Research During the Academic Year: Tips for Time Management & Pursuing your Passions

A schedule board with a plethora of sticky notes containing writings on various obligations.
Whether you’re in a lab or working remotely, fitting in research during the academic year requires above all a willingness to prioritize yourself and good time management. Photo Credit: Jo Szczepanska.

Whether you’re trying to free up your summer to enjoy one of Princeton’s other fully-funded programs, or maybe pave the way for more advanced summer or independent research opportunities, it’s understandable why you might want to get a head start on research during the academic year. But, with jam-packed class schedules, multiple labs, essays to write, and hopefully squeezing in some time for yourself, it can feel impossible to do research on top of life at Princeton. So, how do students do it? Can you really spend 8-10 hours per week on research and still find work-life balance? In short, it depends. The number of classes you’re taking, extracurriculars, and your own unique circumstances all factor into whether research during the academic year is sustainable for your class schedule. For some, research can be a valuable addition to their academic schedules. But, like anything at Princeton, it requires careful planning, time management, and clarifying your own values. Here are three tips for striking balance with research during the academic year.

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My Favorite Introductory Lab at Princeton

Three students stand around a tree in the woods and measure its diameter with a tape measure
Students measure the diameter of a tree much like one of the EEB 211 labs

I had lab from 7:30pm to 10:20pm and it was one of my favorite Princeton memories. I tend to get weird looks when I say that, but it’s true! I took EEB 211 fall of my freshman year, mostly because I thought the name “Life on Earth: Mechanisms of Change in Nature” sounded cool. The course itself is the introductory course to Ecology and Evolutionary Biology, and it is similar to some of what I learned in AP Biology in high school but with much more of a focus on species-wide interactions. Format wise, it is a pretty classic lecture Biology class with only a midterm and final, and while the lectures were super interesting the real fun is the labs.

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The Creation of TigerResearch: An Interview with Vivek Kolli ’24

Photo of Vivek Kolli '24

Vivek Kolli ’24 is Vice Chair of the Julis-Rabinowitz Center for Public Policy and Director of Marketing and Outreach for Scholars of Finance.

A few weeks ago, I interviewed Vivek Kolli ‘24, a junior in the Operations Research and Financial Engineering (ORFE) department. Vivek is one of the three developers for TigerResearch, a comprehensive platform that allows for students to easily navigate through their database of Princeton professors and their research areas. In our interview*, we discuss his vision for the platform, the importance of entrepreneurial ideas in driving the research process, and advice for students who would like to get involved with research at Princeton.

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Missed the First ReMatch Dinners? I Got You Covered

Photo of ReMatch table at the Carl A. Fields Center Multipurpose Room. Mentors and mentees are standing around a tall bistro table covered in an orange tablecloth.
Great conversations were happening at the Computer Science and Quantum Studies table at the October 13th ReMatch Meet and Greet

Princeton gives its farthest walks to its strongest academic weapons. Still, sometimes schlepping all the way to the Carl A. Fields (CAF) Center is just a bit too much. If you felt like this around 6:00pm on Tuesday October 4th or Thursday, October 13th, you just may have missed the first ReMatch dinner. No worries! I am here to fill you in on what you missed and hopefully convince you that the next one is worth the walk. First things first, ReMatch (developed and led by the Office of Undergraduate Research and the Graduate School) is a program that helps match first- and second-year undergraduates interested in research with graduate student and postdoc researcher mentors. Mentor and mentee pairs that develop potentially embark on a summer of research in Princeton fully funded by the university. At the dinners, students can eat catered food, mingle, and chat with researchers at tables. 

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A Budding Field: Finding Opportunities in Psychedelic Research

The logo for the Princeton Science of Psychedelics Club. A large three-dimensional ring with blue radiating ovals.
The logo for Princeton’s Science of Psychedelics Club (PSPC), a student-lead organization seeking to educate students about psychedelics, discuss current trends in psychedelic research, and provide opportunities to other students interested in pursuing psychedelic research. If interested in joining, contact PSPC@princeton.edu or President Camilla Strauss ’23 for more information.

One goal for any budding researcher is to see their work have a tangible public impact. But, with endless hours spent in a lab or hunched over a computer, there are times where research can feel abstract or removed from reality. Neuroscience, in particular, faces this stereotype. True, many (including myself) believe that neuroscience holds the key to understanding our conscience and, by extension, our modern predicament. But the question remains: where can an aspiring neuroscientist find the life-altering research they seek?

Ironically, the answer might just lie in reality-altering substances. From neuroscience to public policy, psychedelics is a budding topic across many different fields of research. While Princeton itself is yet to enter the field, the Princeton Science of Psychedelics Club (PSPC) serves as the hub for all students interested in this emerging field. I sat down with PSPC and senior Neuroscience Major President Camilla Strauss to talk about how students interested in psychedelics research could learn more. 

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No Experience, No Problem! A Guide to Being the Newest in the Lab

Woman in lab coat arranges sample tubes in black ice bucket.
Woman in lab coat arranges sample tubes in black ice bucket.

