Everyone, welcome to the Noob Show. Today I am joined by Matt Heunerfauth, the Dean of
the Galicano College of Computing and Information Science at RIT. In this, we talk about the
value of education, how AI is changing the landscape, asking alumni for money, and so
much more. Joy. Two million people in the audience. No, so I've got one of these fancy
mugs, right? These like Ember mugs that keeps your coffee hot. And I used to be against
it because, one, it's an expensive mug and two, it's like if I'm not drinking the coffee
fast enough, what's the point? But it doesn't prevent it from spilling all over my keyboard,
which I just did. So it's good now. It's good now, but I definitely spilled it everywhere.
You need like a sippy cup version, apparently. Yeah, I do need a sippy cup version. Actually,
fun fact, I met there's a there's a product out there called the Mighty Mug, and it's
one of those mugs where you can't knock it over. Have you seen that wobble? Like one
of those little kids sort of? Yeah, but it like it has like this suction cup device on
the bottom. And so when you hit it from like above 10 or 15% of the you can't knock it
over because it like stays suction to the and I met the guy who co founded that last
weekend coincidentally. But you know, doesn't help me not having one. So how's your how's
your day going? Doing pretty good. Yeah. That's good. It's a snowy day here in Rochester, New
York. Is it snowing? Yes. Well, slowly calming down. Yeah. Okay, I don't necessarily miss
that. But have have have there been any snow days like in the past couple years, like who
you know, it's probably been about four or five years. But the first winter I moved up
here to Rochester, it was a pretty cold one. It was like 2014. And I think they might have
called off twice. But of course, it's not day. Oh, no, no, no, back in 2014. I think, you know,
it wasn't the snow. It was just it was so cold. They were worried about folks going to
school and things like that outside. But no, this year, this year, nothing much. So when I was
there, from, I think we had one, it was during finals, which was awesome. One final got postponed
to the New Year because there was like six feet of snow that just dumped overnight or
something crazy. But that was the only time ever and it was only after two o'clock and the
exam was at like 215. So we got really lucky. Discrete math exam. I don't know if I was ready
for it. But I don't then again, I don't know if I was ready for it after the New Year.
Just got to worry about it all over Christmas. Yeah.
I know that's the worst, right? That's why I like doing them before. Are then our students in
exams right now? They are. Yeah, it started halfway through last week, and it finishes up on
Friday. Okay, okay. So you joined RIT in 2014. I did. Okay. What, what, why the switch to RIT from
from because you were in New York City before, right? Yeah, so I started my career as a faculty
member at City University of New York back in 2006. And then I started everything there and in
Queens College was where my laboratory was. You know, I think my my area of research has always
been using AI technology to try to make useful applications for people who are deaf and hard of
hearing. And so being in a big city like New York, there was lots of people. So that meant that you
certainly could recruit like people who are deaf and hard of hearing to kind of test out some
technology you made or do some experiments or something. And I even used to like run a summer
program there for high school students who were deaf and hard of hearing, so that they could kind of
get like a little research experience over the summer, get them kind of excited about computing
and things. The trouble was, I mean, you know, I think during the summertime when I ran that
program, like the language of my laboratory became sign language, and I would sign and we could
recruit a lot of folks. But then during the rest of the academic year, it wasn't like that, like
there weren't deaf students of any significant numbers at the university. And although I could
get really good computing students to work at the lab, and I could get some great deaf students to
also work at the lab at other times to like recruit folks, I couldn't quite crack the thing to figure
out how to get like a great deaf computing student to be working at my lab. So, you know, back in
2014, a job advertisement was forwarded my way for an opening at RIT. And of course, I knew what
RIT was, I mean, working in, you know, deaf technology, everybody knows about NTID and a
thousand deaf students on the RIT campus because of the National Technical Institute for the deaf
there. And I had visited before. And like the job ad, I read it, and it sounded like somebody wrote
it for me. Like it was, Oh, we're looking for a mid career faculty member to come and join RIT and do
research on accessibility technology for people with disabilities. And I was like, Okay, I have to
apply to this. I was happy in New York City. I wasn't planning to move in. But when I when I saw
that, it just got exciting. Yeah. And I think the big two differences when I moved to RIT, the first
one was because there's all of these really skilled sign language interpreters and a lot of support
services and things like that on the campus. It was really possible to recruit deaf and hard of
hearing master students and then PhD students and have them be successful at actually doing a PhD
here. It's also a place where you can go put up a poster, hang it up on a wall and say, Hey, we need
35 people to like do an experiment, we need 35 deaf and hard of hearing people to do an experiment to
try out some pieces of software we need. And you could have a list signed up in a day. And after all
of the logistical things we used to go through, even in New York City to try to recruit people to test
things out. That was just amazing. And that really like accelerated stuff. So yeah, that that was the
big reason for the leap. Because of the research field I did.
Yeah, that's great. I think it's fascinating. And so but the person who sent you the job posting, did
they read through your 45 pages of resume?
Perhaps. So yes, we have a 45 page resume. Well, in my defense, it's it's
here we go. This is the academic in a club.
It's a thesis review, right? So and grad students that that work in computing or are studying, we
teach them how to create a curriculum vitae, basically a really long resume, that absolutely
everything you have ever done as an academic gets in there every paper, every every little thing.
