Week Five: What new skills and/or knowledge have you gained from your summer experience? Have you met anyone who has been instrumental in shaping/helping you conduct your project? Briefly, how has this person impacted you? What have you learned about leadership from this individual, and how might it influence your actions, work, and self in the future?
As I mentioned in my previous post, my REU advisor put my group in contact with a post-doc at UBuffalo. Her name is Sophie and she runs a wonderful NSF-funded pedagogical/research collective known as GHub (“glaciology hub“).She has been incredibly helpful, both in terms of providing guidance to our group research project and support for my specific Laidlaw project, which focuses on generating educational materials on the mathematical modeling involved in glaciology and geoscience more broadly. In learning more about GHub, the goal of my Laidlaw project has narrowed down to posting a series of educational materials on GHub and then advertising these materials (along with the broader GHub mission) to space-grant consortia and relevant university departments/institutes.
This progression of the project has taught me a lot about the importance of networking in academia. There are a lot of smart people out there doing a lot of different things. Oftentimes it is convenient and more efficient to collaborate. In my case, this is helping my work reach a wider audience, and it is putting me in contact with domain experts––at the same time, I'm helping this budding initiative grow its platform. In my future work, I hope to continue to pinpoint and deliver on these kinds of useful collaborations
Week 4: What challenges and/or difficulties have you encountered and how did you go about resolving them? Speak to a specific challenge you have encountered and some of the ways that you tackled the problem.
I've been encountering a lot of issues with group work and communication. I am working directly with three other undergraduates, but there are a lot of other people who are tied to the work we are doing. This includes not just our PI but also a post-doc helping out with the project, the co-authors of the paper we are trying to extend (they work at U. Buffalo), and a group called G-Hub (short for "glaciology hub") which is building up a website of open-source tools, data, and educational material on the melting of glaciers and the rising of sea levels.
While there are benefits to embedding oneself in such a large and well-connected research community, it is also kind of intimidating. I've had a few moments of proudly suggesting new ideas only to discover (or be told) that they have already been done or, worse, are bad ideas. I've had some trouble coordinating with my immediate research partners because we are all climbing the learning curve at different rates.
I think the approach I've adopted is to accept that I am not going to become an expert in this subject in a matter of weeks. It's okay that I've stepped a bit out of my comfort zone, into a field that's very new to me. I think it's given me much more perspective on how careful one must be when applying mathematical models, how important it is to be patient and willing to learn about the domain of application first.
Week Three: What does a typical day look like this summer? Aside from a narrative description, upload a photo, video and/or other media submission!
It's interesting because the last two days of the week were pretty atypical! Along with the rest of my research team, I took the red line down to MIT to attend this workshop on the computational complexity of statistical problems. The lectures were awesome––they have given me new motivation to tackle daunting theory problems, both in my current research and beyond. I happened to take one picture when I was there: it shows a slide with a list of open questions in fine-grained complexity. I would attach it but this interface does not seem to allow it.
In any case, a typical day in the summer involves waking up at around 8 or 9am and heading to the Joyce Cummings Center, which houses the Math and CS departments, as well as my office. There, I work with the three other members of my group. Most days, we meet with our advisor, and every week, we have a joint seminar with the rest of the program where we listen to a lecture or present our work. I like to take walks on campus during my breaks to clear my mind, but between the weeks of rain and the air pollution, I just wish the weather was nicer!
Q: If your project connects with your research from last summer, explain the ways in which it picks up on themes, issues, or questions that are important to you. How are you expanding on your project from last summer? How is your understanding of this topic evolving?
A: Both last summer's project and my current work are about mathematical modeling, which is a very tricky subject. Unlike pure mathematics, which works in the space of abstractions and propositions, math modeling projects are data-centered. When working with data, there are somewhat competing perspectives at play. In one sense, there is way too much data and we need to distill the most important information, efficiently. In another sense, there is practically never enough data, because we often justify our inferences according to "laws of large numbers" and "central limit theorems," beautiful results that only apply "in the limit," as the number of data points goes to infinity. In any case, this summer, I am gaining a much richer understanding of the mathematical modeling toolbox. I am looking at a wider variety of statistical methods (spline regression, MCMC parameter estimation) compared to last summer, where I almost exclusively used transformer-based language models. This is helpful for me as I begin to work on the pedagogical material for this project.
Week 1 Q: If your project this summer differs from your project last summer, has last summer’s project influenced your project this year, and if so how?
A: I think last summer's project set me up with the "research literacy" to succeed in my current project. For reference, last summer, I conducted a large-scale, automated analysis of the political undertones of Supreme Court transcripts dating back to 1955. This summer, I am simultaneously researching and creating educational content regarding climate modeling. I think research literacy––in the sense of being able to read through sophisticated work and capture the main ideas quickly, while filtering out the auxiliary details––is crucial to balancing these two priorities and coming out of this with the deliverables that I want. The whole goal of this summer, for me, is to explore a new and important area of applied mathematics and create materials that can help other students do the same. Last summer taught me how to scale steep learning curves: this summer, I want to apply those lessons in a pedagogical way.
