Jacqueline Kory-Westlund

A poem to celebrate my year

2018: A year defined by a PhD,
A study, analyses, and a writing spree.
A kid who’s growing; a family, moving.
Always learning, ever improving.

In January, I was glued to a laptop,
Programming robots and testing nonstop.
I recorded dialogue; recruited schools;
Prepped assessments; built software tools.

February is a wild, snowy blur
Of consent forms, paperwork, and red and blue fur.
Kids signed up!
The robot was ready!
All this made me happy, since progress was steady.

As March snow melted, the study began!
I drove to schools and followed my plan.
Eight sessions each, plus pre and post;
The robot was keeping the kids engrossed.

In April, one kid, who wasn’t too shy,
Told me he was “actually part robot, so I can fly!”
(Tega, our robot, it’s worth pointing out,
Just talks, and sits, and looks about.)

By May, I was glad if the robots didn’t break,
But why oh why did I choose this headache?
Long-term studies will be my demise
Why oh why do I do this, you guys?

Oh wait, it’s June, long-term studies are the best!
Look, I have data, totally worth being stressed!
Learning with robots over time—this is nice!
Awesome research, look: data! Worth the price.

In July, let’s mix it up and buy a home,
Way out west where there’s space to roam,
More lakes, more space, and bonus, it’s cheap!
Less traffic, more mountains; more yard upkeep.

In a haze of boxes and packing tape,
The month of August and ggplot graphs take shape.
Let’s leave the humidity and Boston’s heat:
Analyze data; start writing; retreat.

September is data, papers, and writing.
And writing, revising, and then some rewriting.
I find getting three great professors to be
In the same place at the same time isn’t all that easy.

I like watching the colored October leaves from my chair.
They dance and they spin, red and yellow in the air.
Oh wait, I’m still writing. I need a new graph…
Add to this chapter; fix that paragraph….

My baby is two! He’s as tall as a table!
He’s finally stopped trying to eat all our cables!
I’m still writing. Time to start my talk prep.
Defense Day is looming on the doorstep.

Now here’s a day that I’ll remember!
Dissertation defense on the 12th of December.
Crazy year it’s been, that and then some…
But hey: Dr. Jackie, here I come!

—

This article originally appeared on www.media.mit.edu, January 2019

So where do you get good ideas?

Even at MIT, good ideas don't grow on trees.

Instead, I've found that good ideas have two ingredients: preparation and practice.

1. Preparation. The act of acquiring new knowledge and ideas. The foundation on which my good ideas will be built.

2. Practice. Generate lots of ideas. Engage with ideas in new ways. Think about what's next, what could be changed, what can be improved, how things work, what might happen if, implications, extrapolations.

Here's my method.

Preparation

I read outside my field, especially non-fiction. This gives me new information and new perspectives.

For example, I picked up Vera Johnson-Steiner's book Notebooks of the Mind, which was a nice qualitative discussion of creativity. I read Cal Newport's Deep Work, which changed how I approach my work time. Peter Gray's book on self-directed education, Free to Learn, is personally relevant, and discussed a lot of education research about how children learn (including anthropology work about hunter-gatherer tribes!), which influenced how I approach my research on kids, robots, and learning. I've read books on laughter, mutual causality and systems theory, the differences between ancient Chinese and Western medicine, the impact of socioeconomic status and race on language and society, the psychophysiology of stress, and many more.

I read papers in my field. I read the "future work" sections in papers I like. These sections are full of researchers' ideas that didn't quite make it into the current project, ways to extend their work, and ways to improve their work.

I try to have a regular academic reading group. Success has varied. With my lab group, some years we've managed to meet weekly! Some years, we're lucky if meet once a month, if at all. Right now, I'm also in a reading group organized around the broad topic of learning; we've read papers recently on the connections between Piaget and Vygotsky, Bandura's intrinsic motivation theory, and how stress affects learning.

We take turns choosing papers to read, which means I often read papers I may not otherwise have picked up. Some are highly relevant to my work, and some, not so much. One question I always try to ask is "How could I apply the ideas in this paper to my work?" That is, what can I learn from this paper? Having this question in mind helps me ground what I'm reading in what I already know.

