August 23, 2024 • 37 mins
What does it take to make STEM work more accessible and effective? Ashley and Cat introduce their work and their values by answering this question.
Credits
Ashley Juavinett, host + producer
Cat Hicks, host + producer
Danilo Campos, producer + editor
Ashley on teaching coding to neuroscientists:
Juavinett, A. L. (2022). The next generation of neuroscientists needs to learn how to code, and we need new ways to teach them. Neuron, 110(4), 576-578.
Zuckerman, A. L., & Juavinett, A. L. (2024, March). When Coding Meets Biology: The Tension Between Access and Authenticity in a Contextualized Coding Class. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (pp. 1491-1497). PDF here: https://dl.acm.org/doi/pdf/10.1145/3626252.3630966
Sense of Belonging is a widely-studied concept across the psychological sciences. Cat’s work on Developer Thriving includes a measure of Belonging on software teams:
Hicks, C. M., Lee, C. S., & Ramsey, M. (2024). Developer Thriving: four sociocognitive factors that create resilient productivity on software teams. IEEE Software. PDF here: https://ieeexplore.ieee.org/abstract/document/10491133
This recent article provides a helpful commentary, summarizing an impressive collaboration across 22 campuses and 26k+ students: Walton, G. M., Murphy, M. C., Logel, C., Yeager, D. S., Goyer, J. P., Brady, S. T., ... & Krol, N. (2023). Where and with whom does a brief social-belonging intervention promote progress in college?. Science, 380(6644), 499-505. PDF here: https://www.greggmuragishi.com/uploads/5/7/1/5/57150559/walton_et_al_2023.pdf
Mark Appelbaum, Cat’s first stats teacher, had a positive impact on many, many students. You can read about his life here: https://psychology.ucsd.edu/people/profiles/mappelbaum-in-memoriam.html
Schools, Technology and Who gets to Play?
Rafalow, M. H. (2014). The digital divide in classroom technology use: A comparison of three schools. International Journal of Sociology of Education, 3(1), 67-100.
Rafalow, M. H., & Puckett, C. (2022). Sorting machines: digital technology and categorical inequality in Education. Educational researcher, 51(4), 274-278.
Learn more about Ashley:
Learn more about Cat:
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Cat (00:00):
When we started dating, you told me that I was like someone
(00:02):
who grew up in the 1800s.
Ashley (00:04):
I mean, there's literally a photo of you that is
in black and white poking asheep with a stick.
Cat (00:15):
We had a lot of thoughts and feelings about what
technical work in the worldlooks like, what the future of
that work is, how do weunderstand it, how do we make
change about who gets to betechnical.
Ashley (00:28):
This is a podcast about what it's going to take to train
and include the next generationin technical fields.
Cat (00:34):
As, the resident wife guy in this situation, I feel I'm
uniquely qualified to introduceDr.
Ashley Jauvinett.
Ashley is a neuroscientist.
You're an educator.
some might call you an educationactivist, actually, you're a
rabble rousing educator,changing the game for students
in neuroscience.
You're an associate teachingprofessor at UC San Diego.
(00:56):
You're also a musician, issomething that was really
important to me when we firstmet because I fell in love with
you when you were playing anEllie Goulding cover.
We were both in grad school.
Ashley (01:09):
Gosh, that feels really comprehensive.
I'm also your wife.
I feel like that needs to go onmy list of accolades.
Cat (01:13):
So you, a neuroscientist have to introduce the
psychologist.
Now,
Ashley (01:17):
I'm super excited to introduce Dr.
Catherine Hicks.
Who is a social and datascientist and really like a
force for the science ofdevelopers, like someone who is
leading the psychology ofsoftware teams.
She is the creator of thedeveloper thriving framework,
which has really been, I think,one of the first really, really
(01:39):
good examples of someone who isstudying software teams with
evidence based data.
And finally, also someone who'san advocate for open science
someone who believes that fromstart to finish, the kinds of
science we do should beaccessible, reproducible, and
shared with everybody who canbenefit from it.
Cat (01:56):
I think your journey into coding has been really, really
interesting and it's not alwaysone that people hear.
So can you tell me like a littlebit about what got you to being
an assistant teaching professor?
Ashley (02:08):
I don't have any computer science training.
And this is the thing I tell mystudents in my programming
class.
On the first day of class islike, I never took a programming
class,
Cat (02:18):
But you're teaching them.
Ashley (02:19):
But I'm teaching them how to code, right.
And how to think about code.
And so for me, I am aneuroscientist, as you said, and
I started my coding journey whenI was a graduate student and I
was handed a bunch of data,stacks of images and told to
analyze it.
so I had to learn and I got likea MATLAB for neuroscientists
(02:41):
book and did some of theexercises, but it wasn't super
useful.
Like the thing that was actuallyuseful was just having my own
data and having to play with itand edit other people's code.
Cat (02:49):
This is just really plunging me back because we met
in grad school.
I was a psychology graduatestudent I had absolutely no idea
what neuroscientists did at all.
Like, I thought, okay,somebody's there studying the
brain, that's fine.
And then I met you and I waslike, what's happening?
You're in the, this dark lab,you're working with lasers,
there are viruses involved.
(03:10):
Like, just tell the people alittle bit, what did you do in
neuroscience?
Where does this data come from?
Like, what is it like?
Ashley (03:18):
Oh man.
Cat (03:19):
what's MATLAB?
Because I don't think peopleknow that.
Ashley (03:21):
Yeah, neuroscientists, we want to understand how the
brain works.
If you want to understand howthe brain works, you can study
humans, but you can only do somuch in humans.
You know, you can put humans ina fMRI scanner, get some big
pictures of their brain, but itdoesn't tell you about what
neurons are doing.
And if you want to explain howthe brain works, you usually
want to know what neurons aredoing.
So, we turn to animal models,things like mice and sometimes
(03:43):
monkeys or zebrafish.
And we do fancy things in thoseanimal models to target specific
sets of neurons.
So when I was in grad school, Iwas recording lots of images
where I had tagged specific setsof neurons to try to understand
what they were doing in thebrain.
And we were doing this in amouse model and the lasers comes
(04:05):
in because you shoot lasers atthe brain to target these
proteins that change how brightthey are based on how active the
cells are.
Cat (04:14):
I remember feeling like you had access to these superpower
things that I had like nevereven touched at the time in grad
school.
I hadn't even like touched amicroscope because I didn't
really, really, cause I grew uphomeschooled.
And then in college, I didn'ttake any laboratory classes.
My college didn't even havethose open to people who were
not like STEM majors.
Ashley (04:33):
Yeah.
Cat (04:34):
I met you and you were like, Woo lasers.
One day it's a laser.
One day it's a virus.
You know, like you were learninglike a million different skills
a day.
I felt like, did you, do youfeel like you came in like
confident about that?
Ashley (04:46):
I started graduate school with enough research
experience to get me in thedoor, but a pretty narrow slice
of research experience.
I had gone to a small liberalarts school where I didn't take
enough math and I didn't takeenough programming, and then I
was thrown into this, like, verycompetitive, very top notch PhD
program, and I had to learn alot on the spot.
(05:09):
And no, I felt like I knewnothing, and I was learning
everything.
Cat (05:14):
What happened after you were like, I got to teach myself
to code.
Ashley (05:20):
think I spent most of my time in grad school wrangling
MATLAB, which by the way is aprogramming language that is
mostly used by engineers, alittle bit in academia.
It's, uh, it's okay.
It's pretty, it's pretty goodfor some things.
Cat (05:33):
No MATLAB slander is going to occur here.
Ashley (05:35):
It's not open source and we're a big fan of open source
in this room.
You work
Cat (05:38):
in python now, right?
Ashley (05:39):
I do because when I started my job and I started
realizing that I went on thisjourney of learning how to code
and I felt like You know, otherpeople, too, are going to be on
this journey, you know, thestudents that I'm working with,
and I wanted to give them thetools a little more up front and
a little more, you know, in away where everybody has access
(05:59):
to them, um, and feels like theycan get those skills.
And so for me, It didn't feelright to teach MATLAB.
It's not the most ubiquitousprogramming language, especially
outside of academia.
I decided, okay, I'm going tolearn Python.
And when I was in like the firsttwo years of my job, pre tenure
started learning
Cat (06:20):
You actually a professor about to teach it, and you were
like, I better learn this so Ican teach it, right?
Ashley (06:27):
Yeah.
And I'm not the only person whohas like done this, right?
I'm one of many, many people whoeither for research or teaching
or whatever else, right?
Has had to teach themselvesthese skills on the job.
Cat (06:38):
I've done research with all these software developers.
Thousands of developers havecome through my studies at this
point.
And there's always, like, thismoment.
Almost on every topic I've everstudied because I study
psychology and it brings outlike the deep stuff for people
and almost every study that Ido, there's a moment where
(06:58):
people start to say, um, I betyou've never heard this from
anybody before.
