physics research – QueerNeuroMathGirl

NSF Fellowship Application Tips

Around this time last year, I applied to the National Science Foundation Graduate Research Fellowship Program (NSF GRFP). It has a pretty low acceptance rate and is highly dependent on factors outside of our control, such as the review panelists we are randomly assigned and their moods on the day they read our statements. I knew it was a crapshoot, and I was mostly applying because, as a second year, it was my last chance to do so. Getting accepted for the fellowship was a very pleasant surprise. It has made me feel a lot more confident in my abilities and career goals and has made me somewhat more motivated to work through these very difficult times.

I was the only person in my department who applied last year, and most of the resources I used for my application came from online, and from the advice and examples of successful statements by my seniors in Queer organizations on campus I have been participating in. I am also very thankful for my advisor, who gave me thorough suggestions on my proposal. I know at least one person in my department is applying this month, and I would like to pay forward the support I’ve received in the ways I can. I have started a part-time position at my university campus Graduate Writing Center, where I will be reading other students’ statements and providing them with feedback and support. I will not be publishing my statements online, but I will provide some general suggestions and strategies that I have learned here. If you would like to see my statements, you can feel free to contact me, either through the contact form or through other means if we already know each other! If this is helpful for you, my application was in Mathematical Sciences – Mathematical Biology.

Tip # 1: Read the program solicitation. Read all of it. Make sure you understand exactly what they are looking for. The two main review criteria for this fellowship application are Intellectual Merit and Broader Impacts. Make sure you devote enough in your statements to the Broader Impacts portion, as this is a common shortcoming of many applications.

Tip # 2: This is not the time for modesty! If you were meeting a new friend for coffee or going on a date, it might be a good idea not to rattle off a laundry list of all your accomplishments. But you are not trying to get the review panel to like you as a person. You are trying to convince them that you are worth throwing government money at. Make sure to list everything you’ve ever done, especially when it comes to publications and presentations. I will say right now that I did not have any publications when applying, but I am currently working on a first author publication (fingers crossed that it’ll be submission-ready this month!). So I listed this tentative paper with the year 2020, and wrote In Preparation. I would highly recommend this, especially if you currently do not have any publications, or if you are in the process of preparing a first-author publication – which often carries more weight. Also, make sure to list every poster and/or oral presentation you’ve ever done, even if it was just a department-wide poster session or presentation and you don’t think it was a big deal. This is not the time to leave anything out.

Tip # 3: Make sure you give your letter of recommendation writers enough time to write letters, and make sure they are people who know you and know your research well. As a general rule, I would suggest asking them at least a month or three weeks in advance, although earlier is probably better. I would also suggest giving them reminders as the deadline approaches, as you want to make sure everything is submitted on time. It is probably not the best idea to ask a random professor that you never spoke to but got an A in their class. You want to have someone who can vouch for your abilities in research. In addition, make sure to mention to your writers about the Intellectual Merit and Broader Impacts review criteria. The reviewers will be looking for both these things in your letters as well as your statements. If they aren’t familiar with your outreach work, provide them with a CV and/or description of your activities. One thing that I think really helped my application was getting a letter from a woman who was a postdoc in my undergrad lab and is now a tenure-track faculty member. She could speak to my research abilities, but also about the conversations we had as fellow women in a field where there aren’t many (theoretical physics). I also mentioned in my personal statement how seeing that representation in my undergrad lab encouraged me to apply to graduate school and pursue a physics-centered research group.

Tip # 4: Create a narrative about your scientific journey. If you are applying for this fellowship, it is likely that you have a range of professional experiences before this point, whether it is working in a lab, industry, healthcare, or peer-led projects. There is probably something that you’ve gained from each of these experiences that have led you to the project you are proposing today. Make sure that everything you are listing somehow ties into skills or perspectives you’ve gained that have made you more able to conduct the project you are proposing. Make sure you don’t list anything without somehow tying it in to how it has shaped you as the researcher you are today.

