{"id":220,"date":"2016-09-09T18:37:47","date_gmt":"2016-09-09T23:37:47","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?page_id=220"},"modified":"2026-03-11T16:03:40","modified_gmt":"2026-03-11T20:03:40","slug":"machine-learning-sensing-lab","status":"publish","type":"page","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/","title":{"rendered":"Machine Learning & Sensing Lab"},"content":{"rendered":"\n
\"Group<\/figure>\n\n\n\n

<\/p>\n\n\n\n

The Machine Learning and Sensing Laboratory develops machine learning methods for autonomously analyzing and understanding sensor data.<\/p>\n\n\n\n

We investigate and develop artificial intelligence, machine learning, pattern recognition, computational intelligence, signal processing, and information fusion methods for application to sensing. Applications we have studied include landmine and explosive object detection, automated plant phenotyping, sub-pixel target detection, and underwater scene understanding. We have developed algorithms for ground-penetrating radar, hyperspectral imagery, electromagnetic induction data, synthetic aperture SONAR, and minirhizotron imagery. <\/p>\n\n\n\n

<\/a>Current Projects<\/h2>\n\n\n\n

Center for Multiscale Modeling of Multiphase Combustion (CM3C)<\/strong>
Funding Agency:<\/em><\/strong> DOE
Role:<\/em><\/strong> Co-PI; PI:<\/em> S. Balachandar
Dates:<\/em><\/strong> August 2025 \u2013 Current
Related Links:<\/em><\/strong> News Stories<\/a><\/p>\n\n\n\n

Paving the Way for Adoption of New Technologies<\/strong>
Funding Agency:<\/em><\/strong> University of Florida Strategic Funds
Role:<\/em><\/strong> Co-PI; PI:<\/em> S. Thornsbury
Dates:<\/em><\/strong> January 2025 \u2013 Current<\/p>\n\n\n\n

DSFAS: Getting to the Core of Roots: Model-Informed Machine Learning for Phenotyping and Mapping<\/strong>
Funding Agency:<\/em><\/strong> USDA NIFA
Role:<\/em><\/strong> PI
Students:<\/em><\/strong> Aditya Dutt<\/a>, Juan Diaz<\/a>
Dates:<\/em><\/strong> August 2024 \u2013 Current<\/p>\n\n\n\n

Using Machine Learning to Uncover the Role of Tortoises in Shaping Freshwater Ponds in the Gal\u00e1pagos<\/strong>
Funding Agency:<\/em><\/strong> UF Biodiversity Institute
Role:<\/em><\/strong> Co-PI; PI:<\/em> A. Subalusky, UF Biology
Dates:<\/em><\/strong> July 2024 \u2013 Current<\/p>\n\n\n\n

HCC: Medium: Optimizing Interactive Machine Learning Tools to Support Plant Science<\/strong>
Funding Agency:<\/em><\/strong> NSF
Role:<\/em><\/strong> Co-PI; PI:<\/em> L. Anthony, UF CISE
Students:<\/em><\/strong> Diandra Ojo<\/a>, Ayesha Naikodi<\/a>
Dates:<\/em><\/strong> October 2023 \u2013 Current<\/p>\n\n\n\n

Equipment: MRI: Track 2 Acquisition of a Novel Performance-Driven 3D Imaging System for Extremely Noisy Objects (NPIX)<\/strong>
Funding Agency:<\/em><\/strong> NSF
Role:<\/em><\/strong> Co-PI; PI:<\/em> N. Asadi, UF ECE
Dates:<\/em><\/strong> October 2023 \u2013 Current<\/p>\n\n\n\n

Domain Transfer for Continuity of Performance Across Synthetic Aperture Sonar Systems<\/strong>
Investigate domain transfer approaches to translate data and algorithms across SAS sensing systems.
Funding Agency:<\/em><\/strong> ONR
Role: <\/em><\/strong>Co-PI; PI:<\/em> J. Fairbanks, UF CISE
Students:<\/em><\/strong> Sam Gallic, Luke Saleh
Dates:<\/em><\/strong> August 2023 \u2013 Current<\/p>\n\n\n\n

