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Hello, it's me, Asami, again, and yeah, you're gonna hear Len sooner or later, you will hear
his deep, booming voice sooner or later, so don't you worry, he'll be back. But for this
episode, bear with me, and I hope you stick around for a few minutes.
For this episode, I kind of wanted to think out loud, so no structure whatsoever, about
comparing my experience doing research in a museum vs. university setting. For those of
you who don't know, while I was in Hong Kong for the past year or so, I was at a museum,
and in the museum, there's maybe three other scientists at all times, sometimes four,
but usually three, and we were all kind of doing very different kinds of projects.
I mean, that tends to be the case in museums because everyone has completely different
backgrounds. For instance, my background is in experimental physical chemistry,
so my expertise are a lot more in sort of instrumentations or like a, what do you call it,
hardware-software interfacing type of business, and I like tinkering around with optics a lot.
Whereas my other scientists, so one of them will be focusing a lot more on sort of sustainability
projects at the museum, so trying to come up with meaningful index or measures for their
evaluation of their sustainability effort, or various other things. I think she was doing a lot
of different things that all kind of fell under the umbrella of, you know,
fell under the umbrella of preventative conservation, which is a whole new topic.
Maybe I'll dedicate an episode for it at some point, but in short, it's an area of conservation
in artwork where instead of conserving or restoring the art that is already damaged,
we try to prevent that from happening in the first place. So a lot of that is by close monitoring
and meaningful monitoring too, and a lot of artworks rotate in a different storage space,
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so they don't just sit in one space in a storage. They're constantly being shuffled around
in and out of gallery, on loans, etc., and whenever they move around, there's always a
risk associated with it. So, you know, the monitoring system and establishing the baseline
of like, this is how healthy this artwork is, is very, very important in understanding how to
prevent them from deteriorating. So that's the kind of interest that they have, preventative
conservation. So one of the scientists worked dedicated on preventative conservation. The other
scientist worked more with technical analysis of artwork, so this is more actually interacting with
objects and artworks, as opposed to what my project entailed, which was more about tool development
for conservation science field. So she would do something like going to the gallery and measure
the colors of different artworks, and she would also do like sampling of artwork if they need to,
and analyze them using sort of standard chemistry tools like FTIR or Raman spectroscopy,
that sort of things. As you can tell, we all come from very different backgrounds,
some with master's degrees, some with PhDs, so various different degrees of expertise as well.
And because my project was kind of like a standalone project, well, like, it was a bit
harder to share what I'm doing in a meaningful way to these scientists, and also I don't think
any of them had the same kind of vested interest for my project. You know, they were busy doing
their own thing, and you know, of course they were there to help, but no one really was going to be
able to help me, I think. And that's just the nature of workplace, and I think also especially
museum labs, because, you know, we're not at schools, we are research-oriented, but we're not,
like, not all of us are involved in the same project. So we just don't know simply, you know,
what's going on on a day-to-day basis of the project. And sometimes you need that kind of
very close understanding. So like, it was more just like,
Anyway, back in the university lab now, so now I am back in the university setting,
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and sort of like the big umbrella theme of our lab is that we incorporate
machine learning in processing of images and videos. And if you look at our people's project,
we do everything from medical imaging, video games, astrophysics imaging, video data,
all sorts of different kinds of images and different kinds of videos. But we all kind of
fall under the same category of imaging and video processing. And some of us come from more sort of,
let's say, bioinformatics background. Some of us come from, like me, optics and physical chemistry
background. But most of them, I think, are computer scientists or electrical engineers,
and especially in signal processing type of electrical engineers.
So even though we're doing something wildly different, we all know enough about each other's
project. And we have also very slightly different background. So we actually are able to share
and sort of help each other out a lot. For instance, my boss, he is like a super duper
guy when it comes to signal processing. But machine learning, even though he teaches that
course and everything, machine learning only became a thing as far as his career goes,
relatively in the recent half of his career. So that's not what he spent his PhD on. That's not
what he did in his early times. So he's just kind of learning at the same time as we are.
Whereas some of the grad students, they grew up in the world of machine learning. So
that's not new to them. And sometimes they even know more about these new trends, new different
training methods, different architecture of neural networks, and stuff like that.
And even within the machine learning people, there are those who are good at
incorporating different techniques to the training method. And then there's those who are
much better at choosing an appropriate training method for a specific sets of data. Or some people
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even are good at generating that kind of synthetic data. That's what they're good at.
So we're all sharing ideas, sharing two cents about different people's project,
which I really like about this lab. I think we're doing something very different,
but similar enough that we can actually help each other out. And because most of us are sitting in
front of the computer all day long, I think all of us appreciate a chance to have a coffee break
or tea break when somebody goes, hey, can I poke your brain a little bit? Or like, hey, can I have
two minutes from you? And a lot of the times, very good ideas will come out of these kind of casual
chit chat. And that is something I did realize that I missed about university lab setting.
Not to mention the flexibility of hours. Oh, my God. The museum, I loved working there,
but they had this core hour concept of like, okay, can you please be there around nine to six?
Which is fine. I know that that's what most professional work environment is.
But here, you know, it doesn't matter when I show up or when I leave. If I want to come in
on Saturdays, I can, and I don't have to get prior approval to do that. And I also
can come in at like 12 after taking a meeting in the morning or like after going to a gym in
the morning and then come in later, stay later. That's also fine. I don't even think my professor
cares if I'm not there at all, because as long as I'm delivering results, it's kind of fine for him.
Although he himself, I think, prefers to, you know,
I mean, so much so that they're starting a band.
He was like, do you play instruments?
I was like, yeah, I can't play an instrument.
He was like, can you sing?
I was like, I can't sing anymore.
He was like, I'm so disappointed.
He was like, I can't sing anymore.
I was like, I'm so disappointed.
Um, yeah.
Alright.
So, pros and cons.
I do love different things about different workplace.
I guess I appreciate my postdoc exposing me to different environment,
different possibilities.
I guess I appreciate my postdoc exposing me to different environment,
different possibilities.
And hopefully, I find somewhere where I can merge together the-
the clamps.
and hopefully, I find somewhere where I can merge together the-
the clamps.
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y ou know,
the clamps.
I'd like to build workplaces where I can feel quite confident.
One situation I like to think of is,
Um,
Yeah, maybe share us your thoughts.
If you decided to go from
If you decided to go from
academia to industry or
industry to academia?
Thanks for listening.
That's it for the show today.
Thanks for listening and find us on X at Eigo de Science.
That is E-I-G-O-D-E-S-C-I-E-N-C.
See you next time.
