think is actually really phenomenal about our department that

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helps build these connections is the students take on a lot of the work themselves.

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The kinds of projects that people do, whether that's through class and things like senior

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design or VIP or EPICS, or whether it's on your own through the numerous student groups

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that come out of ECE, it blows my mind.

241
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The level of complexity, the level of rigor, the amount of cutting edge technology that

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people are deploying in these projects, it's mind blowing.

243
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It's amazing.

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I know every year we have the Spark competition twice a year, which is our senior design kind

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of showcase and I'm constantly amazed.

246
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I say you built that in a semester and yeah, they're incredible.

247
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Yeah, I wish I had a senior student's capacity to operate on very little sleep.

248
00:13:45,280 --> 00:13:49,480
Yeah, well I asked the team that won last year's Spark competition.

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I said you built this in a semester and they said yeah, I'm like how?

250
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And they said honestly, we don't remember.

251
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Sleep deprivation is how.

252
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But no, I look at what I did 20 years ago in undergrad and what our students are doing

253
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now and there is no comparison.

254
00:14:04,440 --> 00:14:10,720
So beyond kind of the students taking on a lot, what makes Purdue ECE special or stand

255
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out from other programs?

256
00:14:11,960 --> 00:14:16,360
We are constantly rated in the top 10 and so what puts us there?

257
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Yeah, I mean this is one of the things that we're proud about.

258
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We are a very large program and despite being an incredibly large program, we are excellent.

259
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We're constantly rated in the top 10 both for grad and undergraduate education.

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I think a big part of it is that despite being a top research program, we have a lot of faculty

261
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that really care about education and really care about making sure that students are getting

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the right material and having that material taught effectively.

263
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There's, you know, I could go down the list and think about people like Professor Sundaram

264
00:14:46,960 --> 00:14:52,040
or Professor Brinton or Professor Chan, you know, across our department.

265
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There are these faculty that are both excellent researchers and excellent educators and I

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think that really can shine through and can get students excited about this.

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I think another aspect that really helps us here is we have a phenomenal instructional

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staff that works closely with faculty and is constantly talking to faculty and amongst

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themselves about how we can provide a great educational experience at the scale that we

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are.

271
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Right, I go and talk to people at other departments and I tell them, yeah, our circuits, you

272
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know, our fundamentals one class, what other schools might call circuits one, we have a

273
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thousand students a semester.

274
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Wow.

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And it blows their mind.

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It's inconceivable that you could do, that you could be the scale that we are and do

277
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as well as we do.

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But I think that's a testament to our faculty, our staff and again our students for really

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taking on that challenge.

280
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So your passion for teaching is very apparent.

281
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Then several months ago you decided, hey, maybe I'll be the head of the school too.

282
00:15:48,800 --> 00:15:52,600
So what, why did you take on the position of interim head?

283
00:15:52,600 --> 00:15:56,160
Yeah, I ask myself that sometimes.

284
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Why do I do this?

285
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I will say the thing that I miss the most about being interim head is not being in the

286
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classroom and not being able to teach.

287
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I did keep one foot in teaching.

288
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I still advise an epic section.

289
00:16:06,320 --> 00:16:10,520
So I still, you know, on a weekly basis get to interact with students and talk to students

290
00:16:10,520 --> 00:16:14,680
about what they're working on and their passion.

291
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Part of it is that we are a very large school and there's a lot of moving pieces and the

292
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way that we can succeed is by keeping all those moving pieces working together smoothly.

293
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And so it's important to have somebody, I think, you know, one of the things that I

294
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really enjoy about being interim head is the opportunity to see all of these different

295
00:16:38,800 --> 00:16:44,680
threats that are constantly, you know, flowing in our department and understanding how they

296
00:16:44,680 --> 00:16:48,800
all tie together and sort of making sure that everything stays tied together and thinking

297
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about how the research mission feeds into the teaching and the teaching feeds into the

298
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research and all of the different pieces.

299
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So it was a phenomenal challenge.

300
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And I also stepped into some very big shoes.

301
00:17:03,640 --> 00:17:06,880
Our previous head did a great job of elevating our school.

302
00:17:06,880 --> 00:17:10,920
And so it was a little daunting to take it on.

303
00:17:10,920 --> 00:17:13,640
But I'm glad I tried and I'm glad I did.

