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Maria G Lupetini

from San Diego, CA
Age ~70
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Also known as:
Maria G L Ross, Maria G Ross, Maria L Ross, Maria Lupetin
Phone and address:
12742 Kestrel St, San Diego, CA 92129
(858) 252-9261
Related to:
Marion E SimonDavid J Ross, 73Stephen B Seus, 72William M RossAnn E RossThomas J Ross, 68Philip L Ross, 37Thomas J RossWilliam L Ross, 32
Connected to:
Lisa R Lupfer, 59Matthew L Lupfer, 41Nicholas L Lupfer, 36Sarah Lupfer, 100Thomas A Lupfer, 43Juwanera C Luphahla, 62Adam B Lupher, 41Deirdre A Lupher, 43Robert D Lupher, 96Tonja R Lupher, 71

Maria Lupetini Phones & Addresses

  • 12742 Kestrel St, San Diego, CA 92129 (858) 252-9261
  • Glenview, IL
  • Chicago, IL
  • 4695 Whispering Woods Ct, San Diego, CA 92130

Work

Company: Qualcomm technologies incorporated Jun 2004 Position: Engineering advanced analytics and asset management

Education

School / High School: MBA - Universtiy of Chicago, MS Operations Research - University of Minnesota

Skills

Data Mining • Predictive Analytics • Analytics • Business Intelligence • Big Data • Optimization • Machine Learning • Leadership • Sas • Analysis • Cross Functional Team Leadership • Data Science • Crm • Statistical Modeling • Web Analytics • Management • Pattern Recognition • Product Management • Strategy • Operations Research • Semiconductor Industry • Data Scientist • Optimizations • Predictive Modeling • Data Warehousing • Advanced Analytics • Mathematics • Customer Relationship Management • Python

Industries

Wireless

Resumes

Resumes

Maria Lupetini Photo 1

Chief Executive Officer And Chief Data Scientist

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Location:
San Diego, CA
Industry:
Wireless
Work:
Qualcomm Technologies Incorporated since Jun 2004
Engineering Advanced Analytics and Asset Management
InfoMaker Inc Jan 1989 - Jun 2004
President and Founder
Keylime Software (now part of Yahoo) Jan 2000 - Dec 2000
VP Client Services & Chief Privacy Officer
Education:
MBA - Universtiy of Chicago, MS Operations Research - University of Minnesota
University of Chicago
Master of Business Administration (MBA), Finance & Marketing University of Minnesota-Twin Cities
MS, Operations Research
Skills:
Data Mining
Predictive Analytics
Analytics
Business Intelligence
Big Data
Optimization
Machine Learning
Leadership
Sas
Analysis
Cross Functional Team Leadership
Data Science
Crm
Statistical Modeling
Web Analytics
Management
Pattern Recognition
Product Management
Strategy
Operations Research
Semiconductor Industry
Data Scientist
Optimizations
Predictive Modeling
Data Warehousing
Advanced Analytics
Mathematics
Customer Relationship Management
Python

Publications

Us Patents

On-Chip Thermal Management Techniques Using Inter-Processor Time Dependent Power Density Data For Indentification Of Thermal Aggressors

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US Patent:
8595525, Nov 26, 2013
Filed:
Nov 7, 2011
Appl. No.:
13/290456
Inventors:
Jon J. Anderson - Boulder CO, US
Victor A. Chiriac - San Diego CA, US
Sorin A. Dobre - San Diego CA, US
Maria G. Lupetini - San Diego CA, US
Joseph V. Zanotelli - San Diego CA, US
Assignee:
QUALCOMM Incorporated - San Diego CA
International Classification:
G06F 1/32
US Classification:
713320, 713323, 713324, 713330
Abstract:
Various embodiments of methods and systems for controlling and/or managing thermal energy generation on a portable computing device are disclosed. Data discarded from one or more processing core registers may be monitored and analyzed to deduce individual workloads that have been processed by each of the cores over a unit of time. From the deduced workloads, the power consumed by each of the cores over the unit of time in order to process the workload can be calculated. Subsequently, a time dependent power density map can be created which reflects a historical and near real time power consumption for each core. Advantageously, because power consumption can be correlated to thermal energy generation, the TDPD map can be leveraged to identify thermal aggressors for targeted, fine grained application of thermal mitigation techniques. In some embodiments, workloads may be reallocated from the identified thermal aggressors to the identified underutilized processing components.

Generating A Power Model For An Electronic Device

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US Patent:
20110106286, May 5, 2011
Filed:
Nov 5, 2009
Appl. No.:
12/613055
Inventors:
Maria Lupetini - San Diego CA, US
Assignee:
QUALCOMM INCORPORATED - San Diego CA
International Classification:
G06F 1/26
G06F 15/18
G06F 17/50
US Classification:
700103, 700291, 706 12, 703 1, 700121
Abstract:
An apparatus that generates a power model for an electronic device, an apparatus that operates in accordance with a generated power model, and methods for generating a power for an electronic device are disclosed. In a particular embodiment, a method of generating a power model for the electronic device includes reducing training data to identify a subset of operating parameters of an electronic device that contribute most to power consumption of the electronic device and generating the power model for the electronic device based on the reduced training data. The power model is operative to predict a power consumption value corresponding to the electronic device responsive to a set of operating parameter values corresponding to operation of the electronic device.

Generating A Power Model For An Electronic Device

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US Patent:
20130246829, Sep 19, 2013
Filed:
May 6, 2013
Appl. No.:
13/887474
Inventors:
Maria Lupetini - San Diego CA, US
Assignee:
QUALCOMM Incorporated - San Diego CA
International Classification:
G06F 1/28
US Classification:
713340
Abstract:
A method of fabricating a semiconductor device generates a power model for an electronic device. The method includes receiving a data file including design information corresponding to the semiconductor device. The method further includes fabricating the semiconductor device according to the design information, where the semiconductor device includes a processor configured to identify a subset of operating parameters of the electronic device that contribute most to power consumption of the electronic device by reducing training data. The processor is further configured to generate a power model for the electronic device based on the reduced training data. The power model is operable to predict, responsive to a set of operating parameter values corresponding to operation of the electronic device, a power consumption value corresponding to the electronic device.
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Maria G Lupetini from San Diego, CA, age ~70 Get Report