Jascha Sohl-Dickstein

I am a graduate student in the Redwood Center for Theoretical Neuroscience, at University of California, Berkeley. I am a member of Bruno Olshausen's lab.

I am interested in how we learn to perceive the world. That is, how we start receiving sensory input (seeing, hearing, smelling, touching, etc) ... and after a while figure out the way the world works. We learn that the light intensity on adjacent patches of retina is correlated, that the world is made up of edges and surfaces, how occlusion works, that objects exist, that if you drop them they fall and make a noise and hurt... We seem to infer most of human scale physics from examples of sensory input.

How this unsupervised learning problem is solved - how we learn the structure inherent in the world just by experiencing examples of it - is not understood. This is the problem I am interested in tackling.

There are two large (known) parts to this problem. The first is the design of models which are flexible enough to describe *anything* without being unwieldy (greater flexibility frequently comes at the cost of an explosion in the number of parameters, and more computationally costly implementation). The second is practically training these models once they've been designed - estimating their parameters for a particular dataset (almost any model you can write down is impractical to exactly evaluate - a factor called the "partition function", coming from the constraint that probabilities of all states must sum to 1, is intractable to compute). The majority of my work has been on parameter estimation in traditionally intractable models.

Relevant publications

J Sohl-Dickstein, P Battaglino, M DeWeese. Minimum Probability Flow Learning. (2009) http://arxiv.org/abs/0906.4779

C Abbey, J Sohl-Dickstein, B Olshausen. Higher-order scene statistics of breast images. Proceedings of SPIE (2009) http://link.aip.org/link/?PSISDG/7263/726317/1

Papers from my previous life as a Martian

Kinch et al. Dust deposition on the Mars Exploration Rover Panoramic Camera (Pancam) calibration targets. Journal of Geophysical Research-Planets (2007)

Johnson et al. Radiative transfer modeling of dust-coated Pancam calibration target materials: Laboratory visible/near-infrared spectrogoniometry. J. Geophys. Res (2006)

Joseph et al. In-flight calibration and performance of the Mars Exploration Rover Panoramic Camera (Pancam) Instruments. J. Geophys. Res (2006)

Parker et al. Stratigraphy and sedimentology of a dry to wet eolian depositional system, Burns formation, Meridiani Planum, Mars. Earth and Planetary Science Letters (2005)

Soderblom et al. Pancam multispectral imaging results from the Opportunity rover at Meridiani Planum. Science (2004)

Soderblom et al. Pancam multispectral imaging results from the Spirit rover at Gusev crater. Science (2004)

Smith et al. Athena microscopic imager investigation. Journal of Geophysical Research-Planets (2003)

Bell et al. Hubble Space Telescope Imaging and Spectroscopy of Mars During 2001. American Geophysical Union (2001)