Reza Esfandiarpoor

I am a Ph.D. candidate in Computer Science at Brown University, working with Stephen Bach. Previously, I have interned at Google in the AdsAI team. Currently, I am interning at Microsoft in the Office AI team, where I work on LLM agents.
My research focuses on optimizing the interaction between different foundation models to better understand their behavior and improve their performance.
Here is a summary of my research:
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Vision-Language Models. I introduced Extract and Explore, a novel analysis method that uses reinforcement learning to align an LLM with VLM preferences. I then analyze LLM generations and find that non-visual and even spurious information significantly impact VLM concept representations. I introduce Follow-up Differential Descriptions, a new method that adapts LLM-generated concept descriptions to each individual VLM to improve performance.
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Information Retrieval. I introduced SyCL, a new synthetic data generation method that creates fine-grained training data with multi-level relevance labels for training dense retrievers. I also created Trove, an open-source toolkit for dense retrieval that simplifies IR experiments without sacrificing flexibility.
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Learning with Limited Labeled Data. Introduced Extended Few-shot Learning, a novel approach that uses structured knowledge sources and auxiliary data to improve visual classification with small models.
Before Ph.D., I completed my undergraduate studies with Shadrokh Samavi, where I worked on pruning and quantization methods to create efficient medical image analysis models for edge devices.