![\"Five](\"https:\/\/sciences.ucf.edu\/news\/wp-content\/uploads\/sites\/29\/2026\/01\/ATD-Challenge-resized-1024x683.png\")
A team of UCF researchers and students recently earned a second-place finish in the\u00a0National Algorithms for Threat Detection (ATD) 2025 Challenge,\u00a0a national data science competition sponsored by the National Geospatial-Intelligence Agency (NGA) and hosted by Penn State\u2019s Applied Research Lab\u00a0in\u00a0connection with\u00a0the\u00a0National Science Foundation (NSF)-NGA ATD research program.\u00a0The contest pushes participants to develop advanced algorithmic solutions for large-scale,\u00a0complex\u00a0and often incomplete spatiotemporal datasets related to national security.\u00a0<\/p>\n\n\n\n
Each year, principal investigators (PIs) across the country receive the invitation to compete, with only a\u00a0fraction of them\u00a0deciding to\u00a0take on this challenge\u00a0due to the competition\u2019s\u00a0rigor. This year marked the fourth consecutive year\u00a0Associate Professor of Statistics and Data Science\u00a0Hsin-Hsiung\u00a0\u201cBill\u201d\u00a0Huang\u00a0served as PI and oversaw the project\u00a0and his team\u2019s\u00a0participation.\u00a0<\/p>\n\n\n\n
\u201cThis is a nationwide data competition organized by the NSF. Every year they propose a challenging large-scale spatiotemporal\u00a0dataset, and it invites all the PIs,\u201d he says. \u201cOnce you join, you have only about two and a half months to build a model.\u201d\u00a0<\/p>\n\n\n\n
Participants received anonymous vessel observations, known as\u00a0posits,\u00a0which\u00a0include\u00a0time, location,\u00a0speed,\u00a0and\u00a0the additional\u00a0inclusion of\u00a0intentional\u00a0sparsity with injected errors and noise in position and time.\u00a0The teams\u00a0were tasked\u00a0to\u00a0connect\u00a0them\u00a0to\u00a024-hour vessel tracks\u00a0in\u00a0the Gulf Coast and Florida Straits.\u00a0<\/p>\n\n\n\n
\u201cSome of the vessel locations weren’t in the water because of the artificial noise added,\u201d\u202fHuang\u00a0says.\u202f\u201cThat prevents anyone from simply relying on maps or historical data. Everything must be modeled from scratch.\u201d\u00a0<\/p>\n\n\n\n
He explains that the datasets provided are intentionally difficult to work with. They\u00a0contain\u00a0long time series, vast spatial domains, and highly irregular or outlier-heavy data that make standard analytical tools ineffective. Many top universities have\u00a0attempted\u00a0the challenge but\u00a0ultimately withdrew.\u00a0<\/p>\n\n\n\n
\u201cThe challenge is known for its rigor and steep computational demands,\u201d\u00a0he says. \u201cDue to the scale and complexity, not all invited teams submit final solutions.\u201d\u00a0<\/p>\n\n\n\n
Despite the formidable obstacles,\u00a0Huang\u2019s team\u00a0excelled by combining statistical modeling, machine learning techniques, domain\u00a0expertise, and interdisciplinary problem-solving.\u00a0<\/p>\n\n\n\n
Why Reconstructing Incomplete Ship Tracks Matters<\/strong>\u00a0<\/h2>\n\n\n\nHuang explains how this type of research directly supports national security by enabling analysts to detect and\u00a0monitor\u00a0potential threats even when\u00a0identifying\u00a0information is missing or intentionally withheld.\u00a0<\/p>\n\n\n\n
\u201cMany commercial vessels broadcast\u00a0Automatic Identification System\u00a0(AIS), but illegal or high-risk vessels may disable, spoof, or avoid transmitting signals, creating gaps that reconstruction methods aim to address,\u201d Huang says.\u00a0\u201cWe need algorithms that can track them despite missing information.\u201d\u00a0<\/p>\n\n\n\n
These methods extend beyond maritime contexts as he says they apply to aircraft, military operations in terrain that block GPS signals, and even emerging threats.<\/p>\n\n\n\n