Ashley (07:03):
Hmm.
Cat (07:06):
Hmm.
I wasn't trained like everybodyelse.
I'm kind of an imposter.
I got to tell you, it's reallyradicalized me to have like
thousands of people tell me thatthey're the only person like
this.
And it's, I would venture to sayself teaching is kind of like
the majority out there.
Ashley (07:23):
I think that's a hopeful thing because it's, we, we need
those people.
And, you know, there's limitedspace in every introductory
computer science class and everybootcamp.
Like if people aren't teachingthemselves, we're behind.
Cat (07:35):
So you taught yourself to code.
tell me about that classroom,because I think there's some
stuff that's really interestinghere.
Because you're teachingcomputing, you're teaching
coding from outside of computerscience.
Ashley (07:48):
Hmm.
Cat (07:48):
like?
Ashley (07:50):
Yeah, I think I had to do quite a bit of soul searching
and also speaking to colleaguesabout, you know, like, what is
it that people actually need toknow?
You know, I'm not training backend software engineers.
I'm training people who aregoing to go out into the world,
want to do something with theirdata or build a computational
model of some sort.
And they don't need to knowevery single in and out of,
(08:13):
like, object orientedprogramming or something.
I landed on a few principles,which is like, one, we should
try to teach as little syntax aspossible and try to teach as
little like memorization aspossible.
And that's even more true now inthe age of AI assistance.
And two, we need to have it belike much more data focused
than, you know, a typicalcomputer science class.
(08:34):
So a little bit more of a datascience y sort of feel to it.
And.
So that's like the content ofthe class, right?
But that's only half of it.
The other half of it is how youteach the content and how do you
convince biology students, one,that it's worthwhile to do this
and two, that they can do itbecause most of them walk into
the room thinking, wow, gosh,I'm not a hacker.
(08:56):
I'm not a math person.
They have all of thesepreconceived notions about what
it even means to learn coding,right?
Cat (09:01):
Stereotypes.
Ashley (09:02):
They have stereotypes and that stuff gets in the way
of everything else.
Like if you don't talk aboutthat stuff, if you are not
acknowledging that that's in theroom with you, you can't learn
how to code.
just can't.
Cat (09:15):
So, why should those students go learn programming in
a biology classroom, not in acomputer science classroom?
I know you have thoughts aboutthis.
Ashley (09:23):
People should learn computer science in a biology
context because We've thoughtabout what they need to learn,
and it's more tailored to whatthey need, I think, in the end.
And two, there's like a bigidentity piece of it.
So I just co wrote a paper witha student who took one of my
classes who did sort of ventureinto the computer science and
(09:43):
engineering side of campus toget access to some of these
skills.
And she told me, you know, evenin an introductory computer
science class, there werepeople, many, many people in the
room for whom that wasn't theirfirst time, right?
So they a
Cat (09:58):
lie.
Ashley (09:58):
it's a, it's a lie.
Cat (09:59):
intro, CS class is actually an intro CS class on a major
university campus anymore.
Ashley (10:05):
Totally.
And we have different tracks onour campus, but still, right,
there are the students who wouldprefer to take a class that's
like a little bit below theirskill level, so they come into
the room with the knowledge,right?
And so, one, like, she reallyfelt that.
And two, I mean, to be frank, ifyou walk into a computer science
classroom on my campus, it ispredominantly male.
And she really, really feltthat.
(10:26):
And.
Like my class looks dramaticallydifferent than that.
And we can do everything we wantto like, try to make women feel
included in these spaces.
But look at the end of the day,if you're looking around and
it's like mostly dudes, and it'smostly people who aren't in your
major also, who you would neversee outside of this one class,
you know, it's just, it'salienating.
(10:48):
Creating a space for thosestudents where they feel like
they can learn amongst peers,truly peers who feel like
they're in the same boat.
That's really meaningful.
Cat (10:56):
Yeah.
So there's like this concept inpsychology that you know pretty
well called sense of belonging.
Ashley (11:01):
Hmm.
Cat (11:02):
Mm we are constantly looking around our environment,
scanning our environment, andwe're asking, do I belong here?
Do I belong here?
Even, even if they say I belonghere, do I really belong here?
Right?
Like we're smart about this.
This is survival
Ashley (11:16):
hmm.
Cat (11:17):
in a deep way.
This is about what you think ispossible.
I've measured this with softwaredevelopers, even, even with
highly male dominated fields.
This still matters deeply topeople.
So it matters for folks who havean identity that's not
represented.
It also matters, it does damageif you don't have sense of
belonging for everybody.
(11:40):
And, you know, in our research,we've seen like for professional
software teams, the ones thatsay I'm on a team where I really
do feel like we've committed tothis value.
They report being moreproductive and they say my team
is more effective.
Like a very real outcome in theworld.
Ashley (12:02):
that was my entry into computing, right?
Like, what is yours?
Cat (12:05):
You've had years of hearing me say that I don't want to
freaking teach myself to code.
So
Ashley (12:12):
And feeling like you need, feeling like you need
some, some level of something tocall yourself, you know, data
scientist or whatever.
Cat (12:18):
Oh man, design, and I, I discovered that I loved that
stuff.
I really like logic puzzles, andI really like, like, long-form
(12:39):
fiction, like, I like novels, Ilike storytelling, and I had
never thought those things wouldmake me good at math.
Never.
Which I think is, by the way,just like, in general, if you
are good at books, you might begood at math.
Like, computers are there to doa lot of math for us, so we can
do this, like, narrative logicstuff when we're working
professionally with math.
So I started doing statisticsprofessionally, and that was
(13:03):
like a huge, beautiful, gamechanging moment where I was
like, Oh, I actually am good atthis.
I'm smart at this.
I'm actually pretty kick ass atthis.
Heh heh.
But it hadn't translated tocoding for me for a long time.
And I think it was really juststereotypes again I grew up
really like, not with a lot ofexposure to computing.
(13:25):
In fact, um, you know, myparents would like rarely if
ever let us use the computer,they thought the computer was
really dangerous.
I grew up kind of feeling like,this is not for me, whatever is
happening here, like sneak timeon computers at the library,
Ashley (13:42):
where you had to like sign up and like get a specific
computer.
I remember those days.
Yeah.
Cat (13:47):
they're not letting you just sit around and like open, I
mean, I remember the first timeI saw like a little terminal
window and I was like, it's themost terrifying thing I've ever
seen ever.
I didn't need to walk into aclassroom and see it, that it
was all men to feel like me, Youknow, a poor little queer girl,
like, you know, I, I was notwelcome here.
I co founded a tech startup andI worked with the most lovely
(14:11):
People on earth, my co founderchap Snowden, our chief
engineer, Kirk Collins.
And they were both just likecat.
This psychology stuff rocks.
It's so good.
They were like, it's telling uswhat to build.
It's adding so much value.
And I remember this day thatchap was like, you want to sit
down and push some code and gothrough that process and have
(14:32):
someone review your, that code.
We can do that was like thefirst time I felt like someone
looked at me and said, what doyou mean you couldn't be a
developer?
You could be a developer.
Ashley (14:42):
We've talked sort of about like the asymmetry of
these things like It's sounusual for someone to look at
someone with the skill set thatyou had at the time, which was,
you know, predominantly inpsychological, experimental
research, and social science,and say, yeah, no problem,
become a developer, right?
But we do the opposite all thetime.
We say like, you know, yeah,sure, you know how to code?
Like, whatever, play with X, Y,and Z data, play with, you
(15:05):
could, you could tackle whateverdiscipline you want after, you
know, coding.
Like, we have this asymmetry.
Cat (15:10):
I started consulting.
I was like, I can't afford astatistics software anymore.
Now I have to learn R.
And do you remember I was like,had taken on a research contract
and I was like, I'm going toteach myself how to process this
data in R and it's going torequire coding.
And I was sitting on the couchand I like looked at you and I
(15:31):
said, is this what coding is?
Ashley (15:33):
Those were your exact words.
Like, this is it.
Like just typing some words intothis, like, you know, text
console.
You're like, really?
Like, this is, this is the likeenigmatic thing we've been like
skirting around for so manyyears that I've been convinced
that I like, wasn't able to do.
Cat (15:52):
Yeah.
And it's so funny because now Isee those things and I feel like
fondness.
I see like a big messy terminalwindow or, or I see like, you
know, even things I don'tunderstand.
And I'm just like, I love thisstuff.
I love working with developers.
I love thinking about how peoplework in code.
Yeah.
Ashley (16:10):
Yeah.
And here's the thing.
Like you came into coding andinto data science with.
an understanding of howexperimental design works and
also where it goes wrong andlike that sort of really deep
technical logical understandingof how we collect data when we
(16:30):
know there are real differenceslike that we use statistics to
back up at its core is reallyright like logic you came in
with that and I feel like thathas put you and your work you
Way above anybody else who canwhatever like throw a bunch of
data into some fancy Statisticalpipeline without actually
(16:52):
understanding why they're makingthose choices and you know those
things.
