IEEE Elevate Season2.0 Episode 1: Securing Intelligence: From Communication Networks to Trustworthy AI

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The first episode of IEEE ELEVATE 2.0, the continuation of its successful previous season,
brings a compelling story of academic excellence and research ambition, tracing a path from
communication networks and cybersecurity to the frontier of trustworthy artificial intelligence and
from the classrooms of MIST to graduate research at Virginia Tech.
In this session, guest speaker Sadman Saif, Graduate Research & Teaching Assistant at Virginia
Tech and former Lecturer at MIST, will share his journey through research in cybersecurity,
Explainable AI and the reliability and security of Large Language Models. He will reflect on how
he built a strong research profile as an undergraduate, moved from teaching into graduate study
abroad, and navigated the path to a leading US research university.
Whether someone is an undergraduate aspiring to a research career, a final-year student preparing
for higher studies abroad or simply curious about the fast-evolving world of AI security and
trustworthy machine learning, this episode offers practical insights, honest lessons and real
inspiration for taking the next step in your academic journey


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Sadman Saif of Virginia Tech

Topic:

Securing Intelligence: From Communication Networks to Trustworthy AI.

Biography:

Sadman Saif is a Graduate Research and Teaching Assistant at Virginia Tech, USA, where his
research focuses on the reliability and security of complex AI systems, including Large Language
Models and cyber-physical systems. He previously served as a Lecturer in the Electrical,
Electronic and Communication Engineering (EECE) department at the Military Institute of
Science and Technology (MIST), from which he graduated in 2024.
A Dean’s List awardee, Sadman’s research has evolved from cybersecurity for IoT networks,
smart grids, and communication infrastructure toward trustworthy and secure artificial
intelligence. He has extensively applied Machine Learning and Explainable AI (XAI) techniques
such as SHAP to build intrusion detection systems, and now works on fault localization and
reliability in LLMs and AI-augmented human decision-making.
His work has earned an h-index of 4 with over 55 citations, including a widely cited federated
learning approach for IoT botnet intrusion detection published in Complex & Intelligent Systems,
and interpretable machine learning methods for IoT security. He has also gained industry exposure
in Bangladesh’s telecommunications sector through training at Grameenphone and NovoTel
Limited, spanning network operations, data center management, and international gateway
systems.

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