From Environmental Science Student to STELLA Innovator: Bianca's Journey

Video Summary

“Because it’s a low cost instrument, I feel comfortable messing with different sensors and trying to build my own version of STELLA with different codes and different parameters.”

A Serendipitous Meeting with Innovation

Bianca Cilento’s path to becoming a STELLA advocate began with a simple desire to build her own spectrometer. As an environmental science student pursuing a combined bachelor’s and master’s degree program, she had discovered her passion for interdisciplinary approaches to scientific problems during an internship in Sweden. There, she worked with plant compositions using a pseudo spectrometer called the Colorsnap tool, combining remote sensing with ecology to study changing Arctic landscapes.

“I was really excited about this project and thought, this is great – I’m going to get my masters and get a PhD and make my own spectrometer that works a little bit better, looks at near infrared,” Bianca recalls. But fate had other plans. When she met Mike Taylor and was introduced to STELLA, she realized she had found “everything I wanted to build and more.”

This discovery transformed her master’s thesis into “Quantifying Landscape Change using different spectrometers and imagery,” allowing her to compare different tools, collection methods, and mapping techniques. The project perfectly matched her love of looking at problems from multiple angles using interdisciplinary thinking.

Overcoming Initial Intimidation

Despite STELLA’s reputation for accessibility, Bianca’s first encounter with the device was daunting. “Honestly, I was a little overwhelmed. I did not have a very strong coding or manufacturing background, so I was a little scared at first,” she admits. The solution came from an unexpected source – her older brother helped her build her first STELLA.

But the initial fear quickly gave way to appreciation. “It actually was not as bad as I was expecting. It was really easy to put together and use,” Bianca discovered. Even when she encountered problems in the field due to her own mistakes, the troubleshooting process became part of the learning experience rather than a barrier to success.

This experience opened her eyes to STELLA’s potential for other early career scientists and researchers who might not have access to high-cost spectrometers – a realization that would shape her ongoing work with the platform.

Transcript

Could you please tell us,

tell me about your educational background and your current role?

Yeah, definitely.

So I started my degrees or my education back at right.

I started in 2019.

I was enrolled in the environmental science,

bachelor’s program, and about three years.

And to that, I was enrolled in the BSMS program.

So bachelors.

Masters, and I kind of had a hone down and pick a project that,

that I was going to do my thesis on.

And I’ve always been somebody who has like to do,

like to look at projects and problems,

from a lot of different angles and use a very interdisciplinary

ways of thinking to approach things.

So I ended up going to Sweden on an internship

or on a, like a stipend position, through a program called Emerge

that allowed me to look at, plant compositions

and how those are changing using a pseudo spectrometer called the Colorsnap tool.

And I was like, this is great.

I can use, you know, a little bit of remote sensing, a little bit of ecology.

And I can look at how Arctic landscapes are changing over time.

And I was really excited about this project and I was like, this is great.

I’m going to actually get my masters and get a PhD, which I did not do.

And make my own spectrometer that works a little bit better.

Looks at near infrared.

And then I met you and you introduced me to the STELLA,

which was everything I wanted to build and more.

And I incorporated that into my masters.

So my the title of my thesis ended up being Quantifying Landscape

Change using different spectrometers anyway, as imagery.

Yeah.

And so we were able to compare different tools and different

collection methods, different mapping methods,

even different

like GIS systems to

quantify how landscapes were changing over time.

And so that’s kind of what led me to where I am.

I’m working at EagleView right now.

It’s a aerial imagery company based out of, Rochester, New York.

And so I do a lot of the image processing stuff on that side.

But I’m also still working with Paul to kind of see how this fellow works

as a spectrometer and where its value as, like a science tool is.

So I’m testing it, compared to different spectrometers compared to

like its consistency over time, a whole bunch of different, parameters.

So I’m really excited about that.

What was your first impression when you started working with STELLA devices?

Honestly, I was a little overwhelmed.

I did not have a very strong coding

or manufacturing background, so I was a little scared at first.

I actually had my older brother help me do it, or help me build it.

But it actually was not as bad as I was expecting.

It was really easy to put together in use, and I did a little bit

of troubleshooting in the field because I messed a few things up.

But yeah, no, I had a really great time using it and

I think it can be really helpful to like a lot of early career

scientists, early career researchers who might not have access to,

high cost spectrometers.

Fantastic.

And that leads into the next question.

What career skills do you feel that STELLA has helped you develop?

Oh, gosh.

What hasn’t it helped me develop?

I feel I don’t know if I’d a confident programmer,

but I definitely am at least better than where I started.

