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https://chasemgrimm.com/#aboutpage-encoding-jsonld https://chasemgrimm.com/#aboutpage http://schema.org/encoding https://chasemgrimm.com/#aboutpage-encoding-n3 https://chasemgrimm.com/#aboutpage http://schema.org/encoding https://chasemgrimm.com/#aboutpage-encoding-nq https://chasemgrimm.com/#aboutpage http://schema.org/encoding https://chasemgrimm.com/#aboutpage-encoding-nt https://chasemgrimm.com/#aboutpage http://schema.org/encoding https://chasemgrimm.com/#aboutpage-encoding-rdf https://chasemgrimm.com/#aboutpage http://schema.org/encoding https://chasemgrimm.com/#aboutpage-encoding-rj https://chasemgrimm.com/#aboutpage http://schema.org/encoding https://chasemgrimm.com/#aboutpage-encoding-trig https://chasemgrimm.com/#aboutpage http://schema.org/encoding https://chasemgrimm.com/#aboutpage-encoding-trix https://chasemgrimm.com/#aboutpage http://schema.org/encoding https://chasemgrimm.com/#aboutpage-encoding-ttl https://chasemgrimm.com/#aboutpage http://schema.org/image https://images.bighouseenterprise.com/chase-grimm/chase-grimm/primary/chase-pfp-d045a2.webp https://chasemgrimm.com/#aboutpage http://schema.org/inLanguage en https://chasemgrimm.com/#aboutpage http://schema.org/isPartOf https://chasemgrimm.com/#website https://chasemgrimm.com/#aboutpage http://schema.org/mainEntity https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#aboutpage http://schema.org/name Home https://chasemgrimm.com/#aboutpage http://schema.org/url https://chasemgrimm.com https://chasemgrimm.com/#aboutpage http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/AboutPage https://chasemgrimm.com/#answer-50633665 http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#answer-50633665 http://schema.org/text Chase operates from the belief that good systems engineering makes any problem approachable. His pragmatic philosophy—that different tools solve different problems and 'not everything is a snowflake'—cuts through technology hype to match the right approach to each challenge. He emphasizes pattern recognition across domains and applies the same foundational systems thinking principles whether architecting data pipelines or designing quantitative models. https://chasemgrimm.com/#answer-50633665 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Answer https://chasemgrimm.com/#answer-71a04f35 http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#answer-71a04f35 http://schema.org/text Chase's career demonstrates that rigorous systems thinking transfers across seemingly disparate fields. Whether analyzing proteomics literature, predicting traffic incidents, or extracting financial signals, the underlying approach remains consistent: decompose the problem, identify the right tools (rather than forcing fashionable solutions), and build infrastructure that scales. His published research spans biotechnology, agriculture, transportation, and finance—fields united by the systems challenges they present rather than their surface-level subject matter. https://chasemgrimm.com/#answer-71a04f35 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Answer https://chasemgrimm.com/#answer-8bb5a10a http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#answer-8bb5a10a http://schema.org/text As a Senior Engineer at Consilience AI, Chase builds specialized language models for financial and enterprise domains. His work focuses on extracting risk signals and causal relationships from unstructured text sources including regulatory filings, earnings calls, and corporate communications. He architects text analytics infrastructure for processing these documents at scale. https://chasemgrimm.com/#answer-8bb5a10a http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Answer https://chasemgrimm.com/#answer-8f4adb30 http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#answer-8f4adb30 http://schema.org/text Chase spent four years as a Data & Operations Research Scientist at Principal Financial Group, applying optimization and statistical methods across investment divisions to improve portfolio performance and operational efficiency. His work spanned equities, fixed income, and asset management. This foundation, combined with his current work at Consilience AI building NLP systems for financial documents, positions him at the intersection of AI and quantitative finance. https://chasemgrimm.com/#answer-8f4adb30 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Answer https://chasemgrimm.com/#answer-aa47feaf http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#answer-aa47feaf http://schema.org/text Chase has published peer-reviewed research spanning multiple domains united by underlying systems challenges. His work includes analyzing proteomics literature trends using NLP (Journal of Proteome Research, 2024), validating virtual reality simulator fidelity for agricultural equipment training (Computers and Electronics in Agriculture, 2019), investigating relationships between public events and traffic incidents using crowdsourced data (IEEE SIEDS, 2017), and developing knowledge surveys to distinguish types of operator expertise (Human Factors and Ergonomics Society, 2016). https://chasemgrimm.com/#answer-aa47feaf