Practice

Notebooks: I have one. Several, actually (along with some text files and unsent email drafts).  I jot down ideas regularly: thoughts on whatever I'm reading about, interesting things I notice about the world, how concepts connect back to other things I've learned. I review these notes periodically. I look for patterns. When deliberating dissertation topics, I noticed themes in what I highlighted in my notes, which helped me narrow in on what really interested me. I've developed new research ideas and come up with ways of building on my previous work.

Spend time thinking, processing, summarizing, planning, and synthesizing. For me, this often overlaps with "notebook time", in that I do a lot of this thinking and planning on paper. I find writing time (such as working on a paper) is also synthesizing time. The process of writing coherent paragraphs about a topic means I'm clarifying and summarizing my understanding of the topic at the same time. The important thing, however you do it, is to not only accumulate knowledge but also process what you've learned. I find it important to spend connecting ideas and deepening my understanding of how different pieces of knowledge fit together.

Use class projects as an opportunity to explore random ideas. I've benefited from the MIT Media Lab's project-heavy class structure, since there's ample space to try out new things, no long-term vision or research agenda required. In my final project for an Affective Computing class, I tested a hypothesis about the impact of introducing a social robot in a particular way might have on people's social judgements of the robot. I've also made light-up balls that change color in response to accelerometer data (we called them glorbs, and created life-size paper robot silhouettes to ask questions about the "aliveness" of robots.

Other people in my lab have, perhaps, gone in wackier directions—for example, two students did a project about enhancing creativity during early stages of sleep, which involved getting people to fall asleep wearing an EEG cap, and having a robot wake them up with questions every time they started to get comfortably dreamy.

I talk to people. For example, in my lab group, we used to all walk downstairs to get tea or coffee from the 3rd floor kitchen at least twice a day. We'd troop back up to the lab, steaming mugs in hand, and stand around throwing ideas off the wall for half an hour before getting back to work. We discuss some serious stuff, like the ethics of child-robot interaction, as well as random stuff, like ceiling robots that could unobtrusively steal leftover food from other people's meetings.

I also try to talk to people from outside my lab and outside my field. Hearing from people who see things from a different perspective or who need me to explain things in a different way can be incredibly helpful for gaining new insights and seeing things from a different point of view.

In all these conversations, notebooks, and classes, I try to keep asking, "And then what?" If my hypotheses are supported, what next? If I'm wrong about something, what are the implications? Where are the opportunities? What might happen if?

That's where my good ideas come from. Preparation and practice.

—

This article originally appeared on the MIT Graduate Student Blog, June 2018

Exploring how the relational features of robots impact children's engagement and learning

One challenge I've faced in my research is assessment. That's because some of the stuff I'd like to measure is hard to measure—namely, kids' relationships with robots.

During one study, the Tega robot asked kids to take a photo with it so it could remember them. We gave each kid a copy of their photo at the end of the study as a keepsake.

I study kids, learning, and how we can use social robots to help kids learn. The social robots I've worked with are fluffy, animated characters that are more akin to Disney sidekicks than to vacuum cleaners—Tega, and its predecessor, DragonBot. Both robots use Android phones to display an animated face; they squash and stretch as they move; they can playback sounds and respond to a variety of sensors.

In my work so far, I've found evidence that the social behaviors of the robot—such as its nonverbal behavior (e.g., gaze and posture), social contingency (e.g., performing the right social behaviors at the right times), and expressivity (such as using a very expressive voice versus a flat/boring one)—significantly impact how much kids learn, how engaged they are in the learning activities, and how credible they think the robot is.

I've also seen kids treat the robot as something kind of like a friend. As I've talked about before, kids treat the robot as something in between a pet, a tutor, and a technology. They show many social behaviors with robots—hugging, talking, tickling, giving presents, sharing stories, inviting to picnics—and they also show understanding that the robot can turn off and needs battery power to turn back on. In some of our studies, we've asked kids questions about the properties of the robot: Can it think? Can it break? Does it feel tickles? Kids' answers show that they understand that robot is a technological, human-made entity, but also that it shares properties with animate agents.

In many of our studies, we've deliberately tried to situate the robot as a peer. After all, one key way that children learn is through observing, cooperating with, and being in conflict with their peers. Putting the cute, fluffy robot in a peer-like role seemed natural. And over the past six years, I've seen kids mirror robots' behaviors and language use, learning from them the same way they learn from peers.

