Hello! I write about science and technology (with a focus on biotech), as well as food and cooking.
Before moving into industry, I completed a PhD in cognitive neuroscience in Talia Konkle’s Cognitive and Neural Organization Lab at Harvard. You can find links to my academic publications under the “Academic Papers” section of this site.
In all of my work, I value thoughtful problem-solving and clear communication. On this site you’ll find links to my freelance work, as well as blog posts on topics that range from neuroscience to coffee-shop culture to running. If you’re looking for a freelance writer for an interesting project, hit me up!
Interests
Science communication
Food & cooking
Healthcare
Healthy, livable cities
Bikes
Education
PhD in Cognitive Neuroscience, 2021
Harvard University
BA in Linguistics & Cognitive Science (History minor), 2013
It would be an exaggeration to say that I’ll add cardamom to anything – but only slightly.
Truly, I love most of the classic baking spices. Cinnamon sparkles on the tongue, nutmeg seduces with its alluring perfume, allspice dresses up the simplest cake, ginger adds a delicious fire. But cardamom has a special grip on me.
So I add it to everything. I add it to oatmeal, to fruit compote, to coffee.
Here’s something that I can’t unlearn after years of statistical training: when you’re comparing samples of data along some metric, it’s not sufficient to report their averages. You also need to report how much your data varies around those averages. Without information about variability, you might misinterpret your data’s overall patterns. This makes it particularly important to visually mark variability on the plots that we use to communicate findings in a memorable way — for example, with error bars.
I love dessert. I’ll always ask for a dessert menu at a restaurant, and I’m disappointed when the only options are coffee and a scoop of ice cream. A bakery crawl is my idea of a solid weekend plan. My freezer is always – always – stocked with cookie dough and ice cream.
And yet, whenever I get a sweetened drink from a coffee shop, I almost always think that it’s far too sweet – cloyingly sweet, distractingly sweet, off-puttingly sweet.
How do fashion trends emerge evolve over time? Why were dresses with rectangular silhouettes all the rage in the 1920’s, followed by dresses with defined waists in the 1940’s? One theory is that some trends in fashion are influenced by trends in the economy. Specifically, the “hemline index” theory posits a connection between the economy and skirt lengths – skirts have gotten longer during some economically troubling times (such as the Great Depression) and shorter during some prosperous times (such as the 1920’s and 1960’s) 1.
How do you know whether you’ve predicted an outcome correctly? In some cases – for example, a political race – the answer is straightforward. Either the outcome happened or it didn’t. In others, the answer is less straightforward. Often this happens because you can’t observe all of the data that you’d need in order to get a definitive answer.
I currently work as a product manager for a tech organization, and in that capacity I ask myself this kind of question a lot.
Running has been a friend of mine for a long time. This has been especially true during the pandemic – I can always lace up my shoes and go for a run, even if gyms are closed and yoga classes are canceled. Of course, not all runs are equal. Sometimes it feels like a slog, and sometimes it feels like I’m flying. Recently, I’ve become increasingly curious in digging into the factors that influence this variation in how my runs go.
Cooking Trends in the Pandemic It’s a common refrain that people have formed a new bond with their kitchens during the pandemic. In the early days, as restaurants closed and many of us lost jobs or started working from home, my Twitter feed was abuzz with interest in the kinds of slow kitchen projects that keep you ducking in and out of the kitchen all day. Suddenly there was abundant time at home to simmer a slow-cooking pot of beans or bake sourdough.
Feel free to play around with my code, which is available here. Some days - between an unchecked global pandemic, the breakdown of voting rights, widespread police brutality, and whatever else is adding stress to your life - the world feels pretty chaotic. But even amid this apparent chaos, our world contains a lot of predictable structure. This is because we’re surrounded by natural covariance: taller people also have bigger feet, fitter people have a lower heart rate, and being outdoors also means being exposed to more light (and Vitamin D!
All code for these analyses is available here. Imagine that you’re an epidemiologist who’s curious whether people who fall ill with COVID-19 differ from those who don’t along several dimensions that measure their health and demographics. Or, imagine that you’re a cognitive neuroscientist who’s curious whether a particular brain region responds differently to two types of images - for example, large and small objects. In both cases, you might want to use a classification analysis to understand whether the categories (infected vs.
When researchers measure neural responses using fMRI, they’re often faced with a question: which brain regions should they analyze? There’s no single best answer to this question, but I recently published a paper (with my advisor, Talia Konkle) outlining one approach: selecting the voxels with reliable data. The aim is to restrict your analyses to the parts of the brain that respond consistently over multiple presentations of the same stimuli.
In my last blog post, I described how Wasserstein (a.k.a. Earth-Mover’s) distances could be used to measure the dissimilarity between two neural response patterns. The main benefit of “Brain-Mover’s Distance” is that it takes the topology of the brain into account, measuring not just how similarly the voxels in question respond, but also their proximity in the brain. I also worked through an example to demonstrate how this method could be used to assess the replicability or inter-subject reliability of an fMRI dataset.
When analyzing fMRI data, we often seek to measure similarity between two brain responses. For example, we run reliability analyses to ask, “how similar is this subject’s brain when they see the same image again?” or “how similar is this subject to the rest of the group?” Relatedly, Representational Similarity Analyses (RSA) allow us to ask, “Does this brain region respond the same way to all members of a category - like inanimate objects?
What do we understand about the brain? That’s a surprisingly tricky question. But first, here’s another one: what does it mean to “understand” a biological system? There’s currently a lot of disagreement about the first question, which I believe lies primarily in unspoken differences in how we answer the second. So let’s break it down a bit.
The many faces of understanding In colloquial usage, “understanding” has several meanings, which cluster into two camps: (1) comprehension and (2) explanation.