http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Answer https://chasemgrimm.com/#answer-f6841e87 http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#answer-f6841e87 http://schema.org/text Chase holds a B.S. in Industrial Engineering from Iowa State University (2014-2017). During his undergraduate studies, he worked as a Research Assistant conducting user studies for agricultural technology, developed Android applications for equipment automation, and built predictive models using text mining. He also holds CAP (Certified Analytics Professional) and PMP (Project Management Professional) credentials. https://chasemgrimm.com/#answer-f6841e87 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Answer https://chasemgrimm.com/#article-recap-chase-grimm http://schema.org/abstract Knowledge Engineering, System Design, System Test. https://chasemgrimm.com/#article-recap-chase-grimm http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#article-recap-chase-grimm http://schema.org/datePublished 2021-06-23T00:00:00+00:00 https://chasemgrimm.com/#article-recap-chase-grimm http://schema.org/description Knowledge Engineering, System Design, System Test. https://chasemgrimm.com/#article-recap-chase-grimm http://schema.org/headline ReCAP: Chase Grimm https://chasemgrimm.com/#article-recap-chase-grimm http://schema.org/isPartOf https://chasemgrimm.com/#website https://chasemgrimm.com/#article-recap-chase-grimm http://schema.org/name ReCAP: Chase Grimm https://chasemgrimm.com/#article-recap-chase-grimm http://schema.org/publisher https://chasemgrimm.com/#org-informs https://chasemgrimm.com/#article-recap-chase-grimm http://schema.org/url https://pubsonline.informs.org/do/10.1287/LYTX.2021.03.11/full/ https://chasemgrimm.com/#article-recap-chase-grimm http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Article https://chasemgrimm.com/#breadcrumb-item-1 http://schema.org/item https://chasemgrimm.com https://chasemgrimm.com/#breadcrumb-item-1 http://schema.org/name Home https://chasemgrimm.com/#breadcrumb-item-1 http://schema.org/position 1 https://chasemgrimm.com/#breadcrumb-item-1 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/ListItem https://chasemgrimm.com/#breadcrumb-item-2 http://schema.org/item https://chasemgrimm.com https://chasemgrimm.com/#breadcrumb-item-2 http://schema.org/name Home https://chasemgrimm.com/#breadcrumb-item-2 http://schema.org/position 2 https://chasemgrimm.com/#breadcrumb-item-2 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/ListItem https://chasemgrimm.com/#breadcrumb http://schema.org/itemListElement https://chasemgrimm.com/#breadcrumb-item-1 https://chasemgrimm.com/#breadcrumb http://schema.org/itemListElement https://chasemgrimm.com/#breadcrumb-item-2 https://chasemgrimm.com/#breadcrumb http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/BreadcrumbList https://chasemgrimm.com/#country-united-states http://schema.org/name United States https://chasemgrimm.com/#country-united-states http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Country https://chasemgrimm.com/#credential-bachelor-of-science-in-industrial-engineering http://schema.org/credentialCategory degree https://chasemgrimm.com/#credential-bachelor-of-science-in-industrial-engineering http://schema.org/dateCreated 2017 https://chasemgrimm.com/#credential-bachelor-of-science-in-industrial-engineering http://schema.org/name Bachelor of Science in Industrial Engineering https://chasemgrimm.com/#credential-bachelor-of-science-in-industrial-engineering http://schema.org/recognizedBy https://chasemgrimm.com/#eduorg-iowa-state-university https://chasemgrimm.com/#credential-bachelor-of-science-in-industrial-engineering http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/EducationalOccupationalCredential https://chasemgrimm.com/#definedterm-algorithmic-trading http://schema.org/description Developing quantitative trading strategies that apply systems engineering principles to financial markets. Focus on systematic approaches that leverage the same foundational methods used in other technical domains. https://chasemgrimm.com/#definedterm-algorithmic-trading http://schema.org/name Algorithmic Trading https://chasemgrimm.com/#definedterm-algorithmic-trading http://schema.org/sameAs https://www.google.com/search?kgmid=/m/07h5dh https://chasemgrimm.com/#definedterm-algorithmic-trading http://schema.org/termCode intermediate, 4 years https://chasemgrimm.com/#definedterm-algorithmic-trading http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-cue-fidelity http://schema.org/description The degree to which visual and sensory cues in a simulation accurately represent their real-world counterparts. Critical for ensuring that simulator-trained behaviors transfer to actual operational contexts. Requires systematic validation through user studies with domain experts. https://chasemgrimm.com/#definedterm-cue-fidelity http://schema.org/inDefinedTermSet https://chasemgrimm.com/#termset https://chasemgrimm.com/#definedterm-cue-fidelity http://schema.org/name Cue Fidelity https://chasemgrimm.com/#definedterm-cue-fidelity http://schema.org/termCode used extensively, 2019 https://chasemgrimm.com/#definedterm-cue-fidelity