New year: Time to look back at what I did in 2018, and think about how I want the next year to be different.

Thinking about 2018, I can say it was a “scramble” year for me. Above everything else, my tenure examination was looming over my head. Everything that I did, at least until September, was evaluated by “does this help me pass my tenure evaluation?”. On the good side, I did get tenured this year, and this means work security and freedom to make longer termed plans. On the downside, I feel like I did a lot of things that I am really not interested in, just because of tenure, and my research passion suffered accordingly. By the end of the year, I just wanted all these side projects to get over with.

So I guess my aspirations for 2019 is to par down on the number of side projects, and try to focus on things that I really want to grow and love. Say less yes, and focus on things that I think will make me proud down the line. Of course, I still have many leftover projects from 2018. Specially because in August I was scrambling to get funding, any funding, and surprisingly many of my proposals were accepted. Too much of a good thing? I mean, I like these projects, but there are just so *many* of them — I was expecting maybe 1 in 5 of my applications to get accepted. But hey, I should be celebrating, right? :-)

With this in mind, my goal for this year is to try to focus on things that I will feel proud of having done at some point in the future. With this in mind, my very first day (evening?) of the year was spent catching up with neural network frameworks (Keras and Pytorch), so that I can better read and comment on my student’s code. My goal by the end of the week is to: 1- Run an image segmentation model (using U-net?), 2- Run some simple werewolf AI bots, 3- get up to speed with the grad-free library of EC-based neural networks, 4- hack my linux machine to play nice with the Sony Digital paper, and last but not least, 5- Try to blog all of this stuff.