Research into aflatoxin mitigation that is ongoing and proposed by the University of Florida and Mississippi State University in partnership with Premium Peanut<\/strong>
Funding Agency:<\/strong><\/em> USDA\/ARS National Peanut Research Laboratory
Role: <\/em><\/strong>Co-PI; PI:<\/em> B. Tillman, UF Agronomy
Students<\/strong>:<\/strong><\/em> Khaled Hamad<\/a>
Dates:<\/em><\/strong> January 2023 \u2013 Current<\/p>\n\n\n\n

MCA: Improving understanding of controls over spatial heterogeneity in dryland soil carbon pools in the age of ‘big data’<\/strong>
Machine learning techniques related to detecting surface features from imagery in dryland ecosystems.
Funding Agency:<\/strong><\/em> NSF
Role: <\/em><\/strong>Senior Personnel; PI:<\/em> H. Throop, Arizona State University
Students<\/strong>:<\/strong><\/em> Clinton Jules<\/a>
Dates:<\/em><\/strong> August 2022 \u2013 Current<\/p>\n\n\n\n

DSFAS-CIN: Alliance of Plant Phenotyping Software (APPS) Developers: Integration and Interoperability of Open-Source Tools to Support Plant Phenotyping for Agriculture<\/strong>
This effort is to create an open source plant phenotyping software package and platform.
Funding Agency:<\/strong><\/em> USDA NIFA
Role: <\/em><\/strong>Co-PI; PI:<\/em> M. Gehan
Students<\/strong>:<\/strong><\/em> Ayesha Naikodi<\/a>
Dates:<\/em><\/strong> January 2022 \u2013 Current<\/p>\n\n\n\n

AI-HARVEST: Artificial Intelligence Hub for Agricultural Reporting and Verification of Ecosystem Services through Sensing Technologies<\/strong>
<\/em>In this effort, we are developing hybrid- deep learning and crop modeling tools to measure and quantify ecosystem services provided by an agricultural system.
Funding Agency:<\/strong><\/em> FDACS
Role:<\/em><\/strong> Co-PI; PI:<\/em> M. Dukes
Students:<\/em><\/strong> Satya Krishna Pothapragada<\/a>
Dates:<\/em><\/strong> February 2021 \u2013 Current
Related Links:<\/em><\/strong> Smarter Agriculture Takes Root<\/a><\/p>\n\n\n\n

SiTS: Hyperspectral Signals in the Soil<\/strong>
<\/em>In this effort, we will build hyperspectral cameras (which also collect information outside of the visible range collected by standard color cameras) that will be inexpensive, have an automated mechanism to move up and down minirhizotron tubes, and are paired with automated algorithms to process and understand the collected data.
Funding Agency:<\/strong><\/em> USDA NIFA
Role: <\/strong><\/em>PI
Students:<\/strong><\/em> Ritesh Chowdhry<\/a>
Dates:<\/em><\/strong> January 2021 \u2013 Current
Related Links:<\/em><\/strong> UF Article 2020<\/a> | Horti Daily Article 2021<\/a> | WCJB News Story and Video #1 2021<\/a> | WCJB News Story and Video #2 2021<\/a> | Villages Daily Sun 2021<\/a> | Fox 13 Tampa Bay 2021<\/a> | Florida Insider 2021<\/a> | Independent 2021<\/a> | Reuters 2021<\/a> | Smarter Agriculture Takes Root<\/a><\/p>\n\n\n\n

