August 8, 2025 • 44 mins
Dan sits down with guests Mark Daniel Ward and Katie Sanders from The Data Mine at Purdue University to explore how higher education is evolving to meet the demands of the AI-driven workforce. They share how their program blends interdisciplinary learning, corporate partnerships, and real-world data science projects to better prepare students across 160+ majors. From AI chatbots to agricultural forecasting, they discuss the power of living-learning communities, how the data mine model is spreading to other institutions and what it reveals about the future of education, workforce development, and applied AI training.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jerod (00:04):
Welcome to the Practical AI podcast, where we break down
the real world applications ofartificial intelligence and how
it's shaping the way we live,work, and create. Our goal is to
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(00:24):
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Now, onto the show.
Daniel (00:49):
Welcome to another episode of the Practical AI
podcast. This is DanielWightnack. I am CEO at
Prediction Guard, and I'm reallyexcited to follow-up today on on
a topic that, of course, hascome up on the show many times
and even in our last episodecame up, very specifically,
(01:10):
which is how well oureducational system is doing at
kind of training students forthis next generation of jobs,
especially as it relates to datascience, AI, and and kind of the
latest wave of technologies.And, of course, I'm I'm very
privileged because I'm live in atown where there's a great
(01:33):
university, Purdue University.And so I'm I'm very privileged
today to have with me tworepresentatives from Purdue
University and the data mine.
That's that's mine as inpickaxe, not not mined or mime.
The data mine over at Purdue gotMark Daniel Ward, who is the
(01:53):
executive director of the datamine, and also Katie Sander, who
Katie Sanders, who's the chiefoperating officer. Welcome.
Great to have you all.
Katie (02:02):
Thanks for having us.
Daniel (02:03):
Yeah. Great to great to have you here with us. Well, I
kind of teed up a little bit ofthat in the sense that there's a
lot of us in industry who are,of course, being bombarded by
all sorts of changes to ourjobs, the way we do our day to
day tasks, whether that becoding or everything from HR to
(02:27):
administrative things to evenmanufacturing jobs and all sorts
of things. All of these are sortof being transformed by this
latest wave of technologies. AndI know, we'll talk more about
what the data mine is here in abit, but you all are kind of
uniquely positioned to reallythink about this next wave of
(02:48):
students that are coming intothe workforce and and
particularly how things likedata science, AI, etcetera, are
shaping maybe how they need tobe trained or how they should be
prepared for the workforce.
Could you give us a little bitof a sense of your viewpoint on
that and maybe how people thataren't in the education space
(03:10):
could think about you know, someof your perspective on what is
changing or needs to change orwhat you're hoping to see
happen.
Mark (03:20):
You know, one thing we see is higher education is changing
just the way industry ischanging. The students, of
course, still love coming tocollege and graduate school and
all the fundamental learningthat occurs in labs and courses,
research centers on campus. Butmaybe more than ever, I don't
know how you quantify that, butcertainly there's an appetite
(03:41):
among students as ageneralization to see how the
skills really translate intoreal world work. I think
students and of course theirparents and families are really
interested in what they learn incollege and then graduate school
is going to get them and whenthey go into the workplace, how
that's going to translate intocareers more than just that
(04:04):
initial job. What's the long runlook like?
How are they building afoundation while they're here at
Purdue for what they're gonna doin the rest of their career? So
higher education is alsowrestling with those changes as
well.
Katie (04:16):
And I think in previous years, you know, you were good
if you had an internship, yourjunior year, your senior year,
but now the way things havechanged, that's just too late.
Daniel (04:28):
Yeah. And I know that your focus at the data mine is
related to, of course, datascience, maybe the wider
perspective of data engineering,AI machine learning, whatever it
is. Why is that set of skillssomething that maybe students
from across differentdepartments or majors should
(04:52):
have exposure to in a real worldsense? You might think, Oh,
well, computer science majors,certainly they need to know
about AI, but why is this aconcern that should be kind of
interdisciplinary?
Mark (05:05):
Data's just pervasive in industry, you know, aerospace
engineers and people onmanufacturing floors and out in
the field and so on. Everybody'scareer is being remade because
of the pervasiveness of data. Soit's not just the computer
scientists by any means. It'speople wanna do predictions and
build tools and have sensors andall kinds of automation and
(05:28):
workflows that everybody'sleveraging data driven tools and
methodologies, you know, intheir sector of industry.
Katie (05:38):
One thing I learned, I'm not technical at all, so let's
preface with that, is that datalooks different to an engineer
than it does a data scientist.So being able to have students
that are able to look at datadifferently, then they can come
up with the best outcome forwhatever project they're working
(05:58):
on or whatever work that they'redoing.
Daniel (06:00):
Yeah, and would you find, I guess you work with a
lot of corporate partners. Thisis certainly something that I've
seen in industry is often theteam that you're working with,
of course, is not just made upof developers or others, but
there's people from a variety ofbackgrounds that hopefully are
providing input to a problem. Sois that part of what students
(06:24):
need to be ready for is talkingabout maybe technical topics or
data topics in an in a placewhere there's a diversity of of
backgrounds? I don't know. Is isthat part of the the goal there?
Katie (06:39):
I would say yes. Definitely, giving students the
opportunity to work with data nomatter what major they're in. We
have over 160 majors in ourprogram. We have some, actually
some marketing projects wheremarketing teams are working with
data and they need assistancewith that. So, you know, we can
pull marketing students in onthat as well as data science
(07:01):
students or engineeringstudents, whatever they're
looking for in order to get thatblend and to get all, you know,
different insights as to whatthe solution could be.
Daniel (07:12):
Yeah. I think that that is an excellent perspective. And
I think it leads naturally intosome discussion of the data mine
itself, because I was veryintrigued to learn, like I said,
we've been talking about this onthe show, but the sort of need
for creative approaches tohelping students wrestle with
(07:33):
real world problems, but alsowrestle with those in the
context of AI and advancedtechnology, which is maybe
outside of what you could do inkind of a standard classroom
environment. And I was I wasinterested in in what you all
are doing because it does seemlike a very interesting creative
approach. I see on your kind ofdescription of the data mine,
(07:57):
there's maybe a whole lot ofwords that we need to pick apart
here, but there's, you say it'san interdisciplinary living
learning community, again, opento students from every college.
But these students workalongside corporate partners and
they solve real worldchallenges. So maybe if we just
(08:21):
start with a little bit ofpicking apart that definition,
We already talked a little bitabout the interdisciplinary
portion of this, but just togive us a sense of maybe growth
and scale across the wholeuniversity, could you help us
understand the scope of studentsthat are involved in this?
(08:42):
Katie, you mentioned evenmarketing and other things.
Maybe, Mark, could you help usunderstand how this has grown
and how pervasive or ubiquitousit is across the different
departments at Purdue?
Mark (08:57):
You know, DataMind grew out of a grant we had where we
had 20 sophomore undergraduatesa year living and working across
the street here from where we'relocated in the Convergence
Building. And those 20 sophomoreundergraduates every year
produced a ton of researchoutputs with faculty all over
the campus. Well, one of thoseearly career students at the
(09:21):
time wanted to go work with thecompany. The other 99 worked
with faculty. That really setsomething thinking in our mind
about how we could also haveresearch with external partners.
So when that grant was windingdown that last year, our
university administration was sosupportive and said, well, what
if we open this up to anybody oncampus? How quickly would this
(09:43):
grow? And we had 100 studentsthat first year and then 600 and
then 800, and no one's requiredto take this program, the state
of mind that we offer. Studentssee a lot of value in it. We
sense that the employers, thepeople hiring them for both
internships and full timeemployment, see a lot of value
in the skills the students arelearning.
(10:05):
And just coming back to thatinterdisciplinary piece, each
student kind of has their rolein the larger team, an engineer,
someone from Daniels School ofBusiness, someone from a data
science background orcommunications or liberal arts.
The students kind of find theirniche in a larger team within
the larger corporate environmentthat they're working, and it
(10:27):
just seems to be a model that'ssuccessful.
Daniel (10:29):
And could you speak to maybe, Katie, the general
structure of these teams? We'retalking about, I think if I
remember what you said as wewere chatting before recording,
that there's potentially over2,000 students now that could
participate in this, which ispretty incredible. Wide
reaching. How are those studentsfrom different departments,
(10:54):
interdisciplinary? How are theyformed into teams?
