Mohsen Dehghankar

Computer Science PhD Student | Chicago, IL

About

I am a Ph.D. student in Computer Science at the University of Illinois Chicago, advised by Dr. Abolfazl Asudeh. I completed my B.Sc. in Computer Engineering, with a minor in Mathematics, at Sharif University of Technology.

My research focuses on algorithm design for improving different stages of the machine learning and data pipeline. Broadly, my research spans three directions:

  • LLM Inference Efficiency: Designing algorithms that accelerate the inference in LLMs.

  • Retrieval Problems: Problems such as Approximate Nearest Neighbor (ANN) search and ranked retrieval. Exploring how retrieval can be used to enhance inference.

  • Algorithmic Fairness: Algorithms for fairness-aware data management.

In addition, I have an interest in computational geometry algorithms, particularly in their applications to data science and database problems.

Thesis (Proposal) Title: “From Data to Models: Efficient Algorithms for Retrieval and Inference”

Selected Papers

  • An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks
    Mohsen Dehghankar, Mahdi Erfanian, Abolfazl Asudeh
    ICML 2025
    [PDF] | [Code] | [Project Page]

  • HENN: A Hierarchical Epsilon Net Navigation Graph for Approximate Nearest Neighbor Search
    Mohsen Dehghankar, Abolfazl Asudeh
    Preprint (2025)
    [PDF] | [Code]

  • An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring
    Sana Ebrahimi, Mohsen Dehghankar, Abolfazl Asudeh
    AACL 2025
    [PDF]

  • Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks
    Mohsen Dehghankar, Abolfazl Asudeh
    KDD 2025
    [PDF] | [Code]

  • Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups
    Mohsen Dehghankar, Abolfazl Asudeh
    VLDB 2025
    [PDF] | [Code]

  • Fair Set Cover
    Mohsen Dehghankar, Rahul Raychaudhury, Stavros Sintos, Abolfazl Asudeh
    KDD 2025
    [PDF] | [Code] | [Video]

  • Needle: A Generative-AI Powered Monte Carlo Method for Answering Complex Natural Language Queries on Multi-modal Data
    Mahdi Erfanian, Mohsen Dehghankar, Abolfazl Asudeh
    Preprint (2025)
    [PDF] | [Code]

Contact

You can reach me by email at mdehgh2@uic.edu. You can also connect with me on LinkedIn, GitHub, and X.