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Nicole M Beckage
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Portland, Oregon, United States
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Jobs
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Data Scientist for AI Assisted FeaturesNAVEXJun 2025 - Current (1 year 4 months)• Spearheaded a centralized AI evaluation and monitoring ecosystem, defining the core safety, accuracy, and risk benchmarks required for all AI features across Governance Regulations and Compliance (GRC) services
• Optimized a multi-skill analytical AI agent via LLM-as-a-judge frameworks and gold-standard benchmarks to ensure precise and accurate data reporting and summarization
• Developed scalable AI service endpoints, bridging the gap from data science prototype to production-level platform features- Senior Research Scientist, Sociotechnical SystemsIntel labsJan 2023 - Nov 2024 (1 year 11 months)• Designed and executed end-to-end primary research on transparency and trustworthiness of AI foundation models via adversarial machine learning and AI red-teaming
• Contributed to the development internal Responsible AI governance framework, including standardized ethical review protocols and external benchmarks for AI safety
• Engineered and deployed evaluation systems to enable cross-functional teams to systematically assess AI models for governance compliance throughout the development lifecycle. - Senior AI Research Scientist, Multimodal Dialogue SystemsIntel labsAug 2021 - Jan 2023 (1 year 6 months)• Architected and implemented end-to-end state-of-the-art foundation model frameworks for specialized applications including meeting summarization and bias mitigation
• Collaborated with cross-functional teams to develop, deploy, and evaluate production-grade ML systems across educational and manufacturing environments, ensuring integration with existing infrastructure - Deep Learning Research Scientist/Engineer, Brain-Inspired ComputingIntel labsApr 2019 - Aug 2021 (2 years 5 months)• Engineered deep neural architectures, informed by human language processing data, that significantly enhanced model interpretability and achieved measurable improvements in accuracy metrics
• Developed and deployed novel ML architectures that systematically incorporate cognitive processing principles, resulting in optimized fine-tuning methodologies and enhanced efficiency for production-level language processing systems - Assistant Professor (TT) Computer ScienceUniversity of KansasAug 2016 - Jul 2018 (2 years)• Tenure track professor of AI/ML working on personalization and interpretability as informed by human behavior with applications in recommendation systems, aerospace, civil engineering and others
• Developed course work for undergraduate engineering students in statistics, ML, and AI
• Received numerous prestigious grants from both government and private sector funding sources (including NSF, DARPA, NASA, CRA, and NSA) resulting in over 6 million dollars over 5 years
Education
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- PhD - Computer Science; Cognitive ScienceUniversity of Colorado BoulderJan 2016Thesis: Neural network and statistical models of language and concept formation
- MS - Computer ScienceUniversity of Colorado BoulderJan 2015
- MS - Quantitative PsychologyUniversity of california, IrvineJan 2012
- Bachelors - Cognitive Science; Mathematics; Psychology; GermanIndiana UniversityJan 2010
Publications
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- AA Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine LearningJan 2025
- DDecoding biases: Automated methods and LLM judges for gender bias detection in language modelsJan 2024
- AA unifying computational account of temporal context effects in language across the human cortexJan 2023
- SSelecting Informative Contexts Improves Language Model Fine-tuningJan 2021
- SSlower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information DecayJan 2021
- CContext or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization inJan 2021