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:

Network Dynamics, Biophysics, and Mental Illness

[latexpage] This past fall was my first quarter of graduate school, and one of our core courses was Deterministic Models in Biology. For our final project, we chose a quantitative biology paper on a topic of our interest and presented on it to the class. The paper I chose was a review paper, Psychiatric Illnesses as Disorders of Network Dynamics by Daniel Durstewitz, Quentin J.M. Huys, and Georgia Koppe. My undergraduate research focused on the dynamics of neurons at the molecular level, and this paper helped me connect it to specific characteristics of mental illnesses.

This paper proposes that since observable cognitive and emotional states rely on the underlying dynamics of neuronal networks, we should use Dynamical Systems Theory (DST) to characterize, diagnose, and develop therapeutic strategies for mental illness.

The central idea of DST is that there is a set of differential equations that evolve in time. A set of dynamical equations could look as follows:

$\frac{dx_1}{dt} = \dot{x_1} = f_1(x_1, … , x_M, t; \boldsymbol{\theta} )$
$\frac{dx_2}{dt} = \dot{x_2} = f_1(x_1, … , x_M, t; \boldsymbol{\theta})$
$\vdots$
$\frac{dx_M}{dt} = \dot{x_M} = f_M(x_1, … , x_M, t; \boldsymbol{\theta})$

The variables $x_1, x_2, … x_M$ represent the dynamical variables such as voltage or neural firing rate. These equations describe how each of these variables change over time. $\boldsymbol{\theta}$ represents parameters, fixed values that are properties of the system that do not change over time.

We define a fixed point as the point at which the derivatives of all of the variables are equal to 0. Fixed points are stable if activity converges towards them, and unstable if activity diverges from them. Stable fixed points are called attractors. We can define the basin of attraction as the set of points from which activity converges towards the attractor.

The figure below shows an example of a phase plane, a representation of a space spanned by the two variables of a system. Note that it is possible to use dimensionality reduction methods to obtain visual representations for higher dimensional systems. The arrows show the activity of the system. The blue and orange curves represent nullclines, and along each of these curves, the derivative of one of the variables is 0. The green line represents the barrier between the two basins of attractions. It is possible to cross over this barrier as a result of either external influences or random fluctuations.

I will discuss some basic neuroscience before going into the dynamics of mental illnesses. There are many ion currents that pass through a neuron membrane such as sodium, potassium, and calcium. The dynamics of these ions are driven by electrochemical gradients. Spiking activity occurs when there is a rapid influx of sodium ions, producing the spike followed by an efflux of potassium ions, returning the membrane potential to the threshold potential.

We can think of a neuron membrane as a capacitor, where positive and negative charges are accumulated on either side. The current is the rate of charge flowing per time, $I = \frac{dq}{dt}$, and the charge of a capacitor is defined as q = CV. The current through the membrane is this $I_m = C_m \frac{dV_m}{dt}$. We can think of this system as the circuit shown below:

Because of charge conservation, the sum of the currents across the capacitor and each of the resistors must be 0. In mathematical terms, this is $C_m \frac{dV_m}{dt} = -\sum_i I_i$.

If we approximate each of these currents as ohmic, they will satisfy Ohm’s law, V = IR, meaning that the current is proportional to the difference between the membrane voltage and the threshold voltage by a factor of 1/R, or in other words, the conductance.

If the conductance were constant over time, these would be linear. However, the conductance depends on the proportion of ion channels that are open and the proportion of channels that are closed, called the gating variables. For example, a sodium current can be described as

$I_{Na} = g_{max}m^3h(V_m – E_{Na})$

In this system, m and h are the gating variables, and they vary from 0 to 1, and $g_{max}$ is the maximal conductance.

We can think of the dynamical equations for the gating variables as the result of a mass equation. Consider the reaction

$Closed \rightleftharpoons Open$

Suppose $\alpha$ is the rate of opening of a channel, or the forward reaction above, and $\beta$ is the rate of closing, the reverse reaction above, and both of these rates depend on the voltage. If m represents the proportion of channels that are open, the derivative over time is equal to the  forward rate times the concentration of reactants minus the reverse rate times the concentration of products. In other words:

$\frac{dm}{dt} = \alpha(V_m)(1-m) – \beta (V_m)m$

Another form of this dynamical equation commonly seen in the literature is:

$\frac{dm}{dt} = \frac{m_{\infty}(V_m) – m}{\tau_{Na}(V_m)}$

$\tau_{Na}$ is the voltage-dependent time constant, and $m_{\infty}$ is the steady-state proportion of open channels as a function of voltage.

