collaborators

7 papers

cs.DS2025

On Fair Epsilon Net and Geometric Hitting Set

Mohsen Dehghankar, Stavros Sintos, Abolfazl Asudeh

Fairness has emerged as a formidable challenge in data-driven decisions. Many of the data problems, such as creating compact data summaries for approximate query processing, can be…

cs.MA2025

An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring

Sana Ebrahimi, Mohsen Dehghankar, Abolfazl Asudeh

While multi-agent LLM systems show strong capabilities in various domains, they are highly vulnerable to adversarial and low-performing agents. To resolve this issue, in this paper…

cs.DS2025

HENN: A Hierarchical Epsilon Net Navigation Graph for Approximate Nearest Neighbor Search

Mohsen Dehghankar, Abolfazl Asudeh

Hierarchical graph-based algorithms such as HNSW have achieved state-of-the-art performance for Approximate Nearest Neighbor (ANN) search in practice, yet they often lack theoretic…

cs.IR2024

Needle: A Generative AI-Powered Multi-modal Database for Answering Complex Natural Language Queries

Mahdi Erfanian, Mohsen Dehghankar, Abolfazl Asudeh

Multi-modal datasets, like those involving images, often miss the detailed descriptions that properly capture the rich information encoded in each item. This makes answering comple…

cs.LG2024

Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks

Mohsen Dehghankar, Abolfazl Asudeh

Large language models (LLMs) have quickly emerged as practical and versatile tools that provide new solutions for a wide range of domains. In this paper, we consider the applicatio…

cs.LG2024

An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks

Mohsen Dehghankar, Mahdi Erfanian, Abolfazl Asudeh

Despite their tremendous success and versatility, Deep Neural Networks (DNNs) such as Large Language Models (LLMs) suffer from inference inefficiency and rely on advanced computati…