304
00:17:13,640 --> 00:17:15,720
It's been a lot of fun and it's really great.

305
00:17:15,720 --> 00:17:20,840
I get to, as interim head, brag about what everybody else is doing.

306
00:17:20,840 --> 00:17:26,760
And there's no greater sense of accomplishment than being able to talk about your fantastic

307
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colleagues and students.

308
00:17:28,120 --> 00:17:33,200
So we're coming off a pretty several years where we had a lot of growth, both in terms

309
00:17:33,200 --> 00:17:36,280
of facilities and in the classroom.

310
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What do you see as the next challenges and things that Purdue EC will be tackling?

311
00:17:41,000 --> 00:17:42,000
Yeah.

312
00:17:42,000 --> 00:17:43,000
So there's a few things.

313
00:17:43,000 --> 00:17:47,160
So, yeah, here at West Lafayette over the last 10 years or so, we've seen a tremendous

314
00:17:47,160 --> 00:17:51,320
amount of growth in the department, way more faculty, way more students.

315
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Our physical plant, our buildings are starting to keep up.

316
00:17:54,840 --> 00:18:00,040
We have this great new project going on right now where we're completely revamping our undergraduate

317
00:18:00,040 --> 00:18:01,040
instructional labs.

318
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I'm really excited about what that's going to look like next year.

319
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And I'm really jealous of the experience that our students are going to have starting in

320
00:18:07,120 --> 00:18:08,840
the fall.

321
00:18:08,840 --> 00:18:11,680
We show that project to some of our alumni.

322
00:18:11,680 --> 00:18:14,560
This is the first floor of the BHW building.

323
00:18:14,560 --> 00:18:17,960
We show that project to some of our alumni and they say, well, this isn't fair.

324
00:18:17,960 --> 00:18:19,240
They're going to get windows in the labs.

325
00:18:19,240 --> 00:18:20,240
Right.

326
00:18:20,240 --> 00:18:21,400
We didn't have windows.

327
00:18:21,400 --> 00:18:24,200
And then some of them turn around and say, well, you can only do a great job in an ECE

328
00:18:24,200 --> 00:18:25,520
lab if you don't have windows.

329
00:18:25,520 --> 00:18:26,520
Right.

330
00:18:26,520 --> 00:18:27,520
You feel like you're in a cave.

331
00:18:27,520 --> 00:18:28,520
Exactly.

332
00:18:28,520 --> 00:18:29,520
So we'll see how that part plays out.

333
00:18:29,520 --> 00:18:32,520
But I'm really jealous of some of the facilities there and excited about how that's going to

334
00:18:32,520 --> 00:18:34,880
take us to the next level.

335
00:18:34,880 --> 00:18:39,280
I think one of the big challenges that's coming up in the next year is where our growth is

336
00:18:39,280 --> 00:18:44,160
going to be now is not here in West Lafayette.

337
00:18:44,160 --> 00:18:45,160
Right.

338
00:18:45,160 --> 00:18:49,680
We're launching, you know, Purdue is launching this ambitious new mission down in Indianapolis,

339
00:18:49,680 --> 00:18:50,680
right?

340
00:18:50,680 --> 00:18:52,440
A new Purdue campus that is still us.

341
00:18:52,440 --> 00:18:53,440
Right.

342
00:18:53,440 --> 00:18:57,960
It's still our department, our unit, but also teaching folks down in Indianapolis.

343
00:18:57,960 --> 00:19:00,160
And so it's a new territory.

344
00:19:00,160 --> 00:19:01,160
Right?

345
00:19:01,160 --> 00:19:04,280
It's not, I wouldn't say it's green field because we're right in the middle of a big

346
00:19:04,280 --> 00:19:05,280
city.

347
00:19:05,280 --> 00:19:06,280
Right.

348
00:19:06,280 --> 00:19:10,560
It's a new opportunity for us and a new phase of growth for us where we, I think we will

349
00:19:10,560 --> 00:19:14,520
still be growing in terms of faculty, in terms of students, in terms of facilities, but in

350
00:19:14,520 --> 00:19:16,680
a very different way that we have in the past.

351
00:19:16,680 --> 00:19:22,920
Not in our traditional footprint, not in places that we've all become familiar with

352
00:19:22,920 --> 00:19:28,240
over the 120 plus years that the department's been around, but in a brand new space.

353
00:19:28,240 --> 00:19:30,200
And that's really exciting.