Cat (16:58):
thank you.
It's not just about me.
Like, like, think about yourstudents.
Like there was a student ofyours who actually went to
Microsoft, right?
Who's like, had a biologybackground.
Ashley (17:08):
This student of mine She Was a neuroscience major She
took one of my classes where wedid some coding and kept
learning after class andgraduated, went to Microsoft,
but not in like a quote unquotetechnical role, like in some
sort of consumer facing role.
And eventually like launchedherself through a lot of self
(17:32):
advocacy into a role where she'snow a software engineer.
Like, proper, you know,recognized as such with that
skill set.
And, um, I think, you know, youand I are in some, in some cases
in some sort of flavor like thatbecause we are people too who
have learned these skill setsand have had to advocate for
(17:52):
like, okay, no, no, I am someonewho can teach this class or no,
no, like I am someone who can dothis data analysis, you know,
and run this experiment and dothis research.
And, um, there's more of us.
There's so many more of us outin the world.
Cat (18:06):
Yeah, right.
And like, you're missing out,
Ashley (18:11):
Yeah.
Cat (18:11):
Like if you don't have us, if you don't make it, honestly
make it easy for us actually toget into this stuff, right?
Ashley (18:18):
Yeah.
You're missing out on all thepeople who have all of the ideas
and that like diversity ofthinking from other fields
outside of computer science andengineering.
Cat (18:28):
How do biologists think about code that's different?
Ashley (18:32):
Well, I think I wouldn't say we think about code
differently necessarily.
I mean, code is just the meansto an end, right?
But we think about datadifferently.
I mean, The kind of data wecollect is often really noisy,
and you don't know where thatnoise comes from.
You don't know if it's from thething you're using to measure.
(18:53):
You don't know if someone bumpedthe microscope or the animal at
that time.
You don't know if it's realbiological noise, right?
And so we come in withskepticism about data, about
where it came from and where thenoise is from.
Cat (19:05):
Do you feel like people in tech don't come in with enough
skepticism about data?
Yeah.
Ashley (19:13):
of the stuff we've talked about, like in our
conversations about likemessiness, right?
Like what data is not there?
Cat (19:19):
This is a scientist's way of thinking about signals.
Ashley (19:22):
yes.
yes.
Cat (19:23):
are like, Oh my God, I found something.
It's definitely real.
I'm going to believe it forever
Ashley (19:27):
Yeah, totally.
And
Cat (19:28):
to raise a million billion dollars.
Ashley (19:31):
totally.
And you've, and you've all thedata you've played with in your
training as say, like a datascientist is, you know, the Iris
set on Python, which is Perfect.
It's a perfect data set.
There's nothing wrong with it orthe Titanic data set or any of
these other toy data sets.
Cat (19:47):
in a bad way?
Ashley (19:48):
Well, it's, it's, it's just like a complete data set.
There's no, you know, like noisethat is not intentional, right?
And we play with that data.
It's nice and tidy and, youknow, great.
Yeah.
I can run dimensionalityreduction on that.
Cool.
Um, but hand me another dataset.
I can't assume that it's cleanin the same way.
And that's the kind of lens Icome in with as a biologist that
(20:11):
I think a data scientist doesn'talways have.
Cat (20:14):
I have a background in learning science too, and how
people learn, and one thing thatI think is so cool about the
discipline based computing thatyou do is like, you know,
people, need all kinds of thingsto learn and they need like
abstract things and they needgeneral principles and they need
to put it into practice and theyneed applied situations.
(20:36):
And I think that halo effectreally bothers me.
The idea that everybody shouldjust go to a CS department and
that they'll learn these thingsabout how code works.
And that doesn't match up withhow students actually are like
making decisions.
Ashley (20:50):
Oh, totally.
Yeah.
Yeah, totally.
And I think something that'scome out of my research, which I
didn't expect, the short term.
Can I just do something withthis bit of code is like just as
rewarding as the long termthought of like, maybe someday
this will get me a job or allowme to do X, Y, and Z with my
data.
Like the sort of short term,like, can I do like a fun, weird
(21:11):
personal project around this islike super gratifying.
Or like, I do work in a researchlab.
Is there something I can do withthe data I have now that like
actually, you know, could, couldbe the thing that motivates me
to take this class and to learnthese skills?
And I think a lot of like,movements around trying to make
programming more inclusive havefocused on the long term.
(21:33):
Well, like you're going to makemoney, like you're going to make
a ton of money if you get this,more jobs, you know, it's a good
skill set to have.
And like, that's nice, but like,you know, at the end of the day,
you get that like little burstof dopamine just to throw some
neuroscience in it,
Cat (21:49):
Yeah,
Ashley (21:49):
right?
You don't get dopamine from likethinking about your retirement
savings.
You get dopamine from like thestuff you can do today.
Cat (21:57):
totally.
Well, okay, there was thisthing.
So I used to work in schools andon like these all these like
long term education researchprojects.
Right.
And I always had this thing inmy mind, which was like do only
the rich kids get to have fun orwhat?
Like,
Ashley (22:09):
Mm,
Cat (22:11):
is that is that not something we all need?
Like all these inclusionprograms can be so somber.
Ashley (22:20):
Oh, totally,
Cat (22:21):
so like, You know, I on the goal.
And I mean the, the lot, look,it's important to tell people
there are these careers.
We want you to know about it.
We want you to know, but I thinkyou make a really good point.
We cut those people out from.
experimentation.
There's some great work from Ithink Matt Ruffalo about how the
(22:41):
same technology program went outto different schools, and the
same like resources, like samelittle devices for kids, same
curricula, but teachers in thewealthier school had the kids
explore and do self directed.
You know, and, and, right, like,remember me with the computers?
When I got my first computer incollege, I was like, I better
(23:04):
not break this computer.
Ashley (23:05):
Mm hmm, Mm hmm,
Cat (23:07):
one thing that I have to do is not break the most expensive
thing I've ever owned.
Ashley (23:11):
Mm hmm, Yeah,
Cat (23:12):
that is not a situation in which you're going to be coding
a bunch.
Ashley (23:17):
no, and you know, like, so in this big survey I ask of
my class before and after thequarter starts, um, I ask them
one item among many others.
This one item has changed themost out of any other item on
the survey, and it is exactlywhat you just said.
The item is, I worry thatmistakes I make will damage my
(23:37):
computer.
And students start the quarter,and they're like, yeah, I really
worry about that.
And then you think about yourlow income students.
You think about the students whojust bought the first laptop
they'll ever, you know, haveever owned.
And they are deeply concerned,as you were, about literally
breaking the computer.
How do you learn coding when youcan't play?
Cat (23:59):
Hmm.
So what do you do to help themfeel like they can?
Ashley (24:05):
I break my computer in front of them a lot.
I mean, and I really feel likethat's, that's it, right?
Like I walk into the room and Isay, I've never taken a coding
class.
I'm gonna make mistakes in frontof you.
You're gonna ask questions.
I don't understand.
I'm gonna sit here and generatea bunch of errors and I
actually, I have this activity,which I love.
(24:28):
Um, on like the, you know, inthe first week where I have them
intentionally generate multiplekinds of errors because it's,
you got to immediately get overthe fact of like, okay, got an
error.
It didn't, it didn't breakanything.
Right.
It's fine.
Cat (24:42):
You're like, this is actually the assignment.
I bet they have a lot of funwith that.
Ashley (24:46):
Totally.
Because so much of like learninghow to code is interpreting
those errors.
Right.
And so, all right, let's getthem all.
Let's see what they, see whatthey mean and then grow from
there.
Yeah.
Cat (24:57):
That's so cool.
Ashley (25:02):
And someday I probably will accidentally, like, hack
into my computer in the wrongway, and But we haven't done
that yet.
Yeah.
Yeah.
The ultimate learning moment.
Cat (25:19):
Open science is like huge in neuroscience.
Like it's really important.
I don't, I don't know if peopleknow that, like people who
aren't neuroscientists have anyidea of that.
Ashley (25:28):
I recently gave this like very silly nerd night talk
about how anybody can be aneuroscientist, which is just
based on the premise of like,look, there's so much
neuroscience data online.
If you know a little bit ofprogramming, you could get into
it and start taking a look atit.
Um, so that's one side of it,but, but neuroscience.
Yeah.
And I think like a lot ofdifferent fields of science in
(25:50):
general are really into opensource tools and sharing.
You know, not, not remaking thewheel from the beginning,
sharing tools and things likethat.
Yeah, and I know you've, you'vetried to like get this going in
your work, right, with yourresearch So back to that, like
what, what is it that you aretrying to open up in your world?
Cat (26:11):
Yeah.
I lead a team that does socialscience research about software
developers.
And.
That is a small world.