And yep, mine is a exciting page turner of 45 pages.
Do you feel like a badass? Like you go to like kinkos, you print out 45 pages, and then you just slam it
down during the interview, you're like, right?
I haven't tried that one yet. Because mine, mine's like a mine's like a feather.
What was that?
And I mean, you know, something that I often like talk about with students when we're trying to like,
you know, write a paper on something, it's a lot easier to write something that's too long than it
is to actually write something that's short. So actually, the challenge of compressing something
down into just a couple pages, that's really tough. I don't, I don't know. If I ever wasn't doing
academia, and I had to have like a normal resume, compress that thing down to two pages, I don't
know. Well, I mean, that's a great segue into our friends in the AI world would using AI to do
that. That sounds like something that's very common and likely.
It is indeed. I mean, that. So, you know, I feel like during my career, I've gotten to see a couple
different interesting leaps in what computers can do. Certainly, I started studying computing well
before, you know, smartphones and having a computer in your pocket was a thing. And as someone who
studies how people use tech and evaluates whether anything's a good idea and you tested out with
people, the idea that you have another platform like that to do things is really exciting. It opens
up new applications. But then after I started doing work in creating technology and software for
people who are deaf and hard of hearing, a lot of it was about speech and language tech. So some of
it was on tools to make sign language animations or things that tools that could allow for automatic
creating of captions with speech. Yes. And so the second big leap was really maybe about a decade
or so ago, when there was a lot of new neural network based approaches in artificial intelligence.
And there was another just huge leap in performance. And I think suddenly technology like
automatic speech recognition that had always been a little bit niche, like, you know, you could get
like drag and naturally speaking, you could go train it for a long time and use it to your own
voice. Suddenly, about 10 years ago, it started to really become seriously powerful and accurate
enough that I got really excited and started to shift a little more my focus at our laboratory
towards automatic captioning tools. So could you have a meeting like this and auto automatically
have the captions appear, or have somebody go to a lecture and do it? And you know, 10 years ago,
that sounded a little bit iffy, because everybody was was thinking about speech recognition from the
way it had been before this. And so the kind of work we were doing was, okay, this is probably
going to be awful, but how could we make it better for people, right? Could we could we indicate in
the captions, when the tool isn't quite confident of the word it heard, and then maybe that would be
useful for the user and stuff like this, it got better, the tech got better. And suddenly we could
do all these powerful things with those neural based AI techniques. I feel like this is now like the
third leap is all of this generative AI stuff. The I mean, some of it we folks that do, you know,
technology and speech and language had seen large language models for a long time. And we had kind
of known like, these things can look a little bit magical when you interact with them. But the idea
that suddenly it's actually like out there and people are experimenting with it and trying to use
it in all sorts of different ways. That's I think the exciting thing. So similar to that first leap I
mentioned of the smartphone in your pocket, suddenly everybody was trying it out, using it in a new
way, figuring out in their day to day life what they might do with it. It made this ecosystem of
people making apps for these devices, because it would fill these different needs. I think that's
kind of what we've just seen with the generative AI. And so as you said, could I could you get a 45
page resume down to two pages? I haven't tried it yet. But yeah, I'll give it a whirl after this.
Yeah, buffer overflow. Yeah. And I also think, you know, we've a lot of during my career, we've been
studying like how to make like custom AI tools for people with disabilities to do different things
like, I don't know, like, simplify text, if you have trouble reading text, for example, well, the idea
that there's now almost this like utility knife of an AI tool that can do all these things like
summarize things, simplify things, reorganize things, that's going to be really interesting in
many applications, but also in tech for people with disabilities. Because now it's something that's
in every web browser, or you could add chat GPT or whatever else to anybody's phone. And you don't
need a custom specialty app or whatever, you can use the same thing everybody else is using, but
there's all these interesting ways to use it. Yeah, totally. So that, you know, actually, I use whisper,
which was one of the open AI products to transcribe all the audio for this podcast. And then I upload
those and I try to timestamp them to so you can say, at two minutes and 47 seconds, you know, well,
Matt was talking, it's easy in this situation, because it could be half wrong, which Matt, but
and then you can like go to that, or you can just see the text and it works for a number of reasons.
It's not just for, I know the accessibility is huge, but for SEO, for example, if you're searching
on a particular topic, and you can see, oh, we're talking about accessibility or talking about AI,
you can go right to it, and then you can listen in or you can read where it was. So I think it's
pretty cool. Speaking of AI, how is school, how's RIT? I mean, you're the big boss now. How is RIT
embraced AI? Like, you know, I'm sure there's tons of things to think about. There's potential,
you know, everything from cheating to AI pulling in, you know, if you're helping it with a paper
or with code, you know, using source code that may not be open source or licensed properly, like,
but also it's such a great tool. So I'm really, really curious to know, like, how is RIT embracing AI
and what do you, how is education doing that? Well, you call me the big boss. What I'll say is
about two years ago, I crossed more into the academic leadership side of things. I became the
Dean of our Golisano College of Computing and Information Sciences, of which I know you're an
alum. And so asking about sort of, you know, what has RIT been doing in AI? I think actually,
the story is a little bit longer than just kind of the excitement about the generative AI very
recently. So, you know, when I think about sort of the portfolio of all the different areas of
research that the faculty in our college do, really the trend over the past 15 years has just
been a bigger piece of the pie being AI. Or folks that were doing work in some other area of computing,
suddenly there's an AI methodology to what they're doing as well. So they might be working in
cybersecurity, software engineering, some other area, but just about everything has like an AI
twist to what you can do. And that's kind of some of the exciting frontier in many ways.