http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-human-factors-engineering http://schema.org/description Conducting user research and designing systems that account for operator knowledge, preferences, and cognitive limitations. Experience validating simulator fidelity and developing knowledge assessment instruments for population segmentation. https://chasemgrimm.com/#definedterm-human-factors-engineering http://schema.org/name Human Factors Engineering https://chasemgrimm.com/#definedterm-human-factors-engineering http://schema.org/sameAs https://www.google.com/search?kgmid=/m/02m8q https://chasemgrimm.com/#definedterm-human-factors-engineering http://schema.org/termCode intermediate, 3 years https://chasemgrimm.com/#definedterm-human-factors-engineering http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-machine-learning http://schema.org/description Developing and deploying machine learning models for classification, prediction, and pattern recognition. Applications span scientific trend forecasting, traffic incident prediction, and financial signal extraction. https://chasemgrimm.com/#definedterm-machine-learning http://schema.org/name Machine Learning https://chasemgrimm.com/#definedterm-machine-learning http://schema.org/sameAs https://www.google.com/search?kgmid=/m/01hyh_ https://chasemgrimm.com/#definedterm-machine-learning http://schema.org/termCode advanced, 7 years https://chasemgrimm.com/#definedterm-machine-learning http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-natural-language-processing http://schema.org/description Designing and implementing NLP pipelines for extracting structured information from unstructured text. Experience spans scientific literature mining, financial document analysis, and crowdsourced data processing. Core competencies include named entity recognition, relationship extraction, topic modeling, and trend analysis at scale. https://chasemgrimm.com/#definedterm-natural-language-processing http://schema.org/name Natural Language Processing https://chasemgrimm.com/#definedterm-natural-language-processing http://schema.org/sameAs https://www.google.com/search?kgmid=/m/05flf https://chasemgrimm.com/#definedterm-natural-language-processing http://schema.org/termCode expert, 7 years https://chasemgrimm.com/#definedterm-natural-language-processing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-not-everything-is-a-snowflake http://schema.org/description A pragmatic engineering philosophy that resists the tendency to treat every problem as unique and requiring bespoke solutions. Emphasizes pattern recognition across domains and matching established tools to problems rather than reinventing approaches. Serves as a counterweight to technology hype and over-engineering. https://chasemgrimm.com/#definedterm-not-everything-is-a-snowflake http://schema.org/inDefinedTermSet https://chasemgrimm.com/#termset https://chasemgrimm.com/#definedterm-not-everything-is-a-snowflake http://schema.org/name Not Everything is a Snowflake https://chasemgrimm.com/#definedterm-not-everything-is-a-snowflake http://schema.org/termCode used extensively, 2020 https://chasemgrimm.com/#definedterm-not-everything-is-a-snowflake http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-operations-research http://schema.org/description Applying optimization and statistical methods to improve decision-making in investment and operational contexts. Experience with portfolio optimization, resource allocation, and process improvement across equities, fixed income, and asset management divisions. https://chasemgrimm.com/#definedterm-operations-research http://schema.org/name Operations Research https://chasemgrimm.com/#definedterm-operations-research http://schema.org/sameAs https://www.google.com/search?kgmid=/m/0bx8h https://chasemgrimm.com/#definedterm-operations-research http://schema.org/termCode advanced, 6 years https://chasemgrimm.com/#definedterm-operations-research http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-rate-of-rise http://schema.org/description A metric developed for quantifying the progression and emergence velocity of scientific technologies and methodologies within a research corpus. Used to identify which technologies are gaining traction and forecast their trajectory based on publication patterns over time. https://chasemgrimm.com/#definedterm-rate-of-rise http://schema.org/inDefinedTermSet https://chasemgrimm.com/#termset https://chasemgrimm.com/#definedterm-rate-of-rise http://schema.org/name Rate of Rise https://chasemgrimm.com/#definedterm-rate-of-rise http://schema.org/termCode used extensively, 2024 https://chasemgrimm.com/#definedterm-rate-of-rise http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-science-of-science http://schema.org/description An analytical approach that applies data science and NLP methods to scientific literature itself, treating the corpus of published research as a dataset to be mined for trends, patterns, and forecasts about the evolution of scientific fields. https://chasemgrimm.com/#definedterm-science-of-science http://schema.org/inDefinedTermSet https://chasemgrimm.com/#termset https://chasemgrimm.com/#definedterm-science-of-science