How to Land Research Opportunities

"So, Roger, what are you doing this summer?”
“Boring stuff”
“Way to sell yourself. What can I do for ya’ this summer?” 
Those three sentences started it all.
 
Hey, my name is Gian-Mateo, and I’m a current fourth-year imaging science student.

Last spring, as I congratulated my friends on their success in finding jobs, internships, and research opportunities, my job status remained empty, just as the return inbox of my job applications was.
 
Some of you reading this are type ‘A’ sharks. You’ll gladly go out and shake any outstretched hand; you don’t just want an opportunity, you want to be a top candidate at Google. You’re so confident, even Patrick Bateman would go, “You’re good.”
 
However, some of us reading this are more like me: introverted, shy, and shaky when it comes to networking and finding opportunities. Yes, even shy people like us have a fighting chance, and I’ll tell you my story of how.
 
Over the summer, I had the fortunate opportunity to work adjacent to the MISHA Project. MISHA is a sophisticated camera system that takes a picture with 16 LEDs (our phones use 3), with each capture using a specific wavelength ranging from ultraviolet to the human visible, to the near infrared.
 
The perks of MISHA are that more colors = more data. The downside is that our phones and monitors only have 3 LEDs (RGB), so MISHA’s data isn’t always human-realizable, meaning you can’t use human eyeballs to just “look at it”. You need some specialized algorithms.
 
My research revolved around pages from the Archimedes (yeah, that guy) Palimpsest. I worked on extracting the text buried under mold, stains, dust, and wax residue.

My favorite imaging science professor,  Dr. Roger Easton Jr., works daily on uncovering hidden data from multispectral (i.e., more than 3 LED) imagery, such as from MISHA, or other historical documents – like the Archimedes Palimpsest.
 
Roger graciously gave me free range to test a previously dismissed algorithm during the summer, Generalized Orthogonal Subspace Projection for multispectral imagery. By aggregating imagery from every LED, the algorithm constructs a mathematical model of the data and looks for the unique signature of the desired target: in this case, the undertext. It showed promising results!
 
This opportunity provided me with hands-on experience working with multispectral imagery, algorithm development, and optimization, as well as my first real taste of research and discovery (R&D). The R&D will continue, as I will utilize this research as a jumpstart for my now ongoing senior project.
 
Most feel-good success stories will tell you all the great, wonderful things that naturally come to you if you just try.
 
I’d like to leave you all with a different moral: Trying doesn’t automatically get you an internship, co-op, or the greatest research experience of your college career. Trying gets your foot into someone’s door.
  
Try to network with professors who do what you want to do (even if you don’t know what that is yet, just pick a professor and ask what they do!)
 
If you’re scared or timid like I was, just find your favorite professor and start up a chat. That’s networking! Who knows, it might even land you a research job.