Tip # 5: For Broader Impacts, while it might be helpful to mention your own personal adversities and minority status, what will be even more useful is to list the ways that you plan to uplift other marginalized groups on a broad level. If you are not a member of a marginalized group, talk about initiatives you’ve taken to support those in marginalized groups throughout your career, and how to plan to continue doing so as you progress. If you are a member of a marginalized group, a good way to mention it is to bring it up in the context of outreach organizations you’ve participated in, and how you plan to use representation to encourage others in STEM, such as recruiting people to your program and increasing retention by making workspaces safer for marginalized people. If you identify as LGBTQ+, but you have never participated in and do not plan to participate in identity-based orgs, I would suggest not including it. However, if you were inspired by a talk by an LGBTQ+ identifying faculty member and it has shaped your confidence and pursuit of your career in some way, that could be a powerful thing to include.

Tip # 6: If there are any gaps in your records, such as lack of publications due to time limitations in your undergraduate research or lower grades because of some personal and/or financial adversity, I would include some kind of explanation in your personal statement. For example, I included the two projects I was involved in during undergrad, which have stalled in the research group in favor of other projects and my contributions were never published. However, it is best not to make the hardship the focus of your statement and delve too much into it. Instead, you can use this as a testament to your resilience and persistence, something that is incredibly important, as research is hard and will involve a lot of failures that you will have to be prepared to overcome. Remember the purpose of the application, which is convincing a panel of strangers who have never seen you to throw money at your project. You want to make sure that everything you include in your personal statement has some purpose that is highlighting either your intellectual merit or your potential to benefit society as a whole. The overall feel of the statements should be positive.

Tip # 7: For your research statement, I would recommend organizing it in pieces. What I did was start off with a biological introduction, lead into a broad question, and three sub-projects that fall under the umbrella of addressing my broad question. I then created separate paragraphs for each of these three sub-projects. It can be helpful to use bold or italic font to highlight these themes, and the specific steps you plan to take to address these things. You want to show that you have thought about methods, and especially if you are already a grad student, show the panel that the institution you are in has the resources to help you carry out your project. The more clarity and organization you have in laying out your plan, the better. It could be helpful to provide a figure or an equation (if you are in a more mathematical field such as mine). Make sure to address broader impacts of the research, as well as potential broader impacts that come with communicating the research and recruiting and mentoring undergraduate students participating in your research.

Tip # 8: If you don’t get the fellowship, DON’T BE DISCOURAGED. It does not mean anything about you as a scientist. There are so many faculty members I admire and respect who have been rejected by this fellowship, but they still went on to be amazing scientists. There are peers of mine who deserve it just as much as I did, if not more. It is a very random process! I also know someone whose labmate applied one year and got rejected, and applied the next year with nearly the same application and got accepted. That just goes to show that getting accepted and rejected has so much to do with factors that are out of your control. It is always a good idea to try, because you never know (for the same reasons), but just know that even if you don’t get it, you are incredibly awesome and you can still do amazing science!

I hope this was helpful, and feel free to contact me for any feedback! Also, know that these tips are just one person’s opinion, and there are many more resources for advice and support! I will include some that I have personally used:

NSF GRFP Website

Tips Websites:

https://www.alexhunterlang.com/nsf-fellowship

http://www.malloryladd.com/nsf-grfp-advice.html

https://www.profellow.com/tips/8-tips-for-crafting-a-winning-nsf-grfp-application/

http://www.christineliuart.com/writing/2018/8/31/advice-for-applying-to-the-nsf-grfp

http://www.clairemckaybowen.com/fellowships.html

YouTube Videos:

April 2019 Grad School Life Updates

I originally planned to update this blog every week or so during school, but as soon as the quarter started, things got super busy and it was easy to put this off. Hopefully, I will be better about it this quarter!

To give some background, ever since I started thinking about applying to grad programs, I knew that I wanted to come to my school and program, Biomathematics. I did a lot of research on different aspects of the programs, and even more after I was invited to the interview weekends. I chose this place based on a lot of factors, including academic fit, future goals, advisors, general feel of the program, location, and LGBTQ+ friendliness of the campus.

The program has been wonderful so far and has even surpassed my expectations. It is a pretty tiny program, only 15 grad students total, so the classes are very small and everyone in the program knows each other. Every Thursday, the grad students, some of the students who work for our professors but are from neighboring departments such as Math and Biostatistics, and postdocs all go to a nearby bar, Barney’s, for “pub night”, where they basically drink beer, spill (metaphorical) tea, and relieve stress. In my experience with the students, they have all been incredibly helpful, friendly, and inclusive. I have been careful about sharing personal information with them and thus have only come out to one person in my program so far. I hope that I can make closer friendships with the other students over time.