Testing predictions of plant-microbe-environment interactions to optimize climate adaptation and improve sustainability in switchgrass feedstocks
<\/strong>This work will leverage a field-established genetic resource network (S-GENE) to deepen understanding of local adaptation and to identify beneficial traits, genes, and microbial associates that contribute to switchgrass productivity.
Funding Agency:<\/em><\/strong> DOE
Role:<\/em><\/strong> Co-PI; PI:<\/em> T. Juenger, UT Austin
Dates:<\/em><\/strong> September 2020 \u2013 Current
Students:<\/strong><\/em> Diandra Ojo<\/a>, Bakari Kerr
Related Links:<\/em><\/strong> UF Article 2020<\/a> | UT Article 2020<\/a> | Smarter Agriculture Takes Root<\/a><\/p>\n\n\n\n

NSF Engineering Research Center for Internet of Things for Precision Agriculture<\/strong>
This effort is an NSF ERC focused on the development of Internet of Things (IoT) for Precision Agriculture and the infrastructure (including algorithm development) needed to effectively deploy IoT devices for precision agriculture. 
Funding Agency:<\/strong><\/em> NSF
Role:<\/em><\/strong> Co-PI; PI:<\/em> C. Kagan, UPenn
Dates:<\/em><\/strong> September 2020 \u2013 Current
Students:<\/strong><\/em> Anna Hampton<\/a>, Khaled Hamad<\/a>, Chloe Nolan
Related Links:<\/em><\/strong> UF Article #1 2020<\/a> | UF Article #2 2020<\/a> | UF Article #3 2020<\/a> | UF Article #4 2021<\/a> | IoT4Ag Homepage<\/a>| NSF Award Article 2020<\/a> | Purdue Article 2020<\/a> | UC Merced Article 2020<\/a> | WCJB News Story and Video 2020<\/a> | Smarter Agriculture Takes Root<\/a><\/p>\n\n\n\n

Completed Projects<\/h2>\n\n\n\n

Identifying drought-resistance traits and drought stress limits of southeastern peanuts<\/strong>
Developing plant phenotypic approaches to identify drought-resistance and drought stress in peanuts.
Funding Agency:<\/strong><\/em> NPB
Role: <\/em><\/strong>Co-PI; PI:<\/em> W. Hammond, UF Agronomy; Co-PI:<\/em> B. Tillman, UF Agronomy
Dates:<\/em><\/strong> January 2023 \u2013 June 2024<\/p>\n\n\n\n

FreshID: Fruits and Vegetables Quality Evaluation Using Hyperspectral Imaging System<\/strong>
In this effort, we are developing AI-based approaches to detect and predict vegetable “freshness” levels.
Funding Agency:<\/strong><\/em> USDA NIFA and UF Research
Role: <\/em><\/strong>Co-PI; PI:<\/em> T. Liu, UF Horticulture
Dates:<\/em><\/strong> June 2021 \u2013 April 2025
Related Links:<\/em><\/strong> UF Article 01 2021<\/a> | UF Article 02 2021 <\/a> | WCJB News Story and Video 2021<\/a> | Gainesville Sun Article 2021 <\/a><\/p>\n\n\n\n

Adaptive Manifold Learning for Multi-Sensor Translation & Fusion given Missing Data<\/strong>
Funding Agency:<\/em> Center for Big Learning
Role:<\/em> PI
Dates:<\/em> May 2018 \u2013 December 2024<\/p>\n\n\n\n

AI for Climate Smart Florida Agriculture<\/strong>
These funds are to support students interested in exploring the use of artificial intelligence for climate smart agriculture. The funds are dedicated to provide student scholarships to support data collection activities at producer locations throughout the state of Florida.
Funding Agency:<\/em> Solutions from the Land
Role: <\/em>PI; Co-PI:<\/em> J. Dubeux
Dates:<\/em> November 2021 \u2013 December 2024<\/p>\n\n\n\n