And what does a team mean, Iguess, is my question.
Katie (11:02):
That's a loaded question. So we have a couple of different
options, right? So we have ourone credit seminar course where
students are learning differentskills, R, Python, SQL, Bash,
depending on what level theychoose. And then we have the
three credit hour corporatepartner program where students
(11:24):
are working eight to 12 ishstudents per team are working on
a data science project with acompany. They are meeting
weekly.
They have a fifty minute meetingwith their mentor and then they
have a little less than two hourlab that's led by a TA where
they're working in agilemethodology, you know, trying to
(11:48):
solve whatever problem thecompany has presented.
Daniel (11:51):
And is that structure either one of you could let me
know. Have there been multipleiterations of that to learn? I'm
very interested in kind of the alittle bit of the backstory here
because I'm sure there areothers, maybe even at other
universities, but othercorporate maybe folks listening
to the podcast that arewondering, you know, what is the
(12:13):
work that's going on inuniversities to really figure
out how to adapt to whatstudents need to do. So was
there iteration in that,challenges, things that you
tried and then changed? Anythoughts on that and kind of the
history of how that developedinto what it is now?
Mark (12:34):
I guess I could speak to that. You know, in the beginning
we had undergraduate students,Katie already mentioned students
from 160 some majors on campusnow, but we didn't have graduate
students involved. We oftendidn't have very much faculty
involvement. Our relationshipswith many of these external
partners were just starting tobuild. We have really deep
(12:56):
partnerships now with some ofour friends in industry and
students will stay involvedoften throughout their undergrad
study and sometimes even chooseto stay at Purdue for the
graduate study to be in DataMindbecause they recognize what an
opportunity this is.
We've definitely evolved andcontinue to evolve. In addition
(13:16):
to the way we've evolved workingwith companies, I'm sure we'll
speak to this more as we getinto the podcast, but we're
trying to help otheruniversities around the state
and also outside of Indiana toadopt external facing types of
models like the data mine inwhatever sector of their state,
whatever sector of the economythat they're interested to
(13:39):
partner in.
Daniel (13:40):
Interesting. And are there other examples of these
sorts of external facingprograms that are spinning up
around the nation or evenglobally that you're aware of?
Katie (13:52):
Yeah. We have, right now we have a partnership with,
Youngstown State University.They are pretty much operating
their own, you know, data mineinstance. And then we have Data
Mine of the Rockies, which isled by another faculty member
here on campus who is workingwith students at Purdue and
(14:12):
Colorado on different projects.We also have quite a few schools
in Indiana that are working withus in different ways.
We have some that are foldedinto our seminars, some that are
folded into projects and somethat are working with faculty on
research projects. We also havea grant that we're working with
(14:35):
other institutions across TheUnited States. And it's very
similar in that some are workingon projects, some are folded
into our seminar course. But weare working with, I believe it's
over 60 institutions, if notmore.
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Daniel (16:36):
Well, Mark, Katie, great introduction to kind of a little
bit of the structure of this asit relates to kind of this
interdisciplinary and and scalecomponent. I'm interested in
this next component of thedescription, this sort of
living, learning community. Helpme understand what that means
and why it's important.
Mark (16:57):
You know, early when we had our first grant before there
was a data mine, I wasn't awareof how many resources the
university puts towards studentshaving a great first
undergraduate year at Purdue.And then similarly, as students
get ready to graduate the end oftheir junior year and into their
senior year, wow, we have a tonof resources for students making
(17:19):
that transition into theworkforce. But there's this
murky middle, especially onesophomore year where students
are taking their hardest coursesin their major, kind of finding
their footing. You know, they'vegotten through their 100 level
courses and they're thinkingabout how their major is gonna
transition into a career. Andit's not just Purdue, but by no
(17:39):
means.
It's well documented that that'sa time when students start to
question and struggle and reallywrestle with what's their career
going to turn out to be. So welove that many of our students
choose to live together and theywork together. While Ms. Sanders
and I are at home, you know,with our respective families
(18:00):
where all of the staff membersclose-up shop and we have dinner
and go to sleep, the studentsare still working. You know, at
10:30 in the evening, they'rebonding and they're learning
from each other.
And that's when the real work insome sense happens. So by having
many of the students livetogether in a residence hall and
study together and faculty oftenhave office hours there and we
(18:21):
have our meetings there and theuniversity has been so
supportive, we're even buildinga second residence hall
completely devoted to thesestudents' experience. We think
we've kind of been able toreshape some of those struggles
students would have in anintensive science and
engineering environment that isoften corporate facing. And they
(18:42):
end up feeling at home andfinding their friends often who
become lifelong friends andsuch. It really enriches the
experience.
Daniel (18:50):
Really interesting. Prior to this conversation, if
you were to ask me, hey, what,all right, you're going to
transform sort of data scienceand AI across a university
campus. Probably the first thingthat would come to my mind is
not like build a residence hall.But it's interesting to hear
(19:14):
like how this has actually kindof organically, it seems like
somewhat organically come up.And maybe that's, is it part of
the intentionality that you haveof like, there's
interdisciplinary students,maybe they're in their own
buildings and different classes,but there's this kind of cross
(19:35):
interdisciplinary space wherethey are, but you're also
having, like you say, a lot ofthe meetings there and that sort
of thing.
So have you found that thestudents associate that space,
yes, with their living, but alsokind of these projects and what
they're a part of kind of in animpact sort of way. Like we're
(19:57):
together, living together,working on these projects that
have a wider impact. What isyour, I don't know, any
experience of how that sort ofsense of place influences the
students' view of the projects?Any thoughts?
Mark (20:11):
I could say one thing that comes to mind, I think very
fondly to our first year workingwith our friends at Cummins,
because they would bringliterally sometimes two van
loads of Cummins employees allthe way up from Columbus,
Indiana to West Lafayette, asubstantial drive of a couple
hours. And they would meet withthe students in a room in the
(20:32):
residence hall where many of thestudents were living. And I
could just imagine if I were astudent, wow, I have people from
one of the largest companieshere in Indiana who are coming
here to see me. Of course, itputs some pressure on them, but
on the other hand, it shows thatCummins trust them and they see
them as the future workforce ata major corporation like
Cummins. And moreover, ourCummins friends are some of the
(20:55):
nicest people that we work with.
I spent a large portion of themorning, now six years later,
talking to my friends at Cumminsabout what we're gonna do next
year. That model where we arefortunate to meet very good
mentors in industry who care aton about our student success
has tended to provide anotherlayer that maybe a regular
(21:16):
faculty member might not have inthe research team. Students are
getting feedback from people whoaren't faculty, and it's really
well informed because it'sinformed by the industry
experience. So I think that's areally key part of why the
model's been successful is notsomething Katie and I or our
team is doing, but rather we aresurrounded by good friends who
(21:38):
seem to believe in this modeland even more believe in our
students and their potential tocontribute and make a
difference.
Katie (21:45):
I would likely say, I would say two things. One being
the students get more sense of acommunity, right? When you're
seeing the same people everyday, you're walking down the
hall and you're thinking, oh,that's so and so, or you like
walk down campus. And I knowthere's thousands of people on
campus, but it's sometimes niceto see a familiar face. And I
think that sense of communityreally helps with the projects
(22:08):
because they're able to divedeeper into those personal
relationships.
That being said, one thing is wehave our Indianapolis, Data Mine
of Indianapolis, and the mentorsare meeting with students
on-site because of the closeproximity to many of the
companies, which has been liketransformational, like down
(22:30):
there, like it's beentransformative. The students
love it. The companies aregetting that one on one time. We
can't always do that here inWest Lafayette because of the
location, because of location.So that's been really great.
And I forgot to mention thatearlier that Indianapolis, you
know, we started that January'24 with seminar and then kicked
(22:51):
off the corporate partnersprogram this last fall. And it
was very successful and a greatexperience for students,
especially students that arecoming in their freshman year,
they get that experiencefirsthand.
Daniel (23:04):
Yeah, it's been really interesting to hear, you know,
just anecdotally, similar sortof things on the industry side
when we've been engagingcustomers and when we're able to
meet them on-site, to theirconference room, right? It's
like that sort of interactionhas such a meaningful
(23:26):
contribution to building thatrelationship and rapport and the
trust that can be built therethat is really, really difficult
in a virtual environment. And Iknow many people are good at
that and sometimes there's notthe chance to be in the same
room. But I definitely thinkthat instilling that in
(23:47):
students' minds of the kind ofcontinued importance of some
face to face interaction andbuilding that relational
component is really key. That'svery encouraging to me.