354
00:19:30,200 --> 00:19:31,200
It's a little bit scary.

355
00:19:31,200 --> 00:19:33,640
It's ranking and a little bit scary.

356
00:19:33,640 --> 00:19:35,320
But I think the opportunities there are really great.

357
00:19:35,320 --> 00:19:36,320
Yeah.

358
00:19:36,320 --> 00:19:41,000
It's another thing that could set us apart from other programs as well.

359
00:19:41,000 --> 00:19:43,680
So Purdue Compute is a big thing.

360
00:19:43,680 --> 00:19:44,680
Yeah.

361
00:19:44,680 --> 00:19:45,680
Big.

362
00:19:45,680 --> 00:19:48,360
I don't know that we can even, we can probably do a whole podcast just about Purdue Compute.

363
00:19:48,360 --> 00:19:52,960
But it feels to me like ECE is going to play a major role in this effort that the university

364
00:19:52,960 --> 00:19:54,400
is spearheading.

365
00:19:54,400 --> 00:19:56,720
What is our role in Purdue Compute?

366
00:19:56,720 --> 00:19:57,720
Yeah.

367
00:19:57,720 --> 00:20:02,200
So Purdue Compute is this kind of umbrella term that the university is placing over a

368
00:20:02,200 --> 00:20:08,000
bunch of initiatives that are all kind of tied together by their importance or by their

369
00:20:08,000 --> 00:20:10,680
foundation in computing.

370
00:20:10,680 --> 00:20:15,520
And so this involves both growth in terms of core computing work, the kind of stuff that

371
00:20:15,520 --> 00:20:23,160
we do in computer engineering and also other parts of the department and ties with computer

372
00:20:23,160 --> 00:20:26,880
science, which is of course also where core computing work happens.

373
00:20:26,880 --> 00:20:29,720
But it also involves semiconductors.

374
00:20:29,720 --> 00:20:34,160
And all of the investments of the university is making in semiconductors, understanding

375
00:20:34,160 --> 00:20:43,040
that there's a vital national need for excellence in that space, both in terms of research,

376
00:20:43,040 --> 00:20:47,440
but also in terms of workforce development, putting out students.

377
00:20:47,440 --> 00:20:52,440
And if the country is going to come and say we need tens of thousands of new semiconductor

378
00:20:52,440 --> 00:20:56,600
engineers, well, what better place to do it than a place like Purdue where we have fantastic

379
00:20:56,600 --> 00:21:03,440
semiconductor researchers in classes and we just so happen to educate thousands of engineers.

380
00:21:03,440 --> 00:21:06,040
It also includes artificial intelligence.

381
00:21:06,040 --> 00:21:09,240
This is revolutionizing the world.

382
00:21:09,240 --> 00:21:13,360
And there's opportunities, I think especially at Purdue, to think about how artificial intelligence

383
00:21:13,360 --> 00:21:19,360
is going to revolutionize the physical world, the things that we touch and grow and move,

384
00:21:19,360 --> 00:21:20,360
not just computing.

385
00:21:20,360 --> 00:21:27,800
So this is the iPod, the Institute for Physical Artificial Intelligence that Purdue has, Purdue

386
00:21:27,800 --> 00:21:29,640
ECE has a huge role.

387
00:21:29,640 --> 00:21:31,000
It also includes quantum.

388
00:21:31,000 --> 00:21:37,360
So we're talking about things, Purdue gets to talk about doing things at very large scales,

389
00:21:37,360 --> 00:21:41,640
but we also do things at extremely small quantum scale.

390
00:21:41,640 --> 00:21:44,640
And Purdue has a lot of excellence there.

391
00:21:44,640 --> 00:21:49,720
So this is Purdue Compute, this big umbrella thing that spans maybe these four things,

392
00:21:49,720 --> 00:21:52,960
including AI, semiconductors, quantum.

393
00:21:52,960 --> 00:21:58,160
When I look at that, what I find fascinating is that there is not a one of those four things

394
00:21:58,160 --> 00:22:01,120
that our school is not at the center of.

395
00:22:01,120 --> 00:22:04,160
If you want to do computing, you should come talk to us.

396
00:22:04,160 --> 00:22:09,480
If you want to do AI, especially AI as it applies to real world things, you should come

397
00:22:09,480 --> 00:22:10,480
talk to us.