So there's that progression. In the curriculum side of the house, students clamor for AI courses,
right? So those AI courses, the machine learning courses, they fill up real fast, right?
Yeah. Everybody's on the course registration system trying to get into those.
Back in my day, the classes that fill up quickly were like wines and beers of the world.
Well, that one too, but you know, I think it's neck and neck with an AI class nowadays, if that
tells you something. That says a lot. And so, you know, slowly what we've been doing is kind of just
adding more in the core of the degree programs, because we just realized everybody was picking
it as an elective. But you know what, this is now fundamental to being a computing professional
nowadays, to have some of this sensibility about artificial intelligence. So much so that we even
made a whole master's degree in artificial intelligence that opened this year. Now we've had
other ones where I mean, you could do a master's in computer science and just fill up your courses
with a bunch of AI courses. But I think just as another sign of the times, we've created this
whole new program that's a master's in AI. I think maybe where you were starting with the question
though was what has happened with this whole generative AI, because now you can have it to
your homework, right? So I mean, where a lot of folks, I mean, I think a lot of the press about
this has been about like, Oh, no, is this the end of the essay or something like that, writing essays
in college or something. And I think there is a lot of worry about what happens when it's really
easy to generate fluent text in that way. What has a lot of us pondering what we're about to do next
in pedagogy is actually the fact that this stuff can write computer code too, right? Yeah. And you
might if you read some English texts that these things generate, sometimes you can sort of tell
sometimes you can tell a little bit about style, it's getting so good that actually it's hard to
tell. But when it generates computer code, it's trained to make it look really fluent. And the
trouble is, if there's a bug in that code, it's insidious. I mean, the code will look beautiful,
but there's a terrible flaw in it sometimes. So, but it works pretty good on introductory courses.
So what we're now facing is how do we help educate a whole nother generation of computing
professionals, where there's now a tool that you literally can give it your homework assignment
for an introductory programming class, and it does a pretty good job. Yeah. And the trouble is the
output is computer code, right? So it's actually a little tough to catch. Oh, yeah. This is the
topic of a lot of faculty meetings around the college. You know, where I think we're headed.
Well, what we're doing this year, this year, in our college, the rule is, if a faculty member
is teaching a computing course, you have to say something in your syllabus about what your policy
is on whether you can use generative AI or not or for what assignments. And if like you've got
like a course where everybody's teaching like five different sections of the course because
everybody needs intro to programming, you have to have the same policy across them.
But we're treating this semester as a bit of an experiment to kind of encourage faculty to
someone to embrace it, someone to be like, Oh, no, don't use it. That's cheating.
We're, we're trying it all. We're a big college. So we've got about 5000 computing students.
So we can also try this strategy a little bit of, let's try a bunch of things. Let's see what
works something something's going to take. And then this spring, we're going to bring it back
together and have a lot of conversations among faculty about what worked, what didn't work.
You know, early directions that I'm hearing from faculty is if it's an introductory course,
we really have to do something to make sure that folks are able to code themselves.
And maybe that means like more activities in the classroom, live things, that kind of stuff.
As folks get more senior though, through their degree, if they don't know how to use these tools,
that's actually a problem. I mean, they need to be able to do this and use co-pilot or any of these
assistive tools when they go out in the profession. Yeah, 100%. And it's funny, all the things that
you've mentioned resonate so much with me. So the, I was, I use co-pilot a lot. I've been using it
throughout, I was using it throughout the beta and I pay for it, which is the, which is GitHub's
like code, Gen AI on, and it's 100% built on top of open AI. And it wrote some JavaScript code for
me. And I'm like, why doesn't this, I couldn't think about why it wouldn't work for a good 10
minutes. And I was like, pulling my hair out. And I'm not reading it and reading it. And like,
if I didn't know that, you know, about like, you know, like hoisting variables and JavaScript,
I probably would have just given up, but I recognized, I was like, wait a minute, that part's
wrong. So I rewrote it, right. And now it's beautiful. But what I love about it so much is
it's helped keep me fresh. And it's helped teach me things that maybe I've forgotten or didn't know.
Because I can select some, I can, it's kind of weird, like it can be very, very lonely,
but my friend is now is chat GP is GitHub co pilot, because I could select some code and I'd
be like, explain this, or why am I getting this error? Or how would you make this better? Like,
so you have a function or a method or whatever that's ginormous, how would you make this cleaner?
