A few days after the MCMSki conference, I start to see the main lessons gathered there.
- I should really read the full program before attending the next MCMSki. The three parallel sessions looked consistently interesting, and I really regret having missed some talks (in particular Dawn Woodard‘s and Natesh Pillai‘s) and some posters as well (admittedly, due to exhaustion on my part).
- Compared to the previous instance three years ago (in Utah), the main themes have significantly changed. Scalability, approximate methods, non-asymptotic results, 1/n methods … these keywords are now on everyone’s lips. Can’t wait to see if MCQMC’14 will feel that different from MCQMC’12.
- The community is rightfully concerned about scaling Monte Carlo methods to big data, with some people pointing out that models should also be rethought in this new context.
- The place of software developers in the conference, or simply references to software packages in the talks, is much greater than it used to be. It’s a very good sign towards reproducible research in our field. There’s still a lot of work to do, in particular in terms of making parallel computing easier to access (time to advertise LibBi a little bit). On a related note, many people now point out whether their proposed algorithms are parallel-friendly or not.
- Going from the Rockies to the Alps, the food drastically changed from cheeseburgers to just melted cheese. Bread could be found but ground beef and Budweiser were reported missing.
- It’s fun to have an international conference in your home country, but switching from French to English all the time was confusing.
Back in flooded Oxford now!