398
00:22:10,480 --> 00:22:13,320
If you want to do semiconductors, well, you should definitely come talk to us.

399
00:22:13,320 --> 00:22:16,000
And if you want to do quantum, you should come talk to us.

400
00:22:16,000 --> 00:22:21,560
This is, we are at the heart of what the university is trying to do across all of these initiatives.

401
00:22:21,560 --> 00:22:23,680
And it's really exciting.

402
00:22:23,680 --> 00:22:28,040
I don't think that these initiatives can be successful without us and without a successful

403
00:22:28,040 --> 00:22:30,840
ECE.

404
00:22:30,840 --> 00:22:34,880
And having said that, ECE is obviously a huge part of it, but there are other departments

405
00:22:34,880 --> 00:22:35,880
on campus.

406
00:22:35,880 --> 00:22:40,440
I think it's kind of a microcosm of how research and teaching works now.

407
00:22:40,440 --> 00:22:41,760
It's very interdisciplinary.

408
00:22:41,760 --> 00:22:43,400
We're not in our silos anymore.

409
00:22:43,400 --> 00:22:46,840
How much of that is important to what ECE does?

410
00:22:46,840 --> 00:22:52,520
So before I was interim head, I was the associate head for teaching in the department for several

411
00:22:52,520 --> 00:22:53,520
years.

412
00:22:53,520 --> 00:22:58,680
And as part of that, I got to interview basically every faculty candidate that came through.

413
00:22:58,680 --> 00:23:00,000
And we've been in a growth phase.

414
00:23:00,000 --> 00:23:05,240
We've interviewed probably over 100 faculty candidates in that time.

415
00:23:05,240 --> 00:23:07,600
And one of the questions they would always ask me is, what is one of the things that

416
00:23:07,600 --> 00:23:11,400
you really value about ECE?

417
00:23:11,400 --> 00:23:14,160
The thing that I would consistently tell them, and you could go talk to some of the assistant

418
00:23:14,160 --> 00:23:18,040
professors that we've hired over those last years, and they'll confirm this story.

419
00:23:18,040 --> 00:23:21,120
One of the things that I would consistently tell them is just how much our department

420
00:23:21,120 --> 00:23:22,840
values collaboration.

421
00:23:22,840 --> 00:23:29,040
Just how much we think it is important that our faculty work together to tackle the emerging

422
00:23:29,040 --> 00:23:35,160
challenges of the world, whether that's working with an ECE or working between ECE and physics

423
00:23:35,160 --> 00:23:39,560
or ECE in mechanical engineering or ECE in computer science.

424
00:23:39,560 --> 00:23:41,560
We have a very collaborative department.

425
00:23:41,560 --> 00:23:44,000
It's one of the things that I've loved about this school in the 15 years that I've been

426
00:23:44,000 --> 00:23:46,000
here.

427
00:23:46,000 --> 00:23:47,080
And I agree.

428
00:23:47,080 --> 00:23:52,200
This is the way that we're going to make progress on some of these grand challenges of our time.

429
00:23:52,200 --> 00:23:57,360
There are very few things out there where you could say, oh, I can solve this fundamental

430
00:23:57,360 --> 00:23:59,200
problem while staying in my silo.

431
00:23:59,200 --> 00:24:00,200
Yeah.

432
00:24:00,200 --> 00:24:01,200
Yeah.

433
00:24:01,200 --> 00:24:02,200
It's not as much fun either.

434
00:24:02,200 --> 00:24:03,200
That's right.

435
00:24:03,200 --> 00:24:04,200
It's working with other people.

436
00:24:04,200 --> 00:24:07,200
So, I mean, it's not like you don't have a lot of responsibilities between the little

437
00:24:07,200 --> 00:24:09,920
teaching you do, the interim head.

438
00:24:09,920 --> 00:24:13,840
When you do have free time, what kind of things do you like to do?

439
00:24:13,840 --> 00:24:18,280
Well, I would have answered this question very differently 10 years ago.

440
00:24:18,280 --> 00:24:22,240
What I like to do in my free time is, you know, I love cooking.

441
00:24:22,240 --> 00:24:24,920
And so I'm kind of the primary cook for our family.

442
00:24:24,920 --> 00:24:29,960
I read a lot of science fiction and fantasy, but also other things.

443
00:24:29,960 --> 00:24:33,440
I play the piano and the mandolin in my spare time.