I really think I’ve been able.

Look at what I like to look at and things from a lot of different

like perspectives and angles and different ways of thinking.

And I think that STELLA has really helped me do that

because there’s like the manufacturing angle and the machining angle

and the programing angle, and it can help

help solve so many problems, not even just environmental.

So yeah, I would say that it’s a very great tool.

It teaches a lot.

Whatever, whatever you want.

So STELLA can

What career skills do you feel STELLA has helped you develop?

Oh, gosh.

What what career skills hasn’t STELLA helped me develop?

I think, honestly, STELLA.

STELLA is what you make of it.

I’m somebody who was not a very confident programmer.

So being able to have the code

set up for me, I can really look into it and learn,

you know, what commands are helpful, where, how to modify.

I’ve been able to add a few different commands in there to make my research

easier.

Again, I’m not somebody who has very much technical experience.

So it’s really helpful that the build guide is there.

But there’s also options to, you know,

add your own sensors and add GPUs and anything that’s really helpful to you.

You can, you can mix and match.

So that’s been super helpful.

What transferable skills from solo work

do you see applying to other areas of your career?

Oh, that’s a great question.

I think that

using the STELLA has allowed me to see a project from start to finish.

So I’ve seen, you know, the development of different tools and,

you know, even, like the process of writing grants

to try to get more funding,

to pursue more research all the way down to data collection and data analysis.

So I think seeing

like a comprehensive project from start to finish

has really allowed me to hone in on things I like and things I don’t like.

Data collection specifically is a huge one, so

you only have a limited amount of time and a little limited amount of resources.

It’s really important to come up with a great data plan ahead of time.

So I think that’s something that’s really important that I’ve learned.

What skills did you develop

that you didn’t expect when you first started with STELLA?

Oh, I learned how to fail gracefully.

I would say I think that now, this isn’t specifically tied to STELLA,

but working with STELLA has given me the opportunity.

Because it’s a low cost instrument,

I feel comfortable messing with different sensors and trying to build

my own version of STELLA with different codes and different, I guess, parameters.

So, for example, I was trying to write a code in STELLA

that will, run through all the different gain and integration time combinations

so I don’t have to change it every time I run, every time I turn on the STELLA.

I just wanted one to turn it on once, and it’ll run continuously

and just cycle through.

And it was really hard.

I did not expect,

and I didn’t expect how many

times I’d have to, like, try something, test the code.

It didn’t work.

Try it again.

So it really taught me how to just

be patient.

And it’s okay that it doesn’t work the first few times.

And it’s.

It’s okay.

I can fail gracefully and then still sexy later on.

What’s been the most challenging skill to master?

And how did you overcome that challenge?

Was it the programing you were just mentioned?

Yeah, that’s definitely a big one.

Was the programing.

I am very, very little experience and the experience

I do have was hard, hard to even obtain.

Yeah, I would say the programing, but working with Paul

has definitely helped a lot.

He’s been a great coach to kind of gently

guide me without just giving me the answer on how to program things.

And like I

said, seeing the code, like in front of the working code in front of

you with like, already annotated with which steps do what.

That’s been super helpful too.

So I know, like, oh, if I want to mess with the gain

or the integration time, it’s right there in the code.

So I can kind of just take a bite sized piece and not have to

start from scratch if I want to edit it at all.

what motivated you to focus,

specifically on,

developing, these, these protocols for.

So and what are they?

So I’ve been working with Paul

to do sort of a classroom calibration protocol.

And that’s something that I wish I had learned a little bit

more of while I was doing my thesis.

I think it would have given my measurement a little bit more credibility.

So I think that’s something that

if we can teach it earlier on with something like the STELLA,

I think that that’s something that can advance science further.

I think my measurements that I took with STELLA were great and valuable,

but I also think that they could have

been helped if I had done a few measurements first, especially

because I was testing, in a very uncertain environment

like the outdoors, with different light levels and different times of day.

So having controls,

such as like seeing how

the STELLA reacts in different light levels in different

environments can help give.

A more consistent data

set and that can be easier can be compared

to different data sets year over year, different times a lot easier.

So I think that’s where that motivation really came.

And also just to see how the STELLA performs.

It’s still a relatively new instrument.

So having different people test different things and seeing if, you know, seeing.

So the STELLA is still a relatively new instrument.

So having lots of different groups of people testing

similar parameters under different environments kind of gives us a sense

of how reliable the STELLA is as a science tool,

rather just an educational tool.

Okay.

So what are some key considerations when designing your protocols?