This kind of, I guess, naturallyleads into the discussion of
this next bit of the definitionof the data mine that I've been
(24:08):
using, which is this corporatepartnership. We've talked about
the interdisciplinary studentteams. We've talked about even
how those students are livingand working together in these
interesting, you know, living,learning communities, that that
are sounds like are expanding.What about the the corporate
(24:29):
partnership side of things?Maybe if you could just help us
understand the mechanics of howthat works.
I know some capstone projects atdifferent universities or
student projects at someuniversities, I've heard from
colleagues in industry,sometimes they can be viewed as,
(24:52):
It's good that we can help thesestudents, but there's no benefit
to the company, but maybe it'sgood that we can help the
students. But this is definitelya different view, or at least
what I've seen from what's goingon at the data mine, a different
view of what student projectscould be. So maybe in a general
sense, like how do you thinkthat universities and corporate
(25:18):
partners can actually engagetogether in a way that is
mutually beneficial, yes, to thestudents and their future
career, but also to thecorporate partners?
Katie (25:29):
I would say that a lot of the projects that we get,
they're not mission criticalprojects. The experiential
learning piece is not missioncritical, but they are projects
that, you you'd know, reallylike to get to, but you don't
have the time to get to, youdon't have the manpower, things
that have been on the backburner, things that, you know,
(25:50):
need revived a little bit, orsomething that you've always
wanted to do, but just, youknow, haven't had the time to
do. Those are the type ofprojects that these students
work on. And the students are sosmart and innovative. Like it's
crazy to me, honestly, becausewhen I was that age, I was not
doing that.
Like, so it's just been, youknow, every company has a
(26:13):
different reason why they arecoming to the data mine. Some of
it's a talent pipeline. Some ofit is like, look, I just wanna
hire some interns and this is agreat way to get connected with
students. And you're, youalready know their work ethic
because you're working with themfor the nine months prior to
maybe them starting aninternship. Some people, you
know, some companies are, well,we have this project, let's just
(26:35):
try this and see if we get it,see if it works out.
And it's awesome when it doeswork out, right? So there are
different reasons why companieswill join and, you know, our
retention's pretty good as well.So I think that speaks to the
work that the students do.
Mark (26:52):
I was just gonna say, feel like we're growing with some of
our companies, you know, likebefore I came into the podcast,
I went down the hall, the officesuite for Beck's Hybrids, which
is just on the catty corner fromour office. And, you know, when
we first started talking with Ithink they had on the order of
300 employees and they justhired their one thousandth
employee this year. We're just avery, very small part, but we
(27:17):
feel part of that growth. Wereally feel that in our team and
they always make us feel likewe're part of the family at
Beck's. When you were firstbroaching this question, kind of
what hole are we basicallyfilling with some of these
partners?
You know, yesterday I was inChicago and not naming any
names, but I met with thecompany for the very first time
who has 75 employees. It's not ahuge company, but the challenge,
(27:43):
the gap is they don't have anydata scientists yet. They have
one intern and the intern's justamazing. But if they had full
time folks on staff who couldleverage AI and ML and do some
of the predictive analytics andso on, they recognize that they
could go from being one of theplayers in that industry to
becoming a leader. And if theytake a chance on us at DataMine,
(28:04):
they can try the students on forsize and see what kind of
research and development theycan do.
And it's not just they're beingnice to the students or helping
them with the capstone like yousaid, Daniel. It's more like,
okay, we actually sense there'sa real opportunity to advance
our business here with thesefolks at DataMine. So when we're
able to make impacts like thatand the students' work is really
(28:24):
valued by the company, I thinkthat's when we've hit our sweet
spot.
Daniel (28:28):
Yeah, that's great. And just to give a sense, I mean, I
know there's probably somecorporate partners that can't be
mentioned for confidentialityreasons or other things, but
either of you, I don't know ifyou can give a sense of kind of
how many corporate partners youeither are or have worked with
and kind of the range of sizesand just a sense of industries
(28:51):
maybe, because it is interestingthat similar to what you said,
Katie, I remember being anundergrad and doing projects. I
was not exposed to working withactual real companies outside of
if I did pursue an internship orsomething like that formally.
But as part of the actualuniversity side, I wasn't
(29:14):
exposed to that. So I really,just from a personal standpoint,
had no idea how a lot ofindustry even operated or worked
or that sort of thing.
So I don't know, could either ofyou give us a sense of that,
types of companies and numbers,that sort of thing?
Katie (29:30):
Last year, we had 88 projects with about 60 different
companies. And then I don'tknow, Mark, if you wanna maybe
take the rest of that. I knowthat part. I know the numbers
part.
Daniel (29:43):
That's that's you know the data at the data money.
Yeah.
Katie (29:46):
Yeah. I don't know how to work with it, but I know the
numbers.
Mark (29:50):
We try to be really affordable. I mean, we, you
know, we have not doubled ournumber of companies in the last
year or two, but what we've doneis we've gone a lot deeper in
our relationships with thecompanies. And many companies
where we started out just doinga pilot or, you know, a free
project, no cost or whateverhave turned into two and three
and four and five projects withthe companies, all of which are
(30:13):
paid or frequently sponsoredresearch, which is all
proprietary and not disclosed. Ithink that's been the biggest
change in terms of our businessmodel is the depth of work that
we've done with some of thesecompanies. You asked about some
Katie (30:27):
of the
Mark (30:27):
domains of application of these projects. Of course, it's
hard to categorize, but youknow, we think about aerospace,
defense. I mentioned agricultureearlier, our friends at BAX is
an example of that. Talked We alittle bit about manufacturing
some. We haven't really alludedto pharmaceutical science,
(30:48):
computational drug discovery.
Every vertical companies needthese students, you know, so.
Katie (30:55):
I will say too, our previous projects are listed on
our website as well. If anybodywanted to go check out last
year's symposium, there'sposters with videos from the
students. That option is there.
Mark (31:07):
Yeah, it helps when you go to meet a company for the first
time, you don't have to do ahard sell anymore. I never bring
a PowerPoint to a meetinganymore. We simply ahead of
time, we send that link Katiementioned. Here's what we did
last year. Here's who ourfriends in industry are.
Let the work speak for itself.How can we help you next?
Daniel (31:28):
Yeah, thank you all for kind of sharing some of that
about the symposium, the link,the projects, the corporate
partners. Just so our listenersknow, we will include a link to
the DataMine, website in ourshow notes. So go ahead and
scroll down there to clickthrough, take a look. I'm just
(31:48):
scrolling through all of thecorporate partners, are all, you
know, just in incrediblyinteresting from Allison
Transmission to Dow Chemical,Johnson and Johnson, Lockheed
Martin, etcetera. Just very, youknow, names that people will
recognize, but also some namesthat maybe, are are smaller to
(32:09):
to your point, but still havereally interesting interesting
work and and you can explore allthose things.
I'm wondering, all have beenexposed to a lot of amazing
projects that have gone onwithin the data mine. Are there
any that come to mind that, youknow, like you said, Mark, when
(32:29):
you're talking to corporatepartner or Katie, you're,
engaging either on the studentor the corporate side that
typically come to mind like, Oh,if I get to mention this one,
always a really inspiring one oran interesting one where
students overcame a challenge orsomething like that. Any
particular ones that you'd liketo highlight?
Mark (32:51):
What do you think, Katie? Who are we gonna name drop here?
Katie (32:55):
Well, I don't if I wanna name drop, but one of my
favorite ones was, you know,chatbots are like, everybody
wants one now. Everybody wantsto just be able to say, Hey,
where's this and this and findit for me. And so one of the
projects last year, the studentsworked really hard to create a
(33:15):
chatbot. And then I love to seewhen the company like is able to
implement that to make theirprocesses easier. So that was
one of the ones that came tomind for me.
It also, I think, depends on thecompany, right? Like certain
companies might not have aninterest in like what other
companies in a different domainare doing. So I think it kind of
(33:36):
depends on who you're talkingwith. And just to mention too,
from the student side, one of myfavorite stories is we had a
student who was in computerscience and there, you know, a
lot of students are looking atthe bigger companies, right? We
know the big companies, but theyended up doing an internship
(33:56):
with a smaller company.
