Mustafa Doga Dogan is a research scientist at Adobe, based in Basel, Switzerland. He received his Ph.D. and M.S. in Electrical Engineering and Computer Science from MIT, where he worked with Prof. Stefanie Mueller in the Computer Science & Artificial Intelligence Laboratory (CSAIL).

Doga’s research is driven by a fundamental shift in how users access information: We are moving beyond websites and search engines toward LLMs, personal AI agents, and spatial computing platforms. In this new paradigm, AI systems become the primary interface to knowledge. How can these emerging interfaces deliver reliable, context-aware information anywhere, at any moment?

He investigates this question through the lens of grounded ubiquitous intelligence: designing systems, objects, and infrastructures that make the world legible to AI.

Doga’s research spans two complementary directions related to grounded ubiquitous intelligence:

1) GenAI- and agent-driven tools for next-generation information access.
At Adobe, he designs systems that help organizations and creators adapt to an AI-first information ecosystem. This includes the Adobe LLM Optimizer and Sites Optimizer, which support content quality and discoverability in LLM-dominated environments, i.e., for the emerging field of generative engine optimization (GEO). He also develops interfaces for agentic AI, such as Adobe’s Project Face Off  [🏆Adobe Summit’26 Best Sneak award] and Project Get Savvy, and explore multimodal, contextual AI+AR interactions, e.g., augmented object intelligence.

2) Metadata-driven intelligence and interaction for everyday, real-world objects.
During his PhD, Doga developed new ways for physical objects to carry unobtrusive metadata that AI systems can interpret with high reliability. This active line of work — ubiquitous metadata — includes systems such as G-ID, InfraredTags, BrightMarker, and Imprinto, which embed machine-readable information directly into materials. This allows AI and AR systems to perceive objects with guarantees that vision-only algorithms cannot achieve.

Doga is a past recipient of the Adobe Research Fellowship and Siebel Scholarship. His work has been nominated for best paper and demo awards at CHI, UIST, and ICRA. He has served on the program committees of CHI, UIST, DIS, and TEI. Outside of Adobe and MIT, Doga has worked at Google, UCLA, The University of Tokyo, TU Delft, Bogazici University, and the Max Planck Institute for Intelligent Systems.

For more information, please visit his personal website, www.dogadogan.com.