On Thursday and Friday, Bernhard, Rob, and I discussed about one of Rob's works on exoplanet detection. He has been working on it for awhile, and we will see if the current technique can be improved using more sophisticate machine learning techniques.
An extrasolar planet, or exoplanet, is basically a planet outside the Solar System. As far as I understand, an ultimate goal is to discover the Earth 2.0, those extrasolar planets that orbit in the habitable zone where it is possible liquid water to exist on the surface. The detection of exoplanets itself is very difficult, let alone the extraction of molecular composition of the planets, because planets are extremely faint compared to their parent stars.
On Friday morning, I also met Ralf Herbrich, who is currently a director of machine learning science at Amazon. We didn't talk much, but I guess I will meet him again at UAI2013.
This basically concludes my trip to NYC. I will be at ICML (Atlanta, Georgia) next week and looking forward to meeting many renowned machine learning people.
Early this week, Bernhard and I started to discuss about future direction of our research. It is quite difficult to decide because, on the one hand, I think there still be a number of open questions along the line of kernel mean embedding and its applications. Kernel methods have become one of the most important tools in machine learning and I am certain it will still be. On the other hand, this might be a good opportunity to learn something new.
One of the possibilities that we discussed about is "causal inference". Causal inference has been one of the main research directions of our department in Tuebingen (see http://webdav.tuebingen.mpg.de/causality/ for people who work in this area and their contributions). I have to admit that this topic is new to me. I have very little knowledge about causal inference, which is why I am quite excited about it.
In a sense, the goal of causal inference is rather different from standard statistical inference. In statistical inference, given random variables X and Y, the goal is to discover association patterns between them encoded in the joint distribution P(X,Y). On the other hand, causal inference aims to discover the casual relations between X and Y, i.e., either X causes Y or Y causes X or the there is a common cause between X and Y. Since revealing causal relation involves an intervention on one of the variables, it is not trivial how to do so on the non-experimental data. Moreover, there is an issue of identifiability, i.e., several causal models could have generated the same P(X,Y). As a result, certain assumptions about the model are necessary.
It has been a long week. We had the nips deadline on Friday. Fortunately, we manage to submit the papers. Let's keep the finger crossed! I was very fortunate to receive very constructive comments about my nips paper from Ross Fedely. David Hogg also gave last-minute comments which helped improve the paper further (and special thank goes to Bernhard for that). Here is a picture of us working toward the deadline:
After submitting the papers, we hanged out with many people, including Rob Fergus, in the park to celebrate our submissions.
Right. The main topic of this post is about supernova classification. Early this week, Bernhard and I had a quick meeting with astronomy people from CCPP (Center for Cosmology and Particle Physics). They are working on the problem of supernova classification (identifying the type of supernova from their spectra), and are interested in applying machine learning techniques to solve this problem. Briefly, the main challenge of this problem is the fact that the supernova itself change over time. That is, it can belong to different type depending on when it is observed. . Another challenge of this problem is that we have a small dataset, usually in the order of hundred.
According to wikipedia, a supernova is an energetic explosion of a star. The explosion can be triggered either by the reignition of nuclear fusion in a degenerate star or by the collapse of the core of a massive star, Either way, a massive amount of energy is generated. Interestingly, the expanding shock waves of supernova explosions can trigger the formation of new stars.
Supernovae are important in cosmology because maximum intensities of their explosions could be used as "standard candles". Briefly, it helps astronomers indicate the astronomical distances.
One of the previous works used the correlation between the objects' spectra and set of templates to identify their type. I will read the paper on the weekend and see if we can build something better than just simple correlation.