http://schema.org/name Science-of-Science https://chasemgrimm.com/#definedterm-science-of-science http://schema.org/termCode used extensively, 2024 https://chasemgrimm.com/#definedterm-science-of-science http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-system-expert-vs-practice-expert http://schema.org/description A distinction between two types of domain expertise: system experts possess deep understanding of underlying mechanisms and can reason about novel situations, while practice experts have accumulated wisdom through experience but may struggle with unfamiliar scenarios. This distinction informs the design of knowledge assessment instruments and training systems. https://chasemgrimm.com/#definedterm-system-expert-vs-practice-expert http://schema.org/inDefinedTermSet https://chasemgrimm.com/#termset https://chasemgrimm.com/#definedterm-system-expert-vs-practice-expert http://schema.org/name System Expert vs. Practice Expert https://chasemgrimm.com/#definedterm-system-expert-vs-practice-expert http://schema.org/termCode used extensively, 2016 https://chasemgrimm.com/#definedterm-system-expert-vs-practice-expert http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-systems-engineering http://schema.org/description Applying rigorous systems thinking to decompose complex problems across diverse domains. Expertise in requirements analysis, functional decomposition, interface design, and tradeoff analysis. Philosophy centers on the belief that good systems engineering makes any problem approachable. https://chasemgrimm.com/#definedterm-systems-engineering http://schema.org/name Systems Engineering https://chasemgrimm.com/#definedterm-systems-engineering http://schema.org/sameAs https://www.google.com/search?kgmid=/m/06xj3 https://chasemgrimm.com/#definedterm-systems-engineering http://schema.org/termCode expert, 10 years https://chasemgrimm.com/#definedterm-systems-engineering http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-text-analytics http://schema.org/description Building text analytics infrastructure for processing unstructured data at scale. Specialization in financial and enterprise domains including regulatory filings, earnings calls, and corporate communications. Experience extracting risk signals and causal relationships from document corpora. https://chasemgrimm.com/#definedterm-text-analytics http://schema.org/name Text Analytics https://chasemgrimm.com/#definedterm-text-analytics http://schema.org/sameAs https://www.google.com/search?kgmid=/m/01v59d https://chasemgrimm.com/#definedterm-text-analytics http://schema.org/termCode expert, 8 years https://chasemgrimm.com/#definedterm-text-analytics http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://chasemgrimm.com/#definedterm-virtual-reality-simulation http://schema.org/description Evaluating and validating VR systems for training and research applications. 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https://chasemgrimm.com/#person-chase-grimm http://schema.org/knowsAbout https://chasemgrimm.com/#termset https://chasemgrimm.com/#person-chase-grimm http://schema.org/knowsLanguage Chinese https://chasemgrimm.com/#person-chase-grimm http://schema.org/knowsLanguage English https://chasemgrimm.com/#person-chase-grimm http://schema.org/name Chase Grimm https://chasemgrimm.com/#person-chase-grimm http://schema.org/nationality https://chasemgrimm.com/#country-united-states https://chasemgrimm.com/#person-chase-grimm http://schema.org/sameAs https://isni.org/isni/0000000530566213 https://chasemgrimm.com/#person-chase-grimm http://schema.org/sameAs https://muckrack.com/chasegrimm/bio https://chasemgrimm.com/#person-chase-grimm http://schema.org/sameAs https://orcid.org/0009-0001-7969-3190 https://chasemgrimm.com/#person-chase-grimm http://schema.org/sameAs https://scholar.google.com/citations?user=D_piH2sAAAAJ&hl=en https://chasemgrimm.com/#person-chase-grimm http://schema.org/sameAs https://www.linkedin.com/in/chasemgrimm/ https://chasemgrimm.com/#person-chase-grimm http://schema.org/sameAs https://x.com/chasemgrimm https://chasemgrimm.com/#person-chase-grimm http://schema.org/url https://chasemgrimm.com https://chasemgrimm.com/#person-chase-grimm http://schema.org/worksFor https://chasemgrimm.com/#org-consilience-ai https://chasemgrimm.com/#person-chase-grimm http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Person https://chasemgrimm.com/#question-50633665 http://schema.org/acceptedAnswer https://chasemgrimm.com/#answer-50633665 https://chasemgrimm.com/#question-50633665 http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#question-50633665 http://schema.org/name What is Chase Grimm's engineering philosophy? https://chasemgrimm.com/#question-50633665 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Question https://chasemgrimm.com/#question-71a04f35 http://schema.org/acceptedAnswer https://chasemgrimm.com/#answer-71a04f35 https://chasemgrimm.com/#question-71a04f35 http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#question-71a04f35 http://schema.org/name How does Chase Grimm approach applying AI to different domains? https://chasemgrimm.com/#question-71a04f35 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Question