Writing this in September 2017 after the new first-years have arrived on campus, I realize it's now been four years since I started the PhD. Back in 2013, I had just finished a year in Germany on a Fulbright grant, studying social and antipredator behavior in birds at the Max Planck Institute for Ornithology. The work I had helped with there was on its way to being published in Animal Behaviour, as had my undergraduate senior thesis work. I had just received an NSF-GRFP grant – a great vote of confidence from the federal government – and had spent the last weeks of summer traveling and enjoying the Behaviour conference in Newcastle, UK.
I remember feeling that starting the PhD at Princeton was a sign of finally "making it" – years of working hard in high school, college, and Germany to have the opportunity of pursuing a PhD here. I didn't quite think I could relax now that I was here, but I figured that the pace of success I was used to would continue or even accelerate. I definitely didn't expect to have to work much harder for a much slower (but more rewarding) amount of scientific progress.
What would take years for me to learn was how to do science independently. All of the accomplishments I listed above were the products of hard work, sure, but that hard work had been nudged in the correct direction by the people around me. For so many of the little steps along the way, I'd followed the directions of professors, senior grad students, and scholarship advisors, and my efforts had been quickly rewarded. What would be so much more challenging in the years to follow after I woke up that first humid morning in the Graduate College, eager to launch myself at whatever problem was handed me, would be learning to identify research problems myself and how to solve them.
The context for my PhD experience is sufficiently different from what most people go through that I think it's worth explaining every time I write one of these reflections pieces (click here for links to years 1-3). Halfway through my second year, my advisor left Princeton to become the director of a Max Planck Institute for Ornithology in Konstanz, Germany. No event has shaped my PhD as much as that did; when my advisor left, I gained a huge amount of freedom to pursue whatever experiments I wanted, however I wanted, with no requirements in how I structured my days. Our lab had excellent funds, and the group dynamic was little different at the start because the 15 grad students and post docs (yes, that many!) were still working, eating lunch together, and debating ideas like normal.
However, as the huge workload of starting a new department from scratch kicked up in Germany, and as the lab continued to shrink in Princeton, I was left having to figure out a lot of details of the PhD on my own. At the start of my fourth year, there were six grad students regularly working in the lab; at the end of the year, it was down to myself and another student.
Experimentally, a huge benefit of the lab thinning out was that I had world-class facilities pretty much all to myself. I was able to conduct time-intensive experiments (think trials every day, for several hours, for 4-5 weeks at a time) that would have been difficult to navigate if I was balancing my work with others'. Because I was pretty much the only one doing fish research in our lab (and the entire EEB department), I was able to convert 40% of our housing space to larger tanks for a new species. From a data collection standpoint, I was in about as good a position as it gets.
Logistically, however, being the only person doing experiments meant all the burden of picking up fish from hatcheries, acclimating them to lab, feeding and taking care of them, and sorting out logistics with animal husbandry and IACUC fell to me. For the many months of experiments, my work-life balance veered unsustainably towards working constantly because I was coming into lab every day, multiple times a day, to take care of the fish. (I'll definitely give a nod, however, to labmates Joe Bak-Coleman and Colin Twomey for taking time away from their own work to help with the fish when things occasionally got too hectic - much appreciated!)
In this way, there were two major periods of data collection in my fourth year: pilot trials in August and September 2016 for a predator-prey experiment, and then the actual experiment from April - August 2017. To fund these experiments, I applied for and was rewarded an NSF-DDIG for the predator-prey experiment. I'm immensely grateful for the funding and saddened that NSF discontinued the program. Writing the proposal (twice) was excellent training in thinking through all the steps of science, from the concept, a plan for carrying the idea out and budget for logistics, and scientific relevance and broader impacts. The funding allowed me to purchase additional housing for the predator-prey experiment and hire undergraduate assistance for dozens of hours of data processing. In my fifth year, the DDIG will fund my travel to conferences to share the results with scientists.
|A snapshot of a trial from one of the predator-prey experiments I've been working on. Data collection is complete and I'm now at the processing and analysis stages.|
For the other main project in my thesis - investigating how predation risk changes the rules of alarm propagation in fish schools - I finished data processing and began the long process of data analysis and writing. I originally thought of this project in January 2014, so I'm very excited to be in the final stages of preparation before the work can be shared. I covered most of this work in my fourth-year talk to Princeton EEB in April, which was a rush and gave me great feedback.
Outside of the thesis, I continued my outreach involvement with Princeton Open Labs and Highwire Earth. I wrote and delivered a few lectures in statistics, modeling, and programming for the undergraduates helping me with data processing, which was a lot of fun. In this blog, I finished the "Introduction to R" series with a post on the apply functions and started the "Random R" series with a post comparing the computational efficiency of for loops versus apply. I served another year as a voting member of Princeton's IACUC, reviewing protocols at monthly meetings and inspecting lab facilities biannually. Finally, I reviewed articles for the Journal of Animal Ecology, Animal Behaviour, and New Journal of Physics.
Lessons from 4th year
Learn what boosts your productivity... and what hinders it
My motivation is a resource I've had to learn to carefully manage. Older grad students talk about "getting out of here," which I didn't understand when I started but now understand better. While I'm not itching to leave just yet, I see how working on projects with such long time scales easily disassociates the effort you put in with any measurable output. What does it matter that you come into lab today? Why should you read that textbook? There are no deadlines.
The PhD really requires you to step up your psychological game. I've had to learn about myself at a much deeper level than I required before. I can't code late at night or I have trouble sleeping, and I need a lot of sleep to stay focused. No matter how much I want to stay at the office past 8 or 9pm, I have to cut it short because I'm just borrowing from tomorrow's productivity. Take a break, run, cook, do literally anything else. I've found that leaving lab earlier than I'm used to, or cutting back on work on the weekends, can actually increase my productivity for the week because I return to my projects refreshed and excited.
Something I find essential for staying inspired is a sense of a progress towards my goals. What time of day works for you? This "Working Life" piece in Science really made me reevaluate the timing of when I do tasks, and I now try to write and read in the morning. When I write a few paragraphs in a manuscript - this seemingly impossible task of summarizing years of work into a few pages and figures - I go through the rest of the day knowing I've already accomplished something, even if it's small.
Just get started on that thing you've been putting off, seriously
All of the challenges along the way to becoming a full-fledged scientist can sometimes feel insurmountable. How am I supposed to start and finish this side project when my main thesis work takes so much time? I told myself I would learn Python but the last time I visited my CodeAcademy course was 2015! My colleague just published a review article on their topic and citations are rolling in - I should do that, but I just don't have the time...
It's hard to fit everything in. But I've found that sitting down and devoting my full attention to a problem, really focusing on it and writing out and analyzing every single step it would take to address it, usually yields a path through the confusing muck that's easier than I would have expected. Half the stress came from not knowing what to do. Publishing that side project or learning a new programming language will undoubtedly still take plenty of time and effort. But it's worth throwing yourself at it - even for just a few hours - and you might make more progress than you'd think.
All the best,