Like, I mean, I'm looking allthe time for other psychologists
who are working with softwaredevelopers or software teams,
and, um, there are not a lot ofus.
Um, and so what I'm trying to dowith my research is bring
(26:36):
evidence about what helps peopleinnovate and learn and thrive
together, and I think thatevidence has to be shared.
Like as two scientists, we bothfeel like the only way we all
move forward is with likegenerosity from the get go, like
share all the evidence and theneverybody will benefit and
everybody will flourish.
So the research that I lead islike shared out in the open and
(26:59):
also the methods that we use.
Cause it's hard to measure likebig human things.
And I don't think softwaredevelopment always knows how to
ask questions that come frompsychology.
Um, those things matter to me alot.
And then it matters because.
What I found about this was sothat people can trust and
replicate my research, like themore that I share, the more that
someone else can pick up thebaton and build on it.
(27:22):
And how cool is that?
Because I certainly don't haveall the answers.
And so that's like a huge value,you know, of ours.
And I think it really alignswith like the software
developers who say, if we'regoing to build a world that
relies on all this technology,We have to know what it is.
Because we have to be able tolike triage it if it breaks, you
(27:43):
know, and share the load and,and figure out, you know, how to
kind of approach this almostlike a shared infrastructure
that we're all relying on.
I kind of think of scientificevidence that way.
Ashley (27:54):
I love that.
I love that.
Cat (27:55):
from the people.
So it's like from the people tothe people, like the data in our
research belongs to the peoplewho did it.
So
Ashley (28:03):
absolutely.
Absolutely.
And so, I mean, for the, for thefolks who don't know how this
might work when it's not openscience, like how is this
different than a typical sort ofpublishing process in
Cat (28:16):
Oh yeah, of course.
Okay.
So this is a great question.
Cause I did not know this when Iwent to a science grad program,
I was like, I am going to, I gotinto this PhD.
I guess I will.
Um, I, I've thought that sciencewas just like a thing that, you
know, I had that was out there.
Um, uh, I was sorely mistaken.
(28:38):
So there's this huge systemwhere academic scientists
publish in academic journals.
And those journals are notalways open.
In fact, the fight to get themto be open to the general public
has been like a long term, youknow, fight.
And typically you access themlike through your university or
(28:59):
university might have asubscription to them.
Um, Um, and it's kind ofeverything that you get judged
by if you're in academia, ishaving these publications.
That is, it's, it's, people havethis idea of like a beautiful
ivory tower, life of the mind,which I think you and I have
really fought for, right?
Ashley (29:21):
Mm
Cat (29:22):
it's very output driven in a lot of the time and that's a
huge mistake.
I don't think science used to bethat way, but.
I actually think some of thestuff you've done in
neuroscience challenges thatbecause you've been part of
these like many labcollaborations that are like
less competitive,
Ashley (29:38):
mm-Hmm.
Cat (29:38):
yeah, in a nutshell, that's how scientific work goes out in
these journal articles.
And then it's like not veryaccessible to the general
public.
Ashley (29:46):
You started in this environment where it was like,
not only are computers like notfor your gender, right, but also
they're just like this totallyforeign thing of the outside
world and we don't like theoutside world in our house.
It's how I understand yourupbringing.
So, so this is like the startingpoint for you and here you are,
(30:08):
you know, someone who istechnical, is working directly
with software engineers.
Like, how?
Yeah.
How did you overcome this?
Is there still a feeling of, isthis your world?
Like, what is, I don't know,what's your sense of belonging
on the Kinsey scale?
Like what?
Cat (30:27):
When we started dating, you told me that I was like someone
who grew up in the 1800s.
Ashley (30:33):
I mean, there's literally a photo of you that is
in black and white and you areliterally poking a sheep with a
stick.
Cat (30:39):
In a long denim skirt, long hair.
It's really cute.
Ashley (30:45):
It's maybe like my favorite photo that has ever
existed of you ever.
And it looks like it is from the1800s.
Cat (30:51):
Yes.
So I was raised in a veryconservative religious community
and we had sheep, which was ahighlight of my life.
Love them, miss them, um, wouldherd them around this big, big
property that we had.
I was good at all kinds ofthings.
I was really good at repairingelectric fences and I was really
(31:13):
good at canning peaches.
I took a lot of pride in thosethings and I was really good at
reading books.
We would go to the library andthe librarians would just be
like, here's these kids again at11 a.
m.
on a Wednesday for some reason,they're checking out a hundred
books.
Ashley (31:31):
Yeah, did you like max out the number of books you were
allowed to check out at anygiven time?
Cat (31:35):
They didn't have a limit.
Ashley (31:37):
Oh, wow.
Oh, wow.
Cat (31:39):
was my physical strength So I was raised with a lot of big
beliefs that college was not thebest place for women.
Um, uh, so before we even get totechnology, it was like, could
you even be in a classroom?
I went to college anyway.
And that was really, reallyscary for me.
(32:00):
And I remember I got to my firstcollege class.
It was a 9 a.
m.
Spanish class.
Um, and I got there an hourahead of time because I was so
worried I was going to dosomething wrong because I didn't
know how you're supposed to bein a classroom.
Ashley (32:17):
Yeah.
Yeah.
First time?
Cat (32:19):
I was so excited to get homework for the first time.
I was like, I've heard aboutthis.
I'm gonna kill this.
Ashley (32:25):
Didn't you write, like, you had, like, an, uh, a, like,
semester long essay assignmentand you, like, wrote it in the
first
Cat (32:31):
I wrote all my, if I knew what an end of semester
assignment was, I did it in thefirst week of class.
I took my very first statisticscourse in grad school, which was
taught by Mark Applebaum who,um, rest in peace, Mark, one of
my beloved faculty professors ingrad school.
I was sitting there in theclassroom and he was writing and
(32:53):
it, this was a tough class.
Like this had math PhDs in it.
It was like almost a hazingritual.
Like you're either going to getthrough the first year stats
class or you're not.
And it was assumed that you hada whole lot of background that I
definitely did not have.
And he wrote a bunch ofequations out and I didn't know
the Greek symbols for anything.
(33:15):
I didn't know like the, youknow, the symbol for sum and all
of that.
He had written out this big longthing and I was sitting there in
the classroom, Like just withthis mounting almost like this
buzzing in my head like oh mygod.
It was so such a huge deal thatI even got into a PhD program I
had to move, you know I couldbarely I couldn't afford a car
(33:35):
at the time and then I was likeI'm gonna fail in this classroom
right here right Now like thisis it And then he was like, any
questions?
You know, this moment afterdoing all this like equation
work?
And I like, was like, fine.
If I'm gonna fail anyway, Imight as well be like, I've
lost.
So I raised my hand and I was, Iwas like, um, and he's like,
(33:58):
okay, like, what question do youhave, you know, about the
statistical thing?
And I'm like, no, no, no, Dr.
Applebaum, like, I don't knowwhat this symbol means.
and He was like, Oh, okay, likewhich one?
And I was like, honestly, allthe way back to like the top
left hand corner of the board.
Every single one after the firstone, um, I'm having a problem
with.
This is why he was a greatteacher.
(34:19):
I'm tearing up remembering this.
He looked out into the classroomand he said, uh, anybody else?
And several other people raisedtheir hand.
That, that changed my life, henever made fun of me.
And he was like, I better adjustthis class a little bit.
yeah I actually rocked it.
I got an A in the, the second,uh, course in that series.
(34:43):
And then I did a PhDdissertation on disclosure in
classrooms.
Ashley (34:50):
You had so much to lose by putting yourself out there
because of how much work it hadtaken to get to that moment.
Like, what do you think wasgoing through your head
Cat (35:04):
I think I'm willing to be really embarrassed for the
things that I love I, I thinkI'm willing to fall on my face I
think I deserve to be there evenwith all the barriers and all
the doubt and all the like, Icouldn't afford a computer.
I had a laptop that had been mymom's old laptop that was about
to come apart, and I stillbelieved that I deserved to be
(35:28):
there.
And I've just always had that.
Ashley (35:31):
Mmm.
You had this conviction that youWe're here for a reason, because
you had worked hard, because youdeserved it.
Like just that idea, you know,because I think not only do we
need to see this in ourselves sowe can advocate for ourselves,
but we also need to see this inour students, in our colleagues,
(35:51):
in our managers, in the peoplethat are around us.
Cat (35:54):
There are some wicked problems in the world, like
really hard things going on, andcutting ourselves off from like
all the diverse ways of thinkingand problem solving is the
silliest, it's the stupidestthing we could possibly do.
Ashley (36:08):
Yeah, especially if it comes down to just like, not
knowing what a Greek symbolmeans, right?
Like, like that's, like, that'ssuch a low level, like, silly,
you know, thing, like, okay,like, once you know that, great,
fine, you know, like, you can dothe logic to figure out the
math, that's fine.
At the end of the day, like,it's a low level problem to have
Cat (36:29):
it's syntax.
