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תוכן מסופק על ידי UW College of the Environment. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי UW College of the Environment או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
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S3 E4: From Undergrad to Grad Student with Samatha-Lynn Martinez and Trent Vonich

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Manage episode 446560885 series 3448507
תוכן מסופק על ידי UW College of the Environment. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי UW College of the Environment או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

In this episode of FieldSound, we meet two students who found their own paths at the University of Washington, blending their interests in science communication and public safety with research, classes and discovery — all the while laying the groundwork for their future careers.

From an early age, Samantha Lynn-Martinez, a recent graduate of the UW with a dual degree in biology and marine biology, was drawn to the natural environment and wanted to get involved but didn’t know where to start. Then she heard about high school volunteer programs at local organizations, including Seattle Aquarium.

Martinez enjoyed working with Seattle Aquarium visitors — showing them how to touch a sea urchin or how to be a good steward of nature — and she began doing social media engagement for the aquarium. That work introduced her to video-making.

By combining that curiosity for the natural world with her passion for storytelling, Martinez now uses filmmaking and photography as powerful tools for science communication. Through the lens of a camera, she aims to make complex scientific ideas accessible and engaging. And she’s inspiring others to see the world through a new lens.

“If I can introduce people to a topic they’ve never considered before on my Instagram, and then they do their own Google deep dive after, I think that’s a job well done,” Martinez said.

All of the research she’s done has integrated some form of science communication. Martinez sees the value of this work and advocates for it with her supervisors and PIs.

Recently, Martinez worked with NOAA in the Aleutian Islands studying steller sea lion ecology. She gained valuable field experiences working with sea lions, doing drone and photography surveys, photo identification and more.

What began as an interest in marine life as a high school volunteer at the Seattle Aquarium has evolved into a remarkable journey for Martinez, who was named a Husky 100 in 2024. And she’s just getting started.

“I just want to capture that curiosity. Curiosity is really what drives everything I do,” Martinez said. “Stuff that I’m currently doing with my own projects and through the UW have been really helpful in terms of building that portfolio, building those skills.”

Trent Vonich is a Ph.D. student in atmospheric and climate science who studies the predictability of extreme weather. He’s passionate about unlocking the secrets of the world’s most powerful storms by exploring the potential of machine learning to transform meteorological forecasting.

Vonich is not only a full-time student, he’s also an active-duty officer in the United States Air Force. He balances an exhilarating, fast-paced military career as a pararescueman with his studies and scientific research, all while looking ahead toward his future ambitions — NASA’s astronaut program.

Vonich has always been interested in severe weather, but decided to focus on hurricanes after seeing a number of U.S. Navy and Air Force bases sustain damage by severe storms.

When a weather forecast is wrong, that’s when Vonich steps in. His research examines why weather forecasts sometimes fail. Historically, scientists have looked at physics-based weather models to find answers, but machine learning may offer a much simpler way.

“I think the most compelling part of machine learning impacting weather modeling is the speed at which you can now do forecasts. It’s a totally different approach,” he said. It takes a long time to build machine learning models and train them, but once trained, they work quickly.

Now, tech companies have entered the field of weather modeling. For example,

https://environment.uw.edu/podcast

  continue reading

23 פרקים

Artwork
iconשתפו
 
Manage episode 446560885 series 3448507
תוכן מסופק על ידי UW College of the Environment. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי UW College of the Environment או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

In this episode of FieldSound, we meet two students who found their own paths at the University of Washington, blending their interests in science communication and public safety with research, classes and discovery — all the while laying the groundwork for their future careers.

From an early age, Samantha Lynn-Martinez, a recent graduate of the UW with a dual degree in biology and marine biology, was drawn to the natural environment and wanted to get involved but didn’t know where to start. Then she heard about high school volunteer programs at local organizations, including Seattle Aquarium.

Martinez enjoyed working with Seattle Aquarium visitors — showing them how to touch a sea urchin or how to be a good steward of nature — and she began doing social media engagement for the aquarium. That work introduced her to video-making.

By combining that curiosity for the natural world with her passion for storytelling, Martinez now uses filmmaking and photography as powerful tools for science communication. Through the lens of a camera, she aims to make complex scientific ideas accessible and engaging. And she’s inspiring others to see the world through a new lens.

“If I can introduce people to a topic they’ve never considered before on my Instagram, and then they do their own Google deep dive after, I think that’s a job well done,” Martinez said.

All of the research she’s done has integrated some form of science communication. Martinez sees the value of this work and advocates for it with her supervisors and PIs.

Recently, Martinez worked with NOAA in the Aleutian Islands studying steller sea lion ecology. She gained valuable field experiences working with sea lions, doing drone and photography surveys, photo identification and more.

What began as an interest in marine life as a high school volunteer at the Seattle Aquarium has evolved into a remarkable journey for Martinez, who was named a Husky 100 in 2024. And she’s just getting started.

“I just want to capture that curiosity. Curiosity is really what drives everything I do,” Martinez said. “Stuff that I’m currently doing with my own projects and through the UW have been really helpful in terms of building that portfolio, building those skills.”

Trent Vonich is a Ph.D. student in atmospheric and climate science who studies the predictability of extreme weather. He’s passionate about unlocking the secrets of the world’s most powerful storms by exploring the potential of machine learning to transform meteorological forecasting.

Vonich is not only a full-time student, he’s also an active-duty officer in the United States Air Force. He balances an exhilarating, fast-paced military career as a pararescueman with his studies and scientific research, all while looking ahead toward his future ambitions — NASA’s astronaut program.

Vonich has always been interested in severe weather, but decided to focus on hurricanes after seeing a number of U.S. Navy and Air Force bases sustain damage by severe storms.

When a weather forecast is wrong, that’s when Vonich steps in. His research examines why weather forecasts sometimes fail. Historically, scientists have looked at physics-based weather models to find answers, but machine learning may offer a much simpler way.

“I think the most compelling part of machine learning impacting weather modeling is the speed at which you can now do forecasts. It’s a totally different approach,” he said. It takes a long time to build machine learning models and train them, but once trained, they work quickly.

Now, tech companies have entered the field of weather modeling. For example,

https://environment.uw.edu/podcast

  continue reading

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