• Created a Book-to-Bill Monte Carlo model and corresponding Tableau dashboard for finance leadership to leverage in their existing Book-to-Bill forecasts.• Supported the front- and back-end development of a custom-built Streamlit web app that recommends content to users to aid in the proposal writing process.• Led the development of a Top-Down Forecasting Salary ensemble model as an alternative to finance's current approach for forecasting quarterly salary.• Created a CI/CD workflow in AI Team’s GitHub repository that automates the process of deploying an app's dev changes in a Docker image to the app’s Azure container registry.• Researched and implemented LLMs (i.e., Dolly), LLM tools (i.e., LangChain), and vector databases (i.e., ChromaDB) to understand processes like question answering, text generation, vector/embedding storage, etc. for team’s generative AI tasks.• Leveraged the AI Team’s Azure GenAI sandbox environment to construct an LLM-based Req-to-Resume tool that matches job candidates to requisitions in a data-driven manner.

• Gained practical experience in predictive data analysis and visualization using SQL and Python.• Established an understanding of KPIs, specifically in the healthcare domain, for monitoring and improving business production through historical and real-time performance metrics.• Gathered, analyzed, and aggregated patient data from the Sikka API platform as well as employee and provider data from the Ceridian Dayforce API.• Leveraged large-scale data warehousing tools like Microsoft SQL Server Management Studio and Azure Data Studio to pull, push, query, and manage data within relational schemas.• Reinforced the importance of both issue tracking using Jira and version control using GitHub, encouraging agile software development practices.

• Graded assignments weekly for “Methods and Tools in Software Development” within the MSU department of Computer Science & Engineering during the consecutive semesters of Fall 2021 and Spring 2022.• Assisted students with starting and completing assignments, answered class and content-specific questions, and provided constructive feedback on electronic submissions.

• Supported machine learning project tasks pertinent to rules-based emotion detection of COVID-19 social media and web-based data as part of the NSF “RAPID: Analyses of Emotions Expressed in Social Media and Forums During the COVID-19 Pandemic” grant.• Used the NRC Hashtag Emotion Lexicon; an annotated list of Twitter emotion-word hashtags (terms), the term's association with eight emotion categories (affect category), and the association strength (score) between the term and affect category; to derive a comparable dataset for the project’s collected tweets and other posts.

• Worked primarily with an unmanned ground vehicle and secondarily with a drive-by-wire off-road vehicle to create data collection platforms for ground vehicle research.• Instrumented UGV with sensors (including cameras, LiDAR, radar, GPS & IMU) for perception and navigation, an NVIDIA Drive AGX Pegasus system for data processing, and a Quantum ruggedized DC storage chassis for data storage.• Modeled and simulated UGVS in two 3D simulation environments: MAVS and Gazebo.• Established robotics skills using frameworks like ROS, gained exposure to GNU/Linux workflow and Unix networking, and became familiar with safety and testing protocols for off-road vehicles.

• Practiced experimental design methods in phonetics.• Collected background and production speech data in a sound-attenuated booth.• Interpreted data in speech analysis/synthesis software Praat.• Used aerodynamic equipment to measure airflow and pressure while working with human subjects.

• Introduced young women in grades 6-12 to cybersecurity, programming, and computer science principles with the aid of robotics, laptops, and tablets.• Escorted and supervised participants around MSU campus for duration of camp activities to ensure a safe, satisfactory experience.