Sai Gattupalli, Ph.D.

Currently on the job market — open to global opportunities in research, innovation, and teaching. Based in Massachusetts and available to join immediately. Relocation welcome. Use the contact information in the footer to connect.

Sai Gattupalli, Ph.D.

Research Domains: Modeling Translation, Human-Centered AI, AI and Computational Literacy, Learning Technologies, Intelligent Tutoring Systems

Welcome to my Research and Professional Portfolio

As a scientist working at the intersection of computing, data, and human-centered systems, my research focuses on the design, modeling, and empirical evaluation of AI-enabled technologies. I investigate how theoretical, statistical, and computational models translate into real-world implementations, examining how assumptions embedded in algorithms, interfaces, and system architectures shape system behavior, robustness, and human interaction.

A central contribution of my work lies in modeling translation, the process of adapting conceptual and computational models into operational systems and decision contexts. This work emphasizes aligning modeling assumptions with implementation constraints and real-world use, and includes empirical and system-level research examining how adaptive system components, feedback structures, and AI-enabled interfaces behave in practice.

While education serves as a primary applied context for this research, the methods and modeling approaches I use are broadly applicable across domains where AI systems interact with people, data, and organizational constraints. My work spans system prototyping, experimental design, mixed-methods analysis, and human-in-the-loop evaluation, supporting the translation of research insights into deployable, decision-relevant systems.

In parallel, I develop and study interactive AI-driven platforms, contribute to open-access research artifacts and tools, and support capacity-building efforts around computational and AI literacies. Across projects, my work emphasizes rigorous empirical grounding, transparent system design, and scalable approaches to evaluating how AI technologies function in complex, real-world environments.

Explore the AI in Education, Interactive STEM EdTech, and Publications pages for current and prior research projects.