I have a stealth recruiting talk that I give for machine learning at Xpanse. It goes like this:

• If you want a real mission in your work, cybersecurity can deliver.
• My realm of cybersecurity is impossible without AI.
• Doing this job means solving cool, hard problems.

I pretend the talk is all very objective and about teaching you stuff (which hopefully it also does). I also hint at a lot of the problems that I’ve worked on and solved the past few years. Technical people really like being shown problems and getting to chew on them (which is convenient, because I’m not comfortable publicly sharing how I solved these problems!). And I’m sure it helps that I’m earnest – the slides really are what I love most about my job. The talk works, too. We get solid applicants from it.

I was inspired by a talk I saw from Stitch Fix a number of years ago. I have really minimal interest in clothing… but after hearing them give a technical talk about the problems they were solving, I became convinced they were doing really neat modeling and would be worth considering in a job hunt. Pretty effective. So I tried to channel that insight.

The slides I’m linking here conclude with a harder sell than I usually give, as well as some cross-team Palo Alto projects, because I revised this version for an explicitly recruiting context. (One of our senior recruiters had seen me give this talk at the Lesbians Who Tech conference, and he asked me to give it again in a different context.)

Body language can indicate state of mind. Being familiar with body language tells can help people read a room, avoid closing past the sell on a negotiation, and become more self-aware. I wrote and delivered a short orientation to Comfort, distress and dominance: Reading body language as part of a non-technical skills development series within an established team. It is framed as three 2-3 minute topic introductions followed by 5-10 minutes of small group moderated discussion.

Ryan Eberhardt invited Xpanse to give a guest lecture on the last day of a summer session of Stanford CS 110, in that gap between real coursework and the final.

CS 110 is the second course in Stanford’s systems programming sequence. I loved taking it as a student. I loved CS 110 so much that I TAed it twice, even though it’s a really tough course to TA (the students are zillions of new undergrads, there’s a lot of assignments to give feedback on, and the material is pretty hard for them so office hours consist of a never-ending queue of students with questions). My professor Jerry Cain gave me an award for my TA work, so hopefully I did okay by them.

For this re-visit to CS 110, I introduced microservices, containerization, and orchestration. I gave the orientation to why they should care and who we were. Then two sharp coworkers talked about their daily tech of port scanning and functional programming. I concluded the lecture with hinting at the problems solved by Docker and Kubernetes (and the problems created by them), and I asked leading questions that extended some of the core ideas in CS 110: decoupling of concerns, each worker does one thing, pools of workers share a single point of entry, and request/response models.

We spend a lot of time talking about supervised learning when discussing ML with students, but I find reinforcement learning just as interesting and useful.

I developed a talk on reinforcement learning for high school participants in SAIL ON, the year-round diversity and high school outreach program of the Stanford AI Lab that I initiated and led, which follows the SAILORS intensive two-week AI summer camp.

We discuss how reinforcement learning works, how to make decisions given Bayesian bounds, touchstone RL problems and recent applications, and where RL tends to succeed and fail.

Making mistakes in geographic analysis is disturbingly easy. The “Intro to Geographic Analysis” materials briefly discuss computational representations of geographic data. Then I delve into potential gotchas — from spatial databases to hexagonal partitioning, from avoiding analysis on lat-longs to choosing appropriate graphical formats, and more.