444
00:24:33,440 --> 00:24:34,440
Cool.

445
00:24:34,440 --> 00:24:35,440
So, that's a lot of fun.

446
00:24:35,440 --> 00:24:37,800
I love watching movies and going to see movies.

447
00:24:37,800 --> 00:24:41,440
Eight years ago, the story really changed, which is that now I've got an eight-year-old

448
00:24:41,440 --> 00:24:42,720
and an almost six-year-old.

449
00:24:42,720 --> 00:24:46,760
He's turning six in a little bit over a week.

450
00:24:46,760 --> 00:24:49,960
And they take up a lot of the time.

451
00:24:49,960 --> 00:24:53,240
Do you see engineering seeds in them?

452
00:24:53,240 --> 00:24:56,000
Are you doing to them what your parents did with teaching the coding?

453
00:24:56,000 --> 00:24:57,000
Yeah.

454
00:24:57,000 --> 00:25:00,080
I'm doing, I'm engaging in a little bit of brainwashing.

455
00:25:00,080 --> 00:25:01,360
My wife is a social scientist.

456
00:25:01,360 --> 00:25:02,360
She's a psychologist.

457
00:25:02,360 --> 00:25:07,880
So, she's not the biggest fan of my attempts to kind of guide them in an engineering direction.

458
00:25:07,880 --> 00:25:13,760
But you know, my daughter, who's eight years old, above her bed is a little collage of

459
00:25:13,760 --> 00:25:17,400
prominent women scientists and engineers.

460
00:25:17,400 --> 00:25:18,400
Oh, I love that.

461
00:25:18,400 --> 00:25:19,400
Right.

462
00:25:19,400 --> 00:25:22,080
So, Rosalind Franklin and Grace Hopper and Hedy Lamar.

463
00:25:22,080 --> 00:25:24,280
And so there's a little collage of that.

464
00:25:24,280 --> 00:25:26,880
It started early.

465
00:25:26,880 --> 00:25:31,040
When she was two years old, she had to do a little project in her daycare about what

466
00:25:31,040 --> 00:25:32,520
she wants to be when she grows up.

467
00:25:32,520 --> 00:25:34,240
She said she wants to be an engineer.

468
00:25:34,240 --> 00:25:37,920
I definitely saved that and put that somewhere safe.

469
00:25:37,920 --> 00:25:41,040
So, I definitely see those seeds.

470
00:25:41,040 --> 00:25:43,840
I don't want to force them down anyone path.

471
00:25:43,840 --> 00:25:47,080
If they want to be engineers, then I would love that.

472
00:25:47,080 --> 00:25:48,080
Yeah.

473
00:25:48,080 --> 00:25:51,760
And I do love that they are really excited about how the world works and science and

474
00:25:51,760 --> 00:25:53,360
math and things like that.

475
00:25:53,360 --> 00:25:56,320
But they have so many talents and there's so many different amazing things that they

476
00:25:56,320 --> 00:25:57,320
could do.

477
00:25:57,320 --> 00:25:59,760
So, I'm not going to try to put them into a box.

478
00:25:59,760 --> 00:26:00,760
Gotcha.

479
00:26:00,760 --> 00:26:02,200
Well, I think that's all I have.

480
00:26:02,200 --> 00:26:03,200
Thanks so much.

481
00:26:03,200 --> 00:26:04,840
It's been a great time talking to you today.

482
00:26:04,840 --> 00:26:05,840
Thanks for joining us.

483
00:26:05,840 --> 00:26:06,840
Yeah, this was a lot of fun.

484
00:26:06,840 --> 00:26:07,840
Great.

485
00:26:07,840 --> 00:26:12,120
So, that's it for this episode of Engineering Innovations, the official podcast of Purdue

486
00:26:12,120 --> 00:26:15,960
University's Elmore Family School of Electrical and Computer Engineering.

487
00:26:15,960 --> 00:26:19,280
If you liked the show, please rate it and subscribe and make some comments.

488
00:26:19,280 --> 00:26:20,360
It really helps us.

489
00:26:20,360 --> 00:26:24,000
Your feedback helps us craft how the next episodes are going to be.

490
00:26:24,000 --> 00:26:26,920
And taking you to the next episode, we'll have another one next month.

491
00:26:26,920 --> 00:26:39,080
Until then, thanks for joining us.