Chaitanya (Chaitu) Ekanadham's CIMS Page

Contact |    Research   |   Background    |    Publications    |    Links (academic)    |    Notes


Courant Institute for Mathematical Sciences
Office 718
251 Mercer St

New York, NY 10012

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.

Undergraduate thesis






Academic links

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
Complex Analysis
Linear Algebra
Limit Theorems I

Spring 2008
Limit Theorems II
Nonlinear Optimization
Mathematical Statistics

Fall 2008
Applied functional analysis
Numerical Methods I
Mathematical aspects of neurophysiology

While preparing for oral exams I wrote up a set of notes for each topic in the LaTeX format. Please feel free to comment on any of them (especially if there is something incorrect!) or even use them for studying. I also wrote a more abridged version for topics where some of the proofs tend to be long and convoluted. In these versions, I try to use as few math symbols as possible and explain theorems using simple English. The emphasis is on what theorems say, what they depend on, and what they are used for.

General Topics Special Topics
Real analysis                               Real analysis abridged
Complex analysis
ODE                                            ODE abridged
Probability theory                        Probability theory abridged


Compilation of examples

Special topics outline
Special topics notes