How'd you make this better? And it can tell you and it can be like, I think you should do it this
way. And you can accept the rejected. I find it to be really fascinating. But also, I think that
personally, I think that you do need to have some fundamentals of understanding how, you know,
computer science works. And in a lot of fields, the way that you help to cultivate somebody to
be an expert is you usually start with showing them a lot of examples of products that other
folks have produced. So for example, if you want to teach a model, sort of, I mean,
if you want somebody to be a great creative writer, you would have them read a lot of literature and
talk about the literature, talk about the writing, similar in many of the arts, right? You would look
and consume and critique a lot of it. Historically, that has not been a way that we think about
education in computing. But that idea of being a discerning critic of something that might not
be perfect or something that could be improved may need to be more about how we think about and
start teaching computing. If what it means to be a computing professional is also working with these
AI tools that sometimes are producing beautiful looking but wrong code. I mean, we see this in
the profession, if you think about code review, or sometimes like pair programming kind of things,
and maybe sort of, you know, chatting with your buddy, chat GPT or co-pilot while your code is
wherever my rubber duck programming ran away. But like that, that idea of sort of like, be a
critic, be able to kind of consume something that's not quite right, I think is going to have to be
an earlier and really important part of computer programming nowadays. I think that used to be
something that happened later in the training of somebody, maybe when they took a software engineering
course near the end of their computing degree or something, and they learned how to work with a
bigger team of people, maybe they would get into code review and stuff like this. But yeah, I think
it's become the new skill.
This might sound like a sort of a strange analogy, but it's very, I think to me it works.
When I learned how to, before I knew how to use a debugger, I was doing like, you know,
print statement debugging. And once I learned how to use a debugger, I was like, this feels like
cheating, and I'm 10 times better now. And I think AI for me is also helping me get there. Like,
it's not going to solve all the problems, but it's going to help me become more efficient,
less, you know, use less of my own energy, right? Like I'm sort of more of the conductor than I am
the, you know, the guy tightening the guitar strings, or that's a terrible one. But you know
what I mean, like I'm more of the chef than I am the cook that's just flipping burgers.
And so I find, to me, that seems much more interesting because I love creating stuff and
the faster I can create things, the better, I think. Well, that debugger analogy is interesting
because that actually comes up in a lot of discussions and debates about how we ought to
be educating the next gen of computing professionals because, you know, one way of thinking about
how we approach chat GPT or tools like co-pilot from an education perspective is, well, maybe we
should introduce it early on, but then maybe we don't do a ton of hand holding throughout an entire
degree. Instead, we just try to build some competency in it. And then we let the students
use it if they want or figure out their own style of using it. And in many ways, that's more analogous
to how debuggers are treated in the curriculum for a lot of computing programs nowadays.
Some early course or two, you might get taught how to use your debugger. But in general, you're
not going to hear like professors bring it up a lot during your whole degree. They're just going
to kind of assume like you figured it out. Like you showed you what a debugger was. If you needed
it, you'll use it. And maybe that's where this is headed with co-pilot. I don't know. I think
I feel like we're kind of searching for models or analogies that might help us. And a lot of the
ones that I hear about things like chat GPT are stuff like, oh, it's like the calculator. And,
you know, when calculators were invented, it didn't stop the need for math classes and everybody
was worried about them at first. But, you know, we figured it out. Maybe we'll get there. Maybe
it's a calculator. Or maybe it's like how we do debuggers. I don't know. Or it's going to flip
the whole field upside down. I don't know. I think there's a couple of different ways that this could
go. I just, I just can't wait to like, like, you know, tell, you know, I used to write my code
with, you know, hole punches and paver like, man, you sound old. And now it's like, I used to write
my code by hand. Like, there was no AI back in my day. And you're like, wow, you're really old. It's
like, it's like, where are we going to be? You know, anyways. Okay, so I'm curious about education
in general. And, and, you know, obviously, AI is a big thing. And I think at all levels of
education, that's being, you know, high school, middle school, I don't know, maybe elementary
school. There's been a lot of talks about that. But I'm curious what you think the, you know,
what are the current trends in computer science education, and what impact they have on students?
Yeah, so I mean, you know, the big answer on that one is what what's about to happen with AI,
right? So, but I'll set that aside for a moment. Other trends that I've been seeing in computing
education is an approach to the field that really thinks about how computing needs to be considered
in the way that it intersects with other disciplines. That, you know, we we use computing
to do things. And at times, you also need to provide training and education to somebody
that more explicitly gives them competency in a second field as well. So you see examples of this
at some universities that have degrees that are sort of computing plus something else.
I think we see trends of an increased awareness of the importance of electives
and minors and things like that that a student would take along the way,
where they figure out maybe a sub industry or field where, yes, they want to use computing
to do something, but they want to also know about this other intersection with the world.
So at RIT, all students have to do something called an immersion. It's kind of like a miniature
minor where you got to take a couple courses to get a little bit of depth into something.
And then usually you take two more classes, you get a minor, right? There's there's things like
that that you can do if you really got interested. There's the carrot. Yes, yes. You're so close,
just two more classes. The the other side of that, I think is, you know, there's been a lot of
research on what really draws people to the computing field. And I think there are some
students where, you know, they get really excited about tech in high school, or they think of
themselves as like a tech person or a computing person. And they know, like they know they want
to go to university and study computing. I think there's a lot of other folks that we're not catching
yet in the computing field that would be awesome in the field, but we're not capturing their
imagination or attention yet. Because we sometimes present the field as like a puzzle, a techie thing,
a cool gadget, a futurist kind of thing. Whereas in reality, you know, computing changes the world
in many different ways. And it's a powerful way to change the world. And so reframing the field
with that interdisciplinary view of how does computing allow you to address social problems?
