question marks on a purple background

What are you doing with your life? Why?

Last year, I took a seminar for Media Lab PhD students. During one class, we pondered what questions we ought to be asking as we began our journey toward seemingly distant proposals and dissertations.

We asked questions about ourselves. About our research. Why we do what we do. How we can do what we do better. Who we care about. Our visions. Our passions.

We were given a handout with the following list to start us off:

13 Questions Every PhD Student Should Ask

compiled by Prof. Judy Olson, University of Michigan, for HCI graduate students.

  • What is the problem? What are you going to solve?
  • Who cares? Why do people care about this problem?
  • What have other people done about it?
  • Why is that not sufficient? What are the gaps and unanswered questions?
  • What are you going to do about it? (Approach)
  • What are you really going to do about it? (Methods)
  • What do you expect to find?
  • What did you find? (Findings)
  • What does this mean? (Conclusions)
  • So what? (Implications)
  • What are you going to do next?
  • Where are you going to publish?
  • What are you going to be doing in 5 years?

Then we had to brainstorm our own lists of questions. Here's what my seminar class came up with:

Questions from Media Lab PhD students in 2014

  • How are you going to use it in the real world?
  • How are you going to change people's lives?
  • Will other people use it?
  • What is the question or opportunity? Where have we not gone yet - where are the new frontiers?
  • What does your advisor think you should do?
  • Why is it not incremental? How are you changing the conversation?
  • What did you learn?
  • What do you want to learn?
  • Why would the world (or your grandmother) be excited about it?
  • How can other people build on your work?
  • How could you fail?
  • How do you define success?
  • What other skills should you be learning now?
  • How do you take in the right amount of criticism?
  • How do you work with others and collaborate?
  • Who do you want to share your work with?
  • Who should you interact with to learn more about your field?
  • What's the best way to share your research?
  • What's the best way to get media attention?

Then we got to see the questions brainstormed by students in previous years. Here's what they asked:

Questions from Media Lab PhD students in 2012

  • What am I interested in?
  • What do I want to learn?
  • How do I want to learn those things?
  • Why am I here?
  • Why me? What is my uniqueness to solve this problem?)
  • What special skills do I bring to this?
  • Why do this in an academic environment?
  • What is the solution (not the problem)?
  • What is my vision?
  • What is my passion?
  • Why now?
  • What are my "bets"?
  • Who do I want to work with?

Questions from Media Lab PhD students in 2011

  • Does a PhD enable me to accomplish my dreams? Is this what I want?
  • What am I passionate about?
  • How can I leverage resources around me?
  • What new activities can I enable (rather than problems I can solve)?
  • How can I most effectively impact the world?
  • Who should I choose as collaborators?

Questions from Media Lab PhD students in 2010

  • What is my field?
  • How can I balance my research with the rest of my life?
  • How do my strengths contribute to my chosen field?
  • Am I happy?
  • Do I have the right advisor to accomplish what I want?
  • Can I get this done in time? (Scope of work)
  • Do I have the right background for this - should I take additional courses?

Additional questions from Mitch Resnick

  • How will my work expand possibilities and opportunities for others?
  • What principles and values will guide my work?
  • Can I create a map showing how my work relates to what others have done/
  • Who could I collaborate with?
  • What are some compelling examples that highlight the important of this work?
  • What community do I want to be a part of?
  • Can I make progress on this problem through an iterative process?

A lot to think about.

Can you answer them all?


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_a group of people leaning over stuff_

Questions vs answers

Recently, I had a discussion with a friend about the key difference between science and engineering.

As a computer engineer, my friend found that the more advanced his coursework got and the more he learned about electronics, circuits, and microprocessors, the better he understood the subjects as a whole.

Which shouldn't be too surprising. That's the point of a college engineering degree: learn how stuff works and how to make stuff work.

But me, I find that as I learn more about brains and minds, filled with complex interactions between neurons, glial cells, neurotransmitters, and hormones, the picture gets steadily more complicated. The universe is one big dynamic system, full of chaotic pieces, and I keep finding more questions. The more I learn, the less I know.

That's the scientist's perspective on the world: more knowledge means more questions. More astonishment, more confusion.

(This is not a novel pronouncement, merely a recent observation supporting previously suggested differences between the two disciplines.)


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a laptop, textbook, and piles of papers and notes on a carpeted floor

A month after graduation, I'm well on my way to learning all sorts of crazy new things. This summer, I'm learning about...

  • HAM radio. On Tuesday, I attended the first of a summer-long amateur radio FCC licensing class. I know very little about radios and their components - the president of GSFC's amateur radio club told a story about how easy it was to build a circuit to convert 5 volts down to 3.3 volts, and kept throwing out electronics jargon. I'm looking forward to increasing my knowledge of the subject!

  • Computer innards. On a similarly technical note, my laptop's hard drive stopped spinning up last week. With the help of a computer engineering friend, I opened up the laptop and replaced the drive. Didn't even lose a screw! It's a small step into the world of computer hardware, but that was the first time I've opened up a computer, so it counts for a lot.

  • Multiple realizability. That is, that people can take entirely different paths to the same place. People with ridiculously different beliefs can still be thinking exactly the same thing at exactly the same time on ridiculously frequent occasions.