Ashley (36:31):
If that's what we're using the gatekeep, like then
we're, we're just gatekeepingthe kids out who aren't like,
you know, willing to ask
Cat (36:37):
there's like this logical fallacy that you and I talk
about a lot, which is like,people think that something
being rigorous is just smallnumbers, like, a small
percentage of people get throughthis thing.
I mean, like, a small percentageof people will survive getting
hit on the head.
Like, not how we should selectsoftware developers.
(36:59):
You do all this work, which islike, how do we make teaching
better?
What if a teacher is reallygood, and then all their
students succeed?
Should we blame a really goodteacher for having high success
in their classroom?
It's like, ludicrous.
When we started dating, you told me that I was like someone
(00:02):
who grew up in the 1800s.
Ashley (00:04):
I mean, there's literally a photo of you that is
in black and white poking asheep with a stick.
Cat (00:15):
We had a lot of thoughts and feelings about what
technical work in the worldlooks like, what the future of
that work is, how do weunderstand it, how do we make
change about who gets to betechnical.
Ashley (00:28):
This is a podcast about what it's going to take to train
and include the next generationin technical fields.
Cat (00:34):
As, the resident wife guy in this situation, I feel I'm
uniquely qualified to introduceDr.
Ashley Jauvinett.
Ashley is a neuroscientist.
You're an educator.
some might call you an educationactivist, actually, you're a
rabble rousing educator,changing the game for students
in neuroscience.
You're an associate teachingprofessor at UC San Diego.
(00:56):
You're also a musician, issomething that was really
important to me when we firstmet because I fell in love with
you when you were playing anEllie Goulding cover.
We were both in grad school.
Ashley (01:09):
Gosh, that feels really comprehensive.
I'm also your wife.
I feel like that needs to go onmy list of accolades.
Cat (01:13):
So you, a neuroscientist have to introduce the
psychologist.
Now,
Ashley (01:17):
I'm super excited to introduce Dr.
Catherine Hicks.
Who is a social and datascientist and really like a
force for the science ofdevelopers, like someone who is
leading the psychology ofsoftware teams.
She is the creator of thedeveloper thriving framework,
which has really been, I think,one of the first really, really
(01:39):
good examples of someone who isstudying software teams with
evidence based data.
And finally, also someone who'san advocate for open science
someone who believes that fromstart to finish, the kinds of
science we do should beaccessible, reproducible, and
shared with everybody who canbenefit from it.
Cat (01:56):
I think your journey into coding has been really, really
interesting and it's not alwaysone that people hear.
So can you tell me like a littlebit about what got you to being
an assistant teaching professor?
Ashley (02:08):
I don't have any computer science training.
And this is the thing I tell mystudents in my programming
class.
On the first day of class islike, I never took a programming
class,
Cat (02:18):
But you're teaching them.
Ashley (02:19):
But I'm teaching them how to code, right.
And how to think about code.
And so for me, I am aneuroscientist, as you said, and
I started my coding journey whenI was a graduate student and I
was handed a bunch of data,stacks of images and told to
analyze it.
so I had to learn and I got likea MATLAB for neuroscientists
(02:41):
book and did some of theexercises, but it wasn't super
useful.
Like the thing that was actuallyuseful was just having my own
data and having to play with itand edit other people's code.
Cat (02:49):
This is just really plunging me back because we met
in grad school.
I was a psychology graduatestudent I had absolutely no idea
what neuroscientists did at all.
Like, I thought, okay,somebody's there studying the
brain, that's fine.
And then I met you and I waslike, what's happening?
You're in the, this dark lab,you're working with lasers,
there are viruses involved.
(03:10):
Like, just tell the people alittle bit, what did you do in
neuroscience?
Where does this data come from?
Like, what is it like?
Ashley (03:18):
Oh man.
Cat (03:19):
what's MATLAB?
Because I don't think peopleknow that.
Ashley (03:21):
Yeah, neuroscientists, we want to understand how the
brain works.
If you want to understand howthe brain works, you can study
humans, but you can only do somuch in humans.
You know, you can put humans ina fMRI scanner, get some big
pictures of their brain, but itdoesn't tell you about what
neurons are doing.
And if you want to explain howthe brain works, you usually
want to know what neurons aredoing.
So, we turn to animal models,things like mice and sometimes
(03:43):
monkeys or zebrafish.
And we do fancy things in thoseanimal models to target specific
sets of neurons.
So when I was in grad school, Iwas recording lots of images
where I had tagged specific setsof neurons to try to understand
what they were doing in thebrain.
And we were doing this in amouse model and the lasers comes
(04:05):
in because you shoot lasers atthe brain to target these
proteins that change how brightthey are based on how active the
cells are.
Cat (04:14):
I remember feeling like you had access to these superpower
things that I had like nevereven touched at the time in grad
school.
I hadn't even like touched amicroscope because I didn't
really, really, cause I grew uphomeschooled.
And then in college, I didn'ttake any laboratory classes.
My college didn't even havethose open to people who were
not like STEM majors.
Ashley (04:33):
Yeah.
Cat (04:34):
I met you and you were like, Woo lasers.
One day it's a laser.
One day it's a virus.
You know, like you were learninglike a million different skills
a day.
I felt like, did you, do youfeel like you came in like
confident about that?
Ashley (04:46):
I started graduate school with enough research
experience to get me in thedoor, but a pretty narrow slice
of research experience.
I had gone to a small liberalarts school where I didn't take
enough math and I didn't takeenough programming, and then I
was thrown into this, like, verycompetitive, very top notch PhD
program, and I had to learn alot on the spot.
(05:09):
And no, I felt like I knewnothing, and I was learning
everything.
Cat (05:14):
What happened after you were like, I got to teach myself
to code.
Ashley (05:20):
think I spent most of my time in grad school wrangling
MATLAB, which by the way is aprogramming language that is
mostly used by engineers, alittle bit in academia.
It's, uh, it's okay.
It's pretty, it's pretty goodfor some things.
Cat (05:33):
No MATLAB slander is going to occur here.
Ashley (05:35):
It's not open source and we're a big fan of open source
in this room.
You work
Cat (05:38):
in python now, right?
Ashley (05:39):
I do because when I started my job and I started
realizing that I went on thisjourney of learning how to code
and I felt like You know, otherpeople, too, are going to be on
this journey, you know, thestudents that I'm working with,
and I wanted to give them thetools a little more up front and
a little more, you know, in away where everybody has access
(05:59):
to them, um, and feels like theycan get those skills.
And so for me, It didn't feelright to teach MATLAB.
It's not the most ubiquitousprogramming language, especially
outside of academia.
I decided, okay, I'm going tolearn Python.
And when I was in like the firsttwo years of my job, pre tenure
started learning
Cat (06:20):
You actually a professor about to teach it, and you were
like, I better learn this so Ican teach it, right?
Ashley (06:27):
Yeah.
And I'm not the only person whohas like done this, right?
I'm one of many, many people whoeither for research or teaching
or whatever else, right?
Has had to teach themselvesthese skills on the job.
Cat (06:38):
I've done research with all these software developers.
Thousands of developers havecome through my studies at this
point.
And there's always, like, thismoment.
Almost on every topic I've everstudied because I study
psychology and it brings outlike the deep stuff for people
and almost every study that Ido, there's a moment where
(06:58):
people start to say, um, I betyou've never heard this from
anybody before.
Ashley (07:03):
Hmm.
Cat (07:06):
Hmm.
I wasn't trained like everybodyelse.
I'm kind of an imposter.
I got to tell you, it's reallyradicalized me to have like
thousands of people tell me thatthey're the only person like
this.
And it's, I would venture to sayself teaching is kind of like
the majority out there.
Ashley (07:23):
I think that's a hopeful thing because it's, we, we need
those people.
And, you know, there's limitedspace in every introductory
computer science class and everybootcamp.
Like if people aren't teachingthemselves, we're behind.
Cat (07:35):
So you taught yourself to code.
tell me about that classroom,because I think there's some
stuff that's really interestinghere.
Because you're teachingcomputing, you're teaching
coding from outside of computerscience.
Ashley (07:48):
Hmm.
Cat (07:48):
like?
Ashley (07:50):
Yeah, I think I had to do quite a bit of soul searching
and also speaking to colleaguesabout, you know, like, what is
it that people actually need toknow?
You know, I'm not training backend software engineers.
I'm training people who aregoing to go out into the world,
want to do something with theirdata or build a computational
model of some sort.
And they don't need to knowevery single in and out of,
(08:13):
like, object orientedprogramming or something.
I landed on a few principles,which is like, one, we should
try to teach as little syntax aspossible and try to teach as
little like memorization aspossible.
And that's even more true now inthe age of AI assistance.
And two, we need to have it belike much more data focused
than, you know, a typicalcomputer science class.
(08:34):
So a little bit more of a datascience y sort of feel to it.
And.