I began to wonder about the impact of the relational features of the robot on children's engagement and learning: that is, the stuff about the robot that influences children's relationships with the robot. These relational features include the social behaviors we have been investigating, as well as others: mirroring, entrainment, personalization, change over time in response to the interaction, references to a shared narrative, and more. Some teachers I've talked to have said that it's their relationship with their students that really matters in helping kids learn—what if the same was true with robots?

My hunch—one I'm exploring in my dissertation right now via a 12-week study at Boston-area schools—is that yes: kids' relationships with the robot do matter for learning.

But how do you measure that?

I dug into the literature. As it turns out, psychologists have observed and interviewed children, their parents, and their teachers about kids' peer relationships and friendship quality. There are also scales and questionnaires for assessing adults' relationships, personal space, empathy, and closeness to others.

I ran into two main problems. First, all of the work with kids involved assumptions about peer interactions that didn't hold with the robot. For example, several observation-based methodologies assumed that kids would be freely associating with other kids in a classroom. Frequency of contact and exclusivity were two variables they coded for (higher frequency and more exclusive contact meant the kids were more likely to be friends). Nope: Due to the setup of our experimental studies, kids only had the option of doing a fairly structured activity with the robot once a week, at specific times of the day.

The next problem was that all the work with adults assumed that the experimental subjects would be able to read. As you might imagine, five-year-olds aren't prime candidates for filling out written questionnaires full of "how do you feel about X, Y, or Z on a 1-5 scale." These kids are still working on language comprehension and self-reflection skills.

I found a lot of inspiration, though, including several gems that I thought could be adapted to work with my target age group of 4–6 year-olds. I ended up with an assortment of assessments that tap into a variety of methodologies: questions, interviews, activities, and observations.

We showed pictures of the robot to help kids choose an initial answer when asking some interview questions. These pictures were shown for the question, 'Let's pretend the robot didn't have any friends. Would the robot not mind or would the robot feel sad?'

We ask kids questions about how they think robots feel, trying to understand their perceptions of the robot as a social, relational agent. For example, one question was, "Does the robot really like you, or is the robot just pretending?" Another was, "Let's pretend the robot didn't have any friends. Would the robot not mind or would the robot feel sad?" For each question, we also ask kids to explain their answer, and whether they would feel the same way. This can reveal a lot about what criteria they use to determine whether the robot has social, relational qualities, such as having feelings, actions the robot takes, consequences of actions, or moral rules. For example, one boy thought the robot really liked him "because I'm nice" (i.e., because of the child's attributes), while another girl said the robot liked her "because I told her a story" (i.e., because of actions the child took).

The set of circles used in our adapted Inclusion of Other in the Self task.

Some of these questions used pictorial response options, such as our adaptation of the Inclusion of Other in the Self scale. In this scale, kids are shown seven pairs of increasingly overlapping circles, and asked to point to the pair of circles that best shows their relationship with someone. We ask not only about the robot, but also about kids' parents, pets, best friends, and a bad guy in the movies. This lets us see how kids rate the robot in relation to other characters in their lives.

This girl is doing the Robot Sorting Task, in which she decides how much like a person each entity is and places each picture in an appropriate place along the line.

Another activity we created asks kids to sort a set of pictures of various entities along a line—entities such as a frog, a cat, a baby, a robot from a movie (like Baymax, WALL-e, or R2D2), a mechanical robot arm, Tega, and a computer. The line is anchored on one end with a picture of a human adult, and on the other with a picture of a table. We want to see not only where kids put Tega in relation to the other entities, but also what kids say as they sort them. Their explanations of why they place each entity where they do can reveal what qualities they consider important for being like a person: The ability to move? Talk? Think? Feel?

In the behavioral assessments, the robot or experimenter does something, and we observe what kids do in response. For example, when kids played with the robot, we had the robot disclose personal information, such as skills it was good or bad at, or how it felt about its appearance: "Did you know, I think I'm good at telling stories because I try hard to tell nice stories. I also think my blue fluffy hair is cool." Then the robot prompted for information disclosure in return. Because people tend to disclosure more information, and more personal or sensitive information, to people to whom they feel closer, we listened to see whether kids disclosed anything to the robot: "I'm good at reading," "I can ride a bike," "My teacher says I'm bad at listening."

Tega sports several stickers given to it by one child.

