Embracing Uncertainty: The Morrison Group

Anonymous — Fri, 24 May 2024 21:21:32 +0000

Embracing Uncertainty: The Morrison Group Anonymous (not verified) Fri, 05/24/2024 - 15:21

Grace Wilson

Rebecca Morrison, an assistant professor of computer science at ŷ��ڱ��Ƶ Boulder, focuses on the failures and uncertainties inherent in large computational models and how to create models when others fail.

She and her group are also interested in how to create predictive models for datasets that are highly interrelated, but don't follow a traditional 'normal' distribution.

Morrison believes that the key to a successful PhD is a healthy group culture.

"I think there's sometimes a mentality in academia that you need to be stressed, but I try to set the example that it's okay to be okay, to get enough sleep and not be overworked. You can still be working hard enough and be happy and spend time with friends," she said.

Let's learn more about the group's research from its members:

Rileigh Bandy, 5th Year PhD

Rileigh Bandy, a fifth-year PhD student, is researching how to represent and understand the uncertainties in models which represent elements of the natural world.

These physics-based models can help scientists predict weather patterns, tumor growth, stress patterns on bridges and more.

"Generally we can divide those models into two camps," Bandy said. "There are white box systems of equations or black box models that just spit out outputs where we don't know how they're working. My research has been exploring how we can correct both of those models," she said.

Though highly advanced, these models cannot hope to perfectly simulate the chaotic natural world. Understanding how uncertain the models are helps to improve them, compare them and create reasonable trust levels.

Many physics-based models are safety-critical, such as weather forecasts for flight-paths or tumor-growth predictions for cancer treatment, and understanding them well is important to our everyday lives.

Bandy said that there are many paths to a PhD.

"To find your right path, focus on what you're most interested in. Try to find an advisor and collaborators that have common interests that you like to work with, because that's how you'll want to spend five-ish years doing it," she said.

Teo Price-Broncucia, 5th year PhD student

Teo Price-Broncucia, a fifth-year PhD student, is seeking ways to reduce the expense and code-base complexity of large scientific computer models.

''One thing I'm working on is calibration methods to develop cheaper reduced models with slightly lower fidelity that hopefully retain a lot of the original models' usefulness," he said.

Price-Broncucia explained that these models might be useful in education or industry, but also for science itself.

"There are a lot of times you want to make lots of model runs, and that's difficult to do if it takes a full supercomputer three weeks to run a single model run," he said.

He has also been working with the National Center for Atmospheric Research (NCAR) to understand how changes to the code of chaotic climate models impact what they output.

"Really small changes to a chaotic model can result in totally different outputs. It's the butterfly effect. It's difficult to say if something meaningful changed or a single bit changed somewhere, and thus we got a different output, but really the model is the same," he said.

Price-Broncucia believes the best way to pursue a PhD is sustainably, akin to a marathon, not a sprint.

"You can't put your life on hold when you do a PhD. Research is a creative enterprise. You have better ideas when you're not totally sleep-deprived and stressed out and when you're able to have some space to think about things in a nice way," he said.

Ujas Shah, 1st year PhD Student

Ujas Shah's undergraduate education was in politics and economics before he started a master's degree in data science, which he has now transferred into a PhD.

"My master's was the first time where I actually got into the sciences. I would say that we shouldn't be so afraid of switching fields," Shah said.

Shah is focusing on graphical models for datasets that don't follow the classic bell-curve 'normal' distribution, but are still interlinked and affect one another.

These datasets capture the intricacies of many layers of interconnected variables, such as complex company mergers or how animal species interact in a certain area.

"We already have methods established for a normal distribution, but I am building on some of Rebecca's work to create models for data that's not normally distributed and use the model to help predict what other variables will be," he said.

This predictive power could have applications across a wide variety of subjects, from ecology and economics to physics and quantum science.

Noah Peterson, 1st year PhD student

Noah Peterson, a first-year PhD student, is working on data assimilation for space weather.

