Research During Masters

The visions I had for grad school have not quite been realized during my time at UPenn.

The Statement of Purpose I used for my Masters' admissions was filled with the experiences I had when working on projects on during undergrad. I emphasized that if given the chance, my goal was to advance the state of art in robotics through research. The sentiment was genuine and based on whatever experiences I had in undergrad, I was ready to take a lighter course during my masters to make more time for research. I had pretty much made up my mind to take the thesis option at Penn - granting one with the chance to do original research for two full semesters with a 20% reduction in course load to boot!

However, as I mentioned in my previous posts, my plans changed during my first year. I ended up taking time-consuming courses in my first and second semester which left little time to do research. That's not to say it was for a lack of trying - for I did reach out to a couple of labs located at Pennovation Works, a business incubator and massive robotics research facility under one roof.

I had some good introductory conversations with the passionate PhDs and postdocs at these labs. One of the more notable discussions I had was when a postdoc at one of these labs asked me how I would solve one of the open research problems she was working on. Perplexed by the question, I said that I didn't know enough to answer without reading some papers. After all, I had only just started approaching labs to pursue research and I wasn't even sure where to begin till I got warmed up with the problem.

Ignoring my response, she repeated the question and asked me to try harder. When I was stumped for the second time, she said that I should try to pursue solving the problem with whatever experiences I had in my previous projects. It was a epiphanic moment for me, because till then I had always thought research would require substantial background and theory before building off existing work. It was eye-opening to imagine that solving a difficult problem with whatever I knew was a valid research direction in itself. I left that meeting feeling a lot more certain that I had come to the right place. Unfortunately, a couple of weeks after that meeting, COVID-19 caused the college and all research facilities to close for good. With the burden of courses and new lifestyle changes due to COVID, research was no longer at the top of my priority list and it looked like it would have to wait indefinitely.

In a strange twist of fate, it wasn't for long. My summer internship ended up getting cancelled a few weeks before it was going to start, so to stay busy during the summer, I ended up taking a remote research project at a different lab from the ones I had approached earlier in the year. At first glance, the project I was handed looked like a good fit. It was about creating a autonomous system for quadcopters to avoid midair collisions. I felt like I could leverage some of my previous work to get new ideas. However, soon after I started, the cracks in what I knew started to become apparent. The existing work in the project involved using Model Predictive Control (MPC) and convex optimization for planning collision free trajectories. I only knew about MPC in passing and convex optimization was not a topic I could hope to cover in time for doing anything new in a twelve week project. I would end up spending weeks on what I felt was a novel approach, only to find that the team of PhDs had tried it out without much luck long before. My drawers filled with reams of printed research papers from other university labs working on the same problem. I began rethinking the lab's existing work and it troubled me that the current research direction would only work for a very specific configuration of robots, which as anyone working in robotics knows, is rarely something useful for the real world. At some point, it was clear that the project wasn't a good fit for me, so I dropped out of the lab soon after.

When it comes to original research, I feel master's students are in a middling position as the research work they aspire to do often requires coursework which one only completes near the end of the degree. To that end, I suggest taking advanced versions of courses (such as Model Predictive Control or Adavance Computer Perception) early on. It is important to know the background which a particular research project requires, unless your idea is to solve the problem with what you already know. I also suggest prodding an existing research approach for weaknesses, as new insight can patch the holes in a current direction of the research project. It's also best not to commit early to working on a project, at least not until you have a realistic plan of work for the duration you want to work.

My experience with research was less than ideal, but I would be curious to find out if others have been able to overcome the hurdles which come with trying out a project out of one's depth. Do let me know about your experiences in the comments!