Another activity looked at conflict and kids' tendency to share (like they might with another child). The experimenter holds out a handful of stickers and tells the child and robot that they can each have one. The child is allowed to pick a sticker first. The robot says, "Hey! I want that sticker!" We observe to see if the child says anything or spontaneously offers up their sticker to the robot. (Don't worry: If the child does give the robot the sticker, the experimenter fishes a duplicate sticker out of her pocket for the child.)

Using this variety of assessments—rather than using only questions or only observations—can give us more insight into how kids think and feel. We can see if what kids say aligns with what kids do. We can get at the same concepts and questions from multiple angles, which may give us a more accurate picture of kids' relationships and conceptualizations.

Through the process of searching for assessments I need, discovering nothing quite right existed, and creating new ways of capturing kids' behaviors, feelings, and thoughts, the importance of assessment really hit home. Measurement and assessment is one of the most important things I do in research. I could ask any number of questions, hypothesize any number of outcomes, but without performing an experiment and actually measuring something relevant to my questions, I would get no answers.

We've just published a conference paper on our first pilot study validating four of these assessments. The assessments were able to capture differences in children's relationships with a social robot, as expected, as well as how their relationships change over time. If you study relationships with young kids (or simply want to learn more), check it out!

—

This article originally appeared on the MIT Media Lab website, May 2018

Acknowledgments

The research I talk about in this post was only possible with help from multiple collaborators, most notably Cynthia Breazeal, Hae Won Park, Randi Williams, and Paul Harris.

This research was supported by a MIT Media Lab Learning Innovation Fellowship and by the National Science Foundation. Any opinions, findings and conclusions, or recommendations expressed in this article are those of the authors and do not represent the views of the NSF.

Starting a family in grad school

I wasn't married when I got to MIT, but I had a boyfriend named Randy who moved up to Boston with me. Two years in, we discover that it is, in fact, possible to simultaneously plan a wedding and write a master's thesis! Two years after that? I'm sitting uncomfortably in a floppy hospital gown at Mt. Auburn Hospital using my husband's phone to forward the reviews I'd just received on a recent journal paper submission, hoping labor doesn't kick in full force before I finish canceling all my meetings and telling people that I'll be taking maternity leave a month sooner than expected.

Baby Elian is born later that night, tiny and perfect. The next three weeks are spent writing my PhD proposal from the waiting room while we wait for Elian to grow big enough to leave the hospital's nursery.

Our decision to have a baby during grad school did not come lightly. For a lot of students, grad school falls smack in the middle of prime mate-finding and baby-making years. But my husband and I knew we wanted kids. We knew fertility decreases over time, and didn't want to wait too long. In 2016, I was done with classes, on to the purely research part of the PhD program. My schedule was as flexible as it would ever be. Plus, I work with computers and robotts—no cell cultures to keep alive, no chemicals I'd be concerned about while pregnant. Randy did engineering contract work (some for a professor at MIT) and was working on a small startup.

Was it the perfect time? As a fellow grad mom told me once, there's never a perfect time. Have babies when you're ready. That's it.

Okay, we agreed, now's the time. It'd be great, right? We'd have this adorable baby, then Randy would stay home most of the time and play with the baby while I finished up school. He'd even have time in the evenings and on weekends to continue his work.

Naiveté, hello.

Since my pregnancy was relatively easy (I got lucky—even my officemate's pickled cabbage and fermented fish didn't turn my stomach), we were optimistic that everything else would go well, too. The preterm birth was a surprise, sure, but maybe that was a fluke in our perfectly planned family adventure. Then it came time for me to go back to the lab full time. I'd read about attachment theory in psychology papers—i.e., the idea that babies form deep emotional bonds to their caregivers, in particular, their mothers. Cool theory, interesting implications about social relationships based on the kind of bond babies formed, and all that. It wasn't until the end of my maternity leave, when I handed our wailing three-month-old boy to my husband before walking out the door that I internalized it: Elian wasn't just sad that I was going away. He needed me. I mean, looking at it from an evolutionary perspective, it made perfect sense. There I was, his primary source of food, shelter, and comfort, walking in the opposite direction. He had no idea where I was going or whether I'd be back. If I were him, I'd wail, too.

Us: 0. Developmental psychology: 1.

