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Work Background
ESS, Inc.Data Scientist R&D
Oct. 2022 - Mar. 2023Wilsonville, Oregon, United States · On-site• Using multimodal code (Python, SQL, R, and JavaScript) queried and analyzed a large streaming time series based relational database (Java Database Connector) and designed full-stack performance dashboards (Flask, HTML, CSS) to track and visualize key system performance metrics that allowed for the test team to understand the effects of their decisions in specific testing scenarios, thus building feedback loops improving product resiliency. The project succinctly described as ‘completely automated full-stack interactive data visualization product.’
• Developed large-scale deep learning- advanced recurrent neural network (multiheaded attention-based LSTM), model for system optimization and predictive maintenance needs for end use customers. This was made into a data-product in conjunction with site connecting software to be a simple flagging tool for customers to understand their ability to pull power from the energy warehouse, and or, when instead the warehouse needed maintenance.
• Developed key variable analysis using information theory to understand interaction effects among the most important variables making the model as simple and explainable as possible, and most likely to be predictive. This science-based paradigm leverages the uses of multivariate information modeling and reconstructability analysis to statistically determine increased states of probabilistic understanding of common or frequent system states and how to predict the effects of certain relational models on those specific states. This form of data mining is comparable to heuristic based searches, and Bayesian network analysis, yet often much more powerful in its choice of predictive variables and model structures.
• Utilizing partial differential equations, data mining, machine learning (time series specific), and data visualization techniques built replicable, novel scientific algorithmic software for teams within research and development.
International Institute for AnalyticsProject Intern
Aug. 2022 - Oct. 2022Portland, Oregon Metropolitan Area · On-site• Using human behavioral and statistical knowledge within the framework of discrete choice theory and information modeling, facilitated actionable insights from customer feedback responses on surveys.
• Data was processed with through automated workflow I created using KNIME a visually centric code-based business analytics platform to clean and process the dataset from Excel spreadsheets, this allowed for clear visual explanation to non-technical parties involved with the project.
• The data was run through a Portland State made Open-Source machine learning software called OCCAM that runs Reconstructability Analysis an information modeling paradigm that uses sets of discrete models of relatability within data structures to identify patterns in probability giving understanding and predictability to human actions.
• Contributed to the improvement of business KPIs through data-driven awareness practices and recommendations from the analysis in this project specifically.
Pacific UniversityAdjunct Professor (Systems Thinking)
Jan. 2022 - Jan. 2022Forrest Grove, OR · HybridWe live in a world of improbable outcomes, obscured in unexpected, changing, evolving trends. Individual decision making has become usurped by computational Machine Learning and Big Data has become the new oil; life is hard and now further complex. Systems thinking is an alternative paradigm of problem solving that seeks to leverage collective intuition, general principles across scientific disciplines, and the wisdom of the natural world. You will be able to apply these general principles to your own process of problem solving, and will not be alone along the way; Systems Thinking has become transdisciplinary, and is emerging in unexpected fields of science, medicine, and spirituality.
CSD Autism ServicesBehavioral Health Specialist
Nov. 2021 - Dec. 2021Portland, Oregon Metropolitan Area · On-site- Received certification to apply principles of ABA therapy with clients. - Recorded quantitative data on client behavior, and increased adaptive behaviors by applying positive reinforcement when behavioral goals were met according to the psychologists satisfaction.
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