That, so that other folks can follow it

independently?

So when designing a protocol, it’s

really important to make sure that it’s clear

and detailed while still being concise.

So there’s some steps that are a little bit implied

that you don’t necessarily need to repeat.

That just bogs things down and makes people probably overwhelmed.

Looking at a 20 step protocol.

But it’s also really important to make sure that the steps are repeatable.

So anything that could change the variability

or that could change the setup at all needs to be mentioned.

So if I say, for example,

I’m taking measurements of room darkness, I think it’s important

to make sure to mention how I darken the room.

So like the lights need to be off, curtains

need to be up.

You know, no light sources

so that it’s repeatable for other people.

So, yeah.

So you’re developing a classroom, calibration protocols.

Now, how do you balance say scientific accuracy

with classroom practicality in your protocols?

So a lot of the, constraints in the classroom

are those similar to those I’ve seen as an early career researcher.

So, low cost that

not a lot of time and, honestly, like, cramped space.

So when I’m designing it, I’m doing everything in my apartment,

my one bedroom apartment, to try to make sure that this is repeatable by kids.

This is repeatable.

Low budget.

And this is repeatable under time constraints.

So every

section of the protocol, every section of the calibration that I’m setting up,

I’m practicing it in my apartment to try to understand how,

what materials I can kind of scrounge together,

to make it, you know, low cost

and to make sure that

it can be done on time constraint and make sure there’s enough repeatability

where that can account for any uncertainty.

So it’s really I’m practicing it before I’m writing the protocol.

So I’m going through lots of different trials, lots of different experiments

before I really make sure I get it right.

So, speaking of which,

what calibration materials have you experimented with thus far?

This is a great question because it’s been a ton.

Most recently, the one that comes to my mind is Paul

and I are trying to see, reflector reliability as we’re calling it.

So we are trying to test styrofoam capability,

land version capability as, a reliable reflector.

So we want to see.

So let me back up.

We want.

So a good reflector will reflect light

equally from any incoming angle.

So we want to see is regular regular styrofoam

that anybody can get their hands on.

A good way to do that.

So we wanted to test the,

irradiance we’re getting from all these different angles.

So I have my nightstand set up to be at the right height.

I have a lazy Susan on top of it.

And then I have the styrofoam, crosshatch.

So I know where the center is with a protractor on top, so I know the angle.

And then I have floss tied to it

so I can actually turn it to get the angles that I need.

So that has been honestly probably the best way I found to do that.

In general, I also have it’s like a selfie stand

kind of, or like a tripod for photos that I use to keep the STELLA up.

Paul has a great 3D

printed part that holds the STELLA, and it just clicks right in.

And then I have an art easel that I wasn’t using at the time, so I just

that’s how I keep my target up.

That’s awesome.

Were there any other, materials that you’ve tried so far?

Other material, like in terms of reflectors or just like tools?

Reflectors? Yeah.

I have my tools.

Anything else that you found that works?

Yeah.

I have a white reference from,

I can’t remember the name of the photo studio,

but just like a local one in Atlanta that I’ve been using.

I don’t know the results off the top of my head,

but I have a bunch of different things I plan on doing.

But that’s all I have actually done is just give me a name.

Some of the other calibration mediums you plan on using.

Yes. I know we’ve talked about different types of paper,

so like construction paper specifically because it has more texture to it.

We’ve talked about a whiteboard and even just like a mirror

just to see what we’re looking at.

So those are some some things

that we might see upcoming.

Okay.

Yeah.

So what improvements or refinements are you still working on for your protocols?

That’s another great question.

There’s definitely a few methods that I.

I’m having a hard time,

I guess, nailing down.

But those are probably the more detailed ones where I

actually don’t know.

I actually kind of like my protocols.

Let me start over.

So it’s part of the classroom calibration.

We’re creating different protocols to really calibrate, the STELLA.

And so there’s a lot of different ways you can calibrate it.

You can see how it reacts in different temperatures and different light levels

with, different targets, you know, at different incident angles.

So one thing that’s really hard to balance

is how many different protocols or how many different parameters do

we need to test for this to be comprehensive?

So that’s probably

something that we’re working on refining right now.

I would say the biggest, most important things were working.

The most important ones to include, are definitely accounting

for light and then, having a good reflector.

So those are the two biggest, but also, how many can we supplement

to really have a comprehensive analysis while we’re still under these constraints?

We’ve talked about like budget in time and uncertainty levels

and repeatability.

Where do you see this work heading next?

Any exciting developments on the horizon?

In a perfect world,

I think that a paper is on the horizon.