And, you know, their feedbackwas, I would have never known
that they were collecting datathis way. And, you know, I could
work for a company like this. Ididn't know they existed without
my experience in the data mine.So that's just like one of my
favorite things is just exposingstudents to different
opportunities that they mightnot have thought were there.
Mark (34:20):
I could pile on in a similar direction there. Last
night, as I was waiting for myride home from Chicago, I'm
writing letters ofrecommendation. And wow, I write
a lot of letters. I might writeletters for 40 or 50 or 60
people every year. And I hadthree I was putting together
last night and I was tired.
And as I'm writing the letters,you know, I'm checking my email
(34:42):
and a student writes, Doctor.Ward, I just wanted to write and
let you know I got into thisgrad school. I can't believe
this research mentor I'm goingto get to work with. I think
it's really putting me on trackfor after I finish my master's
or their doctorate. Don'tremember, you know, the kind of
career they're going to haveahead.
And they came back over and overto Datamind made that possible
(35:04):
for me. That's priceless, youknow, I mean, like I'm sitting
there working on my letters andI'm exhausted, but that's what
renews you, you know, that'swhat really kind of gives you
hope for the future at thesecompanies. If we think about an
employer like Eli Lilly inIndiana, they're just
transformative to the economyhere. And we love working with
(35:25):
Eli Lilly. They're just thatthey're good beyond measure to
Purdue, but we also feel luckyto work with AstraZeneca and
AbbVie and our friends at Merckand so on.
And when students are able tokind of see the full breadth of
what's going on in an industryand have a lot of choices, we
feel it's good for everybody.
Daniel (35:45):
That's awesome. I'm just, you know, as we speak,
scrolling through projects andreading amazing things that
students have done, I seeeverything from detecting
digital fraud in healthcarewith, L events. There's, you
know, there's forecasting, soyyields. There's, you know,
(36:11):
reliability for NLP machinelearning models, with with Ford,
just a a lot of reallyinteresting things. And that
only scratches the surface.
I really recommend people to goand scroll through the website.
And there's a lot of interestingvideos and posters to take a
look at. And, our listeners willbe interested to know that the
(36:35):
Practical AI podcast hassubmitted a project for this
upcoming year, and there'll be ateam of students working with
us. And, I will not there issome really cool stuff that
they're going to be doing. Iwon't reveal the full extent of
that, but, I think it will besomething that our listeners
(36:56):
will be able to to interactwith, coming out of the coming
out of the project and also, youknow, something that that I
think will be quite fun.
So I'll tease that a little bit,but we're excited to work with
these students and reallyunderstand from our own
(37:17):
experiential level for myselfand Chris, our cohost, and our
listeners actually pulling theminto this type of new creative
way that students are learningdata science and AI. At this
podcast, we're always talkingabout making AI and data science
(37:38):
and these methodologiesaccessible and practical for
everyone. Certainly a part ofthat is workforce development
and education and this kind ofacademic industry partnership.
Really So excited to actuallysee that materialize. So thank
you both for making that happen.
This is really exciting. I'mwondering so number one is we
(38:02):
should mention, explicitly onshow that the data mine is still
looking for corporate partnersand there are many students that
are there's a whole group ofamazing students that still are
looking for projects. If you'relistening to this in August, you
(38:22):
know, July or August of of oftwenty twenty five, go ahead and
reach out to the data mine.There's still the opportunity
for those projects, or maybeyou're listening to this
sometime in the future. I'm I'msure they would still love to,
love to hear from you.
And of course, we'll includethat link below. What what's
maybe just from y'all'sperspective, for corporate
(38:43):
partners that are coming in andand wanting to sponsor projects,
you mentioned a couple of thingsof how different corporate
partners, view this, but what'sthe value proposition that
someone listening to the podcastcould bring to their coworkers
or supervisors at their companyto kind of tell about this
(39:05):
interesting thing that'shappening at Purdue around data
science and AI? What would bethe elevator pitch that you
could help them give?
Katie (39:17):
We always joke that Mark could sell a used plastic bag,
so so go ahead.
Daniel (39:22):
There you go. I need to hire you into into my company as
a salesman.
Mark (39:26):
Yeah. Would you like to buy this coffee scoop? I could
keep it affordable. Think of allthe things you could do with
this. It's it's not just aregular coffee.
Daniel (39:34):
It's multi purpose.
Mark (39:36):
Yeah. Yeah. Yeah. Yeah. But, you know, I mean, it's it's
so affordable affordable forthese companies.
We we sign these five yearagreements with companies so
they don't have to go back totheir lawyers. Purdue doesn't
have to renegotiate anything.Everything's just sort of laid
out so that a manager who's gotan idea can rubber meets the
road, go go work with thesestudents. And then once that
(39:59):
happens, once one company takesa chance on one team, invariably
all of their siblings in thatcompany, all their buddies, all
people in different parts of theorg, when they hear about it,
they want it for themselves too.It's an easy story to tell.
Katie (40:15):
And I think we helped through that process as well. I
mean, Mark was writing projectdescriptions last week, so he
has an eye and an ear and abrain for it. He can definitely
find where there might be holesor suggestions or things like
that. And we'll with you, himand our corporate partners and
(40:37):
data science team as well.
Daniel (40:39):
That's awesome. And as we kind of get closer to the end
of our conversation here, maybejust taking a step back and as
you all of course, have theimmediate things that you're
part of leading into this nextsemester of the data mine and
students engaging with partnersand that sort of thing. But as
(41:01):
you, whatever it is, lie on yourbed and think about the future
of what's happening, what'schanging, especially over even
the last couple of years, justso much has changed, what
excites you or what are youlooking forward to that may be
possible for the type of programthat you're running or for just
(41:22):
this type of education in highered more generally? What are you
looking forward to and what mostexcites you about the future in
that regard?
Mark (41:33):
Can I say world domination? I meet it in the
most The data mine everywhere.The data mine everywhere. It's
not just about something we'redoing in Purdue, it's become a
model for engagement. And Ireally believe it's silly when
we mentioned some of thesecompanies that they're going to
invest time and money and effortto go to 10 or 20 campuses
(41:54):
around the Midwest or maybearound the country to do their
recruiting.
And then they're going to hirein onesies and twosies, people
who they've often never workedwith before and take a chance
and give them a full time job.My sense is that it's so much
more fun to build thingstogether and whoever it
resonates with, both on thestudent side or on the company
(42:15):
side, just have that naturalmatchmaking. Learn by doing all
the R and D and the AI and ML,all the value creation. So my
hope is that this modelcontinues to be adopted by many
different kinds of institutions,not just here in Indiana or in
the Midwest, but all over thecountry. And those of us in
(42:38):
higher ed need to sticktogether.
I mean, we're all wrestling withthe same changes together, just
that industry is as well. Andit's a big tent. Let's continue
to work together on initiativeswe can all just help each other.
Daniel (42:49):
That's great. Any thoughts from your end, Katie?
Katie (42:52):
I would say similar and that, you know, it's exciting to
see what the workforce is gonnalook like with students getting
engaged with companies earlierin their lives versus later. So
anything's possible.
Daniel (43:09):
That's great. That's a good thought to end on. Well,
thank you both for the work thatyou're doing, and the creativity
that you're putting into thistype of program and the
inspiration that it is for bothcorporate partners who are
finding new ways to engage andrecruit, but also other
universities and educators ingeneral who might be at a loss
(43:34):
for how to, you know, find amodel that works in the the
changing ecosystem that we're apart of. So thank you both for
your work, and and lookingforward to, rolling out and
revealing some of what we'reworking on with with Practical
AI as as the year unfolds. So,thank you both for taking time.
Really appreciate it.
Katie (43:54):
Thank you so much.
Mark (43:55):
Yeah. Thank you.
Jerod (44:04):
Alright, that's our show for this week. If you haven't
checked out our website, head topracticalai.fm and be sure to
connect with us on LinkedIn, X,or Blue Sky. You'll see us
posting insights related to thelatest AI developments, and we
would love for you to join theconversation. Thanks to our
partner Prediction Guard forproviding operational support
for the show. Check them out atpredictionguard.com.