The dynamical equation for voltage for the simple NaKL model is as follows:

$\frac{dV}{dt} = g_{Na}m^3h(E_{Na}-V) + g_K n^4 (E_K -V) + g_L (E_L – V) + I_{inj}C^{-1}$

Neuronal networks are the result of multiple neurons connected to one another through synapses. Pre-synaptic neurons deliverer chemicals, called neurotransmitters, to post-synaptic neurons. Some neurotransmitters are excitatory, such as NMDA (N-Methyl-D-aspartic acid), meaning they increase the likelihood of spiking activity, and others are inhibitory, such as GABA (gamma-aminobutyric acid), meaning that they decrease the likelihood of spiking activity. To describe the networks of neuronal networks, each individual neuron has a voltage equation as illustrated above, with additional terms relating to its synaptic currents. These currents depend on the synaptic conductance, the difference between the membrane voltage and the threshold voltage, the strengths of the synaptic connections, and the fraction of open channels for each receptor. The dynamical equation for the fraction of open channels usually depends on properties of the presynaptic neuron.

So far, the variables we have been considering have been the voltage and the gating variables. In order to discuss the dynamics of mental illness, we must think about another important variable: firing rate. This simply describes the rate of voltage spikes over time. Below is an example of a phase plane, where the vertical axis is the average firing rate of inhibitory neurons, and the horizontal axis is the average firing rate of excitatory neurons.

In this system, the fixed points can be thought of as memories or goal-states, and we can use this system to consider the effects of the underlying dynamics on working memory or decision making. Increasing the depth of the basin of attraction can have the effect of increasing the stability of the state, while flattening the basin of attraction reduces the stability of the state.

This paper highlights the key role of dopamine in altering these attractor dynamics. Stimulating the D1 dopamine receptors has the effect of increasing firing activity of both excitatory (NMDA) and inhibitory (GABA) neurons. This alters the parameters of the system, in particular, the strengths of synaptic connections, over time. As a result, the basins of attraction are deepened, and the state is more stable and robust to external perturbations or noise fluctuations.

Stimulation of the D2 dopamine receptors has the opposite effect, flattening the basins of attractions. These flat attractor landscapes could lead to disorganized or spontaneous thoughts that can be experienced as hallucinations that are characteristic of schizophrenia. This can also explain the high distractibility in attention-deficit hyperactive disorder (ADHD). On the other hand, Obsessive Compulsive Disorder (OCD), a disorder characterized by rumination, invasive and recurrent obsessions and compulsions, can be linked to deep basins of attractions that are robust to potential distractors. Major Depressive Disorder characterized by a coexistence of rumination and a negative mood with lack of concentration and distractibility, and one can think of it as an imbalance between multiple attractor states.

The main point this review paper aims to illustrate is that in order to characterize and develop treatments for mental illnesses, one must consider the underlying network dynamics. The suggested role of dopamine in altering the depth of basins of attractions suggest that we might try to target the dynamics of schizophrenia patients, for example, through dopaminergic drugs.

I found the process of reading this review paper and the sources it cited extremely helpful for me in improving my understanding of neurons, neuronal networks, biophysics, and nonlinear dynamics, and linking my previous understanding of neurons to cognitive processes, something that I had not fully understood before. Because the review paper goes over the general information, I read many of the papers it cited to find the basis behind some of its claims. However, I still do not clearly understand the mechanism behind the changes in the attractor dynamics. I would like to learn more about how the parameters are changed, and how these changes, in turn, alter the attractor landscapes.

At this point, I believe that the connection between these dynamics and mental illnesses as presented in this paper seems rather speculative. However, I think that as more data is collected and analyzed, and further models are developed to understand the dynamics of neuronal networks, we can glean more insight towards understanding and developing treatments for mental illnesses.

References:

Durstewitz, D., Huys, Quentin J. M., Koppe, Georgia. (2018). Psychiatric Illnesses as Disorders of Network Dynamics. doi: https://arxiv.org/pdf/1809.06303.pdf

Durstewitz, D. (2009). Implications of synaptic biophysics for recurrent network dynamics and active memory. Neural Networks, 22(8), 1189-1200.

Durstewitz, D., Seamans, J. K. (2008). The dual-state theory of prefrontal cortex dopamine function with relevance to catechol-o-methyltransferase genotypes and schizophrenia. Biological Psychiatry, 64(9), 739-749.

