Astrophysicist • NASA Veteran

Dr. Chandra Vanajakshi, Ph.D.

Scientific AI Reasoning & Alignment

PhD Astrophysicist and NASA Veteran with a Minor in Mathematics and a decade of experience in High-Performance Computing. Specialist in magnetohydrodynamics and the Verification & Validation of non-linear systems—currently leveraging deep domain expertise to audit AI scientific reasoning, eliminate physical hallucinations, and ensure LLM adherence to first-principles physics.

Dr. Chandra Vanajakshi

Research & Publications

1985

Effect of Turbulent Viscosity on the Isothermal Collapse of a Rotating Protostellar Cloud

The Astrophysical Journal, 294, 504–512

Investigation of how turbulent viscosity influences the dynamics of isothermal gravitational collapse in rotating protostellar clouds, modeled through MHD simulations on CRAY XMP architectures.

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1989

Boundary Value Problems in Magnetohydrodynamics (and Fluid Dynamics). I. Radiation Boundary Condition

Journal of Computational Physics — Elsevier

Developed rigorous radiation boundary conditions for MHD and fluid dynamics simulations, addressing the critical challenge of artificial boundary reflections and truncation errors in computational domains.

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Case Study

AI Reasoning Audit: Identifying Physical Hallucinations in Ideal MHD

AI Model Alignment — Scientific Audit

Audited an AI-generated explanation of Ideal MHD and identified a critical reasoning failure regarding magnetic flux conservation. The model incorrectly stated that flux would “diffuse” during plasma expansion. Corrected the reasoning using Alfvén’s Theorem (the Frozen-in Law), demonstrating that in an infinitely conductive fluid, magnetic flux is a topological invariant—preventing a fundamental violation of Maxwell’s Equations in the model’s training set.


Projects & Experience

AI Policy & Governance

AI Policy Committee Member • Austin Community College • 2024–Present

Spearheading institutional policies for the ethical use of generative AI in classroom and administrative settings. Consulting on the integration of AI agents and advanced prompting to optimize scientific learning outcomes. Architecting ethical frameworks for “AI-Resilient” instructional designs.

Ethical AI, Policy, Prompt Engineering, Education

Physical Invariance Auditing

Scientific AI Reasoning Specialist

Specialist in identifying AI “Model Hallucinations” where outputs violate fundamental laws—non-conservation of mass/energy, violations of Alfvén’s Theorem, or broken symmetries. Red-teaming AI-generated scientific proofs to ensure strict mathematical rigor.

V&V, MHD, Red-Teaming, Mathematical Rigor

MHD Simulations at NASA

NASA Scientist • GSFC & Ames Research Center

Conducted advanced research in Star Formation and Solar System Evolution. Developed and executed complex magnetohydrodynamic simulations for rotating and non-rotating protostellar clouds on CRAY XMP architectures. Applied rigorous V&V protocols to ensure simulation data matched theoretical bounds.

FORTRAN, CRAY XMP, HPC, Star Formation, MHD

Enterprise CRM Architecture

Project Manager / Software Designer • Siebel Systems & Asera Inc. • 7 Years

Led cross-functional teams through the full SDLC, managing complex database design and curriculum deployment for enterprise-level CRM and supply chain systems. Ensured technical requirements met high-level business logic and pedagogical needs.

SDLC, Siebel, Asera, CRM, Database Design


Education & Credentials

Ph.D. & M.S. in Astrophysics (Minor: Mathematics)

North Carolina State University

M.S. in Semiconductor Physics

York University, Canada

M.S. in Electronics / B.S. in Physics

University of Madras, India

Certifications

AI in Practice (Advanced Distance Ed) • Problem Solving with AI • Quality Matters (QM) Fellowship

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