Ankur Teredesai

Supervised learning algorithms have been widely applied in
tracking-by-detection based methods for object tracking in recent years.
Most of these approaches treat tracking as a classification problem and
solve it by training a discriminative classifier and exhaustively evaluating
every possible target position; problems thus exist for two reasons. First,
since the classifier describes the common feature of samples in an implicit
way, it is not clear how well the classifier can… Show more

Supervised learning algorithms have been widely applied in
tracking-by-detection based methods for object tracking in recent years.
Most of these approaches treat tracking as a classification problem and
solve it by training a discriminative classifier and exhaustively evaluating
every possible target position; problems thus exist for two reasons. First,
since the classifier describes the common feature of samples in an implicit
way, it is not clear how well the classifier can represent the feature of the
desired object against others; second, the brute-force search within the
output space is usually time consuming, and thus limits the competence
for real-time application. In this paper, we treat object tracking as a
problem of similarity matching for streaming data. We propose to apply
unsupervised learning by Locality Sensitive Hashing (LSH) and use LSH
based similarity matching as the main engine for target detection. In
addition, our method applies a Support Vector Machine (SVM) based
supervised classifier cooperating with the unsupervised detector. Both
the proposed tracker and several selected trackers are tested on some
well accepted challenging videos; and the experimental results demonstrate
that the proposed tracker outperforms the selected other trackers
in terms of the effectiveness as well as the robustness. Show less

Readmission Score as a Service(RaaS) is the first medical risk calculator that utilizes cloud computing capabilities to provide risk of hospital readmission score as a service.

Managing the water quality in an urban environment is extremely challenging. While it flows, the water picks up pollutants such as lawn care chemicals, oil, and pet waste bacteria. In fact, topography plays a factor in where water runoff goes. However, there are many other factors, such as urban density, impermeable surface coverage, weather events and tidal patterns which all have the potential to impact not only the final destination of a particular pollutant but also the rate of travel along… Show more

Managing the water quality in an urban environment is extremely challenging. While it flows, the water picks up pollutants such as lawn care chemicals, oil, and pet waste bacteria. In fact, topography plays a factor in where water runoff goes. However, there are many other factors, such as urban density, impermeable surface coverage, weather events and tidal patterns which all have the potential to impact not only the final destination of a particular pollutant but also the rate of travel along the route. In this paper, we propose a system, named AMADEUS (Azure Marketplace of Applications for Diverse Environmental Use as a Service), which is an interactive, self-service framework that allows end users to explore, analyse, and visualize the environmental data within the context of their applications. As a case study, we present a sample application on AMADEUS which aims to identify contaminant sources in the Puget Sound region. AMADEUS integrates chemical spill data, meteorological data, Puget Sound buoy data, and water runoff models to perform pollutant path tracking and prediction. More specifically, given a water fall location, AMADEUS is able to identify the runoff path, compute the impact of environmental factors. For example, it can trace back the pollutant to its source, and predict the final destination of the pollutant. In addition, AMADEUS provides user friendly visualization to demonstrate the tracking and prediction of pollutants' routes. - See more at: http://cwds.uw.edu/amadeus-system-monitoring-water-quality-parameters-and-predicting-contaminant-paths#sthash.4VyyT3a2.dpuf Show less

In this paper, we present a novel k-means based distributed in-database algorithm for look-alike modeling implemented within the nPario database system. We demonstrate the utility of the algorithm: accurate, invariant of size and skew of the targetable audience(very few positive examples), and dependent linearly on the capacity and number of nodes in the distributed environment. To the best of our knowledge this is the first ever commercially deployed distributed look-alike modeling… Show more

In this paper, we present a novel k-means based distributed in-database algorithm for look-alike modeling implemented within the nPario database system. We demonstrate the utility of the algorithm: accurate, invariant of size and skew of the targetable audience(very few positive examples), and dependent linearly on the capacity and number of nodes in the distributed environment. To the best of our knowledge this is the first ever commercially deployed distributed look-alike modeling implementation to solve this problem. We compare the performance of our algorithm with other distributed and non-distributed look-alike modeling techniques, and report the results over a multi-core environment. Show less