\u201cMissing\u00a0spatiotemporal\u00a0data is a very important problem,\u201d\u202fHuang\u00a0says.\u202f\u201cWhether it\u2019s vessels, airplanes, or even balloons entering our territory, we need to reconstruct paths to identify and respond to potential threats.\u201d\u00a0<\/p>\n\n\n\n
Teamwork as a Competitive Advantage<\/strong>\u00a0<\/h2>\n\n\n\nThe team included Ph.D. students\u00a0John \u201cMahlon\u201d\u00a0Scott and\u00a0Yuh-Haur\u00a0\u201cJackie\u201d\u00a0Chen\u00a0along with\u00a0Joshua White, who\u00a0joined the team during the middle stage of the competition and contributed to the project\u2019s development,\u00a0and\u00a0associate research scientist\u00a0Bulent\u00a0Soykan, whose\u00a0expertise\u00a0in simulation and modeling was critical.\u00a0<\/p>\n\n\n\n
\u201cThere is no single model that can solve the whole problem. We had to partition the U.S. waters into different structures\u2014Tampa Bay, the Mississippi River, the ocean\u2014and then stitch everything together because vessels move between them. That was the main challenge,\u201d\u00a0Soykan\u00a0says.\u00a0<\/p>\n\n\n\n
Each member contributed unique domain knowledge and\u00a0identified\u00a0patterns in the data.\u00a0Huang says\u00a0his observations from\u00a0real local\u00a0environments\u00a0even helped inform modeling decisions.\u00a0<\/p>\n\n\n\n
\u201cOne evening in Miami, I saw many small private boats moving through the harbor,\u201d he says. \u201cThat made me realize how important time of day is in predicting patterns. Living in Florida helps\u2014we can observe real vessel behavior and bring that insight back to the model.\u201d<\/p>\n\n\n\n
How Their Strategy Evolved<\/strong>\u00a0<\/h2>\n\n\n\nThe team initially explored models from\u00a0previous\u00a0ATD challenges, but\u00a0Scott\u00a0explains\u00a0they\u00a0quickly realized the new dataset required a different approach.\u00a0<\/p>\n\n\n\n
\u201cWe started by trying a model from a previous challenge, but it was too restrictive for the size and density of this dataset, so we spent a lot of time searching for alternatives,\u201d\u00a0Scott says.\u00a0<\/p>\n\n\n\n
Eventually,\u00a0the team shifted toward a\u00a0different\u00a0approach, incorporating decision techniques that evaluated multiple\u00a0possible vessel\u00a0connections and chose the most likely path. This allowed the model to adapt to varied environmental conditions and vessel behaviors.\u00a0<\/p>\n\n\n\n
\u201cIf you have ten possible vessels in the past and a new data point appears, you need a way to determine which one it belongs to,\u201d Scott says.\u00a0\u201cThat linking process was key.\u201d\u00a0<\/p>\n\n\n\n
They combined specialized modeling approaches across different maritime environments and designed a scalable workflow to handle the dataset\u2019s size and irregularity.\u00a0<\/p>\n\n\n\n
Connecting the Challenge to Real-World Research<\/strong>\u00a0<\/h2>\n\n\n\nHuang\u2019s broader research focuses on anomaly detection and large-scale Bayesian modeling under the NSF ATD program. The annual competition serves as both a proving ground and a source of real-world insight.\u00a0<\/p>\n\n\n\n
\u201cThis program collaborates with the\u00a0NGA,\u201d\u00a0he\u00a0says. \u201cWe\u00a0have to\u00a0build useful algorithms that can detect anomalies in vessel\u00a0behavior.\u202fFor me, the challenge\u00a0isn\u2019t\u00a0just about the\u00a0competition\u2014we\u00a0learn lessons and gain insight about how models behave on real\u00a0and\u00a0messy datasets.\u201d\u00a0<\/p>\n\n\n\n
This year, the team explored hybrid modeling approaches capable of adapting to drastically different environments,\u00a0such as narrow rivers versus open ocean,\u00a0while still producing cohesive trajectories across regions.\u00a0<\/p>\n\n\n\n