How does computing allow you to do things that benefits people, improves lives? Yeah,
research has shown that that resonates a lot more with students that are currently
underrepresented in the field. So women, people of color, people with disabilities.
And so, you know, our our college, for example, if you do the the wayback machine and take a look
at our college website over the years, a trend you might notice is that now we kind of frame our
college as we prepare students to improve lives and change the world through computing.
And that wasn't an accidental shift. That was a really careful strategy to think about how we
can draw more folks into the field from that perspective. So I think that's that's certainly
a trend I've seen. Yeah. RIT always has had a long history too of co op. So doing a bunch of
internships during during the course of your degree, and helps you pay for your degree too,
because you don't pay tuition when you're doing that, you're making some money for a semester.
I've seen more and more universities go that direction. I mean, so RIT has been there for
like 50 years doing co ops. But I think other folks are kind of catching on to the idea that
a lot changes when a student gets that first workplace or real world experience. And I know
from like the professor side of it, if I'm interacting with a student, I can kind of tell
if they've already been out on co op already, because like the kind of questions that they ask
in the classroom are like, a little more pointed. Actually, when I was doing this, I saw I saw we
were doing it this way, you know, this kind of stuff, which is great. And not normally
something that you would see in the classroom at most universities.
It also keeps us very honest in terms of are we really teaching the absolute latest stuff?
Yes, it's not just going to be alumni coming back and telling us that it's going to be our own
students. As soon as they come back from a co op, we're going to tell you like, oh, no,
you're teaching the old version of this when I was in the my co op last semester, I was using the
new version that kind of thing. Yeah, I don't know. So the co op thing was one of my favorites
at RIT. And I don't know a single person that did a co op that said it wasn't we shouldn't do
these. It wasn't worth it. And everyone that came back said that they learned so much more on the
co op, like, because it's real world experience, you're applying the things you've learned.
And like, you're also getting paid. So it's way more exciting, right? Like,
and it kind of like, it kind of warms you up into the idea of like joining the workforce, like,
you know, instead of like, after four or five years, hey, here you go. And you're like,
hope you can swim. You learn a little bit along the way. And I think that to me, it's,
I don't I'm surprised on every school's already done this. And people will do a co op. And then
they'll realize, Oh, my gosh, I hate this or something, right? You know,
totally lies. Like, you know, a company of that size. Oh, no, I don't want to work there. Or,
or this part of the country that I lived in for my co op that summer. Oh, I didn't like this. And
that's actually really useful too. And better, better you figure it out on like a three month
co op, rather than go off and move to what you thought was a permanent job and then have to
figure this out. 100%. I think Greg, Greg Coburger may or may not be in the audience here. And
he I remember this he one or two of his co ops he did with a startup company. And now he's founded
his own startup company. And it's very successful. Greg, when you when you decide to send me some
money, I'll give you a free ad here, even though your frequent podcast goes. But, but because of
that, he he gained a lot of I believe, you know, I'm speaking for him now, but I think he gained a
lot of confidence in knowing like what it's like to join a startup, what that life looks like,
versus joining something like, you know, Apple, Microsoft, Google, your typical massive companies
that a lot of people want to work at right out of school. So I think it's immensely valuable.
So, okay, so there's always like, you know, if you read the comments, which they said,
don't read the comments on the internet, there's always people saying like, education is broken.
Right. And, you know, I know that there's a lot of really famous tech people that have said this,
and, and there's a lot of different schools of thoughts. And so I'm really what I'm curious
about is around sort of like, where do you one see need for change in education? And two,
what is that change that you think needs to happen? Is it at a small level? Is it, you know,
kind of a US or a global thing? I think the the trend that I've been noticing is it's a much more
crowded market of choices. So there's everything from like a website where you teach yourself to
code to some sort of online massive course you could do to a programming bootcamp. I mean,
other things I'd put on that scale would be sort of like accelerated sort of programs,
maybe like a for profit university. And then you get into things like full degrees that you might
have at a traditional nonprofit university, whether it's a public institution or a private one.
Now, I work at a private nonprofit university that offers four years degrees, but we also do
other stuff too. I mean, we do these kind of certificates that people can do in a shorter
period of time targeted more to professionals. So I think right now, what you're seeing is
there's a crowded market of a lot of players to offering things that these different points on
that spectrum I mentioned, and then even more traditional universities are experimenting with
degree programs or non credit programs even that are shorter in experience shorter in the
amount of time, right, folks get some experience. What I have seen is there is still a very big
value, if someone can do it to doing like a university degree. You know, it can be expensive,
you look at tuition prices, they look kind of surprising. A lot of things there. I mean,
first of all, a lot of public universities have gotten much less government support over the years
and have to raise that money through tuition. The other thing is when you see a tuition number
for a university, that's really sort of the sticker price. I mean, kind of like buying a car,
that's the starting point. But there's usually a lot of financial aid and scholarships. Really,
that's put sort of putting the max end on things. And, you know, from the perspective of supporting
students that don't have the resources to pay for a lot to go to university, it's actually kind of
better that you see higher sticker prices there because what that means is some people might
be paying that price. But then the university is using a lot of that money to offer financial aid
and scholarships to folks that absolutely can't pay that price. And everybody at that university
might be paying a slightly different price depending on what their financial circumstances are.