https://chasemgrimm.com/#question-8bb5a10a http://schema.org/acceptedAnswer https://chasemgrimm.com/#answer-8bb5a10a https://chasemgrimm.com/#question-8bb5a10a http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#question-8bb5a10a http://schema.org/name What kind of work does Chase Grimm do at Consilience AI? https://chasemgrimm.com/#question-8bb5a10a http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Question https://chasemgrimm.com/#question-8f4adb30 http://schema.org/acceptedAnswer https://chasemgrimm.com/#answer-8f4adb30 https://chasemgrimm.com/#question-8f4adb30 http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#question-8f4adb30 http://schema.org/name What experience does Chase Grimm have in financial services? https://chasemgrimm.com/#question-8f4adb30 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Question https://chasemgrimm.com/#question-aa47feaf http://schema.org/acceptedAnswer https://chasemgrimm.com/#answer-aa47feaf https://chasemgrimm.com/#question-aa47feaf http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#question-aa47feaf http://schema.org/name What research has Chase Grimm published? https://chasemgrimm.com/#question-aa47feaf http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Question https://chasemgrimm.com/#question-f6841e87 http://schema.org/acceptedAnswer https://chasemgrimm.com/#answer-f6841e87 https://chasemgrimm.com/#question-f6841e87 http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#question-f6841e87 http://schema.org/name What is Chase Grimm's educational background? https://chasemgrimm.com/#question-f6841e87 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Question https://chasemgrimm.com/#scholarly-an-agricultural-harvest-knowledge-survey-to-distingui-56b11e http://schema.org/abstract Gaining insight into the unique characteristics of participants during user research is a valuable tool for both recruitment and understanding differences within the target population. This work describes an agricultural harvest knowledge survey that was created for user research studies that observed experienced combine operators driving a combine simulator in virtual crop fields. Two variations of the survey were designed, utilized, and evaluated in two separate studies. Both studies found a difference between low and high knowledge operators' performance on the knowledge survey in addition to performance differences. Based on the success of this survey as a population segmentation tool, the authors recommend three criteria for the design of future knowledge surveys in other domains: 1) use real world scenarios, 2) ensure question are neither too difficult nor too easy, and 3) ask the minimum number of questions to identify operator knowledge successfully. Future research aims to create a tool that can discern between system experts (with deep understanding of the system) and practice experts (who primarily have the wisdom of experience). https://chasemgrimm.com/#scholarly-an-agricultural-harvest-knowledge-survey-to-distingui-56b11e http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#scholarly-an-agricultural-harvest-knowledge-survey-to-distingui-56b11e http://schema.org/datePublished 2016-09-08T00:00:00+00:00 https://chasemgrimm.com/#scholarly-an-agricultural-harvest-knowledge-survey-to-distingui-56b11e http://schema.org/description Gaining insight into the unique characteristics of participants during user research is a valuable tool for both recruitment and understanding differences within the target population. This work describes an agricultural harvest knowledge survey that was created for user research studies that observed experienced combine operators driving a combine simulator in virtual crop fields. Two variations of the survey were designed, utilized, and evaluated in two separate studies. Both studies found a difference between low and high knowledge operators' performance on the knowledge survey in addition to performance differences. Based on the success of this survey as a population segmentation tool, the authors recommend three criteria for the design of future knowledge surveys in other domains: 1) use real world scenarios, 2) ensure question are neither too difficult nor too easy, and 3) ask the minimum number of questions to identify operator knowledge successfully. Future research aims to create a tool that can discern between system experts (with deep understanding of the system) and practice experts (who primarily have the wisdom of experience). https://chasemgrimm.com/#scholarly-an-agricultural-harvest-knowledge-survey-to-distingui-56b11e http://schema.org/headline An Agricultural Harvest Knowledge Survey to Distinguish Types of Expertise https://chasemgrimm.com/#scholarly-an-agricultural-harvest-knowledge-survey-to-distingui-56b11e http://schema.org/isPartOf https://chasemgrimm.com/#periodical-proceedings-of-the-human-factors-and-ergonomics-socie-12c8a1 https://chasemgrimm.com/#scholarly-an-agricultural-harvest-knowledge-survey-to-distingui-56b11e http://schema.org/name An Agricultural Harvest Knowledge Survey to Distinguish Types of Expertise https://chasemgrimm.com/#scholarly-an-agricultural-harvest-knowledge-survey-to-distingui-56b11e