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Week 6:
https://docs.google.com/document/d/15UpWR1YE2xhS_dku_z0IhaabmJvZYOmbgD_i4QCP9H4/edit
Week Five: What new skills and/or knowledge have you gained from your summer experience? Have you met anyone who has been instrumental in shaping/helping you conduct your project? Briefly, how has this person impacted you? What have you learned about leadership from this individual, and how might it influence your actions, work, and self in the future?
As I mentioned in my previous post, my REU advisor put my group in contact with a post-doc at UBuffalo. Her name is Sophie and she runs a wonderful NSF-funded pedagogical/research collective known as GHub (“glaciology hub“). She has been incredibly helpful, both in terms of providing guidance to our group research project and support for my specific Laidlaw project, which focuses on generating educational materials on the mathematical modeling involved in glaciology and geoscience more broadly. In learning more about GHub, the goal of my Laidlaw project has narrowed down to posting a series of educational materials on GHub and then advertising these materials (along with the broader GHub mission) to space-grant consortia and relevant university departments/institutes.
This progression of the project has taught me a lot about the importance of networking in academia. There are a lot of smart people out there doing a lot of different things. Oftentimes it is convenient and more efficient to collaborate. In my case, this is helping my work reach a wider audience, and it is putting me in contact with domain experts––at the same time, I'm helping this budding initiative grow its platform. In my future work, I hope to continue to pinpoint and deliver on these kinds of useful collaborations
Week 4: What challenges and/or difficulties have you encountered and how did you go about resolving them? Speak to a specific challenge you have encountered and some of the ways that you tackled the problem.
I've been encountering a lot of issues with group work and communication. I am working directly with three other undergraduates, but there are a lot of other people who are tied to the work we are doing. This includes not just our PI but also a post-doc helping out with the project, the co-authors of the paper we are trying to extend (they work at U. Buffalo), and a group called G-Hub (short for "glaciology hub") which is building up a website of open-source tools, data, and educational material on the melting of glaciers and the rising of sea levels.
While there are benefits to embedding oneself in such a large and well-connected research community, it is also kind of intimidating. I've had a few moments of proudly suggesting new ideas only to discover (or be told) that they have already been done or, worse, are bad ideas. I've had some trouble coordinating with my immediate research partners because we are all climbing the learning curve at different rates.
I think the approach I've adopted is to accept that I am not going to become an expert in this subject in a matter of weeks. It's okay that I've stepped a bit out of my comfort zone, into a field that's very new to me. I think it's given me much more perspective on how careful one must be when applying mathematical models, how important it is to be patient and willing to learn about the domain of application first.
Week Three: What does a typical day look like this summer? Aside from a narrative description, upload a photo, video and/or other media submission!
It's interesting because the last two days of the week were pretty atypical! Along with the rest of my research team, I took the red line down to MIT to attend this workshop on the computational complexity of statistical problems. The lectures were awesome––they have given me new motivation to tackle daunting theory problems, both in my current research and beyond. I happened to take one picture when I was there: it shows a slide with a list of open questions in fine-grained complexity. I would attach it but this interface does not seem to allow it.
In any case, a typical day in the summer involves waking up at around 8 or 9am and heading to the Joyce Cummings Center, which houses the Math and CS departments, as well as my office. There, I work with the three other members of my group. Most days, we meet with our advisor, and every week, we have a joint seminar with the rest of the program where we listen to a lecture or present our work. I like to take walks on campus during my breaks to clear my mind, but between the weeks of rain and the air pollution, I just wish the weather was nicer!
Week Two.
Q: If your project connects with your research from last summer, explain the ways in which it picks up on themes, issues, or questions that are important to you. How are you expanding on your project from last summer? How is your understanding of this topic evolving?
A: Both last summer's project and my current work are about mathematical modeling, which is a very tricky subject. Unlike pure mathematics, which works in the space of abstractions and propositions, math modeling projects are data-centered. When working with data, there are somewhat competing perspectives at play. In one sense, there is way too much data and we need to distill the most important information, efficiently. In another sense, there is practically never enough data, because we often justify our inferences according to "laws of large numbers" and "central limit theorems," beautiful results that only apply "in the limit," as the number of data points goes to infinity.
In any case, this summer, I am gaining a much richer understanding of the mathematical modeling toolbox. I am looking at a wider variety of statistical methods (spline regression, MCMC parameter estimation) compared to last summer, where I almost exclusively used transformer-based language models. This is helpful for me as I begin to work on the pedagogical material for this project.
Week 1
Q: If your project this summer differs from your project last summer, has last summer’s project influenced your project this year, and if so how?
A: I think last summer's project set me up with the "research literacy" to succeed in my current project.
For reference, last summer, I conducted a large-scale, automated analysis of the political undertones of Supreme Court transcripts dating back to 1955. This summer, I am simultaneously researching and creating educational content regarding climate modeling. I think research literacy––in the sense of being able to read through sophisticated work and capture the main ideas quickly, while filtering out the auxiliary details––is crucial to balancing these two priorities and coming out of this with the deliverables that I want. The whole goal of this summer, for me, is to explore a new and important area of applied mathematics and create materials that can help other students do the same. Last summer taught me how to scale steep learning curves: this summer, I want to apply those lessons in a pedagogical way.