So that's like the content ofthe class, right?
But that's only half of it.
The other half of it is how youteach the content and how do you
convince biology students, one,that it's worthwhile to do this
and two, that they can do itbecause most of them walk into
the room thinking, wow, gosh,I'm not a hacker.
(08:56):
I'm not a math person.
They have all of thesepreconceived notions about what
it even means to learn coding,right?
Cat (09:01):
Stereotypes.
Ashley (09:02):
They have stereotypes and that stuff gets in the way
of everything else.
Like if you don't talk aboutthat stuff, if you are not
acknowledging that that's in theroom with you, you can't learn
how to code.
just can't.
Cat (09:15):
So, why should those students go learn programming in
a biology classroom, not in acomputer science classroom?
I know you have thoughts aboutthis.
Ashley (09:23):
People should learn computer science in a biology
context because We've thoughtabout what they need to learn,
and it's more tailored to whatthey need, I think, in the end.
And two, there's like a bigidentity piece of it.
So I just co wrote a paper witha student who took one of my
classes who did sort of ventureinto the computer science and
(09:43):
engineering side of campus toget access to some of these
skills.
And she told me, you know, evenin an introductory computer
science class, there werepeople, many, many people in the
room for whom that wasn't theirfirst time, right?
So they a
Cat (09:58):
lie.
Ashley (09:58):
it's a, it's a lie.
Cat (09:59):
intro, CS class is actually an intro CS class on a major
university campus anymore.
Ashley (10:05):
Totally.
And we have different tracks onour campus, but still, right,
there are the students who wouldprefer to take a class that's
like a little bit below theirskill level, so they come into
the room with the knowledge,right?
And so, one, like, she reallyfelt that.
And two, I mean, to be frank, ifyou walk into a computer science
classroom on my campus, it ispredominantly male.
And she really, really feltthat.
(10:26):
And.
Like my class looks dramaticallydifferent than that.
And we can do everything we wantto like, try to make women feel
included in these spaces.
But look at the end of the day,if you're looking around and
it's like mostly dudes, and it'smostly people who aren't in your
major also, who you would neversee outside of this one class,
you know, it's just, it'salienating.
(10:48):
Creating a space for thosestudents where they feel like
they can learn amongst peers,truly peers who feel like
they're in the same boat.
That's really meaningful.
Cat (10:56):
Yeah.
So there's like this concept inpsychology that you know pretty
well called sense of belonging.
Ashley (11:01):
Hmm.
Cat (11:02):
Mm we are constantly looking around our environment,
scanning our environment, andwe're asking, do I belong here?
Do I belong here?
Even, even if they say I belonghere, do I really belong here?
Right?
Like we're smart about this.
This is survival
Ashley (11:16):
hmm.
Cat (11:17):
in a deep way.
This is about what you think ispossible.
I've measured this with softwaredevelopers, even, even with
highly male dominated fields.
This still matters deeply topeople.
So it matters for folks who havean identity that's not
represented.
It also matters, it does damageif you don't have sense of
belonging for everybody.
(11:40):
And, you know, in our research,we've seen like for professional
software teams, the ones thatsay I'm on a team where I really
do feel like we've committed tothis value.
They report being moreproductive and they say my team
is more effective.
Like a very real outcome in theworld.
Ashley (12:02):
that was my entry into computing, right?
Like, what is yours?
Cat (12:05):
You've had years of hearing me say that I don't want to
freaking teach myself to code.
So
Ashley (12:12):
And feeling like you need, feeling like you need
some, some level of something tocall yourself, you know, data
scientist or whatever.
Cat (12:18):
Oh man, design, and I, I discovered that I loved that
stuff.
I really like logic puzzles, andI really like, like, long-form
(12:39):
fiction, like, I like novels, Ilike storytelling, and I had
never thought those things wouldmake me good at math.
Never.
Which I think is, by the way,just like, in general, if you
are good at books, you might begood at math.
Like, computers are there to doa lot of math for us, so we can
do this, like, narrative logicstuff when we're working
professionally with math.
So I started doing statisticsprofessionally, and that was
(13:03):
like a huge, beautiful, gamechanging moment where I was
like, Oh, I actually am good atthis.
I'm smart at this.
I'm actually pretty kick ass atthis.
Heh heh.
But it hadn't translated tocoding for me for a long time.
And I think it was really juststereotypes again I grew up
really like, not with a lot ofexposure to computing.
(13:25):
In fact, um, you know, myparents would like rarely if
ever let us use the computer,they thought the computer was
really dangerous.
I grew up kind of feeling like,this is not for me, whatever is
happening here, like sneak timeon computers at the library,
Ashley (13:42):
where you had to like sign up and like get a specific
computer.
I remember those days.
Yeah.
Cat (13:47):
they're not letting you just sit around and like open, I
mean, I remember the first timeI saw like a little terminal
window and I was like, it's themost terrifying thing I've ever
seen ever.
I didn't need to walk into aclassroom and see it, that it
was all men to feel like me, Youknow, a poor little queer girl,
like, you know, I, I was notwelcome here.
I co founded a tech startup andI worked with the most lovely
(14:11):
People on earth, my co founderchap Snowden, our chief
engineer, Kirk Collins.
And they were both just likecat.
This psychology stuff rocks.
It's so good.
They were like, it's telling uswhat to build.
It's adding so much value.
And I remember this day thatchap was like, you want to sit
down and push some code and gothrough that process and have
(14:32):
someone review your, that code.
We can do that was like thefirst time I felt like someone
looked at me and said, what doyou mean you couldn't be a
developer?
You could be a developer.
Ashley (14:42):
We've talked sort of about like the asymmetry of
these things like It's sounusual for someone to look at
someone with the skill set thatyou had at the time, which was,
you know, predominantly inpsychological, experimental
research, and social science,and say, yeah, no problem,
become a developer, right?
But we do the opposite all thetime.
We say like, you know, yeah,sure, you know how to code?
Like, whatever, play with X, Y,and Z data, play with, you
(15:05):
could, you could tackle whateverdiscipline you want after, you
know, coding.
Like, we have this asymmetry.
Cat (15:10):
I started consulting.
I was like, I can't afford astatistics software anymore.
Now I have to learn R.
And do you remember I was like,had taken on a research contract
and I was like, I'm going toteach myself how to process this
data in R and it's going torequire coding.
And I was sitting on the couchand I like looked at you and I
(15:31):
said, is this what coding is?
Ashley (15:33):
Those were your exact words.
Like, this is it.
Like just typing some words intothis, like, you know, text
console.
You're like, really?
Like, this is, this is the likeenigmatic thing we've been like
skirting around for so manyyears that I've been convinced
that I like, wasn't able to do.
Cat (15:52):
Yeah.
And it's so funny because now Isee those things and I feel like
fondness.
I see like a big messy terminalwindow or, or I see like, you
know, even things I don'tunderstand.
And I'm just like, I love thisstuff.
I love working with developers.
I love thinking about how peoplework in code.
Yeah.
Ashley (16:10):
Yeah.
And here's the thing.
Like you came into coding andinto data science with.
an understanding of howexperimental design works and
also where it goes wrong andlike that sort of really deep
technical logical understandingof how we collect data when we
(16:30):
know there are real differenceslike that we use statistics to
back up at its core is reallyright like logic you came in
with that and I feel like thathas put you and your work you
Way above anybody else who canwhatever like throw a bunch of
data into some fancy Statisticalpipeline without actually
(16:52):
understanding why they're makingthose choices and you know those
things.
Cat (16:58):
thank you.
It's not just about me.
Like, like, think about yourstudents.
Like there was a student ofyours who actually went to
Microsoft, right?
Who's like, had a biologybackground.
Ashley (17:08):
This student of mine She Was a neuroscience major She
took one of my classes where wedid some coding and kept
learning after class andgraduated, went to Microsoft,
but not in like a quote unquotetechnical role, like in some
sort of consumer facing role.
And eventually like launchedherself through a lot of self
(17:32):
advocacy into a role where she'snow a software engineer.
Like, proper, you know,recognized as such with that
skill set.
And, um, I think, you know, youand I are in some, in some cases
in some sort of flavor like thatbecause we are people too who
have learned these skill setsand have had to advocate for
(17:52):
like, okay, no, no, I am someonewho can teach this class or no,
no, like I am someone who can dothis data analysis, you know,
and run this experiment and dothis research.
And, um, there's more of us.
There's so many more of us outin the world.
Cat (18:06):
Yeah, right.
And like, you're missing out,
Ashley (18:11):
Yeah.
Cat (18:11):
Like if you don't have us, if you don't make it, honestly
make it easy for us actually toget into this stuff, right?
Ashley (18:18):
Yeah.
You're missing out on all thepeople who have all of the ideas
and that like diversity ofthinking from other fields
outside of computer science andengineering.
Cat (18:28):
How do biologists think about code that's different?
