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Federico Pardina-Malbran

from Fort Collins, CO
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Also known as:
Federico Pardina, Malbra Federico Pardina
Phone and address:
2945 Telluride Ct, Fort Collins, CO 80526
(970) 223-6740
Connected to:
Jeffrey Pardinas, 28Jesus O Pardina, 65Roberto Pardinas, 58Bobbin E Pardine, 63Alicia E Pardini, 37April L Pardini, 71Corlissa L Pardini, 48Daniel R Pardini, 67Francesca Pardini, 32Harry J Pardini, 100

Federico Pardina-Malbran Phones & Addresses

  • 2945 Telluride Ct, Fort Collins, CO 80526 (970) 223-6740

Work

Company: John deere Jan 2015 Position: Data scientist

Education

Degree: Doctorates, Doctor of Philosophy School / High School: Colorado State University 2007 to 2009

Skills

Statistics • Analysis • Data Mining • Data Analysis • Gis • Spatial Analysis • Databases • Process Improvement • Forecasting • Arcgis • Sql • Market Analysis • Python • Remote Sensing • Gis Analysis • Spark • Amazon Web Services • Hadoop

Languages

English • Spanish

Ranks

Certificate: Machine Learning

Industries

Research

Resumes

Resumes

Federico Pardina-Malbran Photo 1

Data Scientist

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Location:
Fort Collins, CO
Industry:
Research
Work:
John Deere
Data Scientist
John Deere Jan 2013 - Dec 2014
Manager, Business Analytical Services
Deere & Co May 2005 - Dec 2012
Statistician
Geoveritas John Deere Jun 2004 - May 2005
Gis Analyst and Statistician
Colorado State University Jan 2003 - Jun 2004
Reasearch Assistant
Education:
Colorado State University 2007 - 2009
Doctorates, Doctor of Philosophy Colorado State University 2002 - 2004
Master of Science, Masters, Genetics Universidad Catolica De Cordoba 1997 - 2001
Bachelors, Bachelor of Science, Agronomy, Engineering Colorado State University
Masters
Skills:
Statistics
Analysis
Data Mining
Data Analysis
Gis
Spatial Analysis
Databases
Process Improvement
Forecasting
Arcgis
Sql
Market Analysis
Python
Remote Sensing
Gis Analysis
Spark
Amazon Web Services
Hadoop
Languages:
English
Spanish
Certifications:
Machine Learning

Publications

Us Patents

Machine Control Using Real-Time Model

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US Patent:
20210302925, Sep 30, 2021
Filed:
Jun 10, 2021
Appl. No.:
17/344517
Inventors:
- Moline IL, US
Nathan R. Vandike - Geneseo IL, US
Federico Pardina-Malbran - Fort Collins CO, US
Noel W. Anderson - Fargo ND, US
Michael A. Waldo - Ankeny IA, US
International Classification:
G05B 13/04
A01D 41/14
A01D 41/127
Abstract:
A priori geo-referenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the geo-referenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

Machine Control Using Real-Time Model

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US Patent:
20200323133, Oct 15, 2020
Filed:
Apr 10, 2019
Appl. No.:
16/380564
Inventors:
- Moline IL, US
Nathan R. Vandike - Geneseo IL, US
Federico Pardina-Malbran - Fort Collins CO, US
Bhanu Kiran Reddy Palla - Bettendorf IA, US
International Classification:
A01D 41/127
A01D 43/08
G05B 13/04
H04W 4/021
Abstract:
A priori georeferenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the georeferenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

Machine Control Using Real-Time Model

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US Patent:
20200326674, Oct 15, 2020
Filed:
Apr 10, 2019
Appl. No.:
16/380531
Inventors:
- Moline IL, US
Nathan R. Vandike - Geneseo IL, US
Federico Pardina-Malbran - Fort Collins CO, US
Michael A. Waldo - Ankeny IA, US
Noel W. Anderson - Fargo ND, US
International Classification:
G05B 13/04
A01D 41/127
A01D 41/14
Abstract:
A priori geo-referenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the geo-referenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

Zonal Machine Control

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US Patent:
20200326727, Oct 15, 2020
Filed:
Apr 10, 2019
Appl. No.:
16/380550
Inventors:
- Moline IL, US
Nathan R. VANDIKE - Geneseo IL, US
Federico PARDINA-MALBRAN - Fort Collins CO, US
Noel W. ANDERSON - Fargo ND, US
International Classification:
G05D 1/02
A01D 41/127
A01B 69/00
G05B 13/04
Abstract:
A work machine receives a thematic map that maps values of a variable to different geographic locations at a worksite. Control zones are dynamically identified on the thematic map and actuator settings are dynamically identified for each control zone. A position of the work machine is sensed, and actuators on the work machine are controlled based upon the control zones that the work machine is in, or is entering, and based upon the settings corresponding to the control zone. These control zones and settings are dynamically adjusted based on in situ (field) data collected by sensors on the work machine.

Plant Emergence System

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US Patent:
20170228118, Aug 10, 2017
Filed:
Feb 9, 2016
Appl. No.:
15/018994
Inventors:
- Moline IL, US
MARC LEMOINE - Bettendorf IA, US
FEDERICO PARDINA-MALBRAN - Fort Collins CO, US
International Classification:
G06F 3/0484
G01N 21/29
G06F 3/0482
G01N 33/00
G06T 7/00
G06F 17/30
Abstract:
An unmanned image capture system captures images of a field or work area using a first, spectral image capture system and a second video image capture system. Crop location data that is indicative of the location of crop plants within the field, is obtained. Evaluation zones in the image data generated by the first image capture system are identified based on the crop location data. Crop plants within the evaluation zones are then identified, analyzed to generate a corresponding emergence metric, and linked to a corresponding video image generated by the second image capture system.
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Federico Pardina-Malbran from Fort Collins, CO Get Report