Having no knowledge about Neuroscience at the time, I did not expect my experience taking Advanced Placement Psychology to amount to more than an interesting elective. However, I discovered my passion for research through Dr. Volpicelli-Daley, who came to our class to give a presentation about her research on Parkinson’s Disease with mouse models. She invited us to apply for a position as a research assistant in her lab, and I ended up getting the job. I worked with the Volpicelli-Daley research team at the University of Alabama at Birmingham during the summer between my junior and senior years of high school and then again over my gap year before coming to Princeton.

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Dear First Time Coders, You Can Do It

As a SPIA major, I was worried about coding for the first time. But, after taking POL345, I realized that I actually love statistics and computer science.

      “I can’t code,” I told my friends when I realized that I had to take a statistics course for my major that required coding. “I don’t understand it,” I told them. I had never coded before and the thought of creating algorithms on a computer sent shivers down my SPIA spine. I loved math in high school, and coding always seemed interesting to me, but rumors about Princeton math courses, as well as computer science courses, had me sprinting away from Fine Hall. But then, I realized I had to take a statistics course for SPIA. I had to face my fear of R, or the programming language that most SPIA statistics courses use for statistical computation. I didn’t think that I could do it, but I did. And, I ended up loving it. I faced my fears, learned how to code, and you can too.

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Visualizing COVID-19 Mutations Using PyMOL, a University Provided Resource

These days, it seems like every day we learn of a new variant of SARS-CoV-2 (the virus that causes COVID-19). However, it’s hard to understand what a variant is and how it changes the virus. In this post, I wanted to introduce PyMOL, a program that students have access to through the University. This program can be used to see what the spike protein and its mutations actually look like.

But first, here’s some background on SARS-CoV-2: COVID-19 is a disease caused by a strain of coronavirus called SARS-CoV-2. This virus gets inside the human cells by using something called a spike protein. This spike protein binds to a receptor on the human cell called the ACE2 receptor, and this allows the virus to infiltrate the cell. The variants of SARS-CoV-2 that we keep hearing about typically have different mutations on the spike protein. In the case of the B.1.1.7 variant, which is a variant that is thought to be 30-50 percent more infectious than other variants in circulation, the mutations are at a location that allow the spike protein to bind better to the ACE2 receptor. If you bind better to the receptor, you’re better at infiltrating the cell. The spike is also the target of the vaccine and our natural immune system.

Now, let’s try and look at where these mutations actually are.

This is an illustration of the SARS-CoV-2 virus published by the CDC. The spikes are in red, labeled with a white arrow.
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A Quick Crash Course in Statistics: Part 2

Most people’s New Years Resolutions, I imagine, are not about improving their knowledge of statistics. But I would argue that a little bit of knowledge about statistics is both useful and interesting. As it turns out, our brains are constantly doing statistics – in reality, our conscious selves are the only ones out of the loop! Learning and using statistics can help with interpreting data, making formal conclusions about data, and understanding the limitations and qualifications of those conclusions.

In my last post, I explained a project in my PSY/NEU 338 course that lent itself well to statistical analysis. I walked through the process of collecting the data, using a Google Spreadsheet for computing statistics, and making sense of what a ‘p-value’ is. In this post, however, I walk through how I went about visualizing these results. Interpretation of data is often not complete before getting a chance to see it. Plus, images are much more conducive than a wall of text when it comes to sharing results with other people.

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A Quick Crash Course in Statistics: Part 1

In PSY/NEU 338, From Animal Learning to Changing People’s Minds, my group recently presented our capstone project for the course: we researched irrationality, trying to understand when humans make irrational decisions, how that is implemented in the brain, and if certain things might actually be incorrectly labeled as ‘irrational’. Our emotions are a leading example: although some call them irrational, in practice, they play a key role in fine-tuning our decision-making and reasoning abilities. When you’re happy, for example, everything might be going more positively than expected. Your mood is thus encouraging you to continue the behaviors that led to those rewards, since that positive trend might continue (for a neuroscientific discussion of this topic, see this paper).

To demonstrate this phenomenon first-hand, we had students in the class play what is known as the Ultimatum Game:

You are the proposer. You have been given $100. You are tasked with splitting your money with a stranger, the responder. If the responder accepts the split that you propose, you both keep the money after the game ends. If the responder does not accept, no one keeps the money.

The question: how much money do you decide to offer the responder?

After reading this, students had five seconds to provide their answer. They were then asked to report their mood. The question we wanted to answer was simple:

Is the amount of money people offered statistically different between those who reported “positive” versus “negative” moods?

In this post, I’ll explain some of the basic statistics I used to formally answer this question, bolding some key terms in the field along the way. In my next post, I’ll walk through the programming aspect for visualizing those statistics. 

In Google Spreadsheet, I calculated the mean dollar amount for the “positive” and “negative” categories, equaling $32.2 and $66 respectively. The “equals” sign indicates a function, and D51:D55 corresponds to the cells containing the data for the “positive” category.
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