Ashley (18:32):
Well, I think I wouldn't say we think about code
differently necessarily.
I mean, code is just the meansto an end, right?
But we think about datadifferently.
I mean, The kind of data wecollect is often really noisy,
and you don't know where thatnoise comes from.
You don't know if it's from thething you're using to measure.
(18:53):
You don't know if someone bumpedthe microscope or the animal at
that time.
You don't know if it's realbiological noise, right?
And so we come in withskepticism about data, about
where it came from and where thenoise is from.
Cat (19:05):
Do you feel like people in tech don't come in with enough
skepticism about data?
Yeah.
Ashley (19:13):
of the stuff we've talked about, like in our
conversations about likemessiness, right?
Like what data is not there?
Cat (19:19):
This is a scientist's way of thinking about signals.
Ashley (19:22):
yes.
yes.
Cat (19:23):
are like, Oh my God, I found something.
It's definitely real.
I'm going to believe it forever
Ashley (19:27):
Yeah, totally.
And
Cat (19:28):
to raise a million billion dollars.
Ashley (19:31):
totally.
And you've, and you've all thedata you've played with in your
training as say, like a datascientist is, you know, the Iris
set on Python, which is Perfect.
It's a perfect data set.
There's nothing wrong with it orthe Titanic data set or any of
these other toy data sets.
Cat (19:47):
in a bad way?
Ashley (19:48):
Well, it's, it's, it's just like a complete data set.
There's no, you know, like noisethat is not intentional, right?
And we play with that data.
It's nice and tidy and, youknow, great.
Yeah.
I can run dimensionalityreduction on that.
Cool.
Um, but hand me another dataset.
I can't assume that it's cleanin the same way.
And that's the kind of lens Icome in with as a biologist that
(20:11):
I think a data scientist doesn'talways have.
Cat (20:14):
I have a background in learning science too, and how
people learn, and one thing thatI think is so cool about the
discipline based computing thatyou do is like, you know,
people, need all kinds of thingsto learn and they need like
abstract things and they needgeneral principles and they need
to put it into practice and theyneed applied situations.
(20:36):
And I think that halo effectreally bothers me.
The idea that everybody shouldjust go to a CS department and
that they'll learn these thingsabout how code works.
And that doesn't match up withhow students actually are like
making decisions.
Ashley (20:50):
Oh, totally.
Yeah.
Yeah, totally.
And I think something that'scome out of my research, which I
didn't expect, the short term.
Can I just do something withthis bit of code is like just as
rewarding as the long termthought of like, maybe someday
this will get me a job or allowme to do X, Y, and Z with my
data.
Like the sort of short term,like, can I do like a fun, weird
(21:11):
personal project around this islike super gratifying.
Or like, I do work in a researchlab.
Is there something I can do withthe data I have now that like
actually, you know, could, couldbe the thing that motivates me
to take this class and to learnthese skills?
And I think a lot of like,movements around trying to make
programming more inclusive havefocused on the long term.
(21:33):
Well, like you're going to makemoney, like you're going to make
a ton of money if you get this,more jobs, you know, it's a good
skill set to have.
And like, that's nice, but like,you know, at the end of the day,
you get that like little burstof dopamine just to throw some
neuroscience in it,
Cat (21:49):
Yeah,
Ashley (21:49):
right?
You don't get dopamine from likethinking about your retirement
savings.
You get dopamine from like thestuff you can do today.
Cat (21:57):
totally.
Well, okay, there was thisthing.
So I used to work in schools andon like these all these like
long term education researchprojects.
Right.
And I always had this thing inmy mind, which was like do only
the rich kids get to have fun orwhat?
Like,
Ashley (22:09):
Mm,
Cat (22:11):
is that is that not something we all need?
Like all these inclusionprograms can be so somber.
Ashley (22:20):
Oh, totally,
Cat (22:21):
so like, You know, I on the goal.
And I mean the, the lot, look,it's important to tell people
there are these careers.
We want you to know about it.
We want you to know, but I thinkyou make a really good point.
We cut those people out from.
experimentation.
There's some great work from Ithink Matt Ruffalo about how the
(22:41):
same technology program went outto different schools, and the
same like resources, like samelittle devices for kids, same
curricula, but teachers in thewealthier school had the kids
explore and do self directed.
You know, and, and, right, like,remember me with the computers?
When I got my first computer incollege, I was like, I better
(23:04):
not break this computer.
Ashley (23:05):
Mm hmm, Mm hmm,
Cat (23:07):
one thing that I have to do is not break the most expensive
thing I've ever owned.
Ashley (23:11):
Mm hmm, Yeah,
Cat (23:12):
that is not a situation in which you're going to be coding
a bunch.
Ashley (23:17):
no, and you know, like, so in this big survey I ask of
my class before and after thequarter starts, um, I ask them
one item among many others.
This one item has changed themost out of any other item on
the survey, and it is exactlywhat you just said.
The item is, I worry thatmistakes I make will damage my
(23:37):
computer.
And students start the quarter,and they're like, yeah, I really
worry about that.
And then you think about yourlow income students.
You think about the students whojust bought the first laptop
they'll ever, you know, haveever owned.
And they are deeply concerned,as you were, about literally
breaking the computer.
How do you learn coding when youcan't play?
Cat (23:59):
Hmm.
So what do you do to help themfeel like they can?
Ashley (24:05):
I break my computer in front of them a lot.
I mean, and I really feel likethat's, that's it, right?
Like I walk into the room and Isay, I've never taken a coding
class.
I'm gonna make mistakes in frontof you.
You're gonna ask questions.
I don't understand.
I'm gonna sit here and generatea bunch of errors and I
actually, I have this activity,which I love.
(24:28):
Um, on like the, you know, inthe first week where I have them
intentionally generate multiplekinds of errors because it's,
you got to immediately get overthe fact of like, okay, got an
error.
It didn't, it didn't breakanything.
Right.
It's fine.
Cat (24:42):
You're like, this is actually the assignment.
I bet they have a lot of funwith that.
Ashley (24:46):
Totally.
Because so much of like learninghow to code is interpreting
those errors.
Right.
And so, all right, let's getthem all.
Let's see what they, see whatthey mean and then grow from
there.
Yeah.
Cat (24:57):
That's so cool.
Ashley (25:02):
And someday I probably will accidentally, like, hack
into my computer in the wrongway, and But we haven't done
that yet.
Yeah.
Yeah.
The ultimate learning moment.
Cat (25:19):
Open science is like huge in neuroscience.
Like it's really important.
I don't, I don't know if peopleknow that, like people who
aren't neuroscientists have anyidea of that.
Ashley (25:28):
I recently gave this like very silly nerd night talk
about how anybody can be aneuroscientist, which is just
based on the premise of like,look, there's so much
neuroscience data online.
If you know a little bit ofprogramming, you could get into
it and start taking a look atit.
Um, so that's one side of it,but, but neuroscience.
Yeah.
And I think like a lot ofdifferent fields of science in
(25:50):
general are really into opensource tools and sharing.
You know, not, not remaking thewheel from the beginning,
sharing tools and things likethat.
Yeah, and I know you've, you'vetried to like get this going in
your work, right, with yourresearch So back to that, like
what, what is it that you aretrying to open up in your world?
Cat (26:11):
Yeah.
I lead a team that does socialscience research about software
developers.
And.
That is a small world.
Like, I mean, I'm looking allthe time for other psychologists
who are working with softwaredevelopers or software teams,
and, um, there are not a lot ofus.
Um, and so what I'm trying to dowith my research is bring
(26:36):
evidence about what helps peopleinnovate and learn and thrive
together, and I think thatevidence has to be shared.
Like as two scientists, we bothfeel like the only way we all
move forward is with likegenerosity from the get go, like
share all the evidence and theneverybody will benefit and
everybody will flourish.
So the research that I lead islike shared out in the open and
(26:59):
also the methods that we use.
Cause it's hard to measure likebig human things.
And I don't think softwaredevelopment always knows how to
ask questions that come frompsychology.
Um, those things matter to me alot.
And then it matters because.
What I found about this was sothat people can trust and
replicate my research, like themore that I share, the more that
someone else can pick up thebaton and build on it.
(27:22):
And how cool is that?
Because I certainly don't haveall the answers.
And so that's like a huge value,you know, of ours.
And I think it really alignswith like the software
developers who say, if we'regoing to build a world that
relies on all this technology,We have to know what it is.
Because we have to be able tolike triage it if it breaks, you
(27:43):
know, and share the load and,and figure out, you know, how to
kind of approach this almostlike a shared infrastructure
that we're all relying on.
I kind of think of scientificevidence that way.
Ashley (27:54):
I love that.
I love that.
Cat (27:55):
from the people.
So it's like from the people tothe people, like the data in our
research belongs to the peoplewho did it.
So
Ashley (28:03):
absolutely.
Absolutely.
