Today was the launch event for the Moore-Sloan Data Science Environment at NYU, of which I am the Executive Director. This would not be research (see The Rules at right) except that the event featured six extremely good, short science talks.
- Cranmer (NYU Physics) talked about the discovery and measurement of the Higgs. He showed that the complicated "Data Science" part of the problem was combining the code and scientific analyses of dozens of disparate groups doing disparate things with the data.
- Bonneau (NYU Biology and CS) talked about relationships between genes and microbes. Much of what they do is infer networks of interactions among genes. But he showed some beautiful stuff on inferring differential equations from noisy data, which is relevant across many disciplines.
- Pesaran (NYU Neural Science) spoke about massive (relatively) non-invasive recording efforts in monkey and human brains. His lab can record from thousands of sites simultaneously, but at the brain's surface. He gets large data volume at some loss of resolution, and can record in normal situations, while also capturing movement and behavior with large networks of cameras and other devices. He is working on the brain–behavior relationships, but also has a good chance of being able to make very high capability brain–computer interfaces!
- Freire (NYU Engineering) showed what can be done with data from cities; focusing on amazing things they have learned by visualizing the huge amounts of data NYC keeps about taxicabs. They can see many interesting events in the data, and are looking at automatically flagging and identifying events both in historical data and in real time.
- Tucker (NYU Politics) spoke about political information available on the web from Twitter and similar sources. He showed results on events and subjects where political polarization is high, others where it is low, and others where it changes quickly in time (one example: Sandy Hook shooting).
- Fergus (NYU CS) showed our work on Oppenheimer's (AMNH) P1640 data and how it is possible to extract faint signals from data with a generative model. Both Fergus and Cranmer showed non-trivial probabilistic graphical models, which was super.