A Result Built on Innovation and Collaboration<\/strong>\u00a0<\/h2>\n\n\n\nThe team\u2019s\u00a0second-place achievement highlights the depth of their technical innovation and the power of cross-disciplinary collaboration. Their work not only excelled in the competition but also advanced their ongoing research into complex anomaly detection and trajectory reconstruction,\u00a0fields with significant implications for national security.\u00a0<\/p>\n\n\n\n
As Huang summarized, the ATD Challenge is more than an annual contest. It is an incubator for new methods,\u00a0new ideas, and new ways of understanding vast and noisy real-world data,\u00a0a\u00a0technological\u00a0space where UCF continues to\u00a0stand out.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"
UCF researchers\u00a0excel in\u00a0anomaly detection to support national security\u00a0research\u00a0and advance real-world data science.\u00a0 Written by: Emily Dougherty | Published:\u00a0January\u00a08,\u00a02025\u00a0 A team of UCF researchers and students recently earned a second-place finish in the\u00a0National Algorithms for Threat Detection (ATD) 2025 Challenge,\u00a0a national data science competition sponsored by the National Geospatial-Intelligence Agency (NGA) and hosted by Penn State\u2019s […]<\/p>\n","protected":false},"author":250,"featured_media":35038,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[38,39,40,5377,6525,6491,28,29],"tags":[736,1573,2856,6418],"class_list":["post-35036","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-breaking-news","category-faculty-news","category-graduate-student-news","category-research","category-sdmss","category-statistics-data-science","category-top-news","category-ucf-news","tag-college-of-sciences","tag-hsin-hsiung-huang","tag-research","tag-statistics-and-data-science"],"yoast_head":"\n
UCF Team Secures Second Place in National Algorithms for Threat Detection Challenge\u00a0 - College of Sciences News<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sciences.ucf.edu\/news\/ucf-team-secures-second-place-in-national-algorithms-for-threat-detection-challenge\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"UCF Team Secures Second Place in National Algorithms for Threat Detection Challenge\u00a0 - College of Sciences News\" \/>\n<meta property=\"og:description\" content=\"UCF researchers\u00a0excel in\u00a0anomaly detection to support national security\u00a0research\u00a0and advance real-world data science.\u00a0 Written by: Emily Dougherty | Published:\u00a0January\u00a08,\u00a02025\u00a0 A team of UCF researchers and students recently earned a second-place finish in the\u00a0National Algorithms for Threat Detection (ATD) 2025 Challenge,\u00a0a national data science competition sponsored by the National Geospatial-Intelligence Agency (NGA) and hosted by Penn State\u2019s […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sciences.ucf.edu\/news\/ucf-team-secures-second-place-in-national-algorithms-for-threat-detection-challenge\/\" \/>\n<meta property=\"og:site_name\" content=\"College of Sciences News\" \/>\n<meta property=\"article:published_time\" content=\"2026-01-07T21:54:13+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-01-12T17:59:40+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sciences.ucf.edu\/news\/wp-content\/uploads\/sites\/29\/2026\/01\/ATD-Challenge-resized.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"800\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Emily Dougherty\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Emily Dougherty\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/sciences.ucf.edu\\\/news\\\/ucf-team-secures-second-place-in-national-algorithms-for-threat-detection-challenge\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/sciences.ucf.edu\\\/news\\\/ucf-team-secures-second-place-in-national-algorithms-for-threat-detection-challenge\\\/\"},\"author\":{\"name\":\"Emily