Durstewitz, D. (2006). A few important points about dopamine’s role in neural network dynamics. Pharmacopsychiatry, 39(S 1), 72-75.

Izhikevich, E. M. (2007). Dynamical Systems in Neuroscience: MIT Press.

Johnston, Daniel, Wu, Samuel Miao-Sin . (2001) Foundations of Cellular Neurophysiology. MIT Press.

Rolls, E. T., Loh, M., Deco, G. (2008). An attractor hypothesis of obsessive-compulsive disorder. European Journal of Neuroscience, 28(4), 782-793. doi: 10.1111/j.1460-9568.2008.06379.x

Strogatz, S. H. (2018). Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering: CRC Press.

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:

1. $HVC_I$ neurons have only inhibitory connections with the others
2. $HVC_{RA}$ and $HVC_X$ neurons have only excitatory connections with $HVC_I$ neurons
3. $HVC_{RA}$ have a sequence of excitatory connections with each other that store the bird’s own song
4. There are no direct connections between $HVC_{RA}$ and $HVC_X$ neurons
5. There can be multiple inhibitory connections on a single $HVC_X$ neuron
6. 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.

Introduction

Hello to everyone reading this!

I am a first-year grad student in California, and I decided to create this blog to document some of my experiences on this path working towards becoming a scientist. I would like to use this page as a record of some of my ideas in research, as well as some personal reflections about research, classes, teaching experiences, social experiences, and pursuing hobbies.

My department, Biomathematics, is a small basic science research department within the school of medicine. It focuses on theoretical, computational, and statistical modeling in biology and biomedicine. My research interests are in neuroscience, and the project I have been starting to work on this year focuses on applying tools from physics and applied mathematics to model neuronal networks.

I started college as a chemistry major, but after a while, I realized that while I loved the theoretical side of chemistry, experiments were very much not my strong suit. I changed my major to mathematics/applied science, which allowed me to take theoretical chemistry classes along with a set of courses in applied mathematics. I have always found myself interested in neuroscience, and in my last two years of undergrad, I worked in a research group that studied neurons and neuronal networks from the perspective of theoretical biophysics. There, I picked up a lot of skills in programming and applied mathematics. More than anything, I learned how to find the background information I need for a given task, which has been tremendously helpful transitioning into graduate school.

Aside from curiosity, my main motivation to study neuroscience comes from a desire to improve our understanding of the brain and mental health from a quantitative perspective. Mental health diagnoses are often based on self-reported qualitative data such as questionnaires, which are imprecise and very susceptible to bias. I believe that a greater understanding of the brain and cognitive processes from a theoretical perspective could not only better inform diagnosis and therapeutic intervention, but could reduce the stigma surrounding mental illness. Mental illnesses such as depression, anxiety, attention-deficit hyperactive disorder (ADHD), and obsessive-compulsive disorder (OCD) are often not taken as seriously as physical illness. As a result, many people suffer in silence and do not seek the treatment they need. A greater understanding of the mechanistic aspects of cognitive disorders is, I believe, a step towards recognizing the biological basis of mental illnesses and validating the health concerns of those affected. It motivates me to think about this as a long term goal, and that my studies in science are not only for my own benefit, but towards the benefit of society as a whole.

During my free time, I like finding content on the internet, in the form of blogs, art, and youtube videos. Since school is obviously a large part of my life, I like content about college and graduate school. I have found some content about life as a STEM student in graduate school, and I have felt a sense of inspiration and motivation from watching others working towards their research goals while simultaneously pursuing their hobbies. However, since my field, the interface between biology and mathematics, is relatively new, I rarely find content from students who are studying similar things that I can relate to. So I decided that if it doesn’t already exist, why not create it?

I believe it will be helpful for me to have a record of my progress in learning the material I need to know for my research, and writing things out in a pedagogical way would probably aid my own understanding of the things I’m learning. I also think that it will help me hold myself accountable, not only for my research progress, but also towards personal goals and hobbies, such as drawing and painting, swimming, dance, making new friends, and putting myself out there in the queer community.

It is likely that this blog will mostly be for my own record, and maybe some of my friends who might be interested in what I am doing. However, part of the reason I found it difficult to identify with other people in STEM is that I am often surrounded by peers who are very different from me. I have often benefitted from meeting other women, people of South Asian origin, and queer people in my field, and I know from my own experience how important representation is. I would love to know if anyone relates to any part of my experience, so please do not hesitate to contact me.

Here’s to a fruitful new year, and I am excited to begin this new journey.