  • Tae Kwon Do. An activity I'd never done before: martial arts! All the interns/apprentices in my lab this summer were encouraged to try it out, since the GSFC club is so friendly. We've learned miscellaneous self-defense maneuvers and more ways of kicking than I remember names for - I even got to kick through a board!

  • And software... My lab group is using a variety of software tools and open source code libraries that are new to me: ROS (the Robot Operating System), a code repository via SVN, the MRPT libraries, the point cloud library (PCL), and many more. I'm remembering C++, delving into path planning algorithms, and reading up on SLAM (simultaneous localization and mapping). Yes, it's a whirlwind of acronyms.


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backs of students heads, wearing black motorboard hats and tassels - photo by Terry Bolstad

Don't ever stop

This one's a life update post, but it's also a "here's some cool science!" post.

A few days ago, I graduated from Vassar College with a Bachelor of Arts in Cognitive Science and a correlate in Computer Science. I was decorated with general honors, departmental honors, membership to Psi Chi, and membership to Sigma Xi. My time there was awesome.

What's next?

No lazy summer!

Well, no lazy summer break for me! I've already spent three days in my summer lab at NASA Goddard Space Flight Center, where I'll be working on a number of software development projects. The primary one is a LIDAR-assisted robotic group exploration project, in which we're going to have a small fleet of robots -- a mothership and some workerbots -- use 3D LIDAR data to autonomously map and plot paths through an area. This kind of robot fleet could, eventually, be used to explore other planets. One of the big challenges will be dealing with the 3D image data. I'm looking forward to learning more image processing algorithms!

Another project is the redesign of the Greenland Robotic Vehicle, a big autonomous rover that'll drive across Greenland, collecting a data about snowfall, mapping, and exploring. Did you know there's ice on that country two miles thick? I may also get to play with a robot that has stereo vision.

You can see some of these robots (and what life in the lab may be like) in this great video about last year's interns.

So far, I've met a bunch of intelligent, friendly folks, started catching up on already-written code, and begun to delve into the platforms, libraries, and algorithms we'll be using and developing this summer. Our mentors have already proven themselves to be enthusiastic and helpful. Just yesterday, one of them told us,

"You're engineers at NASA. You want to go where things are, and then go beyond."

That may end up being our theme for the summer.

A little overwhelming?

shiny silver model of a space shuttle

There's going to be so much going on. It'd be easy to get overwhelmed -- especially now, jumping in and floundering around in the code, the projects, the people. So much to learn.

But as I sat in the lab today, reading about ROS, going through tutorials, reading about PCL and feature detection in point clouds, digging through last summer's confusing pile of C# and C++ programs, I realized I wasn't overwhelmed. And it was because of all the other experiences I've had that've gotten me to this point.

Confidence. My first URSI summer, flailing through Microsoft Robotics Studio and complicated conceptual theories. Figuring out how to deal with webcams and image data my second URSI summer, reading papers on optical flow and implementing algorithms. Last summer: excavations of an open source flight simulator, the Aeronautics Student Forum, dealing with different work styles and communication styles in my LARSS lab. And more.

I think about all those experiences, and I'm not afraid of this summer. I could almost be overwhelmed -- perhaps thinking that everyone else has more of the right kind of experience; I wasn't trained as a classic engineer -- but I know I can succeed. My non-engineering, cognitive science background sets me apart and lets me look at problems a little differently than everyone else. I'm an asset.

I know how to learn. I know how to do research.

I can conquer this summer.


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Summer plans

My first post-graduation plans have been finalized: I'll be returning to the fine world of software development and robotics for a summer internship at NASA Goddard Space Flight Center. I'll be working with a diverse bunch of engineers and interns on what I expect will be super exciting, super cool projects.

Thesis!

_red and blue simulated robots in a flat simulated world_

On Friday, I turned in my undergraduate cognitive science thesis. It's been a year in the making -- I started brainstorming ideas last April, spent all summer reading up on relevant literature, and all of this school year developing my model, programming the simulation, running experiments, and finally, writing about all of that.

It's a little weird to realize that I don't have to constantly be thinking about this project any more. I don't have to be, but ever since handing it in, my thoughts continue to swirl around what further analyses to do on the data I collected, how to fix up the studies I did to get more powerful results, which studies would make sense as the next step...

Here's the abstract:

A biologically inspired predator-prey study of the effects of emotion and communication on emergent group behavior

Any agent that functions successfully in a constantly changing world must be able to adapt its behavior to its current situation. In biological organisms, emotion is often highlighted as a crucial system for generating adaptive behavior. This paper presents a biologically-inspired predator-prey model to investigate the effectiveness of an emotion-like system in guiding the behavior of artificial agents, implemented in a set of simulated robots. The predator's behavior was governed by a simple subsumption hierarchy; the prey selected actions based on direct sensory perceptions dynamically integrated with information about past motivational/emotional states. Aspects of the prey's emotion system were evolved over time. The first study examined the interactions of a single prey with the predator, indicating that having an emotion system can led to more diverse behavioral patterns, but may not lead to optimal action selection strategies. In the second study, groups of prey agents were evolved. These agents began to utilize alarm signaling and displayed fear contagion, with more group members surviving than in groups of emotionless prey. These results point to the pivotal role emotion plays in social scenarios. The model adds to a critical body of research in which important aspects of biological emotion are incorporated into the action selection mechanisms of artificial agents to achieve more adaptive, context-dependent behavior.


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