Hello! I am a PhD student in the Image and Video Computing (IVC) Group at Boston University, advised by Professor Kate Saenko and Professor Bryan A. Plummer. I study why vision and vision-language models fail. Most of my work involves identifying spurious correlations, understanding how training data can mislead models, and building benchmarks that reveal where models break. I like diagnostic work: tracing a problem back to its source and figuring out what to do about it.

I've recently been collaborating with NASA on geospatial foundation models—check out our work below.

Selected Publications

Bias & Robustness in Vision-Language Models

• Maan Qraitem, Piotr Teterwak, Kate Saenko, Bryan A. Plummer. Web Artifact Attacks Disrupt Vision Language Models. ICCV 2025. [PDF] [Code]
• Maan Qraitem, Nazia Tasnim, Piotr Teterwak, Kate Saenko, Bryan A. Plummer. Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks. NeurIPS 2024 Workshop. [PDF] [Code]
• Maan Qraitem, Kate Saenko, Bryan A. Plummer. From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations. ECCV 2024. (Oral) [PDF] [Code]
• Maan Qraitem, Kate Saenko, Bryan A. Plummer. Bias Mimicking: A Simple Sampling Approach for Bias Mitigation. CVPR 2023. [PDF] [Code]

Geospatial & Earth Observation AI

• Paulo A Arevalo, Maan Qraitem, Sujit Roy. Detecting Key Land Surface Phenometrics with the Prithvi Foundation Model and Harmonized Landsat Sentinel-2 Data. AGU 2025. [Code]
• Prithvi-EO: An Open-Access Geospatial Foundation Model Advancing Earth Science through Global Collaboration. AGU 2025. [40+ authors; contributed model adaptation pipelines for phenology forecasting and mining detection.]

Evaluation & Reasoning in Multimodal Models

• Eunice Yiu, Maan Qraitem, Charlie Wong, Anisa Noor Majhi, Yutong Bai, Shiry Ginosar, Alison Gopnik, Kate Saenko. KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models. ICLR 2025. [PDF] [Code]
• Maan Qraitem, Kate Saenko, Bryan A. Plummer. Breaking the Assistant Mold: Modeling Behavioral Variation in LLM Based Procedural Character Generation. In Submission. [PDF] [Code]