esrh

In my case, I had worked in Japan for a year at a domestic megacorp (which is how I met my prof), had attended a few conferences, was very fluent in Japanese, and knew with certainty that the enjoyment of living in Japan would make up for any objective drawbacks.

My main issue with grad school is that except for an exceedingly small number of labs that regularly aim for top conferences, the process feels very un-serious. I think the standards are low; very few professors care a lot about doing really good work; the classes are unnecessary, low quality (nobody cared about teaching much, in my dept), mostly surface level; and there's not much of a bar for student research. I'd argue that domestic conference work is not only less prestigious an the author says, but it's also in most cases worse and less interesting, which is the bigger problem. This might be exacerbated by the fact that the vast majority of undergrads begin research in their last year and continue into the MS program by default so there are generally a lot of less motivated students. I still had a few exceptionally smart colleagues, but I never collaborated with them (in fact, our lab has hardly ever published a paper with >2 authors). I generally felt that their hard work was misplaced.

About a year into my MS, after pushing a bit of slop at international conferences, I more or less gave up on working on my project and got a remote job based in the US. Like the article says, it is exceptionally convenient that you can work for 28 hours a week on a student visa, since 28 hours of a US tech job is more than enough to support a nice lifestyle for 1 in tokyo. I might genuinely have dropped out and moved back home if it wasn't for it. DO get the auth at immigration time, it's much more annoying later.

In any case, I really enjoyed it in hindsight. As a final note, Science Tokyo is very simple to enter for international students -- the only requirement is that you find a professor willing to take you in. There are many structures in place for international students and I'd say it's fairly nice and comfortable.

I think the results here are much less important and surprising than what some people seem to be thinking. To summarize the core of the paper, we took stable diffusion (which is a 3-part system of an encoder, u-net, decoder), and replaced the encoder to use WiFi data instead of images. This gives you two advantages: you get text-based guidance for free, and the encoder model can be smaller. The smaller model combined with the semantic compression from the autoencoder gives you better (SOTA resolution) results, much faster.

I noticed a lot of discussion about how the model can possibly be so accurate. It wouldn't be wrong to consider the model overfit, in the sense that the visual details of the scene are moved from the training data to the model weights. These kinds of models are meant to be trained & deployed in a single environment. What's interesting about this work is that learning the environment well has become really fast because the output dimension is smaller than image space. In fact, it's so fast that you can basically do it in real time... you turn on a data collection node and can train a model from scratch online, in a new environment that gets decent results with at least a little bit of interesting generalization in ~10min. I'm presenting a demonstration of this at Mobicom 2025 next month in Hong Kong.

What people call "WiFi sensing" is now mostly CSI (channel state information) sensing. When you transmit a packet on many subcarriers (frequencies), the CSI represents how the data on each frequency changed during transmission. So, CSI is inherently quite sensitive to environmental changes.

I want to point out something that most everybody working in the CSI sensing/general ISAC space seems to know: generalization is hard and most definitely unsolved for any reasonably high-dimensional sensing problem (like image generation and to some extent pose estimation). I see a lot of fearmongering online about wifi sensing killing privacy for good, but in my opinion we're still quite far off.