David Pfau: March 27th (at 5PM)

I'll be presenting on work in progress in collaboration with Bijan Pesaran's group (Yan Wong, Mariana Vigeral, David Putrino) and Josh Merel on building a high degree-of-freedom brain-machine interface.  I'll focus on the Bayesian paradigm for decoding, and two practical problems for pushing that paradigm beyond the commonly-used Kalman filtering approach: building better likelihoods, and building better priors.  The first amounts to fitting tuning curves for various neurons.  Other groups have shown a nonlinear dependence of firing rate on hand position in 3D space, here I will show some preliminary results on fitting tuning curves for large numbers of joint angles.  The second amounts to building better generative models of reach and grasp motions.  As a first step in that direction, I've looked at PCA and ICA for reducing the dimension of reach-and-grasp signals.

Kamiar Rahnama Rad: Feb. 21

Two following questions will be discussed:  1. How does embedding low dimensional structures in high dimensional spaces decreases the learning complexity significantly? I will consider the simplest model, that is a linear transformation with additive noise.  2. Modern datasets are accumulated (and in some cases even stored) in a distributed or decentralized manner. Can distributed algorithms be designed to fit a global model over such datasets while retaining the performance of centralized estimators?   

The talk will be based on the following two papers: 

http://www.columbia.edu/~kr2248/papers/ieee-sparse.pdf  

http://www.columbia.edu/~kr2248/papers/CDC2010-1.pdf