There's a big jump in what you're earning is after getting a university degree, especially
if it's in a very career relevant field, right? So I'm in computing. I feel good about this. I
know you go, you somebody go gets goes and gets a computing degree. Yeah, it's going to be worth it,
right? Because they're going to they're going to earn more. And then, you know, in studies of, well,
what happens after graduation? Because, you know, they didn't work for four years, they went to
university and and now they have that debt from some tuition debt. When does it catch up? You
catch up pretty quick if you're in computing. So depends on how much you spend and other things.
But usually in your mid to late 30s, it's you're catching up, right? Yeah, because the higher
earning power that you've got from the degree. I think, you know, going to a university, I think
also has a really formative kind of experience for students too, because for many, if they're
doing a residential kind of program, they're staying at the university, they're not working,
they're not commuting from home. It might be their first experience living on their own,
trying out kinds of new social environments and joining a club and something they never thought
they would join and you never know what might happen. You know, engaging in an entrepreneurial
activity on their campus, for example, those kind of things, you just don't know where it's going to
head. And a lot of that spontaneity and being part of that environment and that setting
is a big part of the experience too. I think it's good that there's options at different levels,
and there's ways that folks could do like a mid career switch and take a programming boot camp
and do that as well. I think there's always going to be some folks for whom a four year
university degree is the right answer for a lot of those reasons I mentioned. But even four year
universities are getting into the business of more masters or certificates or things like this too,
because we realize some folks want other options. Yeah, interesting. Alexis Ohanian, one of the
founders of reddit.com, people would ask him a lot. He would do a lot of live talks, and I don't
know if it's because he wants to be like a politician or something one day. But it seems like it.
But he gets asked all the time, like, is it worth going to school? Actually, Gary Vaynerchuk
also gets asked this all the time by parents. Is it worth going paying for four year degree?
They're really expensive. Is it worth it? Because then you also hear about the people who didn't go
and like, you know, maybe they're just edge cases like Bill Gates and the dropouts like Bill Gates
and Zuckerberg and those people. But what he said, which I thought to be a really good point,
is like, if you're able to go go, you can always try your entrepreneurial ideas while you're there.
And if you fail, you're still in a safe spot. You haven't like put all your chips in and like,
you know, you're not all in on the idea. Because what happens when you're all in and you and now
you've got nothing to fall back on or you've got no support system there. So I really liked his
answer on that. Should you go to college, basically. And Gary Vaynerchuk, I think, says a lot of
similar things to that regard. I think it's also very disciplined specific too, because I think
it really depends on kind of the field that someone is studying. When it's an area that you know
there's really strong demand for folks to work in that field, I think it can change the calculus
on that quite a bit. Yeah. Yeah. So that's an interesting question. Like, do you think school
should encourage students into those fields or let them do whatever they want?
I think that we need to provide the choices for students. But I think I think the world's a better
place if we give folks more information, and then they can make an informed choice, right?
So I don't think that every high schooler is in a family circumstance where maybe their parents
didn't go to college, or maybe they're not getting advice about what is the best area to go work in,
or what is a hot field or something, right? Some do, but many don't. And they may just look at
like kind of the catalog of all the choices and just sort of pick one that sounds interesting or
something. And I think that's great. I think it's good to be able to try a class or two and things
and then see if it's your passion. But getting some more of that information to students about,
oh, yeah, like, here's what we're expecting is job trends in that field over the next couple years.
And what was the average, you know, starting salary for people who graduated in that program
over the past couple years? Oh, that's interesting. And what percentage of people had a job in six
months after they did that degree program, right? Sort of more intentionally choosing your degree
than just sounds all right, or it's easy for me or I'll be fine in four years. Now, if it's something
that you try it and you hate it, right? I mean, it kind of doesn't matter at that point if you
could get a good job in it, right? So there is a matchmaking to this, right? And you're hoping to
find kind of a sweet spot among all those different factors. But if we don't give that information
or expose it really clearly to students also, I think we're doing them a disservice. So I agree.
Some universities do a better job at this than others where they are very clear on, you know,
starting salary stuff and things like that. But it is a debate in higher education. I mean, because
certainly folks in some of the the science, technology, engineering and mathematics stem
sort of fields. Sure, we like this because usually our students are doing pretty good when we graduate.
But individuals from the humanities, you know, would really talk about kind of the way that
education transforms your mind and forms you as a person. And some of this sort of career and
dollar sign oriented stuff feels very different than that, right? So like everything in higher
education, there is debate. And I think that's good. But but getting that information and choices
out to students is important. Yeah, I think it's good. I think it's a really good idea to get it
in front of them, let them see the options and sort of, you know, hey, look, this is what things
are going to look like or look like now. But you still can make your own choice. I think that's
important. So speaking of after you graduate. And you so you graduate, you know, you're paying
off your loans if you have them. And then how do you approach Okay, so how do you approach the
topic of asking alumni for money after they had just spent the money on school? This is a question
I had so I had to get in here. Because I desperately want to know. Sure. Well, so when folks move
into sort of higher education leadership positions, a big part of the job is kind of the
advancement side of things of kind of reaching out to alumni or companies or supporters to try
to raise funds for the university. Although the tuition price can look high, it actually doesn't
pay for everything that the university needs to operate. And so universities depends on the
type of university and things like this, a good chunk of how it operates may come from philanthropic
dollars, or philanthropic dollars that at one point went into an endowment for them institution
that produces funds for them to operate. I think also when a university wants to do new things,
philanthropic dollars wind up being the thing that allows that to happen. That's kind of a new
program or some new opportunity or space for students or things like that.
