“Artificial neural networks for psychology, neuroscience, and climate change”
That’s the tagline on the front page of this website. You may notice that one of those things is not like the others. In this inaugural lab blog post, I’m going to explain why.
At some point during the pandemic, I got really into reading about climate change. It may have been because I had my first kid, though that didn’t feel like the conscious reason. It may have been because Biden got elected and a lot of my other fears washed a way a bit to reveal this one big one. Or maybe it was just a desire to distract myself from one global disaster by focusing on another. Whatever the reason, while I’d had climate change on the backburner of my mind for many years (simmering away as the planet was…), at some point in early 2021, figuring out just how bad things were going to be became a priority.
And it was devastating and paralyzing.
Once I had really internalized it, I couldn’t stop thinking about the impact of my every move: the temperature I set my home to, every item of food I puchased, any time spent in a car. Simply existing—especially with a small child and all the cheap and disposable items that seem to inevitably come along with them—felt untenable. The Onion headline “Amount Of Water Man Just Used To Wash Dish To Be Prize Of Hand-To-Hand Combat Match In 2065” ran through my head more as prophecy than satire. My husband and I listened to a This American Life episode about a father who ruined his family by forcing them into further and further extremes of climate activism. We talked about how his actions were crazy, but also in a way, he was right, wasn’t he? I didn’t want to do that, but I had to do something.
So I focused on the personal side of things. I looked for the most impactful places to donate. I did a lot research into sustainable clothes and where to buy green household products. After being pescetarian for many years I decided to start eating chicken again but stop eating cheese because the CO2-to-protein conversion is better for poultry. This new awareness about better ways to live my everyday life offered some relief.
And then I just sort of realized…I can work on this. In addition to common lifestyle changes, I could use real skills of mine to do something. I didn’t have to just be a bystander, patronizing and supporting the businesses and organizations trying to make a change. I could put my energy into it and contribute directly myself—and wouldn’t that go a lot further in helping alleviate the guilt and concern, and make me feel like I can actually go on?
So I looked into what someone with my skillset could do. And I also looked into getting new skillsets. But switching topic area and skills at once is hard, so applying what I already knew a bit about—machine learning—to climate seemed the most reasonable way in. I joined a bunch of online communities (which I will collect in an upcoming blog post on working on climate), I took online courses, listened to podcasts, and found some chances to start doing climate data science.
Immersing yourself in an entirely new field—or fields really—as a 30-something year old is weird. You can feel like you don’t even know where to start. But it’s also totally fun. Especially with something like climate change work, which touches absolutely every aspect of everything about your life. I started to think about materials — what is everything around me made of and why? And how could it be done differently to make it all more doable for the longer term. I started to pay a lot of attention to shipping too—where were things coming from and how did they get here? On a jetski tour to the Statue of Liberty, I got distracted by the barges coming over the horizon. And weather! Clouds had so much more meaning after I learned about global wind and pressure patterns. Same for agriculture, energy, ecology, etc. The sense of empowerment that came from knowing more about how the world around me works was an unexpected benefit.
And it’s true that action is the antidote to depression. The community of people working towards climate mitigation and adaptation are so inspiring. They really make you think that change can happen and that you can—in fact, you must—be a part of it, in small ways and big. And the future of clean energy powered cities and sustainable agriculture and so on has so many other beneficial side effects that its crazy that we aren’t there already. A climate change resilient future is not one of deprivation and de-growth, at least not in the ways that matter.
So I knew I had to work on climate. But it’s odd, as someone 4 years out of a 6 year PhD, to try to work in a totally new field. I felt crazy knowing that there are people who are as expert in climate topics as I am in my niche neuro-modeling field, and yet I’m trying to contribute something at all to their work. And what about all my hard-earned neuroscience knowledge? Is it worth it—to me and to the field that trained me—to throw that away to become a novice in a new (though more important) area?
Then I learned about this position at NYU. I am currently a joint assistant professor of Psychology and Data Science. Granted, my computational work on the brain is the main reason for a Data Science title, I saw in that affiliation a chance to grow what had been a side hobby during my postdoc into a proper component of my research. So I am now advertising that, in addition to my work on neuroscience and psychology, my lab does computer vision for climate change. And while that is composed of just one project at the moment, I am excited to grow and work with more partners and students to help solve some of the ML problems people have while trying to save the world.
In some ideal setting, I could just keep working on the neuroscience and psychology and AI questions I find most interesting, without a care for the planet. But the world is far from ideal. There is a Good Place episode wherein it is revealed just how negatively impactful the simple act of buying a tomato can be on the world. I may want to just do interesting things everyday but my attempts to do that are contributing to the destruction of the planet and humanity. Knowing this makes every bit of time I spend not working on climate a pure indulgence.
Having this attitude makes the normal “stressors” of academic life feel less stressful. Applying to grants, writing papers…getting my grants and papers rejected… It’s all fine! I’m on borrowed time here. The fact that I get to spend any time speculating about the brain and mind is a gift. And I’m paying it forward by putting some effort into making a more sustainable planet, so that us humans can keep speculating on the brain and mind for years to come.
It also turns out that I find the climate work pretty academically stimulating. I just bought a textbook on remote sensing and I am itching to dive in. For whatever reason, I love thinking about vision, be it human or computer. Maybe in a different random seed of the universe I’d have been doing this work from the start.
To be clear, I don’t know if any of this will work out. I don’t know if academia is built for this kind of split interest. I don’t even really know how I’ll run my lab yet, logistically, with such different projects going on. But as climate futurist and writer Alex Steffen so beautifully informs us, discontinuity is the job. The way things were done in a pre-climate-change-aware era are no indication of how they will be done now. The change is coming, one way or another. And this is one small change I’m making.
I aimed to make my lab logo gently reflect these mixed priorities. I played around with the text-to-image generator Craiyon and ended up with a prompt along the lines of “brain with a sun in it logo”. One of the outputs was this pretty nice blue and yellow brain that I covered in purple to make it more NYU-y. The more I look at, the more I appreciate how much it represents: the sun and the blue indicate the climate, its placement near the occipital lobe highlights the visual system, and the sun’s reach across the brain captures the concept of attention—another favorite topic of mine. If a simple AI-generated lab logo can make it all work together, then hopefully I can too.