Verónica Elze

About Me

20+ years in tech, now leading AI transformation at the edge of UX, ethics, and human-AI adaptation. I blend AI strategy, user experience design, and future-facing technology with a sharp analytical mindset. My work bridges the technical and human-centered—ensuring the AI we build today creates meaningful, equitable experiences tomorrow.

Professional Background

• Senior Project Manager – Managed multi-million-dollar AI and infrastructure projects for startups to Fortune 500s.
• Senior Program Manager – Led Spokane’s mayoral Community Safety Initiative with six departments and cloud-based tech.
• Principal Product Owner – Oversaw UX and technical design and implementations for enterprise energy management platforms.
• Senior Business Analyst & Consultant – Delivered smart grid, telemedicine, and dashboarding solutions in the U.S. and Canada.

Education

• MS in Artificial Intelligence – City University of Seattle, 2025 – GPA 3.9
• BBA in Management Information Systems – University of New Mexico, 2009 – GPA 3.0
• Certifications: IBM AI Engineering, Mendix Rapid Developer, CJIS, BLS & EMT

Projects

• Emotion-aware AI Tutor – Leveraged RL, OpenAI Whisper, and GPT-4o with real-time sentiment adaptation.
• Dance & AI Series (CS506) – Explored data science and machine learning tools across Pandas, Scikit-learn, and PyTorch using a dance-movement dataset to analyze classification accuracy, feature importance, and model design.
     • Part 1 – Pandas
     • Part 2 – Scikit-learn
     • Part 3 – PyTorch
• Voice-Driven Adaptive Game - CNN-based speech emotion recognition and Genetic Algorithm–inspired tuning for real-time difficulty adaptation.
• Speech-to-Action Robotics – Integrated Speech-to-command robot system using OpenAI Whisper and GPT-4o-mini with Bluetooth LE.
• Adaptive Language Tutor – Adaptive Spanish vocabulary tutor using Q-learning, deployed with FastAPI and React.
• Robotic Arm Optimizer – Modeled kinematics with gradient descent and simulation frameworks, improving computational efficiency.
• Classification – Built CNN models in PyTorch/Scikit-learn for movement recognition; explored transformers for classification accuracy.
• Probabilistic Graph Modeler – Benchmarked graph neural networks and probabilistic algorithms for optimized pathfinding.
• RL Navigation Agents – Trained PPO, SAC, and DQN agents in Gymnasium/Minigrid; deployed on AWS, achieving faster navigation.

Let’s Connect

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