And so, I mean, for the, for thefolks who don't know how this
might work when it's not openscience, like how is this
different than a typical sort ofpublishing process in
Cat (28:16):
Oh yeah, of course.
Okay.
So this is a great question.
Cause I did not know this when Iwent to a science grad program,
I was like, I am going to, I gotinto this PhD.
I guess I will.
Um, I, I've thought that sciencewas just like a thing that, you
know, I had that was out there.
Um, uh, I was sorely mistaken.
(28:38):
So there's this huge systemwhere academic scientists
publish in academic journals.
And those journals are notalways open.
In fact, the fight to get themto be open to the general public
has been like a long term, youknow, fight.
And typically you access themlike through your university or
(28:59):
university might have asubscription to them.
Um, Um, and it's kind ofeverything that you get judged
by if you're in academia, ishaving these publications.
That is, it's, it's, people havethis idea of like a beautiful
ivory tower, life of the mind,which I think you and I have
really fought for, right?
Ashley (29:21):
Mm
Cat (29:22):
it's very output driven in a lot of the time and that's a
huge mistake.
I don't think science used to bethat way, but.
I actually think some of thestuff you've done in
neuroscience challenges thatbecause you've been part of
these like many labcollaborations that are like
less competitive,
Ashley (29:38):
mm-Hmm.
Cat (29:38):
yeah, in a nutshell, that's how scientific work goes out in
these journal articles.
And then it's like not veryaccessible to the general
public.
Ashley (29:46):
You started in this environment where it was like,
not only are computers like notfor your gender, right, but also
they're just like this totallyforeign thing of the outside
world and we don't like theoutside world in our house.
It's how I understand yourupbringing.
So, so this is like the startingpoint for you and here you are,
(30:08):
you know, someone who istechnical, is working directly
with software engineers.
Like, how?
Yeah.
How did you overcome this?
Is there still a feeling of, isthis your world?
Like, what is, I don't know,what's your sense of belonging
on the Kinsey scale?
Like what?
Cat (30:27):
When we started dating, you told me that I was like someone
who grew up in the 1800s.
Ashley (30:33):
I mean, there's literally a photo of you that is
in black and white and you areliterally poking a sheep with a
stick.
Cat (30:39):
In a long denim skirt, long hair.
It's really cute.
Ashley (30:45):
It's maybe like my favorite photo that has ever
existed of you ever.
And it looks like it is from the1800s.
Cat (30:51):
Yes.
So I was raised in a veryconservative religious community
and we had sheep, which was ahighlight of my life.
Love them, miss them, um, wouldherd them around this big, big
property that we had.
I was good at all kinds ofthings.
I was really good at repairingelectric fences and I was really
(31:13):
good at canning peaches.
I took a lot of pride in thosethings and I was really good at
reading books.
We would go to the library andthe librarians would just be
like, here's these kids again at11 a.
m.
on a Wednesday for some reason,they're checking out a hundred
books.
Ashley (31:31):
Yeah, did you like max out the number of books you were
allowed to check out at anygiven time?
Cat (31:35):
They didn't have a limit.
Ashley (31:37):
Oh, wow.
Oh, wow.
Cat (31:39):
was my physical strength So I was raised with a lot of big
beliefs that college was not thebest place for women.
Um, uh, so before we even get totechnology, it was like, could
you even be in a classroom?
I went to college anyway.
And that was really, reallyscary for me.
(32:00):
And I remember I got to my firstcollege class.
It was a 9 a.
m.
Spanish class.
Um, and I got there an hourahead of time because I was so
worried I was going to dosomething wrong because I didn't
know how you're supposed to bein a classroom.
Ashley (32:17):
Yeah.
Yeah.
First time?
Cat (32:19):
I was so excited to get homework for the first time.
I was like, I've heard aboutthis.
I'm gonna kill this.
Ashley (32:25):
Didn't you write, like, you had, like, an, uh, a, like,
semester long essay assignmentand you, like, wrote it in the
first
Cat (32:31):
I wrote all my, if I knew what an end of semester
assignment was, I did it in thefirst week of class.
I took my very first statisticscourse in grad school, which was
taught by Mark Applebaum who,um, rest in peace, Mark, one of
my beloved faculty professors ingrad school.
I was sitting there in theclassroom and he was writing and
(32:53):
it, this was a tough class.
Like this had math PhDs in it.
It was like almost a hazingritual.
Like you're either going to getthrough the first year stats
class or you're not.
And it was assumed that you hada whole lot of background that I
definitely did not have.
And he wrote a bunch ofequations out and I didn't know
the Greek symbols for anything.
(33:15):
I didn't know like the, youknow, the symbol for sum and all
of that.
He had written out this big longthing and I was sitting there in
the classroom, Like just withthis mounting almost like this
buzzing in my head like oh mygod.
It was so such a huge deal thatI even got into a PhD program I
had to move, you know I couldbarely I couldn't afford a car
(33:35):
at the time and then I was likeI'm gonna fail in this classroom
right here right Now like thisis it And then he was like, any
questions?
You know, this moment afterdoing all this like equation
work?
And I like, was like, fine.
If I'm gonna fail anyway, Imight as well be like, I've
lost.
So I raised my hand and I was, Iwas like, um, and he's like,
(33:58):
okay, like, what question do youhave, you know, about the
statistical thing?
And I'm like, no, no, no, Dr.
Applebaum, like, I don't knowwhat this symbol means.
and He was like, Oh, okay, likewhich one?
And I was like, honestly, allthe way back to like the top
left hand corner of the board.
Every single one after the firstone, um, I'm having a problem
with.
This is why he was a greatteacher.
(34:19):
I'm tearing up remembering this.
He looked out into the classroomand he said, uh, anybody else?
And several other people raisedtheir hand.
That, that changed my life, henever made fun of me.
And he was like, I better adjustthis class a little bit.
yeah I actually rocked it.
I got an A in the, the second,uh, course in that series.
(34:43):
And then I did a PhDdissertation on disclosure in
classrooms.
Ashley (34:50):
You had so much to lose by putting yourself out there
because of how much work it hadtaken to get to that moment.
Like, what do you think wasgoing through your head
Cat (35:04):
I think I'm willing to be really embarrassed for the
things that I love I, I thinkI'm willing to fall on my face I
think I deserve to be there evenwith all the barriers and all
the doubt and all the like, Icouldn't afford a computer.
I had a laptop that had been mymom's old laptop that was about
to come apart, and I stillbelieved that I deserved to be
(35:28):
there.
And I've just always had that.
Ashley (35:31):
Mmm.
You had this conviction that youWe're here for a reason, because
you had worked hard, because youdeserved it.
Like just that idea, you know,because I think not only do we
need to see this in ourselves sowe can advocate for ourselves,
but we also need to see this inour students, in our colleagues,
(35:51):
in our managers, in the peoplethat are around us.
Cat (35:54):
There are some wicked problems in the world, like
really hard things going on, andcutting ourselves off from like
all the diverse ways of thinkingand problem solving is the
silliest, it's the stupidestthing we could possibly do.
Ashley (36:08):
Yeah, especially if it comes down to just like, not
knowing what a Greek symbolmeans, right?
Like, like that's, like, that'ssuch a low level, like, silly,
you know, thing, like, okay,like, once you know that, great,
fine, you know, like, you can dothe logic to figure out the
math, that's fine.
At the end of the day, like,it's a low level problem to have
Cat (36:29):
it's syntax.
Ashley (36:31):
If that's what we're using the gatekeep, like then
we're, we're just gatekeepingthe kids out who aren't like,
you know, willing to ask
Cat (36:37):
there's like this logical fallacy that you and I talk
about a lot, which is like,people think that something
being rigorous is just smallnumbers, like, a small
percentage of people get throughthis thing.
I mean, like, a small percentageof people will survive getting
hit on the head.
Like, not how we should selectsoftware developers.
(36:59):
You do all this work, which islike, how do we make teaching
better?
What if a teacher is reallygood, and then all their
students succeed?
Should we blame a really goodteacher for having high success
in their classroom?
It's like, ludicrous.
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I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
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Does hearing about a true crime case always leave you scouring the internet for the truth behind the story? Dive into your next mystery with Crime Junkie. Every Monday, join your host Ashley Flowers as she unravels all the details of infamous and underreported true crime cases with her best friend Brit Prawat. From cold cases to missing persons and heroes in our community who seek justice, Crime Junkie is your destination for theories and stories you won’t hear anywhere else. Whether you're a seasoned true crime enthusiast or new to the genre, you'll find yourself on the edge of your seat awaiting a new episode every Monday. If you can never get enough true crime... Congratulations, you’ve found your people. Follow to join a community of Crime Junkies! Crime Junkie is presented by audiochuck Media Company.
Ridiculous History
History is beautiful, brutal and, often, ridiculous. Join Ben Bowlin and Noel Brown as they dive into some of the weirdest stories from across the span of human civilization in Ridiculous History, a podcast by iHeartRadio.