I think,

Paul and I are working really hard to to,

you know, test all these calibration methods and see

how, you know, consistent the STELLA is across

all these different parameters we’ve talked about.

So I think once we hone down on what specific parameters

we need to include and we can get the data analyzed,

I think we might have an exciting paper in the works.

Who knows?

Yeah.

No measuring extraneous light.

Tell me about it. What is it?

so extraneous.

Light is, light in a completely dark room that we just kind of can’t get rid of.

So all the windows are down, all the curtains are up,

all the lights are off.

But there’s still

there’s still light coming from somewhere because we don’t live in a vacuum.

So that’s something to account for because it can be different

at different points of the day, different days of the week,

different seasons of the year.

So one way we do that is by saturating the lens.

So we max out, gain an integration time and a completely dark room.

And we see how much of this extraneous light we’re seeing.

And we measure that across different conditions.

And if it’s consistent, we can account for it. And,

we can account for it at once

and just kind of do some subtraction.

But if it’s not, there’s different ways we can account for it individually.

So it’s something you can

So you can measure extraneous light at the start of your measurements.

And see if it’s something

that needs to be accounted for or if it’s something that can be

that’s just negligible because of the small amount

compared to the other measurements you’re taking.

what has you most excited about STELLA?

My favorite thing about STELLA is

how easy it is for anybody to access it.

It’s available for students, researchers, teachers, professors.

It’s a great way for anybody to get involved in science.

You know,

I think there’s potential for environmental stuff, but

there’s also capabilities for engineering and machining and programing.

And any teacher who wants to get involved and implement it into their classroom,

there’s a way to do it.

Any sort of math, any sort of science, any sort of

Stem field.

There’s the opportunity for it.

And I think having something so hands on in a real world

application is what will get kids and people excited about science,

and that’s what we need.

I guess one thing that I think is really cool

is that everybody I talk to is really interested.

Like, my mom works in a school, so all the different teachers

I talk to are like, wow, tell me more about this tool.

So it’s really cool to say, like,

yeah, I’m I’m actually pretty comfortable with it, you know?

I know it really well.

Or I can explain it to people and give a demonstration and,

you know, do a quick data analysis right in front of them to show

the difference between different plants and asphalt and concrete.

So I would say that’s pretty cool, everybody.

I think once it’s out there, people will

like once people know about it, I think they’re really excited about it.

And that’s something that’s really cool to be a part of.

“I think it can be really helpful to like a lot of early career scientists, early career researchers who might not have access to high cost spectrometers.”

A Tool That Grows With You

What makes STELLA particularly powerful, according to Bianca, is its adaptability. “STELLA is what you make of it,” she explains. As someone who wasn’t a confident programmer, she found the pre-written code accessible while still offering opportunities for customization. “Being able to have the code set up for me, I can really look into it and learn what commands are helpful, where, how to modify.”

The platform’s modularity proved essential to her research. She could add different sensors, incorporate GPS capabilities, and mix and match components based on her specific needs. This flexibility allowed her to enhance her programming skills incrementally, adding commands to make her research easier without being overwhelmed by starting from scratch.

Learning to Fail Forward

Perhaps the most unexpected skill Bianca developed was what she calls “failing gracefully.” This lesson came while attempting to write code that would automatically cycle through different gain and integration time combinations, eliminating the need to manually adjust settings each time she turned on STELLA.

“It was really hard. I didn’t expect how many times I’d have to try something, test the code – it didn’t work, try it again,” she reflects. “It really taught me how to just be patient. It’s okay that it doesn’t work the first few times.” This experience with iterative problem-solving became invaluable preparation for professional research environments where persistence and patience are essential.

The low cost of STELLA made this experimentation possible. “Because it’s a low cost instrument, I feel comfortable messing with different sensors and trying to build my own version of STELLA with different codes and different parameters,” Bianca notes. This comfort with experimentation, enabled by affordability, became a crucial part of her learning process.

From User to Developer

After completing her master’s degree, Bianca joined EagleView, an aerial imagery company in Rochester, New York, where she applies her image processing skills professionally. But her relationship with STELLA didn’t end with graduation. Instead, she began collaborating with Paul Mirel and Mike Taylor to evaluate STELLA’s potential as a serious scientific instrument, testing its consistency over time and comparing it against other spectrometers.

This work led to an important realization: while her STELLA measurements were “great and valuable,” they could have been enhanced with better calibration protocols. “I think it would have given my measurements a little bit more credibility,” she reflects. This insight sparked her current focus on developing classroom calibration protocols that could advance science education and research.

