Anni Zimina
MS Computer Science
Stanford University
MS Computer Science · Stanford University
Anni Zimina
Anni is a Computer Science student at Stanford Engineering focused on agentic evaluation and multimodal classification. She has built Target-Evaluate-Improve (TEI) Loop, an open-source agent evaluation framework, and a multimodal classification system classifying millions of social media profiles across thousands of parameters.
Papers
- 01
AI-Driven Real-Time Persona Fusion Agents for Adaptive Multimodal Marketing Intelligence
Presented at the 2025 Stanford Causal Science Center Conference on Experimentation
- 02
TEI Loop: Structural Fixes and Pareto-Front Optimization for Agentic Systems
Presented at AI + Science: Accelerating Discovery, Stanford HAI - 03
Confluence: A Unified-Percept Architecture for Multimodal Creator Classification at Scale
Conferences
- 01
Target-Evaluate-Improve (TEI) Loop for Self-Improving Agents and Discovery
Presented at AI + Science: Accelerating Discovery, Stanford HAI
May 5, 2026
- 02
Removing the Hidden Cost of Unstandardized Evaluation: How Standardization Drives Efficiency for Markets Deploying AI Agents
Presented at Market Design in the Age of AI, Stanford Center for Computational Market Design
February 27, 2026
- 03
Optimization of Agentic Systems for Automated Marketing Intelligence
Presented at 2025 SC² Student Conference, Stanford Causal Science Center
November 19, 2025
- 04
AI-Driven Real-Time Persona Fusion Agents for Adaptive Multimodal Marketing Intelligence
Presented at Stanford Causal Science Conference on Experimentation
May 23, 2025
- 05
Real-Time Persona Fusion Agents for Adaptive Multimodal Marketing Intelligence
Presented at 2025 Stanford Data Science Conference
April 29, 2025