Grad school classes have been an adjustment in a lot of ways. On one hand, there is a lot more material covered per class – there have been times when the entirety of a math class I had taken in undergrad was covered in just two lectures. Moreover, it is impossible to get all the required background simply from attending class, and it’s necessary to do a lot of extra reading. One thing that has been surprising for me is that in my program’s core courses, as well as the neuroscience course I took, it didn’t seem as difficult as it was in undergrad to get good grades (despite the material being a lot more daunting). I think this is probably because in undergrad, there were more tricks on exams that were designed to weed people out, and now, the focus is on learning, asking questions that may or may not have answers, and being self-motivated to seek extra references for more information, but we aren’t being directly or comparatively evaluated for those things.

Another difference is that there is a lot more emphasis on reading papers and critical thinking, such as proposing potential experiments or critically examining the presentation of data and results in published papers. Some of my core biomathematics courses had homework problems that had no analytic solutions, or that there were multiple possible approaches, and the professors just wanted to see us come up with ideas, defend our assumptions, and solve as far as analytically (or numerically) possible. This is obviously quite different from undergraduate mathematics or chemistry classes, where there are standard solutions to most classical problems either in the back of the book or somewhere on the internet! But I suppose it is moving more reflective of problems in research that have not been previously solved.

I have particularly enjoyed the aspect of courses that involve choosing papers to review for final presentations, and it has allowed me to explore applications of mathematics and computation to neuroscience and has made me more excited about research. When I was in undergrad, although I studied in a theoretical physics group that looked at neuron dynamics, I wasn’t sure if I was doing it only because that was the main opportunity that came my way, but not out of real passion. I think I was too stressed about the prospect of grad school at the time to really develop my passion in research. However, I have always found myself drawn to related topics for class projects and during our department seminars. Biomathematics is a broad field, and I was originally considering exploring the statistical genetics route that is popular in my department, but after starting here, I think that my interests truly lie in neuroscience and mathematical physics, and I am now much more certain in choosing my research focuses and courses.

My department has many course requirements (4 core biomath courses, 2 biomath electives, 6 applied math courses, and 6 biology courses), and as a result, unlike some of the more experimentally focused departments like biology and engineering, they encourage us to focus on coursework and passing the qualifying exams during the first year. We don’t have official research rotations, and we don’t have to decide on an advisor until the end of the second year. However, all of my classmates have started working with potential advisors.

Although I unofficially attended research meetings in fall quarter, this winter quarter was my first official quarter of directed research. At the same time, one of my core courses was taught by my potential advisor (or PI, although my friends who are not in science keep thinking I mean “private investigator” when I use that term). He was an amazing lecturer; he wasn’t the kind of professor who continuously spews information while we furiously try to scribble everything down, but he led us to certain ideas by asking questions. One thing I really like about working with him, both through the course and during the research meetings and updates, is that although his work is clearly mathematically oriented (his background is in particle physics – interestingly, just like my PI in undergrad), unlike a lot of mathematicians and physicists, he has a very conceptual and biologically relevant approach. Some people in our program prefer more mathematical rigor, but for me, it seemed to be a perfect blend.

My advisor has done a lot of previous work on cardiovascular networks and the scaling of radius and length of individual vessels across levels of the network. I came to visit him before applying to the program, and when I told him that I was interested in neuroscience, he said that he could imagine the possibility of applying the same methods of analysis to study neuronal networks. Since I came to the group, I have been working on formulating this problem, solving for scaling ratios using Lagrange multipliers (more details about this method in my First Quarter Research Progress post), and analyzing data, both from images and quantitative data from 3D reconstructions of neurons. I have reformulated this problem so that instead of minimizing the power loss due to dissipation, I am minimizing conduction time. For neurons, one of the major evolutionary driving factors is the speed in conducting signals. For example, if you touch something hot like a stove, it would be helpful to have this sensory information relayed as soon as possible so you can pull your hand away before burning it! I have also been reading some papers from the fifties about conduction velocity in neurons and the effects of myelination (fatty layers that provide insulation for nerve fibers) on this speed, and have recently incorporated the degree of myelination as a parameter. I am also looking to modify the space filling constraint to fit neuronal systems, but I am not quite sure how to do this yet. Taking neuroscience courses concurrently with this project is helpful because sometimes I will get random ideas from class that I might be able to translate to math in a way that I can incorporate it into my model. Sometimes, I watch videos of talks by researchers in biology about dendritic morphology and structural neuroscience and feel somewhat overwhelmed, because I am obviously making a lot of simplifying assumptions and not taking into consideration factors such as genetic influences.