Finding a balance

This was going to be more difficult than we'd thought. For various financial and personal reasons, we had already decided not to put the baby in daycare. Other people's stories ("when he started daycare, he cried for a month, but then he got used to it") weren't our cup of tea. But our plans of me spending my days in the lab while the baby was back at home? Not so much. In addition to Elian's distress at my absence, he generally refused pumped breast milk in favor of crying, hungry and sad.

So, we made new plans. These plans involved bringing Elian to the lab a lot (pretty easy at first: he'd happily wiggle on my desk for hours, entertained by his toes). Coincidentally, that's when I began to feel pressure to prove that what we're doing works. That I can do it. That I can be a woman, who has a baby, who's getting a PhD at MIT, who's healthy and happy and "having it all". "Having it all." No matter what I pick, kids or work or whatever, I'm making a choice about what's important. We all have limited time. What "all" do I want? What do I choose to do with my time? And am I happy with that choice?

Now, Elian's grown up wearing a Media Arts & Sciences onesie and a Personal Robots Group t-shirt. I'm fortunate that I can do this—I have a super supportive lab group and I know this definitely wouldn't work for everyone. Not only does our group do a lot of research with young kids, but my advisor has three kids of her own. My officemate has a six-year-old who I've watched grow up. Several other students have gotten married or had kids during their time here. As a bonus, the Media Lab has a pod for nursing mothers on the fifth floor, and a couple bathrooms even have changing tables. (That said, it's so much faster to just set the baby on the floor, whip off the old diaper, on with the new. If he tries to crawl away mid-change, as is his wont these days, he can only get so far as under my desk.)

Randy comes to campus more now, too. It's a common sight to see him from the Media Lab's glass-walled conference rooms, pacing the hallway with a sleeping baby in a carry pack while he answers emails on his tablet. I feed the baby between meetings, play for a while when Randy needs to run over to the Green Building for a contractor meeting, and it works out okay. We keep Elian from licking the robots and Elian makes friends from around the world, all of whom are way taller than he is. The best part? He's almost through the developmental stage in which he bursts into tears when he sees them!

I also have the luxury of working from home a lot. That's helped by two things: first, right now, I'm either writing code or writing papers— i.e., laptop? check. Good to go. Second, my lab has undergone construction multiple times the past year, so no one else wants to work there either with all the hammering and paint fumes.

Stronger, faster, better?

But it's not all sunshine, wobbly first steps, and happy baby coos. I think it's harder to be a parent in grad school as a woman. I know several guys who have kids; they can still manage a whole day—or three—of working non-stop, sleeping on a lab couch, all-night hacking sessions, attending conferences in Europe for a week while the baby stays home. Me? Sometimes, if I'm out of sight for five minutes, Elian loses it. Sometimes, we make it three hours. Some nights, waking up to breastfeed a sad, grumpy, teething baby, it's like I'm also pulling all-nighters, but without the getting work done part.

Times when I'm feeling overwhelmed, I remember a fictional girl named Keladry. The protagonist of Tamora Pierce's Protector of the Small quartet, she was the first girl in the kingdom to openly try to become a knight—traditionally a man's profession (see the parallel to academia?). She followed the footsteps of another girl, Alanna, who opened the ranks by pretending to be a boy throughout her training, revealing her identity only when she was knighted. I remember Keladry because of the discipline and perseverance she embodied.

I remember her feeling that she had to be stronger, faster, and better than all the boys, because she wasn't just representing herself, she was representing all girls. Sometimes, I feel the same: That as a grad mom, I'm representing all grad moms. I have to be a role model. I have to stick it out, show that not only do I measure up, but that I can excel, despite being a mother. Because of being a mother. I have to show that it's a point in our favor, not a mark against us.

I remember Keladry's discipline: getting up early to train extra hard, working longer to make sure she exceeded the standard. I remember her standing tall in the face of bullies, trying to stay strong when others told her she wasn't good enough and wouldn't make it.

So I get up earlier, writing paper drafts in the dawn light with a sleeping baby nestled beside me. I debug code when he naps (even at 14 months, he still naps twice a day, lucky me). I train UROPs, run experimental studies, analyze data, and publish papers. I push on. I don't have to face down bullies like Keladry, and I'm fortunate to have a lot of support at MIT. But sometimes, it's still a struggle.

When I was talking through my ideas for this blog with other writers, one person said, "I'm not sure how you do it." I didn't have a good answer then, but here's what I should have said: I do it with the help of a super supportive husband, a strong commitment to the life choices I've made, and a large supply of earl grey tea.