HYPERspectral Plant HYdration Status (HYPER-PHYS): a novel and scalable method for sensing plant function, dysfunction, and failure on a warming planet<\/strong>
Develop HYPERspectral Plant HYdration Status (HYPER-PHYS), a novel and scalable method for sensing plant function, dysfunction, and mortality. Developing HYPER-PHYS will allow monitoring of plant water status in high-throughput agricultural plant breeding programs, while also enabling ecosystem-scale monitoring of plant water status in biodiverse natural systems.
Funding Agency:<\/em> UF Research
Role: <\/em>Co-PI; PI:<\/em> W. Hammond; Co-PI:<\/em> G. John
Dates:<\/em> June 2022 \u2013 December 2024<\/p>\n\n\n\n

Countermine Division\u2019s Electro-Optical and Infrared Detection Algorithms<\/strong>
Develop and validate target detection algorithms.
Funding Agency:<\/em> DoD
Role: <\/em>PI; Co-PI:<\/em> J. Fairbanks; J. Wilson
Dates:<\/em> August 2022 \u2013 December 2024<\/p>\n\n\n\n

MRA: Disentangling Cross-scale Influences on Tree Species, Traits, and Diversity from Individual Trees to Continental Scales<\/strong>
Trees are essential to ecosystems. They store carbon, reduce erosion, and serve as habitat for other species. The factors influencing trees, and the spatial scales at which they are managed, range from an individual tree to entire continents. Since there are approximately three trillion trees in the world collecting data on every tree over large areas is impossible using traditional methods. Therefore, it is necessary to use new technology to measure and describe individual trees over large geographic areas. This research will address this fundamental challenge by combining high resolution remote sensing data with field data on trees.
Funding Agency:<\/em> NSF
Role:<\/em> Co-PI; PI:<\/em> E. White, Univ. Florida
Dates:<\/em> August 2019 \u2013 August 2024
Related Links:<\/em> Project Website<\/a><\/p>\n\n\n\n

Robust and Reversible Deep Nets for Synthetic Aperture Sonar Analysis<\/strong>
<\/em>This project is developed automated outlier detection and competency awareness techniques for deep learning architectures with specific application to synthetic aperture sonar image analysis.
Funding Agency:<\/em> Naval Surface Warfare Center
Role: <\/em>PI
Dates:<\/em> August 2020 \u2013 July 2024<\/p>\n\n\n\n

Superpixel Segmentation and Texture Feature Learning for Multi-Aspect Underwater Scene Understanding
<\/strong>In this project, we are developing segmentation and texture feature analysis methods for automated underwater scene understanding using synthetic aperture sonar.
Funding Agency:<\/em> DOD
Role:<\/em> PI
Dates: May 2020 \u2013 April 2024<\/p>\n\n\n\n

Understanding pre-harvest and post-harvest factors that can help to mitigate aflatoxin in peanut
<\/strong>This project is investigating and working to identify both pre- and post-harvest risk factors for Aflatoxin develop in peanut production. The effort involves imaging to directly detect evidence of aflatoxins as well as advancing models to combine environmental and management factors to predict aflatoxin risk.
<\/strong>Funding Agency:<\/em> Industry Sponsors
Role: <\/em>Co-PI; PI:<\/em> B. Tillman, Univ. Florida
Dates:<\/em> February 2021 \u2013 December 2023<\/p>\n\n\n\n

Hurricane effects on the distribution and management of plant invasions in coastal habitats<\/strong>
In this effort, we are using on-the-ground plant measurements and aerial hyperspectral sensing to evaluate the effects of hurricanes on Brazilian peppertree.
Funding Agency:<\/em> Everglades National Park
Role:<\/em> Co-PI; PI:<\/em> L. Flory, UF Agronomy
Dates:<\/em> September 2020 \u2013 December 2023
Related Links:<\/em> UF Article 2021<\/a><\/p>\n\n\n\n

SENTINEL: SENsing Threats In Natural Environments using Ligand-receptor modules<\/strong>
Funding Agency: <\/em>DARPA
Role:<\/em> Co-PI; PI:<\/em> D. Nusinow, Danforth Plant Science Center
Dates:<\/em> September 2018 \u2013 September 2023<\/p>\n\n\n\n

