Natalie Malaszenko

Vice President of Marketing at Omni Hotels & Resorts

Location
Dallas/Fort Worth Area
Industry
Hospitality
Recommendations25 people have recommended Natalie
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Background

Summary

Marketing Executive recognized as industry leader by "Bloomberg BusinessWeek". Skilled in building and motivating Global teams with strong track record of developing, optimizing and implementing successful revenue generating activities. Innovative and passionate with an inherent drive for results and decisive action.

Game changing contributions to the future of the digital landscape, including a US Patent for online navigation that brings symptom-based customer solutions to other customers in real-time, launching the World’s first live stream concert on Twitter and helping HP become the largest brand on LinkedIn and the 1st to hit 1M followers.

Proudly served as a member of multiple boards including LinkedIn's Marketing Advisory Group and AOL's Tech Advisory Board.

Patents

Dynamic Online Presentation of Solutions Based on Customer Symptoms(Link)

United States 20100077008
Issued March 25, 2010

A knowledge management system is disclosed providing the ability to use an issue's symptoms as search criteria for potential solutions within a solution network. User, system, and diagnostics information is received by the solution network. Symptoms of the issue are provided, which are then used with the user, system, diagnostics, and additional information to search the solution network for potential solutions.

Inventors:

Publications

Independent Components of Magnetoencephalography: Localization(Link)

Neural Computation
August 2002

We applied second-order blind identiŽcation (SOBI), an independent component analysis method, to MEG data collected during cognitive tasks. We explored SOBI’s ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identifiŽcation and localization. We compare localization of the SOBI-separated components to localization from unprocessed sensor signals, using an equivalent current dipole modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations. Furthermore, this preprocessing allowed the detection of neuronal source activations that were otherwise undetectable. This increased probability of neuronal source detection and localization can be particularly beneŽcial for MEG studies of higher-level cognitive functions, which often have greater signal variability and degraded signal-to-noise ratios than sensory activation tasks.

Authors:

Independent Components of Magnetoencephalography: Single-trial Response Onset Time Estimation(Link)

NeuroImage
2002

We recently demonstrated that second-order blind identification (SOBI), an independent component analysis (ICA) method, can separate the mixture of neuronal and noise signals in magnetoencephalographic (MEG) data into neuroanatomically and neurophysiologically meaningful components. When the neuronal signals had relatively higher trial-to-trial variability, SOBI offered a particular advantage in identifying and localizing neuronal source activations with increased source detectability (A. C. Tang et al.,2002, Neural Comput. 14, 1827–1858). Here, we explore the utility of SOBI in the analysis of temporal aspects of neuromagnetic signals from MEG data. From SOBI components, we were able to measure single-trial response onset times of neuronal populations in visual, auditory, and somatosensory modalities during cognitive and sensory activation tasks, with a detection rate as high as 96% under optimal conditions. Comparing the SOBI-aided detection results with those obtained directly from the sensors, we found that with SOBI preprocessing, we were able to measure, among a greater proportion of trials, single-trial response onset times that are above background neuronal activity. We suggest that SOBI ICA can improve our current capability in measuring single-trial responses from human subjects using the noninvasive brain imaging method MEG.

Authors:

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