http://schema.org/sameAs https://doi.org/10.1177/1541931213601465 https://chasemgrimm.com/#scholarly-an-agricultural-harvest-knowledge-survey-to-distingui-56b11e http://schema.org/url https://journals.sagepub.com/doi/10.1177/1541931213601465 https://chasemgrimm.com/#scholarly-an-agricultural-harvest-knowledge-survey-to-distingui-56b11e http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/ScholarlyArticle https://chasemgrimm.com/#scholarly-investigating-the-relationship-between-traffic-incide-24797b http://schema.org/abstract Large social events can influence traffic conditions and possibly lead to jams and incidents. This study leverages crowdsourced data to analytically evaluate the relationship between social events and traffic incidents in the city of Chicago. In particular, we collected data on social events from scraping online webpages, as well as traffic data from a twitter account that posted irregular traffic incidents based on a crowdsourced navigation application (Waze). Using these two sources the relationship between social events and the occurrence of traffic incidents was investigated. The total number social events and their categories for each region and its neighboring regions were used to build models that predicted the chance of a traffic incident occurrence. Based on the analysis, we demonstrated the variables that indicated significant influence on the chance of a traffic incident occurrence in the same day, such as the number of festivals and fairs, total number of events in the (neighboring) region. We have also developed and tested several models to predict the traffic incidents. The results indicated that using solely online listed social events may not be sufficient for traffic prediction. Although the accuracies are not considerable for an independent model, it clearly indicates that the additional information provided by social events will be a valuable addition to the existing traffic prediction models. https://chasemgrimm.com/#scholarly-investigating-the-relationship-between-traffic-incide-24797b http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#scholarly-investigating-the-relationship-between-traffic-incide-24797b http://schema.org/datePublished 2017-04-01T00:00:00+00:00 https://chasemgrimm.com/#scholarly-investigating-the-relationship-between-traffic-incide-24797b http://schema.org/description Large social events can influence traffic conditions and possibly lead to jams and incidents. This study leverages crowdsourced data to analytically evaluate the relationship between social events and traffic incidents in the city of Chicago. In particular, we collected data on social events from scraping online webpages, as well as traffic data from a twitter account that posted irregular traffic incidents based on a crowdsourced navigation application (Waze). Using these two sources the relationship between social events and the occurrence of traffic incidents was investigated. The total number social events and their categories for each region and its neighboring regions were used to build models that predicted the chance of a traffic incident occurrence. Based on the analysis, we demonstrated the variables that indicated significant influence on the chance of a traffic incident occurrence in the same day, such as the number of festivals and fairs, total number of events in the (neighboring) region. We have also developed and tested several models to predict the traffic incidents. The results indicated that using solely online listed social events may not be sufficient for traffic prediction. Although the accuracies are not considerable for an independent model, it clearly indicates that the additional information provided by social events will be a valuable addition to the existing traffic prediction models. https://chasemgrimm.com/#scholarly-investigating-the-relationship-between-traffic-incide-24797b http://schema.org/headline Investigating the relationship between traffic incidents and public events: A case study https://chasemgrimm.com/#scholarly-investigating-the-relationship-between-traffic-incide-24797b http://schema.org/isPartOf https://chasemgrimm.com/#periodical-2017-systems-and-information-engineering-design-sympo-2bacdd https://chasemgrimm.com/#scholarly-investigating-the-relationship-between-traffic-incide-24797b http://schema.org/name Investigating the relationship between traffic incidents and public events: A case study https://chasemgrimm.com/#scholarly-investigating-the-relationship-between-traffic-incide-24797b http://schema.org/sameAs https://doi.org/10.1109/sieds.2017.7937715 https://chasemgrimm.com/#scholarly-investigating-the-relationship-between-traffic-incide-24797b http://schema.org/url https://ieeexplore.ieee.org/document/7937715 https://chasemgrimm.com/#scholarly-investigating-the-relationship-between-traffic-incide-24797b http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/ScholarlyArticle https://chasemgrimm.com/#scholarly-parsing-20-years-of-public-data-by-ai-maps-trends-in-6d5020 http://schema.org/abstract The trends of the last 20 years in biotechnology were revealed using artificial intelligence and natural language processing (NLP) of