Sometimes it's tough to fit that in your standard operating budget and save up all those funds to
create something new like this. A gift can be the thing that makes the step change in that case.
So then I think when approaching alumni or supporters about this, I think part of it is
really about listening. You can't go in with a whole list of, well, we need this, we need this,
here's our laundry list or something. Sure, somebody might donate some money, but in general,
if you get a chance to talk with alums, you're actually getting really good data from them
about what their experience was and what they're seeing in the world.
So my disciplinary background is human-computer interaction. What that means is using psychology
methods like interviews and focus groups and experiments to study things regarding people in
tech. So the idea of having a really good interview conversation with somebody and learning a lot
from it is just kind of baked in to the way I think about stuff as a scientist. And so the idea
that you get to go around and talk to a bunch of alums and hear their story and hear from them
like, what did you think was most important? And what do you think we ought to be thinking about
next? That alone is hugely important. Sometimes they'll even donate their time and they'll come
back and talk to our students or be like peer mentors. And then if they have some money to give,
and what you want to understand is, well, what did they care about? What actually matches with
something that resonates with them and something they would love to see us be able to do for students
next. And a lot of that really comes from them reflecting on their own experience. Maybe something
happened during their time at university where somebody was able to step in and help at a certain
point or they realized that they had a challenge during their time at university. And they could
imagine that, well, if I made this donation, it would make a scholarship or help create like a
center for students to help them if they're having some kind of challenge and it might evoke
what they had experienced themselves. And I think that is really sort of where it all comes down to.
It has to be authentic. It has to actually relate to what the person cares about.
I think you also have to talk about them with what that institution means to them
and how it impacted their life. And sure, sometimes it's a philanthropic donation,
but a lot of times it's really more just staying connected, having them interact with students,
be a mentor, or just get some good advice. Especially when you're chatting with folks
that have had really interesting life experiences or have been leaders of different companies
themselves. Those are folks who probably would charge for their time if they were just giving
out advice to people, right? So sure, I'll take the free consulting advice. I don't know. I think
you've got to go into it that way. Yeah, okay. I think that's a very good way of thinking of it.
Sort of building a community, but also you're getting value that's more than just monetary value.
However, I guess money doesn't hurt, right? Well, I mean, so for example,
when I chat with a lot of alums, I'll ask them about, you know, well, what was the most impactful
thing that you thought, you know, during your time? And they'll talk about different things,
but co-op comes up a lot. So the fact that they had that internship experience, right?
And what I use that for as a dean is kind of a compass that tells me that we're on the right course
by continuing to keep that going, right? Because if alumni are telling me that like,
that's the thing that like changed it all for me. And that's the thing that really helped
me figure out what was next. Okay, we got to protect that got to keep it going.
When I've had conversations with alums about, well, what are you seeing with like this generative AI
stuff being used in the computing field? And I ask them things like, okay, are you somebody who
hires computing folks? Okay, let's say in a couple years, you know, in the future, you were having
an interview with somebody for a position at your company. What if they didn't know how to use
co-pilot or didn't know how to use these AI tools? What would you think about that? Yeah,
they tell me like, Oh, no, that would be a problem. Yes, that's that's a that's a sound bite that I
can bring back to the college, talk about with faculty, and we can really think about like, okay,
this is what I'm hearing from alumni. I have chatted with like, 35 alums at these variety of
companies, and they've all told me this and this. And then it steers the course, right? And so that
kind of insight, we're going to get from from keeping these connections alive. Yeah, 100%.
Actually, that's, that's, you know, when we met in San Jose, like Campbell, California, that's
some of the stuff that we talked about was, you know, AI in the workplace and, and what trends
are happening. And, and I'm pretty sure I have a have a knack for talking too much. And I'm pretty
sure we were scheduled to meet for like a half an hour an hour. And I think it took like a couple
hours. But I enjoyed the conversation. It was a good chat. Yes. What you were part of a big study
I was sort of doing of talking with a lot of folks around Silicon Valley about like, where's this AI
stuff headed? What do you think we got to worry about next, right? Yeah. And that was really
formative. Yeah. Yeah. And for anyone, you know, listening or watching, I do want to make one
point that I think, if you are not familiar with some of the gen AI stuff in a year from now, like,
catch up, or, you know, like, don't don't fall too far behind. Because I think it's,
it's like, it's, you know, it's like ignoring the computer, because it's like new and scary.
I think it's just going to be, it's going to be everywhere. I don't think you can avoid it in the
future. Okay. So, let's see, the last, I guess the last thing I sort of want to, well, there's
a couple things I want to finish up with. One of them is how do you support in underrepresented,
you know, people and sort of what does the school do? And how do you look at that?
