School of Engineering
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Jay Prakash Thakur
Graduate, Stanford Center for Professional Development
BioJay Prakash Thakur is a Senior Software Engineer at Google building agentic AI systems for security, specializing in AI agents, multi-agent systems, and responsible agentic AI innovation. Featured in WIRED Magazine for insights on AI agent accountability and the Stanford LEAD Quarterly, Jay bridges technical excellence with strategic thinking to shape the future of human-AI collaboration.
As an open-source contributor to leading agentic frameworks and advisor to industry committees, he focuses on building scalable systems that drive business growth while serving society ethically. His deep expertise spans agentic AI architecture, multi-agent systems, big data, deep learning, and machine learning.
Previously built scalable AI/ML solutions at Microsoft, Amazon, and Accenture, Jay has published research on frontier technologies and actively contributes to leading tech communities including IEEE (Senior Member), ACM, and AAAI. He serves as a business and startup advisor, holds multiple patents, and is a global speaker, conference session chair, and panelist on Agentic AI innovation and ethics.
With thought leadership articles reaching global readers and trending on major tech platforms, Jay has established himself as a prominent voice shaping the future of agentic AI development at the intersection of innovation, strategic leadership, and responsible technology. -
Jack Topper
Graduate, Stanford Center for Professional Development
BioJack Topper is a Scientific Software Engineer at NASA’s Community Coordinated Modeling Center (CCMC), where he designs and operates large-scale scientific data and modeling systems supporting the global space-weather research community. His work focuses on automating high-performance computing workflows, building resilient data pipelines, and translating complex scientific models into reliable, user-facing services.
He collaborates closely with domain scientists to bridge research objectives and production-grade software, and has taken on technical leadership responsibilities spanning system architecture, reliability, and user adoption. His interests sit at the intersection of optimization, decision systems, machine learning, and large-scale infrastructure, with an emphasis on how mathematical models and data-driven methods inform real-world operational decisions.
Jack is currently pursuing Stanford’s Data, Models, and Optimization Certificate through the Stanford Center for Professional Development, including coursework in convex optimization and related decision-science foundations.