Dougherty\",\"@id\":\"https:\\\/\\\/sciences.ucf.edu\\\/news\\\/#\\\/schema\\\/person\\\/b0fef91c1b645235c964a6ec2a451c95\"},\"headline\":\"UCF Team Secures Second Place in National Algorithms for Threat Detection Challenge\u00a0\",\"datePublished\":\"2026-01-07T21:54:13+00:00\",\"dateModified\":\"2026-01-12T17:59:40+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/sciences.ucf.edu\\\/news\\\/ucf-team-secures-second-place-in-national-algorithms-for-threat-detection-challenge\\\/\"},\"wordCount\":1079,\"image\":{\"@id\":\"https:\\\/\\\/sciences.ucf.edu\\\/news\\\/ucf-team-secures-second-place-in-national-algorithms-for-threat-detection-challenge\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/sciences.ucf.edu\\\/news\\\/wp-content\\\/uploads\\\/sites\\\/29\\\/2026\\\/01\\\/ATD-Challenge-resized.png\",\"keywords\":[\"College of Sciences\",\"Hsin-Hsiung Huang\",\"research\",\"Statistics and Data Science\"],\"articleSection\":[\"COS News\",\"Faculty News\",\"Graduate Student News\",\"Research\",\"School of Data, Mathematical, and Statistical Sciences\",\"Statistics & Data Science\",\"Top News\",\"UCF News\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sciences.ucf.edu\\\/news\\\/ucf-team-secures-second-place-in-national-algorithms-for-threat-detection-challenge\\\/\",\"url\":\"https:\\\/\\\/sciences.ucf.edu\\\/news\\\/ucf-team-secures-second-place-in-national-algorithms-for-threat-detection-challenge\\\/\",\"name\":\"UCF Team Secures Second Place in National Algorithms for Threat Detection Challenge\u00a0 - 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The team included Ph.D. students\u00a0John \u201cMahlon\u201d\u00a0Scott and\u00a0Yuh-Haur\u00a0\u201cJackie\u201d\u00a0Chen\u00a0along with\u00a0Joshua White, who\u00a0joined the team during the middle stage of the competition and contributed to the project\u2019s development,\u00a0and\u00a0associate research scientist\u00a0Bulent\u00a0Soykan, whose\u00a0expertise\u00a0in simulation and modeling was critical.\u00a0<\/p>\n\n\n\n
\u201cThere is no single model that can solve the whole problem. We had to partition the U.S. waters into different structures\u2014Tampa Bay, the Mississippi River, the ocean\u2014and then stitch everything together because vessels move between them. That was the main challenge,\u201d\u00a0Soykan\u00a0says.\u00a0<\/p>\n\n\n\n
Each member contributed unique domain knowledge and\u00a0identified\u00a0patterns in the data.\u00a0Huang says\u00a0his observations from\u00a0real local\u00a0environments\u00a0even helped inform modeling decisions.\u00a0<\/p>\n\n\n\n
\u201cOne evening in Miami, I saw many small private boats moving through the harbor,\u201d he says. \u201cThat made me realize how important time of day is in predicting patterns. Living in Florida helps\u2014we can observe real vessel behavior and bring that insight back to the model.\u201d<\/p>\n\n\n\n
How Their Strategy Evolved<\/strong>\u00a0<\/h2>\n\n\n\nThe team initially explored models from\u00a0previous\u00a0ATD challenges, but\u00a0Scott\u00a0explains\u00a0they\u00a0quickly realized the new dataset required a different approach.\u00a0<\/p>\n\n\n\n
\u201cWe started by trying a model from a previous challenge, but it was too restrictive for the size and density of this dataset, so we spent a lot of time searching for alternatives,\u201d\u00a0Scott says.\u00a0<\/p>\n\n\n\n
Eventually,\u00a0the team shifted toward a\u00a0different\u00a0approach, incorporating decision techniques that evaluated multiple\u00a0possible vessel\u00a0connections and chose the most likely path. This allowed the model to adapt to varied environmental conditions and vessel behaviors.\u00a0<\/p>\n\n\n\n
\u201cIf you have ten possible vessels in the past and a new data point appears, you need a way to determine which one it belongs to,\u201d Scott says.\u00a0\u201cThat linking process was key.\u201d\u00a0<\/p>\n\n\n\n
