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My research interests lie in the area of statistical machine learning. As a research scientist at Siemens my current research is focused on developing novel supervised learning algorithms to deal with imperfect supervision---especially subjective (crowdsourcing), noisy (multiple instance learning), and partial label information (survival analysis). I am also currently working on empirical Bayesian methods for sparse high-dimensional prediction and estimation problems. As a graduate student my doctoral research focused on developing fast scalable machine learning algorithms for massive data sets using ideas inspired from computational physics and computational geometry.
LEARNING WITH PARTIAL / IMPERFECT SUPERVISION
HIGH DIMENSIONAL CLASSIFICATION
FAST METHODS FOR COMPUTATIONAL STATISTICS AND MACHINE LEARNING
MEDICAL IMAGING APPLICATIONS / COMPUTER AIDED DIAGNOSIS
MICROPHONE ARRAY POSITION CALIBRATION.
LOCALIZATION OF DISTRIBUTED ACOUSTIC SENSOR / ACTUATOR ARRAYS.
EXTRACTING THE PINNA SPECTRAL NOTCHES.
TIME DELAY ESTIMATION USING EXCITATION SOURCE INFORMATION.