Improved system assessment of aflatoxin risk utilizing novel data and sensing<\/strong>
Funding Agency:<\/em> USDA NIFA
Role: <\/em>Co-PI; PI:<\/em> D. Rowland at Univ. Florida
Dates:<\/em> May 2018 \u2013 January 2023
Related Links:<\/em> UF|IFAS Article<\/a><\/p>\n\n\n\n

Coordinated Adaptive Phenotyping (CAPs) for Improving Soil Water Acquisition<\/strong>
Funding Agency:<\/em> USDA NIFA
Role: <\/em>Co-PI; PI:<\/em> B. Tillman at Univ. Florida
Dates:<\/em> June 2018 \u2013 December 2022 Related Links:<\/em> UF|IFAS Article<\/a><\/p>\n\n\n\n

Satellite estimation of Bahiagrass yield and crude protein<\/strong>
In this effort, satellite remote sensing imagery is being used to estimate parameters of interest in pasture used for cattle ranching.
Funding Agency:<\/em> Industry Partners
Role:<\/em> Co-PI; PI:<\/em> C. Wilson, UF Agronomy
Dates:<\/em> May 2020 \u2013 August 2022
Related Links:<\/em> UF Article 2021<\/a> | Gainesville Sun News Story 2021<\/a><\/p>\n\n\n\n

AEROFUSION-MLUQ: Mars Lander Aerodynamic Model Data Fusion using Machine Learning<\/strong>
In this effort, the team is developing machine learning tools to quantify uncertainty in planetary landing systems. The effort will advance sensitivity analysis, outlier identification, multi-sensor fusion, and associated uncertainty quantification on sensor analysis performed.
Funding Agency:<\/em> NASA
Role:<\/em> Co-PI; PI:<\/em> M. Lee, NASA
Dates: <\/em>August 2020 \u2013 July 2022<\/p>\n\n\n\n

Environmentally-aware Feature Extraction\/Selection and Classification of Underwater Objects in Synthetic Aperture Sonar Imagery for Mine Countermeasures<\/strong>
Funding Agency:<\/em> Office of Naval Research
Role: <\/em>Co-PI;  PI:<\/em> J. Keller, Univ. of Missouri
Dates:<\/em> May 2016 \u2013 Dec. 2021<\/p>\n\n\n\n

What mind matters? Machine learning approaches to linking structural variation in the brain to individual differences in spatial behavior<\/strong>
Funding Agency:<\/em> UF Research, AI Catalyst Funds
Role:<\/em> Co-PI; PI:<\/em> S. Weisberg, UF Psychology
Dates:<\/em> Nov. 2020 \u2013 Dec. 2021
Related Links:<\/em> UF Article 2021<\/a><\/p>\n\n\n\n

Parasitic Nematode Identification with Deep Learning<\/strong>
Funding Agency:<\/em> UF Research, AI Catalyst Funds
Role:<\/em> PI
Dates:<\/em> Nov. 2020 \u2013 Nov. 2021
Related Links:<\/em> UF Article 2020<\/a> | UF IFAS Article 2021<\/a> | UF Explore Article 2021<\/a> | Growing Produce Article 2021<\/a><\/p>\n\n\n\n

Rays for Roots: Integrating Backscatter X-Ray Phenotyping, Modeling, and Genetics to Increase Carbon Sequestration and Resource Use Efficiency<\/strong>
Funding Agency: <\/em>ARPA-E
Role:<\/em> PI
Dates:<\/em> May 2017 \u2013 Aug 2021
Related Links:<\/em> ARPA-E ROOTS Project Descriptions<\/a> | ARPA-E Announced $70M in Funding<\/a> | UF HWCOE Article<\/a> | UF Research Awards<\/a><\/p>\n\n\n\n

Cyber-Physical Systems Security through Robust Adaptive Possibilitistic Algorithms: a Cross Layered Framework<\/strong>
Funding Agency:<\/em> NSF
Role:<\/em> Co-PI; PI:<\/em> A. Bretas, Univ. Florida
Dates:<\/em> August 2018 \u2013 July 2021<\/p>\n\n\n\n

