Glass brain plots from the data analysis of the project I’m working on with my mentor, who spends a couple hours every week going through the fundamentals of coding in neuroscience with me. When I started working with him, I didn’t even know I could make plots like these. Our weekly meetings paid off.
When I first came to Princeton, already interested in neuroscience research, I kept hearing about all the incredible opportunities available to undergraduates. Professors conducting groundbreaking neuroscience studies, cutting-edge labs filled with brilliant minds—it all sounded amazing. But as a first-year student, I had no idea how to actually get involved. Everyone seemed to know what they were doing, while I was stuck wondering: Where do I even start? Will a professor really take time to mentor someone like me? If I cold-email them, will they even read it?
The Majorana 1 Chip.Photo by John Brecher for Microsoft.
In light of recent discussions in the scientific and engineering community, I wanted to take a closer look at Microsoft’s latest announcement in quantum computing. As someone deeply interested in the intersection of research and innovation, I was curious about what this means for the field. Is this truly a turning point in quantum computing, or is there still more work to be done? As part of Now & Next, a new series dedicated to exploring current events, groundbreaking research, and forward-looking trends in engineering, this post delves into Microsoft’s research, the promise of topological qubits, and how the research community is responding. This could be the dawning age for quantum computing, or another step in a long journey. Let’s dive into what’s going on now and what’s coming next with Microsoft’s quantum computing announcement.
A figure of national research stations in Antarctica, which I recently created for my research using the Python library Cartopy.
Ever since I was a child, I’ve always loved maps—I was a major geography nerd growing up. Jumping forward to today, my like-minded roommates are just as obsessed as I am: the walls of our dorm are literally covered floor to ceiling with maps. These include (but are not limited to) a glaciological map of Antarctica, public transport maps of numerous cities (Toulouse, Christchurch, and New York are just some examples), and a road map of my home state of Washington!
Maps aren’t just a fun hobby: They’re also enormously important in numerous research fields (in addition, of course, to just being plain useful). Whether your research field of interest is history or meteorology or epidemiology, there’s a good chance that you’ll be reading—and making!—some maps. In my own field of glaciology, maps are of paramount importance, whether it’s a map of glacier melt contribution from southeast Alaska or a map of Antarctic ice core sites. I’ve written this guide to provide some helpful resources and tools for making maps for your research, so hopefully it will serve as a good starting point! I should note that this isn’t a tutorial, but plenty of great tutorials should exist on the Internet for all of these tools.
Organic Chemistry Lab Procedure in Frick, taken by Haya Elamir
For STEM majors, lab components of classes can be cumbersome. They can add stress to the classroom experience–not to mention the long hours. Unlike research in a lab as part of a thesis or independent work, these labs may not allow for self-direction, and can feel very methodical. Sure, they apply what we are taught in class, but for me personally, the rates at which lab and lecture move can be quite different, and I do not fee the benefits of the lab experience until later on in the semester when it finally clicks for me.
Blackboard featuring some neuroscience concepts from a neuro class taken at Princeton
We all have to do it: read research papers. They can be jargon-y, long, confusing, and all in all an upsetting experience, but there’s no way around it.
First of all, let’s start by approaching this with a more positive mindset. Reading research papers can give us access to a bucketload of information that no other resource can provide. It is the most updated source on your favorite scientific topics, a Vogue magazine for the scientific world if you will. As such, reading them can be fun–but only if you know how. Now that we are a bit more optimistic about reading them, we can start with the first few steps.
Anna Calveri ’26 is a junior in the Computer Science department. On campus, she is a member of Princeton University Robotics Club, Sympoh Urban Arts Crew, and Colonial Club.
The senior thesis is a hallmark of the Princeton experience, giving students the opportunity to conduct original research under the mentorship of a faculty adviser. Every senior is required to write a thesis, with the exception of Computer Science majors in the Bachelor of Science in Engineering (B.S.E.) degree program. Instead, these students are required to undertake a substantial independent project, called independent work (IW), which can take the form of a traditional one-on-one project with an adviser, an IW seminar where a small group of students independently conduct projects tied to the seminar’s main theme, or an optional senior thesis.
In 2022, I interviewed Shannon Heh ’23 about her experience in an IW seminar, where she highlighted the structure and guidance the professor and course seminar. This year, I wanted to explore the perspective of a B.S.E. Computer Science student who pursued a different option: the one-on-one IW project.
Anna Calveri ’26 stood out as the perfect person to speak with, not just because of her exciting research at the Princeton Vision & Learning Lab led by Professor Jia Deng, but also because she began her project during the summer as a ReMatch+ intern and built on it during the fall semester. While many students only work on their IW within a single semester, Anna’s approach of extending her research across both the summer and fall gave her the chance to deepen her research and hit the ground running with impressive progress.
For Princeton students, it’s not premature to start thinking about summer. If anything, this post may be a little behind for some of those proactive students. Rest assured though, you are not behind if you have not started the search for summer internships (even though many students will say they’ve already applied). Opportunities are aplenty, and no, you are not behind if you didn’t start applying for research internships back in the womb.
For most STEM majors at Princeton, one of the requirements is a course known informally as Core Lab. This class aims to equip students with laboratory skills required to succeed as a scientist in the field. It is usually composed of two 3-hour labs and one lecture per week. As a neuroscience major, I am currently taking NEU 350: Laboratory in Principles of Neuroscience, a class designed to introduce students to modern methods of analyzing neural activity—from the level of single neurons to large-scale networks underlying cognition. The course covers a range of techniques, including intracellular and extracellular recordings, optogenetics, EEG, and fMRI. After weeks of conducting designed experiments, it culminates in an independent research project where students design and conduct their own experiments based on knowledge and skills learned throughout the semester.
A sample image of computer code that works with data. Photo credit: Markus Spiske.
“Big data” and “data science” are some of the buzzwords of our era, perhaps second only to “machine learning” or “artificial intelligence.” In our globalized, Internet-ized society of plentiful information galore, data has become perhaps the most important commodity of all. Across all kinds of academic disciplines, working with large amounts of data has become a necessity: universities and corporations advertise positions for “data scientists,” and media outlets warn ominously of the privacy risks associated with the rise of “big data.”
This isn’t an article that discusses the broader, societal implications of “big data,” although I highly encourage all readers to learn more about this important topic. Instead, I’m here purely to provide you some (hopefully) broadly applicable tips to working with large amounts of data in any academic context.
In my own field of climate science, data is paramount: researchers work with gigantic databases and arrays containing millions of elements (e.g., how different climate variables, such as temperature or precipitation, change over both space and time). But data, and opportunities for working with data, are present in every field, from operations research to history. Below is an overview of some existing data operation tools that can hopefully assist you on your budding data science career!
You’ve finished a research project and now you’re on to the final step: presenting your work! It’s time to share the incredible work you’ve done with the general public, and one of the best ways to do so is to create a poster conveying the significance and conclusions of your research. This will be an essential skill during your time at Princeton whether for a course or as a part of your junior and senior independent work. If this is your first time creating a poster presentation, check this blog out!
Sara Akiba ‘26 with her poster presentation on “Foraminifera-bound δ13C as a Paleo CO2 Proxy: Methods Testing” for the Geosciences Junior Poster Presentations! If you want a poster as great as hers, continue reading below for some advice.