Overall, although research is messy and involves a lot of seeking information from various fields, as well as catching up on basic electrodynamics, fluid mechanics, and neuroscience that I never learned in a class, I am enjoying it a lot. This is my first time having my own project, as in undergrad I was for the most part working as a minion, completing menial coding tasks for grad students’ projects. My office mate in my undergrad research group, now a fourth-year grad student in the same group, came to visit me over spring break and told me I seemed a lot more confident than I was last year. Which is strange to me because I feel more overwhelmed and confused the more I learn! I suppose the “confidence” might come from accepting that I don’t know everything, or even a lot, and I’m more comfortable with being uncomfortable, if that makes any sense at all.

As I anticipated, making friends has been quite difficult for me in grad school. It was especially difficult in fall quarter, when I avoided going to LGBTQ+ specific events out of fear of the unknown, mostly, and just went to the weekly department pub nights every now and then, and spent the rest of my time shut up in my own room. My department mates are wonderful and lovely, but aside from the fact that I am not hugely into drinking, the conversations were centered around heteronormative romantic experiences, and I found myself feeling isolated a lot of the time – especially since I’m not out to most of them. When I talked to my mom about it over winter break, she suggested that I add queer org meetings to my schedule rigidly, with the same priority as classes, just so that I could feel more of a sense of community. I decided that this was a good idea, as mental health is an important thing to commit to.

In winter quarter, I regularly attended two queer orgs. One of these is called QSTEM, or Queers in STEM. It was founded by a second year PhD student in Geochemistry who identifies as a gay man. This org is mostly other graduate students, and the vast majority of them are men, which is not entirely unexpected. I have enjoyed participating in social events such as board game nights and ice cream socials. They also have a lot of outreach opportunities, which I hope I have time to get more involved in as my courses finish up and some time is freed up.

The second org I attended was called Queer Girl, and is only open to women and non-binary people. I was the only one there who wasn’t an undergrad, but was a nice social space to discuss things like queer representation in media (or the lack thereof, especially when it comes to women) – it gave me the opportunity to talk about Shay Mitchell in Pretty Little Liars and a random Korean webtoon I found called “Fluttering Feelings.” There’s definitely a lot I could learn from these women, as they would talk about their sexuality openly, which is something I’ve never been comfortable doing. Being around other women like me helped normalize my experiences a little. One of the coordinators of the group was a fellow Asian woman from San Diego (when I went to undergrad), and it was nice to meet someone I could vent to about missing San Diego and people always assuming we’re straight (being Asian/South Asian and having long hair is a surefire way to convince everyone you’re straight).

One of the social events in this club was a trip to Cuties Coffee, a queer owned and themed coffee shop in East Los Angeles that is designed to be a daytime, sober space for queer socialization and an alternative to the gay bars in West Hollywood. I loved visiting this place so much that I have now made it part of my weekend routine – I go there from around noon to four almost every Saturday to either study for classes or work on coding for research. I have included a picture from that day, and used the rainbow pride flag emojis to cover faces for the privacy of the other org members.

I can’t stress how important it has been for me to have a queer sober space to go to, as I would say I’m pretty far on the introverted side of the spectrum and I never quite feel comfortable meeting new people in bars or nightclubs. (I still mostly keep to myself, drink my coffee/tea, and study during my trips to Cuties, but I hope I will cross the barrier of talking to strangers soon!). At the beginning of winter quarter, I went to West Hollywood a few times to check out the gay bars and nightclubs. Although I love walking on the main strip in West Hollywood, and enjoyed the experience to some extent, it’s not ideal for me because 1) the bars and clubs are largely catered towards gay men – Wednesdays are the only nights specifically for women, and there are no specific clubs for women, and 2) for some reason, being in these spaces where I’m (theoretically) approaching random strangers who are making snap judgments and impressions about me solely based on my physical appearance spiked some of my body insecurities, and to be honest, that’s not a headspace I want (or need) to be in. Right now, the focus for me is on meeting new queer friends and building community, and I’m grateful for these multiple sober spaces I have had access to this quarter.