—

This article originally appeared on the MIT Graduate Student Blog, February 2018

Undervaluing hard work in grad school

Wow, you're at MIT? You must be a genius!"

Um. Not sure how to answer that. Look down at my shoes. Nervous laugh.

"Uh, thanks?"

The random passerby who saw my MIT shirt and just had to comment on my presumed brilliance seems satisfied with my response. Perhaps the "awkward genius" trope played in my favor?

See, I'm no genius. And I'll let you in on a little secret: Most of us at MIT aren't inherent geniuses, gliding by on the strength of a vast, extraordinary intellect.

We're not born super smart. Instead, we do things the old-fashioned way: with copious amounts of caffeine, liberally applied elbow grease, and emphatic grunts of effort that would make a Cro-Magnon proud.

The reality on campus is not exactly the effortless, glamorous image the media likes to paint. You know, headlines like:

• MIT physicists create unbelievable new space dimension!
• MIT scientists discover that chocolate and coffee cure cancer!
• MIT engineers fly to the moon in a ship they built out of carbon nanotubes and crystal lattices!
• Look, it's MIT! Land of the Brilliant, the Inventive, the Brave!

The reality is more like the Land of the Confused, the Obstinate, and the "Let's try it again and see if maybe it works this time so we can get at least one significant result for a paper!"

Yes, I'm exaggerating a little. I have, after all, met a ton of amazing, brilliant people here -- but they're amazing and brilliant because of their effort, curiosity, tenacity, and enthusiasm. Not their inherent genius. None of them are little cartoon figures with cartoon lightbulbs flashing around them like strobe lights as they are struck with amazing idea after amazing idea.

They're people like my labmate, who routinely shows up late to group meetings because he accidentally stays up all night trying to implement some cool machine learning algorithm he found in an obscure-but-possibly-relevant paper (eventually, I'm sure, the effort will pay off!).

They're people like my professors, who set aside entire days each week just for meeting with their students, to hash out ideas and go over paper drafts.

They're people like me, who spend 260% more effort than strictly necessary on making a child-robot interaction flow right, even though the study would probably be fine with subpar dialogue (for the curious: I work on fluffy robots that help kids learn stuff).

The reality is long hours in the library—reading papers, trying to understand what other people have already done and how it relates to my research—and long hours in the lab—trying to put that understanding to use (often learning in the process that I didn't really understand something after all and should probably do more reading).

I think MIT's reputation as being full of inherent geniuses gives many of us the short stick and fails to recognize the sheer amount of hard work and failure that goes into nearly every discovery and invention that's made. Sure, sometimes people get lucky.

There are certainly a few things that someone got right the first time, but let's be honest. The last time my Python code ran on the first try, I went looking for bugs anyway because that never happens (and I was right; hours later, there were still bugs aplenty). Likewise, the last time I got a really interesting experimental result, it was after months of thinking and re-planning, months of programming and testing on the robot, and months of wrangling participants in the lab. All the amazing insights that show up in the final paper draft only come after a lot of analysis, realizing the analysis missed something, rewriting all the R code to do the analysis right, and re-analyzing.

Think of it this way, if a PhD student has signed on to work in a lab for the next indefinite-but-hopefully-only-five-or-maybe-seven years (with a small stipend if they're lucky) and have no idea what magical, impactful dissertation topic will be their ticket out, they're probably already one of those people who likes a challenge. Maybe perseverance is their middle name.

And that's what I think being at MIT is actually about: Learning to fail, struggling to succeed, and knowing the value in the struggle.

Of the real "geniuses" I know, they're people who just want to know what's going on and are okay with doing a lot of hard work to find out.

They're people who keep asking "and then what? and then what?" after they learn something, and spend months or years chasing down answers. For example: "So I find that 5-year-olds mirror the robot's phrases when playing storytelling games with it, and learn more when they do—Why? What does this say about rapport and peer learning? What modulates this effect? What are the implications for educational technology more generally?"

They're people who dive wholeheartedly into each rabbit hole to see how far it goes and what useful tidbits of scientific knowledge can be gleaned along the way.

They're people who keep probing. Sometimes, that leads to dramatic headlines. More often, it doesn't.

—

This article originally appeared on the MIT Graduate Student Blog, February 2018