Chaitanya (Chaitu)
Ekanadham's CIMS Page
Contact
Courant Institute for Mathematical Sciences
Office
251 Mercer St
Email: chaitu
at cims
dot nyu dot edu
Research
interests
I am a Ph.D. candidate in applied mathematics at the Courant Institute of Mathematical Sciences
in New
York University. My advisors are Eero Simoncelli and Dan Tranchina.
My
research
lies at the intersection of statistics, applied
mathematics, and signal processing. I am particularly interested in
applications for analyzing neurophysiological data, acoustic data, and
images. I currently am working on two projects: (1) a method for
decomposing signals generated by transformation-invariant processes,
and (2) generalized linear models for retinal ganglion cell responses
that account for slow-timescale adaptation to input statistics. In
mathematics, my primary areas of interest are probability theory,
statistics, and statistical learning. I am also interested in
information theory, optimization,
scientific computing/numerical
methods, (particularly their
applications to
neuroscience).
I
received my bachelors degree from Stanford
University
in June 2007, majoring in Math &
Computational Science and Symbolic
Systems. For my
undergraduate honors thesis,
I worked in the AI laboratory with Honglak
Lee and Prof.
Andrew Ng on using deep
belief networks to model how visual area V2 may process
angular
stimuli and other complex shapes (see here
and here
for examples in the neuroscience literature). Previously, I've
also worked on projects related to speech
recognition, natural language understanding systems, robotics, and
"bio-inspired" artificial intelligence.
CV
Undergraduate thesis
Current
Previous
Patents
Seminars I regularly attend
CNS colloquium
Comp neuro
and bio seminar
Applied math seminar
CIMS grad student
seminar
Student
probability seminar
Some theoretical neuroscience labs
Lab for Computational Vision @ NYU
(Simoncelli)
Neurotheory group @ Columbia
Paninski lab @ Columbia
Redwood center for theoretical
neuroscience @ Berkeley
Bialek's page
@ Princeton
Gatsby Unit @ UCL
Some neurotechnology blogs (a fairly recent interest of mine, although
I know close to nothing about it...)
Neurotech@MIT
Group
blog
Brain stimulant
Ed Boyden's blog
Neurodudes (neuroscience/ai forum)
Free resources and texts
Statistical
Learning
lecture
notes
(Berkeley)
Convex Optimization
(Free Boyd/Vandenberghe book)
Spiking neural
models (Free Gerstner book)
Stochastic
processes
lecture
notes
(Stanford)
Fall 2007
Real Variables |
Spring 2008 |
Fall
2008
Applied functional
analysisNumerical Methods I Mathematical aspects of neurophysiology |
General Topics | Special Topics |
Complex analysis ODE ODE abridged Probability theory Probability theory abridged |
Special
topics
outline Special topics notes |