Another extracurricular activity I participated in this winter was a club that does educational outreach in the form of presenting posters about various neuroscience to elementary through high school students to get them excited about learning about the brain. I was part of this Committee called Project Glia, which is responsible for designing and creating posters. I really wanted a way to keep in touch with my art – it can be extremely cathartic and rewarding, and I also want to catch up on the neuroscience background I never had in undergrad for my research, so this was the perfect opportunity for me. I designed this poster for “Music and the Brain”, and I was working with two undergrads who did a lot of the neat typography and shading. The director of Project Glia is a senior undergrad who happens to be taking one of my current graduate neurosciences classes with me, The Biology of Learning and Memory.

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One thing I found strange in participating in these activities is that sometimes the undergrads I interact with seem to look up to me in a way, or think that I know things because I am a grad student. One of the students was talking to me the other day about imposter syndrome and comparing yourself to other people, and I ended up saying something along the lines of “Oh, I totally understand that feeling because I used to do that too. But honestly, you can drive yourself crazy comparing yourself to other people – I know because I have done it too, but I realized it was no longer serving me, and I realized I don’t have to be this ‘star student’ to still enjoy what I’m doing.” “That’s SO true,” she had responded sincerely, and meanwhile I was internally panicking during this entire interaction. It was different than listening to a friend, someone who considers me a colleague, and I was suddenly aware of the power dynamic and how much responsibility I had. I think because I’m currently a woman in a grad school program in a related field, some of these women who have goals of grad or med school see me as a sort of safe person to vent to who knows what it’s like to go through this kind of application process and how demoralizing it can be. I was quite nervous about saying the right thing, and having the right mix of relatability and encouragement – all without sounding too preachy or pretentious. When I talked about this later at pub night later with a sixth-year in my program, someone who has significant teaching experience, he reiterated that I have the power to reduce these young women’s imposter syndrome in STEM simply by listening to them and encouraging them. Which is exciting, but also intimidating, because just a year ago, I was that undergrad.

Anyways, that is the (long-winded) gist of the updates of my grad school life over the past quarter. I have some ideas for future, more focused posts, but hope to update more often with these topics as they come up! Until then, I have an exam coming up in my cell neurobiology course, a data analysis assignment, and a research presentation coming up. Wish me luck!

Undergraduate Research Experience

[latexpage]My most important experience in undergrad was working in a group in theoretical physics studying neurons, both on the level of individual neurons and beginning to build simple models for neuronal networks. My group studied a range of nonlinear dynamical systems, and my research focused on dynamics at the molecular level.

When I first began working in the group, my primary prior experience had been undergraduate coursework in chemistry. I had taken only lower-level undergrad courses in math, physics, and to a lesser extent, biology, and my only programming experience was one week of an online course in Python. It definitely didn’t feel like enough at first, and it was an extremely steep learning curve. After my two years of working there, I picked up a lot of skills in programming, learned some basic neuroscience and physics concepts, was able to put the material from my coursework in mathematics, numerical analysis, and programming into practice, and most importantly, learned how to teach myself new material on the fly.

The data I had access to for my research was current and voltage data from current clamp experiments. This means that during the experiment, a current was injected into a cell, and the resulting potential was measured at discrete time intervals of 0.02 milliseconds. Although we only had data from one of the variables, since the dynamical equation of voltage depends on the dynamics of the gating variables and a set of parameters such as the maximal conductances of the ion channels, we can extract this information from the voltage time series. We do this by minimizing a cost function, which has terms for both measurement error and model error. We fix the measurement error and begin with an initial model error, obtaining an initial guess for the minimum, and then me slowly enforce the model constraints until we arrive at a global minimum. We use this state to estimate the most likely values of parameters and time series for the variables.

The first project I worked on was estimating parameter values for induced human neurons. Our experimental collaborators in neuroscience were able to create these cells by converting human skin stem cells to cells with neuronal properties. They were able to obtain current and voltage data through current-clamp experiments. The goal of the project was to estimate parameters for both healthy cells and cells from Alzheimer’s patients. In comparing the results, if we are able to find separation in the parameter space, we might even use this to classify unknown cells based on their current and voltage activity. Moreover, we can learn more about the dynamics and modify our model for induced human neurons as needed.

