Turning data into decisions and AI into impact. Specialized in Data & AI Strategy, Advanced Analytics, AI Governance, and Agentic AI Systems.
I'm a Data & AI strategist who helps organizations harness the full power of data, analytics, and artificial intelligence to drive measurable business outcomes.
My expertise spans the full AI value chain, from advanced analytics and machine learning to agentic AI systems that autonomously reason, plan, and execute complex workflows. I design intelligent solutions that don't just analyze data, they act on it.
I'm equally focused on the governance side of AI: building responsible AI frameworks, compliance strategies, and risk management practices that allow organizations to adopt AI confidently and at scale.
With a foundation in enterprise consulting and technical delivery, I bring the rare combination of deep technical fluency and strategic business acumen, translating AI capabilities into executive-level value propositions and operational impact.
Designing end-to-end data and AI strategies that connect analytics, ML, and agentic systems to real business value.
Building governance frameworks, ethical guardrails, and compliance practices that make AI adoption safe and auditable.
Building autonomous AI agents that reason, plan, and execute, moving beyond static models to systems that take action.
Enterprise AI & Analytics Consulting
Data & Technology Consulting
Consulting & Enterprise Technology
Designing and deploying AI/ML solutions, from predictive models and NLP to LLM-powered tools and intelligent automation.
Building autonomous AI agents that reason, plan, and execute multi-step workflows with tool use and human-in-the-loop safety.
End-to-end analytics from data pipelines to executive dashboards. Turning raw data into strategic intelligence.
Designing responsible AI frameworks, governance policies, and compliance strategies, including guardrails for agentic systems.
Proficient in Python for data science, ML engineering, AI agent development, and rapid prototyping across the modern AI stack.
Architecting cloud-native data platforms, ML pipelines, and scalable AI infrastructure on modern ecosystems.
Leading complex AI and data programs from strategy through delivery. Managing cross-functional teams and executive stakeholders.
Deep foundation in SAP S/4HANA and enterprise ERP. Understanding how AI and analytics integrate with core business systems.
AI capability is accelerating faster than AI oversight. As autonomous agents become enterprise-ready, organizations need leaders who understand not just what AI can do, but how to govern it, especially when AI acts independently. I bridge that gap.
Designing structured governance models with clear accountability, oversight processes, and decision rights for both ML and agentic AI systems.
Defining boundaries, human-in-the-loop checkpoints, and safety constraints for autonomous AI agents acting within authorized scope.
Embedding ethical principles into AI lifecycles, addressing bias, fairness, transparency, and explainability across all AI systems.
Navigating the evolving regulatory landscape, including EU AI Act alignment, industry standards, and emerging rules for autonomous and generative AI.
Identifying and mitigating risks including model drift, data quality, adversarial attacks, LLM hallucination, and unintended agent behavior.
Building AI roadmaps that balance innovation velocity with governance maturity for confident, enterprise-scale AI deployment.
Designed and built agentic AI systems capable of autonomously researching, analyzing, and executing multi-step business workflows with reasoning chains and human-in-the-loop checkpoints.
Developed an enterprise AI governance framework encompassing risk assessment, ethical review processes, agentic AI safety guardrails, and compliance documentation.
Designed and deployed an enterprise analytics platform providing real-time KPI visibility. Automated data pipelines enabling AI-augmented decision support.
Built ML-powered automation using Python to identify patterns, predict bottlenecks, and autonomously trigger process optimizations, a precursor to full agentic workflows.
Architected a centralized data platform connecting disparate enterprise systems with quality rules, lineage tracking, and governance standards powering downstream AI.
Applied machine learning to financial markets, building predictive models for volatility forecasting, pattern recognition, and algorithmic strategy evaluation.
The names I follow and hold, with live quotes and fresh headlines on every visit. Personal watchlist, not investment advice.
S&P 500 ETF
Gold ETF
NVIDIA
Advanced Micro Devices
Palantir Technologies
Micron Technology
Japanese Yen ETF
Freeport-McMoRan (copper)
Bitcoin
Space Exploration Technologies
Anduril Industries
Destiny Tech100 (SpaceX + Anduril proxy)
Crunching 12 months of daily prices...
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Auto-updated from my latest repositories
MS, Analytics, Analytical Tools & Computational Data Analytics
Expected May 2027
B.B.A., Computer Information Systems, Dual Track: Application Development & Artificial Intelligence & Business Analytics
July 2021
The frontier topics I'm actively researching, experimenting with, and forming perspectives on, beyond the mainstream AI conversation.
How do you govern AI systems that act autonomously? Current frameworks weren't built for agents that reason, plan, and execute across tools. I'm exploring accountability models, audit trails, and kill-switch architectures for multi-agent systems.
AI is leaving the screen. From autonomous robots in warehouses to AI-powered surgical systems, physical AI raises entirely new questions about safety, liability, and real-world decision-making at the edge.
Nations are racing to build their own AI stacks, from compute to models. I'm studying how sovereign AI policies, data residency laws, and geopolitical tensions are reshaping enterprise AI strategy and cross-border deployments.
If you can't break it, you can't trust it. I'm diving into structured red teaming methodologies, prompt injection defenses, and how organizations should stress-test AI systems before they reach production.
Every AI system depends on upstream models, data pipelines, and third-party APIs. A single poisoned dataset or compromised model can cascade. I'm exploring how to map, monitor, and mitigate AI supply chain vulnerabilities.
The future isn't AI replacing humans or humans overseeing AI. It's fluid collaboration. I'm researching cognitive load frameworks, trust calibration, and how to design systems where humans and AI agents genuinely complement each other.
Markets don't behave one way. Trending, mean-reverting, calm, and stressed regimes each break strategies built for another. I run ADX regime filters in my live trading and I'm exploring hidden Markov models and volatility clustering for systematic regime switching.
Direct indexing turns one portfolio into hundreds of individually harvestable positions. I'm studying how tax alpha is generated and measured: wash-sale constraints, tracking-error budgets, and the point where harvesting stops paying for itself.
Mean-variance is where I started, not where it ends. I'm digging into Black-Litterman views, CVaR objectives, and drawdown-constrained allocation, plus the estimation-error problem that quietly breaks naive optimizers.
My NLP pipeline linked earnings-announcement language to firm outcomes. Next up: LLM agents for research workflows, alternative data signals, and the validation discipline needed to keep AI-generated alpha claims honest.
Beta only tells part of the story. I'm going deeper on factor exposures, Barra-style risk decomposition, and performance attribution that explains where returns actually came from, and whether they'll persist.
A backtest is one path through history. Monte Carlo simulation, bootstrapped scenarios, and stress tests show how a portfolio behaves in the histories that didn't happen, yet. Grounded in my Georgia Tech simulation coursework.
Whether you're exploring a collaboration, career opportunity, or simply want to connect, I'd love to hear from you.
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