(44:26):
Also, to Breakmaster Cylinderfor the Beats and to you for
listening. That's all for now,but you'll hear from us again
next week.
Welcome to the Practical AI podcast, where we break down
the real world applications ofartificial intelligence and how
it's shaping the way we live,work, and create. Our goal is to
help make AI technologypractical, productive, and
accessible to everyone. Whetheryou're a developer, business
leader, or just curious aboutthe tech behind the buzz, you're
(00:24):
in the right place. Be sure toconnect with us on LinkedIn, X,
or Blue Sky to stay up to datewith episode drops, behind the
scenes content, and AI insights.You can learn more at
practicalai.fm.
Now, onto the show.
Daniel (00:49):
Welcome to another episode of the Practical AI
podcast. This is DanielWightnack. I am CEO at
Prediction Guard, and I'm reallyexcited to follow-up today on on
a topic that, of course, hascome up on the show many times
and even in our last episodecame up, very specifically,
(01:10):
which is how well oureducational system is doing at
kind of training students forthis next generation of jobs,
especially as it relates to datascience, AI, and and kind of the
latest wave of technologies.And, of course, I'm I'm very
privileged because I'm live in atown where there's a great
(01:33):
university, Purdue University.And so I'm I'm very privileged
today to have with me tworepresentatives from Purdue
University and the data mine.
That's that's mine as inpickaxe, not not mined or mime.
The data mine over at Purdue gotMark Daniel Ward, who is the
(01:53):
executive director of the datamine, and also Katie Sander, who
Katie Sanders, who's the chiefoperating officer. Welcome.
Great to have you all.
Katie (02:02):
Thanks for having us.
Daniel (02:03):
Yeah. Great to great to have you here with us. Well, I
kind of teed up a little bit ofthat in the sense that there's a
lot of us in industry who are,of course, being bombarded by
all sorts of changes to ourjobs, the way we do our day to
day tasks, whether that becoding or everything from HR to
(02:27):
administrative things to evenmanufacturing jobs and all sorts
of things. All of these are sortof being transformed by this
latest wave of technologies. AndI know, we'll talk more about
what the data mine is here in abit, but you all are kind of
uniquely positioned to reallythink about this next wave of
(02:48):
students that are coming intothe workforce and and
particularly how things likedata science, AI, etcetera, are
shaping maybe how they need tobe trained or how they should be
prepared for the workforce.
Could you give us a little bitof a sense of your viewpoint on
that and maybe how people thataren't in the education space
(03:10):
could think about you know, someof your perspective on what is
changing or needs to change orwhat you're hoping to see
happen.
Mark (03:20):
You know, one thing we see is higher education is changing
just the way industry ischanging. The students, of
course, still love coming tocollege and graduate school and
all the fundamental learningthat occurs in labs and courses,
research centers on campus. Butmaybe more than ever, I don't
know how you quantify that, butcertainly there's an appetite
(03:41):
among students as ageneralization to see how the
skills really translate intoreal world work. I think
students and of course theirparents and families are really
interested in what they learn incollege and then graduate school
is going to get them and whenthey go into the workplace, how
that's going to translate intocareers more than just that
(04:04):
initial job. What's the long runlook like?
How are they building afoundation while they're here at
Purdue for what they're gonna doin the rest of their career? So
higher education is alsowrestling with those changes as
well.
Katie (04:16):
And I think in previous years, you know, you were good
if you had an internship, yourjunior year, your senior year,
but now the way things havechanged, that's just too late.
Daniel (04:28):
Yeah. And I know that your focus at the data mine is
related to, of course, datascience, maybe the wider
perspective of data engineering,AI machine learning, whatever it
is. Why is that set of skillssomething that maybe students
from across differentdepartments or majors should
(04:52):
have exposure to in a real worldsense? You might think, Oh,
well, computer science majors,certainly they need to know
about AI, but why is this aconcern that should be kind of
interdisciplinary?
Mark (05:05):
Data's just pervasive in industry, you know, aerospace
engineers and people onmanufacturing floors and out in
the field and so on. Everybody'scareer is being remade because
of the pervasiveness of data. Soit's not just the computer
scientists by any means. It'speople wanna do predictions and
build tools and have sensors andall kinds of automation and
(05:28):
workflows that everybody'sleveraging data driven tools and
methodologies, you know, intheir sector of industry.
Katie (05:38):
One thing I learned, I'm not technical at all, so let's
preface with that, is that datalooks different to an engineer
than it does a data scientist.So being able to have students
that are able to look at datadifferently, then they can come
up with the best outcome forwhatever project they're working
(05:58):
on or whatever work that they'redoing.
Daniel (06:00):
Yeah, and would you find, I guess you work with a
lot of corporate partners. Thisis certainly something that I've
seen in industry is often theteam that you're working with,
of course, is not just made upof developers or others, but
there's people from a variety ofbackgrounds that hopefully are
providing input to a problem. Sois that part of what students
(06:24):
need to be ready for is talkingabout maybe technical topics or
data topics in an in a placewhere there's a diversity of of
backgrounds? I don't know. Is isthat part of the the goal there?
Katie (06:39):
I would say yes. Definitely, giving students the
opportunity to work with data nomatter what major they're in. We
have over 160 majors in ourprogram. We have some, actually
some marketing projects wheremarketing teams are working with
data and they need assistancewith that. So, you know, we can
pull marketing students in onthat as well as data science
(07:01):
students or engineeringstudents, whatever they're
looking for in order to get thatblend and to get all, you know,
different insights as to whatthe solution could be.
Daniel (07:12):
Yeah. I think that that is an excellent perspective. And
I think it leads naturally intosome discussion of the data mine
itself, because I was veryintrigued to learn, like I said,
we've been talking about this onthe show, but the sort of need
for creative approaches tohelping students wrestle with
(07:33):
real world problems, but alsowrestle with those in the
context of AI and advancedtechnology, which is maybe
outside of what you could do inkind of a standard classroom
environment. And I was I wasinterested in in what you all
are doing because it does seemlike a very interesting creative
approach. I see on your kind ofdescription of the data mine,
(07:57):
there's maybe a whole lot ofwords that we need to pick apart
here, but there's, you say it'san interdisciplinary living
learning community, again, opento students from every college.
But these students workalongside corporate partners and
they solve real worldchallenges. So maybe if we just
(08:21):
start with a little bit ofpicking apart that definition,
We already talked a little bitabout the interdisciplinary
portion of this, but just togive us a sense of maybe growth
and scale across the wholeuniversity, could you help us
understand the scope of studentsthat are involved in this?
(08:42):
Katie, you mentioned evenmarketing and other things.
Maybe, Mark, could you help usunderstand how this has grown
and how pervasive or ubiquitousit is across the different
departments at Purdue?
Mark (08:57):
You know, DataMind grew out of a grant we had where we
had 20 sophomore undergraduatesa year living and working across
the street here from where we'relocated in the Convergence
Building. And those 20 sophomoreundergraduates every year
produced a ton of researchoutputs with faculty all over
the campus. Well, one of thoseearly career students at the
(09:21):
time wanted to go work with thecompany. The other 99 worked
with faculty. That really setsomething thinking in our mind
about how we could also haveresearch with external partners.
So when that grant was windingdown that last year, our
university administration was sosupportive and said, well, what
if we open this up to anybody oncampus? How quickly would this
(09:43):
grow? And we had 100 studentsthat first year and then 600 and
then 800, and no one's requiredto take this program, the state
of mind that we offer. Studentssee a lot of value in it. We
sense that the employers, thepeople hiring them for both
internships and full timeemployment, see a lot of value
in the skills the students arelearning.
(10:05):
And just coming back to thatinterdisciplinary piece, each
student kind of has their rolein the larger team, an engineer,
someone from Daniels School ofBusiness, someone from a data
science background orcommunications or liberal arts.
The students kind of find theirniche in a larger team within
the larger corporate environmentthat they're working, and it
(10:27):
just seems to be a model that'ssuccessful.
Daniel (10:29):
And could you speak to maybe, Katie, the general
structure of these teams? We'retalking about, I think if I
remember what you said as wewere chatting before recording,
that there's potentially over2,000 students now that could
participate in this, which ispretty incredible. Wide
reaching. How are those studentsfrom different departments,
(10:54):
interdisciplinary? How are theyformed into teams?
And what does a team mean, Iguess, is my question.