To test the validity of our estimates, we use the parameter estimates at the end of our time window and use the model to integrate forward the voltage equation, obtaining a time series prediction for voltage. If these predictions match the data closely, we can place more confidence in our estimates.

Using the simple NaKL model, where we were only considering sodium and potassium currents, we got the following results for predictions:

As we can see, although the model predicts the spiking regions well, the subthreshold regions are less accurate. As a result, I tried adding a hyperpolarization-activated inward current to the voltage equation, which added two more variables to the system. The results of the predictions using the estimated parameters were as follows:

Another project I started working on was modeling the network of neurons in HVC, the premotor nucleus of a songbird called a zebra finch. Songbirds are good models for human language learning because male songbirds spend their youth listening to a tutor, producing syllables and listening to themselves, and eventually establishing a pattern of song syllables unique to themselves.

Within HVC, there are three types of neurons. The $HVC_{Ra}$ neurons lead to the premotor pathway for the song, the $HVC_{X}$ neurons are essential for learning and memory, and the $HVC_I$ neurons have inhibitory connections with the other two types of neurons.

We built a simple model of the connections with the following assumptions, determined from the results of in vivo experiments:

$HVC_I$ neurons have only inhibitory connections with the others
$HVC_{RA}$ and $HVC_X$ neurons have only excitatory connections with $HVC_I$ neurons
$HVC_{RA}$ have a sequence of excitatory connections with each other that store the bird’s own song
There are no direct connections between $HVC_{RA}$ and $HVC_X$ neurons
There can be multiple inhibitory connections on a single $HVC_X$ neuron
The auditory input, which is converted to a current, directly influences all of these neurons to some extent

Below is an illustration of the simplest form of our model, with only three neurons of each type:

When I was working in the group, we did not yet have experimental data. However, we attempted to create simulated data with pre-determined parameters and use our methods to estimate them. We planned to use the results of these twin experiments to design experiments for our collaborators.

We used song recordings from the lab and extracted pressure wave data from the mp3 files, and then used a transfer function to convert this to a current. Then, we used this current and parameters values we determined, integrating the model’s dynamical equations and obtaining time series data for voltage and the gating variables. In this model, there are nine neurons, and each of these has its own voltage equation and corresponding gating variable equations.

I was only able to complete the twin experiments for this simple model before coming to grad school, but during my time in the group, I developed a script in C that would automatically write the model equations and organize the relevant information into the files we need for data assimilation.

My code makes use of the connection matrix, where the column on the left refers to the presynaptic neuron and the column on the right refers to the postsynaptic neuron, and the synaptic connections strengths are either 0, signaling no connection, or 1, signaling a connection. The code asks the user to manually list the connections using coordinates.

The code can easily be modified for more complex models, such as varying the size of the connection matrix, or varying the strengths of the synaptic connections. When I first wrote the files for data assimilation for this model with a network that has three neurons of each type, it took a couple weeks to complete manually, with some trial and error. My hope that this code will make it more efficient to run twin experiments for larger and more complex models.

I am happy with the research experiences I have had in undergrad, and I feel that it has prepared me to approach independent research here in graduate school. However, our models are very simple and not very biologically realistic. Since my program has a greater emphasis on not only physics, but biological training, I will be able to understand the properties and behavior of neurons at a deeper level, and develop models that are not simply mathematically elegant, but capture the essence of the biology as accurately as possible.

References

Armstrong, E., Abarbanel, H. D. (2016). Model of the songbird nucleus HVC as a network of central pattern generators. Journal of neurophysiology, 116(5), 2405-2419.

Daou, A., Ross, M., Johnson, F., Hyson, R., Bertram, R. (2013). Electrophysiological characterization and computational models of HVC neurons in the zebra finch. Journal of neurophysiology, 110, 1227-1245.

Long, M. A., Jin, D. Z., Fee, M. S. (2010). Support for a synaptic chain model of neuronal sequence generation. Nature, 468(7322), 394.

Mooney, R., Prather, J. F. (2005). The HVC microcircuit: the synaptic basis for interactions between song motor and vocal plasticity pathways. Journal of Neuroscience, 25(8), 1952-1964.

Williams, H. (2004). Birdsong and singing behavior. Annals of the New York Academy of Sciences, 1016(1), 1-30.