1. Rahul Biswas, PhD
Center for Gravitational Wave and Astronomy ( A NASA University Research Center)
80 Fort Brown, Brownsville, Texas , 78521 +1 956 832 3441 Rahul.Biswas@utb.edu
Quick Details
MY PROFILE
Working with Condor as a
A NASA University Center Computational Physicist working on some of the interesting scheduler to process jobs
problems in Astrophysics. Experienced in analyzing 250 GB of raw time-series to in beowulf clusters
find possible astrophysical sources of gravitational-wave sources. Equally interested
in applying ideas to financial market, data modeling, test trading strategies. Methods Obtained Department of
involved ANOVA, Fourier analysis, Bayesian Statistics, likelihood estimation. Heavy Energy (DOE) Grid
coder in Python, scipy, numpy and statistical packages R using Rpy API. Experienced certificate.
in using machine learning algorithms, libSVM, PyNN, Random Forest. www.opensciencegrid.org
Working in open source
CAREER OBJECTIVE environment. All codes
are available under GNU
public license.
I am very eager to work with a team in the role of Quantitative Analyst, conduct Member of American
research in financial market, develop scalable models for prediction. Interested in
areas of time series analysis, high frequency data analysis, pattern recognition, data Physical Society (APS)
mining algorithm, quantitative analysis. www.aps.org
Member of LIGO
PROGRAMMING SKILLS Scientific Collaboration
(LSC).
www.ligo.org
Python, pylab, numpy, scipy, SQL binding for python. pysqlitedb. Rpy statistical
package using Python. MATLAB, C/C++, familiarity with HADOOP and
MapReduce. Experience in using various numerical packages BLAS, LAPACK. AWARDS
XGobi for parallel coordinates system (highly efficient in visualizing multi-
parameter). Primary work OS:Linux, Unix. Knowledge in scripting language BASH.
Chancellors Fellowship,
University of Wisconsin
EDUCATION Milwaukee 2004-09
Junior Research Fellowship
PhD in PHYSICS Award,
University of Wisconsin Milwaukee SEPTEMBER 2010 (3.470/4.0) Council of Scientific and
Industrial Research, India
MSc. in PHYSICS 2003
Indian Institute of Technology AUGUST 2003 (7.255/10.0)
BSc. in PHYSICS
University of Delhi MAY 2001 Not GPA Based
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2. Publications of Interest
RESEARCH EXPERIENCE
[1] A New approach to TIme
Domain Classification of
Broadband Noise in
CENTER FOR GRAVITATIONAL WAVE AND ASTRONOMY Gravitational Wave Data,
( A NASA University research Center)
Post Doctoral Research Associate OCTOBER 2010 - PRESENT S Mukherjee, P. Rizwan and
Rahul Biswas
Analyzing 250 GB volume of astrophysical data collected from LIGO-Virgo http://arxiv.org/abs/1201.4400
network of detectors to analyze patterns of noise transients. Understanding their origin
and classifying them based on their waveform, time series pattern. [2] Search for Gravitational
Waves from Compact
Binary Coalescence in
Key Projects: LIGO and Virgo Data
from S5 and VSR1,
Designed and tested an automatic work-flow to determine the origins of non-
Abbot et al, Phys. Rev. D.
stationary noise transients using Longest Common Subsequence method. The 82: 102001
method determines common sequences between two time series. Further classification
using the k-means clustering algorithm was done to classify them and given a [3] The Loudest Event Statistic:
ranking order. Achieved 10 times better performance in speed from past classifiers of General Formulation,
similar kind. [1] Properties and Application,
Implemented the Multi Variate Statistical Classifier (MVSC) to determine the Rahul Biswas, Patrick R.
significance of various short noise transients in the astrophysical data. The training Brady, Jolien D.E. Creighton
and Stephen Fairhurst,
was done using Random Forest Technology. Compared to similar algorithms like
Class. Quant. Grav (26):
Support Vector Machines and ANN. Achieved 90% efficiency at a low false alarm 175009
probability (ROC figure of merit). This work was done with collaborators at MIT,
NASA and Caltech.
UNIVERSITY OF WISCONSIN MILWAUKEE
Research Assistant AUGUST 2004 - SEPTEMBER 2010
During my PhD, I performed the analysis of LIGO-Virgo experimental data to
search for possible astrophysical sources of gravitational-wave. This was the first time
data from four detectors had been analyzed. The data was recorded at 16KHz and
down sampled to 2KHz for analysis. Matched filtering technique was used after
whitening the data. [2]
Key Projects:
Formulated a method to combine the Bayesian upper limits from various
experiments based on the most significant event in a astrophysical search. This
method is the only way to combine experimental results in my field and determine the
expected rate of binary neutron stars in the Universe. [3]
Developed the non-central glitch model using the non-central chi-square distribution
and studied the statistical significance of instrumental artifacts in the data. This
method was further developed to estimate the noise estimates with false alarm
probability of 10 -6 .
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