Katie (11:02):
That's a loaded question. So we have a couple of different
options, right? So we have ourone credit seminar course where
students are learning differentskills, R, Python, SQL, Bash,
depending on what level theychoose. And then we have the
three credit hour corporatepartner program where students
(11:24):
are working eight to 12 ishstudents per team are working on
a data science project with acompany. They are meeting
weekly.
They have a fifty minute meetingwith their mentor and then they
have a little less than two hourlab that's led by a TA where
they're working in agilemethodology, you know, trying to
(11:48):
solve whatever problem thecompany has presented.
Daniel (11:51):
And is that structure either one of you could let me
know. Have there been multipleiterations of that to learn? I'm
very interested in kind of the alittle bit of the backstory here
because I'm sure there areothers, maybe even at other
universities, but othercorporate maybe folks listening
to the podcast that arewondering, you know, what is the
(12:13):
work that's going on inuniversities to really figure
out how to adapt to whatstudents need to do. So was
there iteration in that,challenges, things that you
tried and then changed? Anythoughts on that and kind of the
history of how that developedinto what it is now?
Mark (12:34):
I guess I could speak to that. You know, in the beginning
we had undergraduate students,Katie already mentioned students
from 160 some majors on campusnow, but we didn't have graduate
students involved. We oftendidn't have very much faculty
involvement. Our relationshipswith many of these external
partners were just starting tobuild. We have really deep
(12:56):
partnerships now with some ofour friends in industry and
students will stay involvedoften throughout their undergrad
study and sometimes even chooseto stay at Purdue for the
graduate study to be in DataMindbecause they recognize what an
opportunity this is.
We've definitely evolved andcontinue to evolve. In addition
(13:16):
to the way we've evolved workingwith companies, I'm sure we'll
speak to this more as we getinto the podcast, but we're
trying to help otheruniversities around the state
and also outside of Indiana toadopt external facing types of
models like the data mine inwhatever sector of their state,
whatever sector of the economythat they're interested to
(13:39):
partner in.
Daniel (13:40):
Interesting. And are there other examples of these
sorts of external facingprograms that are spinning up
around the nation or evenglobally that you're aware of?
Katie (13:52):
Yeah. We have, right now we have a partnership with,
Youngstown State University.They are pretty much operating
their own, you know, data mineinstance. And then we have Data
Mine of the Rockies, which isled by another faculty member
here on campus who is workingwith students at Purdue and
(14:12):
Colorado on different projects.We also have quite a few schools
in Indiana that are working withus in different ways.
We have some that are foldedinto our seminars, some that are
folded into projects and somethat are working with faculty on
research projects. We also havea grant that we're working with
(14:35):
other institutions across TheUnited States. And it's very
similar in that some are workingon projects, some are folded
into our seminar course. But weare working with, I believe it's
over 60 institutions, if notmore.
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Daniel (16:36):
Well, Mark, Katie, great introduction to kind of a little
bit of the structure of this asit relates to kind of this
interdisciplinary and and scalecomponent. I'm interested in
this next component of thedescription, this sort of
living, learning community. Helpme understand what that means
and why it's important.
Mark (16:57):
You know, early when we had our first grant before there
was a data mine, I wasn't awareof how many resources the
university puts towards studentshaving a great first
undergraduate year at Purdue.And then similarly, as students
get ready to graduate the end oftheir junior year and into their
senior year, wow, we have a tonof resources for students making
(17:19):
that transition into theworkforce. But there's this
murky middle, especially onesophomore year where students
are taking their hardest coursesin their major, kind of finding
their footing. You know, they'vegotten through their 100 level
courses and they're thinkingabout how their major is gonna
transition into a career. Andit's not just Purdue, but by no
(17:39):
means.
It's well documented that that'sa time when students start to
question and struggle and reallywrestle with what's their career
going to turn out to be. So welove that many of our students
choose to live together and theywork together. While Ms. Sanders
and I are at home, you know,with our respective families
(18:00):
where all of the staff membersclose-up shop and we have dinner
and go to sleep, the studentsare still working. You know, at
10:30 in the evening, they'rebonding and they're learning
from each other.
And that's when the real work insome sense happens. So by having
many of the students livetogether in a residence hall and
study together and faculty oftenhave office hours there and we
(18:21):
have our meetings there and theuniversity has been so
supportive, we're even buildinga second residence hall
completely devoted to thesestudents' experience. We think
we've kind of been able toreshape some of those struggles
students would have in anintensive science and
engineering environment that isoften corporate facing. And they
(18:42):
end up feeling at home andfinding their friends often who
become lifelong friends andsuch. It really enriches the
experience.
Daniel (18:50):
Really interesting. Prior to this conversation, if
you were to ask me, hey, what,all right, you're going to
transform sort of data scienceand AI across a university
campus. Probably the first thingthat would come to my mind is
not like build a residence hall.But it's interesting to hear
(19:14):
like how this has actually kindof organically, it seems like
somewhat organically come up.And maybe that's, is it part of
the intentionality that you haveof like, there's
interdisciplinary students,maybe they're in their own
buildings and different classes,but there's this kind of cross
(19:35):
interdisciplinary space wherethey are, but you're also
having, like you say, a lot ofthe meetings there and that sort
of thing.
So have you found that thestudents associate that space,
yes, with their living, but alsokind of these projects and what
they're a part of kind of in animpact sort of way. Like we're
(19:57):
together, living together,working on these projects that
have a wider impact. What isyour, I don't know, any
experience of how that sort ofsense of place influences the
students' view of the projects?Any thoughts?
Mark (20:11):
I could say one thing that comes to mind, I think very
fondly to our first year workingwith our friends at Cummins,
because they would bringliterally sometimes two van
loads of Cummins employees allthe way up from Columbus,
Indiana to West Lafayette, asubstantial drive of a couple
hours. And they would meet withthe students in a room in the
(20:32):
residence hall where many of thestudents were living. And I
could just imagine if I were astudent, wow, I have people from
one of the largest companieshere in Indiana who are coming
here to see me. Of course, itputs some pressure on them, but
on the other hand, it shows thatCummins trust them and they see
them as the future workforce ata major corporation like
Cummins. And moreover, ourCummins friends are some of the
(20:55):
nicest people that we work with.
I spent a large portion of themorning, now six years later,
talking to my friends at Cumminsabout what we're gonna do next
year. That model where we arefortunate to meet very good
mentors in industry who care aton about our student success
has tended to provide anotherlayer that maybe a regular
(21:16):
faculty member might not have inthe research team. Students are
getting feedback from people whoaren't faculty, and it's really
well informed because it'sinformed by the industry
experience. So I think that's areally key part of why the
model's been successful is notsomething Katie and I or our
team is doing, but rather we aresurrounded by good friends who
(21:38):
seem to believe in this modeland even more believe in our
students and their potential tocontribute and make a
difference.
Katie (21:45):
I would likely say, I would say two things. One being
the students get more sense of acommunity, right? When you're
seeing the same people everyday, you're walking down the
hall and you're thinking, oh,that's so and so, or you like
walk down campus. And I knowthere's thousands of people on
campus, but it's sometimes niceto see a familiar face. And I
think that sense of communityreally helps with the projects
(22:08):
because they're able to divedeeper into those personal
relationships.
That being said, one thing is wehave our Indianapolis, Data Mine
of Indianapolis, and the mentorsare meeting with students
on-site because of the closeproximity to many of the
companies, which has been liketransformational, like down
(22:30):
there, like it's beentransformative. The students
love it. The companies aregetting that one on one time. We
can't always do that here inWest Lafayette because of the
location, because of location.So that's been really great.
And I forgot to mention thatearlier that Indianapolis, you
know, we started that January'24 with seminar and then kicked
(22:51):
off the corporate partnersprogram this last fall. And it
was very successful and a greatexperience for students,
especially students that arecoming in their freshman year,
they get that experiencefirsthand.
Daniel (23:04):
Yeah, it's been really interesting to hear, you know,
just anecdotally, similar sortof things on the industry side
when we've been engagingcustomers and when we're able to
meet them on-site, to theirconference room, right? It's
like that sort of interactionhas such a meaningful
(23:26):
contribution to building thatrelationship and rapport and the
trust that can be built therethat is really, really difficult
in a virtual environment. And Iknow many people are good at
that and sometimes there's notthe chance to be in the same
room. But I definitely thinkthat instilling that in
(23:47):
students' minds of the kind ofcontinued importance of some
face to face interaction andbuilding that relational
component is really key. That'svery encouraging to me.