Multi-Sensor Data Fusion for Buried Target Detection<\/strong>
Funding Agency:<\/em> Army Research Office
Role:<\/em> PI
Dates: <\/em>April 2017 – May 2021<\/p>\n\n\n\n

CAREER: Supervised Learning for Incomplete and Uncertain Data<\/strong>
Funding Agency:<\/em> National Science Foundation
Role:<\/em> PI
Dates:<\/em> May 2014 \u2013 Apr. 2021
Related Links:<\/em> Project Website | <\/a>NSF Award Abstract <\/a> | <\/a>MU Article<\/a>| <\/a>MIZZOU Magazine Article<\/a><\/p>\n\n\n\n

Climate Adaptation and Sustainability in Switchgrass: Exploring Plant-Microbe-Soil Interactions across Continental Scale Environmental Gradients<\/strong>
Funding Agency:<\/em> Department of Energy
Role: <\/em>Co-PI;  PI:<\/em> T. Juenger, Univ. of Texas at Austin
Dates:<\/em> August 2015 \u2013 March 2021
Related Links:<\/em> UT Austin Article<\/a> | UF HWCOE Article<\/a> | Smarter Agriculture Takes Root<\/a><\/p>\n\n\n\n

Development of Competitive Analytic Research Methodologies with Universities, Volunteer Geospatial Information Methodologies GEOINT Curriculum Development and Scientific Exchange<\/strong>
Funding Agency:<\/em> NGA
Role:<\/em> Co-PI; PI:<\/em> P. Gader, Univ. Florida
Dates:<\/em> March 2018 \u2013 March 2021<\/p>\n\n\n\n

Multi-Aspect Underwater Scene Understanding<\/strong>
Funding Agency:<\/em> Office of Naval Research
Role:<\/em> PI
Dates:<\/em> Apr. 2014 \u2013 Dec. 2020
Related Links:<\/em> MU Article 2015<\/a> | MU Article 2016<\/a><\/p>\n\n\n\n

Taking the next steps towards making drone data applicable: novel approaches for directly relating UAV images to peanut maturity, disease, and drought<\/strong>
Funding Agency:<\/em> National Peanut Board
Role:<\/em> PI
Dates:<\/em> Jan. 2019 \u2013 June 2020<\/p>\n\n\n\n

Peanut Volatile Organic Compounds Analysis<\/strong>
Funding Agency:<\/em> National Peanut Board
Role: PI
Dates:<\/em> Jan. 2019 \u2013 June 2020 
Related Links:<\/em> Georgia Tech Article<\/a><\/p>\n\n\n\n

Biodiversity Data from Insect Songs: New hardware and software for monitoring insect bioacoustics, and new opportunities for public outreach<\/strong>
Funding Agency:<\/em> UFII & UFBI
Role:<\/em> Co-PI; PI:<\/em> B. Stucky, Univ. Florida
Dates:<\/em>  Nov. 2018 – May 2020<\/p>\n\n\n\n

Peanut seed quality research<\/strong>
Funding Agency:<\/em> APSA
Role:<\/em> Co-PI; PI:<\/em> D. Rowland, Univ. Florida
Dates:<\/em> Aug. 2018 – July 2019<\/p>\n\n\n\n

Robust Hyperspectral Image Analysis via Computational Topology<\/strong>
Funding Agency:<\/em> UFII
Role:<\/em> Co-PI; PI:<\/em> P. Bubenik, Univ. Florida
Dates:<\/em> Aug. 2017 – Aug. 2018<\/p>\n\n\n\n

Algorithm and Decision Support for Buried Object Detection<\/strong>
Funding Agency:<\/em> Army Research Office
Role:<\/em> PI
Dates: <\/em>October 2016 – April 2017<\/p>\n\n\n\n