publicly available data. Implementing this 'science-of-science' approach, we capture convergent trends in the field of proteomics in both technology development and application across the phylogenetic tree of life. With major gaps in our knowledge about protein composition, structure, and location over time, we report trends in persistent, popular approaches and emerging technologies across 94 ideas from a corpus of 29 journals in PubMed over two decades. New metrics for clusters of these ideas reveal the progression and popularity of emerging approaches like single-cell, spatial, compositional, and chemical proteomics designed to better capture protein-level chemistry and biology. This analysis of the proteomics literature with advanced analytic tools quantifies the Rate of Rise for a next generation of technologies to better define, quantify, and visualize the multiple dimensions of the proteome that will transform our ability to measure and understand proteins in the coming decade. https://chasemgrimm.com/#scholarly-parsing-20-years-of-public-data-by-ai-maps-trends-in-6d5020 http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#scholarly-parsing-20-years-of-public-data-by-ai-maps-trends-in-6d5020 http://schema.org/datePublished 2024-02-02T00:00:00+00:00 https://chasemgrimm.com/#scholarly-parsing-20-years-of-public-data-by-ai-maps-trends-in-6d5020 http://schema.org/description The trends of the last 20 years in biotechnology were revealed using artificial intelligence and natural language processing (NLP) of publicly available data. Implementing this 'science-of-science' approach, we capture convergent trends in the field of proteomics in both technology development and application across the phylogenetic tree of life. With major gaps in our knowledge about protein composition, structure, and location over time, we report trends in persistent, popular approaches and emerging technologies across 94 ideas from a corpus of 29 journals in PubMed over two decades. New metrics for clusters of these ideas reveal the progression and popularity of emerging approaches like single-cell, spatial, compositional, and chemical proteomics designed to better capture protein-level chemistry and biology. This analysis of the proteomics literature with advanced analytic tools quantifies the Rate of Rise for a next generation of technologies to better define, quantify, and visualize the multiple dimensions of the proteome that will transform our ability to measure and understand proteins in the coming decade. https://chasemgrimm.com/#scholarly-parsing-20-years-of-public-data-by-ai-maps-trends-in-6d5020 http://schema.org/headline Parsing 20 Years of Public Data by AI Maps Trends in Proteomics and Forecasts Technology https://chasemgrimm.com/#scholarly-parsing-20-years-of-public-data-by-ai-maps-trends-in-6d5020 http://schema.org/isPartOf https://chasemgrimm.com/#periodical-journal-of-proteome-research https://chasemgrimm.com/#scholarly-parsing-20-years-of-public-data-by-ai-maps-trends-in-6d5020 http://schema.org/name Parsing 20 Years of Public Data by AI Maps Trends in Proteomics and Forecasts Technology https://chasemgrimm.com/#scholarly-parsing-20-years-of-public-data-by-ai-maps-trends-in-6d5020 http://schema.org/sameAs https://doi.org/10.1021/acs.jproteome.3c00430 https://chasemgrimm.com/#scholarly-parsing-20-years-of-public-data-by-ai-maps-trends-in-6d5020 http://schema.org/url https://pubs.acs.org/doi/10.1021/acs.jproteome.3c00430 https://chasemgrimm.com/#scholarly-parsing-20-years-of-public-data-by-ai-maps-trends-in-6d5020 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/ScholarlyArticle https://chasemgrimm.com/#scholarly-the-importance-of-operator-knowledge-in-evaluating-vi-5a28c0 http://schema.org/abstract Research, development, testing, and operator training of large agricultural harvesting equipment has become increasingly expensive and complex. Operational simulators can offset these costs, but it is critical to evaluate the fidelity of the simulator experience to ensure that it will lead to operator behaviors that match those in the real world. This research describes a validation process for new visual cues within simulators and focuses on the presentation and fidelity of cues in a combine harvest simulator. The fully functional immersive combine simulator in this work provides a mixed-reality interface that combines a physically accurate operator interface with displays of virtual reality (VR) combine header and grain. Grain combine operators participated to assess how well they perceived the VR cues as representing actual field and crop activity. Results showed that operators successfully identified 85% of the visual cues in the combine simulator and recognized when these cues indicated machine settings adjustment needs but that operators' ability to choose the correct action to take correlated with their knowledge of the combine. Results also showed wide variation in the number of the grain combine's head reel adjustments made by operators, likely reflecting strong individual preferences or habits around reel adjustment. Operators' number of adjustment interactions with the reel was also significantly correlated with the