This kind of, I guess, naturallyleads into the discussion of
this next bit of the definitionof the data mine that I've been
(24:08):
using, which is this corporatepartnership. We've talked about
the interdisciplinary studentteams. We've talked about even
how those students are livingand working together in these
interesting, you know, living,learning communities, that that
are sounds like are expanding.What about the the corporate
(24:29):
partnership side of things?Maybe if you could just help us
understand the mechanics of howthat works.
I know some capstone projects atdifferent universities or
student projects at someuniversities, I've heard from
colleagues in industry,sometimes they can be viewed as,
(24:52):
It's good that we can help thesestudents, but there's no benefit
to the company, but maybe it'sgood that we can help the
students. But this is definitelya different view, or at least
what I've seen from what's goingon at the data mine, a different
view of what student projectscould be. So maybe in a general
sense, like how do you thinkthat universities and corporate
(25:18):
partners can actually engagetogether in a way that is
mutually beneficial, yes, to thestudents and their future
career, but also to thecorporate partners?
Katie (25:29):
I would say that a lot of the projects that we get,
they're not mission criticalprojects. The experiential
learning piece is not missioncritical, but they are projects
that, you you'd know, reallylike to get to, but you don't
have the time to get to, youdon't have the manpower, things
that have been on the backburner, things that, you know,
(25:50):
need revived a little bit, orsomething that you've always
wanted to do, but just, youknow, haven't had the time to
do. Those are the type ofprojects that these students
work on. And the students are sosmart and innovative. Like it's
crazy to me, honestly, becausewhen I was that age, I was not
doing that.
Like, so it's just been, youknow, every company has a
(26:13):
different reason why they arecoming to the data mine. Some of
it's a talent pipeline. Some ofit is like, look, I just wanna
hire some interns and this is agreat way to get connected with
students. And you're, youalready know their work ethic
because you're working with themfor the nine months prior to
maybe them starting aninternship. Some people, you
know, some companies are, well,we have this project, let's just
(26:35):
try this and see if we get it,see if it works out.
And it's awesome when it doeswork out, right? So there are
different reasons why companieswill join and, you know, our
retention's pretty good as well.So I think that speaks to the
work that the students do.
Mark (26:52):
I was just gonna say, feel like we're growing with some of
our companies, you know, likebefore I came into the podcast,
I went down the hall, the officesuite for Beck's Hybrids, which
is just on the catty corner fromour office. And, you know, when
we first started talking with Ithink they had on the order of
300 employees and they justhired their one thousandth
employee this year. We're just avery, very small part, but we
(27:17):
feel part of that growth. Wereally feel that in our team and
they always make us feel likewe're part of the family at
Beck's. When you were firstbroaching this question, kind of
what hole are we basicallyfilling with some of these
partners?
You know, yesterday I was inChicago and not naming any
names, but I met with thecompany for the very first time
who has 75 employees. It's not ahuge company, but the challenge,
(27:43):
the gap is they don't have anydata scientists yet. They have
one intern and the intern's justamazing. But if they had full
time folks on staff who couldleverage AI and ML and do some
of the predictive analytics andso on, they recognize that they
could go from being one of theplayers in that industry to
becoming a leader. And if theytake a chance on us at DataMine,
(28:04):
they can try the students on forsize and see what kind of
research and development theycan do.
And it's not just they're beingnice to the students or helping
them with the capstone like yousaid, Daniel. It's more like,
okay, we actually sense there'sa real opportunity to advance
our business here with thesefolks at DataMine. So when we're
able to make impacts like thatand the students' work is really
(28:24):
valued by the company, I thinkthat's when we've hit our sweet
spot.
Daniel (28:28):
Yeah, that's great. And just to give a sense, I mean, I
know there's probably somecorporate partners that can't be
mentioned for confidentialityreasons or other things, but
either of you, I don't know ifyou can give a sense of kind of
how many corporate partners youeither are or have worked with
and kind of the range of sizesand just a sense of industries
(28:51):
maybe, because it is interestingthat similar to what you said,
Katie, I remember being anundergrad and doing projects. I
was not exposed to working withactual real companies outside of
if I did pursue an internship orsomething like that formally.
But as part of the actualuniversity side, I wasn't
(29:14):
exposed to that. So I really,just from a personal standpoint,
had no idea how a lot ofindustry even operated or worked
or that sort of thing.
So I don't know, could either ofyou give us a sense of that,
types of companies and numbers,that sort of thing?
Katie (29:30):
Last year, we had 88 projects with about 60 different
companies. And then I don'tknow, Mark, if you wanna maybe
take the rest of that. I knowthat part. I know the numbers
part.
Daniel (29:43):
That's that's you know the data at the data money.
Yeah.
Katie (29:46):
Yeah. I don't know how to work with it, but I know the
numbers.
Mark (29:50):
We try to be really affordable. I mean, we, you
know, we have not doubled ournumber of companies in the last
year or two, but what we've doneis we've gone a lot deeper in
our relationships with thecompanies. And many companies
where we started out just doinga pilot or, you know, a free
project, no cost or whateverhave turned into two and three
and four and five projects withthe companies, all of which are
(30:13):
paid or frequently sponsoredresearch, which is all
proprietary and not disclosed. Ithink that's been the biggest
change in terms of our businessmodel is the depth of work that
we've done with some of thesecompanies. You asked about some
Katie (30:27):
of the
Mark (30:27):
domains of application of these projects. Of course, it's
hard to categorize, but youknow, we think about aerospace,
defense. I mentioned agricultureearlier, our friends at BAX is
an example of that. Talked We alittle bit about manufacturing
some. We haven't really alludedto pharmaceutical science,
(30:48):
computational drug discovery.
Every vertical companies needthese students, you know, so.
Katie (30:55):
I will say too, our previous projects are listed on
our website as well. If anybodywanted to go check out last
year's symposium, there'sposters with videos from the
students. That option is there.
Mark (31:07):
Yeah, it helps when you go to meet a company for the first
time, you don't have to do ahard sell anymore. I never bring
a PowerPoint to a meetinganymore. We simply ahead of
time, we send that link Katiementioned. Here's what we did
last year. Here's who ourfriends in industry are.
Let the work speak for itself.How can we help you next?
Daniel (31:28):
Yeah, thank you all for kind of sharing some of that
about the symposium, the link,the projects, the corporate
partners. Just so our listenersknow, we will include a link to
the DataMine, website in ourshow notes. So go ahead and
scroll down there to clickthrough, take a look. I'm just
(31:48):
scrolling through all of thecorporate partners, are all, you
know, just in incrediblyinteresting from Allison
Transmission to Dow Chemical,Johnson and Johnson, Lockheed
Martin, etcetera. Just very, youknow, names that people will
recognize, but also some namesthat maybe, are are smaller to
(32:09):
to your point, but still havereally interesting interesting
work and and you can explore allthose things.
I'm wondering, all have beenexposed to a lot of amazing
projects that have gone onwithin the data mine. Are there
any that come to mind that, youknow, like you said, Mark, when
(32:29):
you're talking to corporatepartner or Katie, you're,
engaging either on the studentor the corporate side that
typically come to mind like, Oh,if I get to mention this one,
always a really inspiring one oran interesting one where
students overcame a challenge orsomething like that. Any
particular ones that you'd liketo highlight?
Mark (32:51):
What do you think, Katie? Who are we gonna name drop here?
Katie (32:55):
Well, I don't if I wanna name drop, but one of my
favorite ones was, you know,chatbots are like, everybody
wants one now. Everybody wantsto just be able to say, Hey,
where's this and this and findit for me. And so one of the
projects last year, the studentsworked really hard to create a
(33:15):
chatbot. And then I love to seewhen the company like is able to
implement that to make theirprocesses easier. So that was
one of the ones that came tomind for me.
It also, I think, depends on thecompany, right? Like certain
companies might not have aninterest in like what other
companies in a different domainare doing. So I think it kind of
(33:36):
depends on who you're talkingwith. And just to mention too,
from the student side, one of myfavorite stories is we had a
student who was in computerscience and there, you know, a
lot of students are looking atthe bigger companies, right? We
know the big companies, but theyended up doing an internship
(33:56):
with a smaller company.