New Investigator Program Award: Functions of Multiple Instances for Hyperspectral Analysis<\/strong>
Funding Agency:<\/em> National Geospatial-Intelligence Agency
Dates:<\/em> September 30, 2014 \u2013 October 2017
Role:<\/em> PI
Note: <\/em>Research conducted at Dr. Zare’s prior appointment at the Univ. of Missouri<\/p>\n\n\n\n

Machine Learning Techniques for Handheld Subsurface Object Detection<\/strong>
Funding Agency:<\/em> Army Research Office
Role:<\/em> PI
Dates:<\/em> November 1, 2013 \u2013 October 2016
Note: <\/em>Research conducted at Dr. Zare’s prior appointment at the Univ. of Missouri<\/p>\n\n\n\n

Understanding root growth using X-ray CT Imaging to increase crop yields<\/strong>
Funding Agency:<\/em> Mizzou Advantage
Role: <\/em>Co-PI;  PI:<\/em> S. Kovaleski
Dates:<\/em> June 2014 \u2013 September 2016
Note: <\/em>Research conducted at Dr. Zare’s prior appointment at the Univ. of Missouri<\/p>\n\n\n\n

Adaptive Underwater Target Detection<\/strong>
Funding Agency:<\/em> MU Research Board
Role:<\/em> PI
Dates:<\/em> January 1, 2014 \u2013 May 2015
Note: <\/em>Research conducted at Dr. Zare’s prior appointment at the Univ. of Missouri<\/p>\n\n\n\n

Environmentally-Adaptive Target Recognition Systems<\/strong>
Funding Agency:<\/em> Naval Surface Warfare Center
Role:<\/em> PI
Dates:<\/em> April 2014 \u2013 May 2015
Note: <\/em>Research conducted at Dr. Zare’s prior appointment at the Univ. of Missouri<\/p>\n\n\n\n

Mathematical Models for Describing and Reasoning with Geographic and Human Cultural Features<\/strong>
Funding Agency:<\/em> DOD
Role: <\/em>Co-PI;  PI:<\/em> J. Keller
Dates:<\/em> October 2010 \u2013 December 2014
Note: <\/em>Research conducted at Dr. Zare’s prior appointment at the Univ. of Missouri<\/p>\n\n\n\n

Explosive Object Detection with Electromagnetic Induction Sensors<\/strong>
Funding Agency:<\/em> DOD
Role:<\/em> PI
Dates:<\/em> September 2011 \u2013 May 2014
Note: <\/em>Research conducted at Dr. Zare’s prior appointment at the Univ. of Missouri<\/p>\n\n\n\n

Probabilistic Hyperspectral and LIDAR Fusion<\/strong>
Funding Agency:<\/em> DOD
Dates:<\/em> October 1, 2010 \u2013 October 31, 2013
Note: <\/em>Research conducted at Dr. Zare’s prior appointment at the Univ. of Missouri<\/p>\n\n\n\n

Airborne Multispectral Imagery (MSI) and ground-based Ground Penetrating Radar (GPR) Fusion<\/strong>
Funding Agency:<\/em> DOD
Role:<\/em> PI
Dates:<\/em> September 2010 \u2013 October 2011
Note: <\/em>Research conducted at Dr. Zare’s prior appointment at the Univ. of Missouri<\/p>\n","protected":false},"excerpt":{"rendered":"

The Machine Learning and Sensing Laboratory develops machine learning methods for autonomously analyzing and understanding sensor data. We investigate and develop artificial intelligence, machine learning, pattern recognition, computational intelligence, signal processing, and information fusion methods for application to sensing. Applications we have studied include landmine and explosive object detection, automated plant phenotyping, sub-pixel target detection, […]<\/p>\n","protected":false},"author":468,"featured_media":0,"parent":0,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"page-templates\/page-sidebar-none.php","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-220","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/pages\/220","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/468"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=220"}],"version-history":[{"count":4,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/pages\/220\/revisions"}],"predecessor-version":[{"id":15895,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/pages\/220\/revisions\/15895"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=220"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}