"We don't often know if we have either all the physics included or if our initial parameters are entirely correct, so sometimes the model is off, and we also have observations, but sometimes those observations can have somewhat large amounts of error in them," he said.

Data assimilation is the practice of combining those models and observations with different levels of uncertainty to get a more accurate estimate of true conditions.

Peterson said that in order to get the most out of his PhD, he had to learn to let go.

"I was so used to putting all of my time into getting perfect grades, but now that's not the most important thing, the research is," he said.

Rach Washington, 1st year MSCS Coursera

Rach Washington is a first-year MSCS Coursera student working with Morrison's group, with Bandy as her mentor.

"In the project I am currently working on," Washington said, "there is a computationally expensive model that works well to describe the system and a reduced model that poorly describes the system. Our goal is to use a less computationally expensive model that describes the system as well as the complex model."

Washington recommends that students interested in research reach out and try to build relationships.

"Even as a Coursera student, I am treated as an on-campus student. I would recommend going to office hours. Course facilitators are always kind and happy to help," she said.

Sienna Amorese, fourth year undergraduate student

Sienna Amorese is a fourth-year undergraduate student studying statistics and data science. Morrison is Amorese's research advisor, while Price-Broncucia has been mentoring her.

"My experience has been wonderful. All members of the group are open to help guide me. It's amazing to see my studies in the real world and set goals that encourage me to explore," Amorese said.

Amorese is working with Price-Broncucia to create graphical models from NCAR's weather dataset.

"My research focuses on decoupling random climate variables from a weather dataset at NCAR. I've explored the normality and covariance of variables, and am working on creating graphical models to represent their relationships based on their precision matrix," she said.

The Morrison group focuses on how to overcome and adapt to the failures and uncertainties in computational models of often chaotic systems.

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'My work starts when models start to fail'

Anonymous — Tue, 05 May 2020 16:49:54 +0000

'My work starts when models start to fail' Anonymous (not verified) Tue, 05/05/2020 - 10:49

Assistant Professor Rebecca Morrison has developed a new mathematical tool that could help scientists to deliver more accurate predictions of how diseases, including COVID-19, spread.

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Assistant professor wins research award from Johnson & Johnson

Anonymous — Fri, 12 Apr 2019 15:47:09 +0000

Assistant professor wins research award from Johnson & Johnson Anonymous (not verified) Fri, 04/12/2019 - 09:47

The Women in STEM²D Scholars Award aims to fuel the development of women and inspire career paths in their respective fields.

Assistant Professor Rebecca Morrison has been named a winner of the Johnson & Johnson Women in STEM²D (WiSTEM²D) Scholars Award.

She will receive three years of mentorship and $150,000 in funding from Johnson & Johnson for her research into flexible algorithms for climate modeling. The award had more than 400 applications this year, and Morrison is one of six winners, representing the disciplines of science, technology, engineering, math, manufacturing and design.

Morrison, who won in the math category, is interested in identifying flexible algorithms that can run mathematical calculations on variables like temperature, weather and rainfall more quickly and accurately as they shift over time. This will allow for more precise computational, physics-based and data-driven models of those changes so it's easier to address complex issues like climate.

“It really is an honor to receive this award, and I want to thank Johnson & Johnson for supporting women in STEM,” Morrison said. “I’m excited to learn more about how these complex models interact and see if we can’t make them faster and more streamlined, but just as reliable for predictions.”

Launched in June 2017, the Johnson & Johnson WiSTEM²D Scholars Award aims to fuel development of female STEM²D leaders and feed the talent pipeline by awarding and sponsoring women at critical points in their careers, in each of the STEM²D disciplines.

"Through this Award and other programs, Johnson & Johnson is hoping to increase the participation of women in STEM²D fields worldwide," said Cat Oyler, vice president of global public health, tuberculosis, for Johnson & Johnson and a WiSTEM²D university sponsor. “We want to nourish the development of women leaders building a larger pool of highly trained female researchers so that they can lead STEM²D breakthroughs in the future.”

For more information and to submit nominations for the 2020 Awards, visit www.jnj.com/wistem2d.

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