And, you know, their feedbackwas, I would have never known
that they were collecting datathis way. And, you know, I could
work for a company like this. Ididn't know they existed without
my experience in the data mine.So that's just like one of my
favorite things is just exposingstudents to different
opportunities that they mightnot have thought were there.
Mark (34:20):
I could pile on in a similar direction there. Last
night, as I was waiting for myride home from Chicago, I'm
writing letters ofrecommendation. And wow, I write
a lot of letters. I might writeletters for 40 or 50 or 60
people every year. And I hadthree I was putting together
last night and I was tired.
And as I'm writing the letters,you know, I'm checking my email
(34:42):
and a student writes, Doctor.Ward, I just wanted to write and
let you know I got into thisgrad school. I can't believe
this research mentor I'm goingto get to work with. I think
it's really putting me on trackfor after I finish my master's
or their doctorate. Don'tremember, you know, the kind of
career they're going to haveahead.
And they came back over and overto Datamind made that possible
(35:04):
for me. That's priceless, youknow, I mean, like I'm sitting
there working on my letters andI'm exhausted, but that's what
renews you, you know, that'swhat really kind of gives you
hope for the future at thesecompanies. If we think about an
employer like Eli Lilly inIndiana, they're just
transformative to the economyhere. And we love working with
(35:25):
Eli Lilly. They're just thatthey're good beyond measure to
Purdue, but we also feel luckyto work with AstraZeneca and
AbbVie and our friends at Merckand so on.
And when students are able tokind of see the full breadth of
what's going on in an industryand have a lot of choices, we
feel it's good for everybody.
Daniel (35:45):
That's awesome. I'm just, you know, as we speak,
scrolling through projects andreading amazing things that
students have done, I seeeverything from detecting
digital fraud in healthcarewith, L events. There's, you
know, there's forecasting, soyyields. There's, you know,
(36:11):
reliability for NLP machinelearning models, with with Ford,
just a a lot of reallyinteresting things. And that
only scratches the surface.
I really recommend people to goand scroll through the website.
And there's a lot of interestingvideos and posters to take a
look at. And, our listeners willbe interested to know that the
(36:35):
Practical AI podcast hassubmitted a project for this
upcoming year, and there'll be ateam of students working with
us. And, I will not there issome really cool stuff that
they're going to be doing. Iwon't reveal the full extent of
that, but, I think it will besomething that our listeners
(36:56):
will be able to to interactwith, coming out of the coming
out of the project and also, youknow, something that that I
think will be quite fun.
So I'll tease that a little bit,but we're excited to work with
these students and reallyunderstand from our own
(37:17):
experiential level for myselfand Chris, our cohost, and our
listeners actually pulling theminto this type of new creative
way that students are learningdata science and AI. At this
podcast, we're always talkingabout making AI and data science
(37:38):
and these methodologiesaccessible and practical for
everyone. Certainly a part ofthat is workforce development
and education and this kind ofacademic industry partnership.
Really So excited to actuallysee that materialize. So thank
you both for making that happen.
This is really exciting. I'mwondering so number one is we
(38:02):
should mention, explicitly onshow that the data mine is still
looking for corporate partnersand there are many students that
are there's a whole group ofamazing students that still are
looking for projects. If you'relistening to this in August, you
(38:22):
know, July or August of of oftwenty twenty five, go ahead and
reach out to the data mine.There's still the opportunity
for those projects, or maybeyou're listening to this
sometime in the future. I'm I'msure they would still love to,
love to hear from you.
And of course, we'll includethat link below. What what's
maybe just from y'all'sperspective, for corporate
(38:43):
partners that are coming in andand wanting to sponsor projects,
you mentioned a couple of thingsof how different corporate
partners, view this, but what'sthe value proposition that
someone listening to the podcastcould bring to their coworkers
or supervisors at their companyto kind of tell about this
(39:05):
interesting thing that'shappening at Purdue around data
science and AI? What would bethe elevator pitch that you
could help them give?
Katie (39:17):
We always joke that Mark could sell a used plastic bag,
so so go ahead.
Daniel (39:22):
There you go. I need to hire you into into my company as
a salesman.
Mark (39:26):
Yeah. Would you like to buy this coffee scoop? I could
keep it affordable. Think of allthe things you could do with
this. It's it's not just aregular coffee.
Daniel (39:34):
It's multi purpose.
Mark (39:36):
Yeah. Yeah. Yeah. Yeah. But, you know, I mean, it's it's
so affordable affordable forthese companies.
We we sign these five yearagreements with companies so
they don't have to go back totheir lawyers. Purdue doesn't
have to renegotiate anything.Everything's just sort of laid
out so that a manager who's gotan idea can rubber meets the
road, go go work with thesestudents. And then once that
(39:59):
happens, once one company takesa chance on one team, invariably
all of their siblings in thatcompany, all their buddies, all
people in different parts of theorg, when they hear about it,
they want it for themselves too.It's an easy story to tell.
Katie (40:15):
And I think we helped through that process as well. I
mean, Mark was writing projectdescriptions last week, so he
has an eye and an ear and abrain for it. He can definitely
find where there might be holesor suggestions or things like
that. And we'll with you, himand our corporate partners and
(40:37):
data science team as well.
Daniel (40:39):
That's awesome. And as we kind of get closer to the end
of our conversation here, maybejust taking a step back and as
you all of course, have theimmediate things that you're
part of leading into this nextsemester of the data mine and
students engaging with partnersand that sort of thing. But as
(41:01):
you, whatever it is, lie on yourbed and think about the future
of what's happening, what'schanging, especially over even
the last couple of years, justso much has changed, what
excites you or what are youlooking forward to that may be
possible for the type of programthat you're running or for just
(41:22):
this type of education in highered more generally? What are you
looking forward to and what mostexcites you about the future in
that regard?
Mark (41:33):
Can I say world domination? I meet it in the
most The data mine everywhere.The data mine everywhere. It's
not just about something we'redoing in Purdue, it's become a
model for engagement. And Ireally believe it's silly when
we mentioned some of thesecompanies that they're going to
invest time and money and effortto go to 10 or 20 campuses
(41:54):
around the Midwest or maybearound the country to do their
recruiting.
And then they're going to hirein onesies and twosies, people
who they've often never workedwith before and take a chance
and give them a full time job.My sense is that it's so much
more fun to build thingstogether and whoever it
resonates with, both on thestudent side or on the company
(42:15):
side, just have that naturalmatchmaking. Learn by doing all
the R and D and the AI and ML,all the value creation. So my
hope is that this modelcontinues to be adopted by many
different kinds of institutions,not just here in Indiana or in
the Midwest, but all over thecountry. And those of us in
(42:38):
higher ed need to sticktogether.
I mean, we're all wrestling withthe same changes together, just
that industry is as well. Andit's a big tent. Let's continue
to work together on initiativeswe can all just help each other.
Daniel (42:49):
That's great. Any thoughts from your end, Katie?
Katie (42:52):
I would say similar and that, you know, it's exciting to
see what the workforce is gonnalook like with students getting
engaged with companies earlierin their lives versus later. So
anything's possible.
Daniel (43:09):
That's great. That's a good thought to end on. Well,
thank you both for the work thatyou're doing, and the creativity
that you're putting into thistype of program and the
inspiration that it is for bothcorporate partners who are
finding new ways to engage andrecruit, but also other
universities and educators ingeneral who might be at a loss
(43:34):
for how to, you know, find amodel that works in the the
changing ecosystem that we're apart of. So thank you both for
your work, and and lookingforward to, rolling out and
revealing some of what we'reworking on with with Practical
AI as as the year unfolds. So,thank you both for taking time.
Really appreciate it.
Katie (43:54):
Thank you so much.
Mark (43:55):
Yeah. Thank you.
Jerod (44:04):
Alright, that's our show for this week. If you haven't
checked out our website, head topracticalai.fm and be sure to
connect with us on LinkedIn, X,or Blue Sky. You'll see us
posting insights related to thelatest AI developments, and we
would love for you to join theconversation. Thanks to our
partner Prediction Guard forproviding operational support
for the show. Check them out atpredictionguard.com.
(44:26):
Also, to Breakmaster Cylinderfor the Beats and to you for
listening. That's all for now,but you'll hear from us again
next week.
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