They combined specialized modeling approaches across different maritime environments and designed a scalable workflow to handle the dataset\u2019s size and irregularity.\u00a0<\/p>\n\n\n\n
Connecting the Challenge to Real-World Research<\/strong>\u00a0<\/h2>\n\n\n\nHuang\u2019s broader research focuses on anomaly detection and large-scale Bayesian modeling under the NSF ATD program. The annual competition serves as both a proving ground and a source of real-world insight.\u00a0<\/p>\n\n\n\n
\u201cThis program collaborates with the\u00a0NGA,\u201d\u00a0he\u00a0says. \u201cWe\u00a0have to\u00a0build useful algorithms that can detect anomalies in vessel\u00a0behavior.\u202fFor me, the challenge\u00a0isn\u2019t\u00a0just about the\u00a0competition\u2014we\u00a0learn lessons and gain insight about how models behave on real\u00a0and\u00a0messy datasets.\u201d\u00a0<\/p>\n\n\n\n
This year, the team explored hybrid modeling approaches capable of adapting to drastically different environments,\u00a0such as narrow rivers versus open ocean,\u00a0while still producing cohesive trajectories across regions.\u00a0<\/p>\n\n\n\n
A Result Built on Innovation and Collaboration<\/strong>\u00a0<\/h2>\n\n\n\nThe team\u2019s\u00a0second-place achievement highlights the depth of their technical innovation and the power of cross-disciplinary collaboration. Their work not only excelled in the competition but also advanced their ongoing research into complex anomaly detection and trajectory reconstruction,\u00a0fields with significant implications for national security.\u00a0<\/p>\n\n\n\n
As Huang summarized, the ATD Challenge is more than an annual contest. It is an incubator for new methods,\u00a0new ideas, and new ways of understanding vast and noisy real-world data,\u00a0a\u00a0technological\u00a0space where UCF continues to\u00a0stand out.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"
UCF researchers\u00a0excel in\u00a0anomaly detection to support national security\u00a0research\u00a0and advance real-world data science.\u00a0 Written by: Emily Dougherty | Published:\u00a0January\u00a08,\u00a02025\u00a0 A team of UCF researchers and students recently earned a second-place finish in the\u00a0National Algorithms for Threat Detection (ATD) 2025 Challenge,\u00a0a national data science competition sponsored by the National Geospatial-Intelligence Agency (NGA) and hosted by Penn State\u2019s […]<\/p>\n","protected":false},"author":250,"featured_media":35038,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[38,39,40,5377,6525,6491,28,29],"tags":[736,1573,2856,6418],"class_list":["post-35036","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-breaking-news","category-faculty-news","category-graduate-student-news","category-research","category-sdmss","category-statistics-data-science","category-top-news","category-ucf-news","tag-college-of-sciences","tag-hsin-hsiung-huang","tag-research","tag-statistics-and-data-science"],"yoast_head":"\n
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Huang\u2019s broader research focuses on anomaly detection and large-scale Bayesian modeling under the NSF ATD program. The annual competition serves as both a proving ground and a source of real-world insight.\u00a0<\/p>\n\n\n\n
\u201cThis program collaborates with the\u00a0NGA,\u201d\u00a0he\u00a0says. \u201cWe\u00a0have to\u00a0build useful algorithms that can detect anomalies in vessel\u00a0behavior.\u202fFor me, the challenge\u00a0isn\u2019t\u00a0just about the\u00a0competition\u2014we\u00a0learn lessons and gain insight about how models behave on real\u00a0and\u00a0messy datasets.\u201d\u00a0<\/p>\n\n\n\n
This year, the team explored hybrid modeling approaches capable of adapting to drastically different environments,\u00a0such as narrow rivers versus open ocean,\u00a0while still producing cohesive trajectories across regions.\u00a0<\/p>\n\n\n\n