Bringing Science to Everyday Spaces

Bianca’s approach to protocol development reflects her commitment to accessibility. She designs and tests everything in her one-bedroom apartment, ensuring that procedures can be replicated by students with limited space, time, and budgets. “I’m practicing it in my apartment to try to understand what materials I can kind of scrounge together to make it low cost and to make sure that it can be done under time constraints,” she explains.

Her creative solutions demonstrate the ingenuity that STELLA encourages. For testing reflector reliability, she uses a nightstand adjusted to the right height, a lazy Susan for rotation, styrofoam crosshatched to identify the center, a protractor for angle measurement, and dental floss for manual rotation. A selfie stand holds the STELLA, while an unused art easel supports targets.

This DIY approach isn’t just about cost savings – it’s about proving that quality science can happen anywhere, with materials anyone can access.

The Science of Calibration

Bianca’s calibration work addresses a fundamental challenge in scientific measurement: how do you ensure your instrument is giving you accurate, comparable data? Her protocols tackle multiple variables that can affect STELLA’s performance, from different light levels and temperatures to various target materials and incident angles.

One particularly important aspect is measuring “extraneous light” – the unavoidable illumination present even in seemingly dark rooms. “All the windows are down, all the curtains are up, all the lights are off, but there’s still light coming from somewhere because we don’t live in a vacuum,” she explains. By saturating the sensor and measuring this background light under different conditions, researchers can account for it in their data analysis.

Her work with reflector materials has tested everything from basic styrofoam to professional white references from photo studios, with plans to evaluate construction paper, whiteboards, and mirrors. Each material offers different advantages in terms of cost, availability, and optical properties.

Balancing Rigor with Practicality

Creating protocols that maintain scientific accuracy while remaining classroom-friendly requires careful balance. Bianca’s approach emphasizes clarity and repeatability while avoiding overwhelming detail. “It’s really important to make sure that it’s clear and detailed while still being concise,” she notes. “There’s some steps that are a little bit implied that you don’t necessarily need to repeat – that just bogs things down.”

The key is identifying which variables truly matter for reproducible results. When describing room darkness measurements, for example, she specifies that lights must be off and curtains must be closed – details that might seem obvious but are crucial for consistent results across different users and environments.

A Vision for Broader Impact

Bianca’s excitement about STELLA extends far beyond her own research. Working now at EagleView while continuing her STELLA research, she sees the instrument’s potential to democratize scientific access across multiple fields. “It’s available for students, researchers, teachers, professors. It’s a great way for anybody to get involved in science,” she enthuses.

Her mother’s work in schools has given her direct insight into educators’ reactions. “All the different teachers I talk to are like, ‘Wow, tell me more about this tool,'” she reports. Being able to demonstrate quick data analysis showing differences between plants, asphalt, and concrete creates immediate engagement and understanding.

Looking Toward the Future

The culmination of Bianca’s calibration work may be a research paper that establishes STELLA’s credibility as a scientific instrument rather than just an educational tool. “Paul and I are working really hard to test all these calibration methods and see how consistent the STELLA is across all these different parameters,” she explains.

This research addresses a crucial question for any emerging scientific instrument: How reliable is it? By having different groups test similar parameters under various environmental conditions, the scientific community can better understand STELLA’s capabilities and limitations.

The Ripple Effect of Accessibility

What excites Bianca most about STELLA is its potential to get people excited about science through hands-on, real-world applications. “I think having something so hands on in a real world application is what will get kids and people excited about science, and that’s what we need,” she reflects.

Her journey from intimidated student to confident developer illustrates STELLA’s power as both an educational tool and a research platform. By removing traditional barriers – high cost, complex programming requirements, need for specialized facilities – STELLA opens scientific exploration to anyone willing to learn and experiment.

A Model for Accessible Innovation

Bianca’s story demonstrates how accessible scientific tools can transform not just individual learning experiences, but entire approaches to research and education. Her work developing calibration protocols ensures that future STELLA users can achieve greater scientific rigor while maintaining the platform’s essential accessibility.

Her apartment-based testing protocols prove that quality science doesn’t require expensive laboratories or specialized facilities – just creativity, persistence, and the right tools. As she continues her professional work in aerial imagery while advancing STELLA’s scientific capabilities, Bianca exemplifies how accessible innovation can create pathways for the next generation of scientists and engineers.

Through her journey from overwhelmed student to confident researcher and protocol developer, Bianca has become living proof that STELLA’s promise of democratizing science education and research is not just aspirational – it’s achievable, measurable, and already happening.

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