Step 83: Survive!

I’m alive! This blogging hiatus was due to the busiest semester at Oxy so far. It’s the first time I’ve had a three-course teaching load (well, two and a half), and on top of that I was involved in both the computer scientist search and the proposal for a Computer Science department/major. Everything below probably deserves a blog post of its own as I return to my weekly schedule, but in the mean time are the highlights:

  • Department/Major Proposal: The major news of the semester (pun intended) is that the faculty voted on and passed a resolution to establish a Computer Science department and major. This would not have happened without lots of work by other people, both on the intellectual side as well as navigating the college bureaucracy. As a junior faculty, I actually felt understanding and working through the process of proposing a major was more work than designing the major itself. I don’t want to go into the politics of proposing a department/major, but I’ve learned a lot about the different interests at the college. I will just mention that the allocation of resources between departments was a concern, which I think will be a focus of the faculty for the near future.
  • CS Search: Slightly before the department stuff really heated up, we also hired a new faculty! Kathryn Leonard will be joining us from CalState Channel Island in the fall, as a full professor. Her is on shape modeling from a mathematical perspective… but I can’t really do her research justice. She has also done a lot of teaching and outreach for underrepresented students, and started a Data Science Minor at CSUCI. I’m super-excited for her lead CS@Oxy.
  • Research: I experimented a little this semester with having a lot more students – from 2-3 the previous semesters to 6-7 this semester. The main reason is that, with all the classes I’m teaching, it’s hard for me to find time focusing on research. More students means more meetings, but that actually blocks out time for me to think about what they are doing. I am still figuring out how to split up my main projects for these students, so not every project will lead to publication-level work. I also abandoned group meetings this semester, partially because we changed how students get research credit, but also because the students’ projects do not directly overlap. Still, I think the experiment was a success, and I intend to keep this many students in future semesters.
  • Intro to Cog Sci: I’m not sure how much I talk about this course, since most of this blog is focused on computer science. As the name suggests, this is the first course that students take in Cognitive Science, and is usually co-taught by three professors. The ideal would to be to have one philosopher, one cognitive psychologist, and one mathematician/computer scientist, but this year it’s just me and my Cog Sci colleague. Because the teaching team changes every year, the first semester is often time to figure out how you work together, with the second semester to polish the class a little. We were lucky to have great students this semester, which let us push the concepts a little harder, but also meant more time rethinking what each lecture should be about.
  • Data Structures: This is the first time I’ve taught this course, but it went better than the first time I taught CS1. For some reason, over the semester I keep feeling as though there are topics I am not covering, but when I compare my syllabus to those at other colleges, the topics have significant overlap. My one big mistake was trying to useful multiple free online textbooks, but the readings ended up being disorganized and disjoint. Other than that, this class was surprisingly easy to teach, and I thoroughly enjoy drawing box-and-arrow diagrams again.
  • Topics in AI: The first time I taught this course was my first semester at Oxy. At the beginning of the semester, I thought I would rethink the course given what I know now about Oxy students, but in retrospect the course did not change as much as I thought it would. The balance between cognitive science and computer science remains a problem, and the assignments from last time were good enough that I reused most of them. I did introduce more computer science/machine learning into the course, but had to be careful that the mathematical details did not exceed my student’s abilities. I mostly relied on visualizations in IPython, which worked well. This course may change in the near future as Cognitive Science revisits their curriculum in light of Computer Science, so it’s unclear if I will teach this course again.

As I said, I will likely spend a full blog posts on each of these, and other smaller topics as well. I’m excited to have time to write again!

Step 83: Survive!

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