number of correct actions chosen, suggesting that careful attention to the reel is related to good farming performance. This research demonstrates the importance of taking operator knowledge and preferences into account when designing new agricultural products and offers a systematic method of validating cues within an agricultural simulator. https://chasemgrimm.com/#scholarly-the-importance-of-operator-knowledge-in-evaluating-vi-5a28c0 http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#scholarly-the-importance-of-operator-knowledge-in-evaluating-vi-5a28c0 http://schema.org/datePublished 2019-05-01T00:00:00+00:00 https://chasemgrimm.com/#scholarly-the-importance-of-operator-knowledge-in-evaluating-vi-5a28c0 http://schema.org/description Research, development, testing, and operator training of large agricultural harvesting equipment has become increasingly expensive and complex. Operational simulators can offset these costs, but it is critical to evaluate the fidelity of the simulator experience to ensure that it will lead to operator behaviors that match those in the real world. This research describes a validation process for new visual cues within simulators and focuses on the presentation and fidelity of cues in a combine harvest simulator. The fully functional immersive combine simulator in this work provides a mixed-reality interface that combines a physically accurate operator interface with displays of virtual reality (VR) combine header and grain. Grain combine operators participated to assess how well they perceived the VR cues as representing actual field and crop activity. Results showed that operators successfully identified 85% of the visual cues in the combine simulator and recognized when these cues indicated machine settings adjustment needs but that operators' ability to choose the correct action to take correlated with their knowledge of the combine. Results also showed wide variation in the number of the grain combine's head reel adjustments made by operators, likely reflecting strong individual preferences or habits around reel adjustment. Operators' number of adjustment interactions with the reel was also significantly correlated with the number of correct actions chosen, suggesting that careful attention to the reel is related to good farming performance. This research demonstrates the importance of taking operator knowledge and preferences into account when designing new agricultural products and offers a systematic method of validating cues within an agricultural simulator. https://chasemgrimm.com/#scholarly-the-importance-of-operator-knowledge-in-evaluating-vi-5a28c0 http://schema.org/headline The importance of operator knowledge in evaluating virtual reality cue fidelity https://chasemgrimm.com/#scholarly-the-importance-of-operator-knowledge-in-evaluating-vi-5a28c0 http://schema.org/isPartOf https://chasemgrimm.com/#periodical-computers-and-electronics-in-agriculture https://chasemgrimm.com/#scholarly-the-importance-of-operator-knowledge-in-evaluating-vi-5a28c0 http://schema.org/name The importance of operator knowledge in evaluating virtual reality cue fidelity https://chasemgrimm.com/#scholarly-the-importance-of-operator-knowledge-in-evaluating-vi-5a28c0 http://schema.org/sameAs https://doi.org/10.1016/j.compag.2019.03.026 https://chasemgrimm.com/#scholarly-the-importance-of-operator-knowledge-in-evaluating-vi-5a28c0 http://schema.org/url https://www.sciencedirect.com/science/article/abs/pii/S0168169918309657?via%3Dihub https://chasemgrimm.com/#scholarly-the-importance-of-operator-knowledge-in-evaluating-vi-5a28c0 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/ScholarlyArticle https://chasemgrimm.com/#termset http://schema.org/author https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#termset http://schema.org/hasDefinedTerm https://chasemgrimm.com/#definedterm-cue-fidelity https://chasemgrimm.com/#termset http://schema.org/hasDefinedTerm https://chasemgrimm.com/#definedterm-not-everything-is-a-snowflake https://chasemgrimm.com/#termset http://schema.org/hasDefinedTerm https://chasemgrimm.com/#definedterm-rate-of-rise https://chasemgrimm.com/#termset http://schema.org/hasDefinedTerm https://chasemgrimm.com/#definedterm-science-of-science https://chasemgrimm.com/#termset http://schema.org/hasDefinedTerm https://chasemgrimm.com/#definedterm-system-expert-vs-practice-expert https://chasemgrimm.com/#termset http://schema.org/name Glossary https://chasemgrimm.com/#termset http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTermSet https://chasemgrimm.com/#website http://schema.org/description Chase Grimm is an AI systems engineer transforming complex challenges into robust AI systems through rigorous engineering and pragmatic design. https://chasemgrimm.com/#website http://schema.org/inLanguage en https://chasemgrimm.com/#website http://schema.org/name Chase Grimm's Personal Website https://chasemgrimm.com/#website http://schema.org/publisher https://chasemgrimm.com/#person-chase-grimm https://chasemgrimm.com/#website http://schema.org/url https://chasemgrimm.com https://chasemgrimm.com/#website http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/WebSite urn:x-rdflib:default