So, I mentioned that, you know, my background's human computer interaction. So a lot of it is
studying this intersection with people and tech. And I believe it because I've seen it,
that if you have got folks that actually are reflecting the diversity of the world
on a team, or as part of a project, you wind up learning a lot more. So, you know, for example,
a lot of my work is on tech for people who are deaf and hard of hearing. And we've been able to have
a lot of deaf and hard of hearing research team members, whether it's faculty collaborators at
other universities or PhD students at our lab or master students undergrads. And, you know,
I've been working in that field of accessibility for over 20 years. And there are still times where
we will do a study or something or we'll interview somebody and ask them how they want to use some
tech. And there will be some quote in an interview. And I don't know what to make of it, right? And
then I'll talk about it with one of my deaf and hard of hearing colleagues. And they're like,
oh, no, yeah, it means this, because their own personal experience gives them that window of
insight, right? It also gives them the idea of how should we as a field be prioritizing the agenda
of what we ought to be working on next in a way that doesn't just represent a tiny slice of the
world, right? So, part of what we do is, as I mentioned before, you know, rebranding a little
bit the field, like it's about computing impacting the world. And that brings in more students that
might not have otherwise been interested in tech. But then once we get them here, we've got to support
them. So, at our college, we have a big diversity initiatives office that has a large program called
Women in Computing, and then another program called Computing Organization for Multicultural
Scholars that comes, see OMS. And both of those groups are kind of like clubs. They're sort of
affinity groups for students that want to connect with peers in that space. But we supercharge them
by adding a professional staff member and admin support to the clubs and a budget. So, they can
do a lot more than they might have been able to do if they had to just self organize and do all the
fundraising themselves for things. Right. And so, it creates like a community inside the college
where students can find peers. And we also wind up having a lot of their activities be
things that reaches out to local middle schools. So, for example, the Women in Computing Organization
does a lot of activities with local Girl Scout troops to kind of get them excited about computing.
And so, it's kind of like a long game to kind of get a bigger pipeline of students interested.
But then they're all doing it together. So, then they've got a community sense. And then because
they had that experience together, the alums from that program come back and connect and do
mentorship stuff. And so, it's kind of connecting a lot of dots to create this community and this
activity. And similarly for students of color, we've got that other group comms that I mentioned.
So, where we're going with a lot of this is really trying to increase the diversity of students in
our college who are women or people of color. So, the Women in Computing Group has been around for
over a decade. And over the past decade, we've more than doubled the percentage of women that are
coming into our undergraduate class in the college. So, it's telling us that something there is working,
which is why we're kind of using that model of affinity groups, supercharged with professional
staff and kind of things to do this support. For students with disabilities, especially Deaf and
Hard of Hearing, RIT also has a lot of supports. I mean, there's really amazing interpreters and
captioners on the campus. A lot of other sort of student clubs and support that, you know, I
mentioned I moved to RIT because I wanted to be able to have Deaf and Hard of Hearing students
on our team. And, you know, if you imagine like a graduate mathematics course that PhD students
in computing have to take, you know, it takes a really skilled interpreter to be able to in real
time interpret all of that into American Sign Language consistently during a whole semester
and using the same vocabulary that the student might have used in the last class in the sequence,
so they don't get confused to really get a student through a graduate education there.
And we're doing it. We're graduating students through the pipeline that reflects some of this
diversity. So, yeah, there's a lot of things to do in this space and those are a couple of
things we do at RIT, but I believe in it because I know it's important. As a computing field,
we are not going to be as good a computing field as we can be if we're only recruiting a tiny slice
of the world. We're going to ignore things. We're going to not realize certain things are important
and we're not even going to know what to make of feedback from our users because we don't have
the perspective to understand it. It sort of reminds me of, I use this a lot at the allegory of the
cave, if you're familiar, with the allegory of the cave, where Socrates, Aristotle and Socrates
are discussing like there's these, if you will, prisoners and they're chained up and the only
thing they can see is a shadow is projected on the wall in front of them. And it's not until one
of the prisoners is able to, and they don't know the world has color in three dimensions and all the
sounds and smells and butterflies and whatever, but when one of the prisoners gets out, he's
able to see all these things and comes back in to sort of free the rest of the people in there.
They're skeptics, right? Like, oh, we don't need to do that. This is life here. And so it reminds
me a bit of that because then you get a bigger picture of the world as it is. And I think it
helps in entrepreneurship, especially at least coming from my point of view, because you're not
just serving like you said, just a slice of the population, you can serve, you know,
more people, which I think is huge. All right, well, on that note, I, you know, I greatly
appreciate your time. I do, I do, you know, enjoyed this conversation and I hope to have you
again on the podcast soon. I know you're a very busy person. So thank you very much for being here.
Happy holidays. Happy holidays. This was really fun. It was great to pick it up and I enjoyed
talking with you all about this. Yeah. And I know it's snowing up there. So I don't envy. I don't
think I'll be visiting right now, but maybe, maybe when it's a little bit warmer and everyone
comes out from the tunnels underground. Indeed. Thanks again, Matt. I really appreciate it.
Bye bye. Bye.