Razieh Negin Rahimi

Razieh Negin Rahimi serves as an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, conducting research at the Center for Intelligent Information Retrieval (CIIR). Her work focuses on developing interpretable information access systems that reduce user cognitive burdens through advanced modeling techniques.

She earned her PhD, MS, and BS in Computer Engineering from the University of Tehran. Her educational foundation in computational methods directly informs her research approach to complex information challenges.

Dr. Rahimi's research spans explainable search systems, cross-language information retrieval, and entity representation. She pioneers methods for generating human-interpretable explanations of ranking models to build user trust, develops techniques for entity relationship inference from unstructured text, and creates frameworks for multilingual search in resource-constrained languages. Her work consistently bridges theoretical innovation with practical user experience improvements, particularly in handling diverse information needs across linguistic boundaries.

Analysis of her recent publications reveals accelerating integration of large language models for explainable ranking tasks, with growing emphasis on bias discovery and search result diversification. Her 2023-2025 work shows sophisticated adaptation of neural architectures for listwise explanation generation and reasoning-aware retrieval.

Her scientific contributions have been recognized through:

• 2022 Google Research Scholar Award
• Manning/IALS Innovation Award (with James Allan)
• 2019 Amazon Research Award (with James Allan)

Dr. Rahimi has secured significant research funding including an NSF CAREER grant (2024) for explanation-based optimization of diversified retrieval systems. She actively mentors within the CIIR lab and collaborates with industry partners through her awarded research projects. Her teaching portfolio includes graduate courses in information retrieval where she emphasizes practical implementation of advanced ranking models.

Within the Center for Intelligent Information Retrieval, her team develops next-generation search technologies focusing on interpretability and user-centered design, with current projects exploring the intersection of neural retrieval and human cognition.