Research Group

ASQI Lab

Autonomous Systems and Quantum Intelligence Laboratory

Directed by Dr. Mohammad Arif Hossain, Department of Engineering Technology, Middle Tennessee State University

The lab brings together graduate students and collaborators working on the boundary between intelligent systems and next-generation networks. Current work spans agentic AI safety, distributed and collaborative learning, mobile edge intelligence, and quantum-assisted approaches to hard optimization problems in next-generation networks.

What we work on

Agentic LLM Security

Securing agentic LLM systems against prompt injection, tool misuse, and adversarial manipulation, with autonomous defense for multi-agent workflows.

Distributed Machine Learning

Collaborative and privacy-aware distributed learning architectures that enable efficient model training across edge nodes.

Edge Intelligence & IoT

Optimizing computation offloading, resource allocation, and scheduling for mobile edge computing in heterogeneous IoT environments.

Generative AI

Generative models for network management, synthetic data, and intelligent automation in communication systems.

AI-Native 6G Networks

Integrating AI/ML natively into 5G/6G architectures for adaptive slicing, ISAC, and self-optimizing radio access.

Quantum-Assisted ML

Quantum computing paradigms to accelerate ML model training and optimization in next-generation networks.

Current members

MH

Dr. Mohammad Arif Hossain

Principal Investigator

SM

Sharmin Milu

PhD in Computational Science, since Fall 2025

TF

Tanzimul Fahim

PhD in Computational Science, since Fall 2026

MR

Matthew Radice

PhD in Computational Science, since Fall 2026

YS

Yeahia Sarker

MSc in Engineering Technology, since Spring 2026

HR

Most. Humaira Rime

MSc in Engineering Technology, since Fall 2026

SS

Md Shahariar Islam Shanto

MSc in Engineering Technology, since Fall 2026

Join the lab

We are seeking motivated M.S. students interested in Quantum-assisted Machine Learning and Autonomous Systems. See the students page for requirements and how to apply.

Prospective students