AI and Machine Learning Expert

From Algorithms to Outcomes

Artificial Intelligence and Machine Learning are transforming every major industry, and my work is focused on leveraging these technologies to solve complex, high-impact problems. By combining advanced academic research with practical application, I am bridging the gap between theoretical AI and real-world results that deliver measurable value.

“The future won’t be about AI replacing people, but about people who understand AI shaping the future.” — Ariel Miller

Advanced Academic Pathways

I am currently pursuing advanced studies in Artificial Intelligence and Machine Learning through the Graduate Program at the University of Texas at Austin, McCombs School of Business, with a path toward a PhD in AI-ML Data Sciences. This academic foundation allows me to remain at the forefront of algorithmic development, optimization techniques, and emerging research in neural networks, reinforcement learning, and predictive modeling. I also hold an elite AI certification—earned by fewer than 2,000 people worldwide—which positions me uniquely to deploy advanced algorithms at scale for real-world applications.

Applied AI Projects

My academic and collaborative work gave me the chance to deploy AI across industries, turning algorithms into measurable impact. These projects built the foundation of my technical range:

  • Ensemble Power: Bagging, boosting, and XGBoost for high-stakes forecasting, risk reduction, and market optimization.

  • Deep Learning in Action: Neural networks tuned for predictive maintenance — identifying failures before they occur in manufacturing and energy systems.

  • Computer Vision: CNNs and transfer learning applied to image classification challenges, from crop yield improvements to medical diagnostics.

  • Next-Gen NLP: Retrieval-augmented generation (RAG) pipelines, transformer-based sentiment analysis, and domain-specific LLMs that surface sharper, context-aware insights.

  • Generative & Multimodal AI: From text-to-image synthesis to audio-to-text speech recognition, integrating multiple modalities into a unified intelligence layer.

  • Fraud, Churn & Risk Models: Predictive frameworks for banking and credit systems, where fractional accuracy gains deliver real-world financial impact.

  • Deploying for Impact: Containerized ML models and SQL-driven integrations that bridge experimentation with production-ready solutions.

Hands-On Systems That Deliver

Beyond working on structured projects, I’ve taken the lead in designing and developing my own proprietary AI systems: building products, platforms, and tools that reflect my vision of where AI is headed. I’ve led proprietary projects that extend AI into entirely new domains:

  • The Ari Bets Sports Wagering System: Developed a subscription-based model that empowers bettors to run their own predictions using equilibrium detection, probability modeling, and line-tracking tools. In

  • Horseracing CAW Systems: Built an advanced CAW (Computer Assisted Wagering) system that analyzes stride length, sectional timing, and hidden variables for sharper betting edges.

  • AI Health Coaches for Ola Health: Designed RAG-based systems that deliver accurate, evidence-backed health answers in real time, paired with ML-driven protocol optimization for sleep, recovery, nutrition, and performance. This blends medical expertise with scalable personalization.

  • QuickPub for Authors: Applied AI/ML to streamline publishing workflows — proofreading, formatting, error detection, and personalized market insights — lowering barriers for independent authors while enhancing professional output.

  • Fernwood: AI-Interactive Storytelling: Built frameworks for generative storytelling and gamified worlds, where AI models allow characters, plotlines, and player choices to evolve dynamically. This merges entertainment, gaming, and narrative AI in a way that pushes the boundaries of interactive media.

Quantum Collective: Applied Leadership

Our approach at Quantum Collective is to build flexible architectures that can be adapted across industries. The same foundational technologies used for sports wagering equilibrium detection can be extended to financial portfolio optimization, supply chain forecasting, or even smart energy grid management. AI isn’t a single solution—it’s a flexible toolset that, when properly designed, can solve problems once considered beyond reach.

As CEO of Quantum Collective, I lead teams dedicated to building AI/ML systems for industries where traditional analytics have fallen short. The work we do goes beyond conventional data analysis. By integrating deep learning frameworks, reinforcement learning strategies, and neural network optimization, our models are capable of adapting dynamically as new information flows in. This is critical in environments such as live sports markets, where milliseconds matter, and in healthcare, where patient outcomes depend on accurate, real-time predictions.

The AI/ML field is evolving rapidly, and my goal is to contribute to that evolution by pairing academic insight with practical leadership. Whether creating models that challenge the limits of sports betting strategies, designing predictive analytics that transform medical care, or building tools to manage emerging markets, my focus remains on turning raw data into actionable intelligence—changing not only how decisions are made, but what decisions are possible in the first place.