Manzoor Hussain

Welcome, and thank you for visiting my portfolio website.

I have completed my Ph.D. in Computer Science at Chungbuk National University, Republic of Korea, where I worked as a Graduate Research Assistant in the Software Intelligence Engineering Laboratory. I earned my B.S. degree in Software Engineering from the University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan, in 2018.

My research interests span cyber-security of intelligent systems, AI safety and security, robust AI, adversarial attacks and defense, machine/deep learning, and software development. Since 2020, I have successfully completed three National Research Foundation (NRF) projects related to cyber-physical systems safety in the autonomous driving domain.

In addition to my academic research, I have over three years of professional experience in the software industry, having worked with leading companies in Pakistan including Syntax Solution, NineSole Technology, and GIKI. I am also actively serving as a reviewer for several reputed international journals from publishers such as IEEE, Springer Nature, Wiley, Elsevier, and MDPI.

Research Interests:

•AI Safety • AI Security • Adversarial Attacks and Defense • Deep Learning • Machine Learning • Cyber-Physical Systems • Safety Analysis • Hazard Analysis • Autonomous Driving Systems • Robotics

Current Research Project:

Improvement of Intelligent Autonomous Systems based on Adversarial Deep Reinforcement Learning and its Quality Evaluation

Educational Background:

M. S. Leading to Ph. D. in Computer Science

Institution: Chungbuk National University, Cheongju, Chungbuk 28644, Republic of Korea.

Supervisor: Prof. Jang-Eui Hong

PhD Thesis: A Robust Unified Transformer for Security Resilience in Intelligent Systems.

BS. in Software Engineering

Institution: The University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan

Current Position:

Post Doctoral Researcher (Nov. 2025~ current)

Division of Mechatronics, KTH Royal Institude of Technology, Sweden.

Key Responsibilities: I will be conducting research and development in AI safety and security, with a focus on robust AI modeling for safety-critical applications in autonomous driving. Specifically, my work will involve developing collaborative perception systems for intelligent autonomous vehicles to detect occluded and beyond-sensor-range objects, thereby improving overall driving safety.

Past Positions:

Post Doctoral Researcher (September. 2025 ~ November 2025)

Software Intelligence Engineering Lab, Chungbuk National University, Cheongju, Republic of Korea.

Key Responsibilities: Research and development on AI safety and Security, Robust AI modeling for Safety-critical application, Tool designing for safety analysis, Simulation and testing of autonomous driving systems, Working on NRF projects, Team management, project management (Technical).

Graduate Research Assistant (Mar. 2020 ~ August 2025)

Software Intelligence Engineering Lab, Chungbuk National University, Cheongju, Republic of Korea.

Teaching Assistant (Sep. 2020 ~ August 2025)

Chungbuk National University, Cheongju, Republic of Korea.

Course: Object Oriented Programming, Objected Oriented Design, Software Quality Assurance, Software Testing || Main Instructor: Prof. Jang-Eui Hong

Key Responsibilities: Preparing lecture materials, Consulting with Students, Helping students with their semester projects, Conducting Quiz and Assignments, Addressing queries and after classes.

Team Lead (Mar. 2021~ 2025)

Software Intellingence Engineering Lab, Chungbuk National University, Cheongju, Republic of Korea.

Key Responsibilities: Team management, project management (Technical), technical report writing, proposal writing, and task scheduling.

Skills:

Frameworks:

• Pytorch • Tensorflow

IDE:

• Pycharm • Jupyter Notebook • Visual Studio • Android Studio

Programming:

• Python • Java • C++• C • MATLAB

Simulator:

• CARLA• Udacity • LGSVL• Unity

Language:

• English (Professional proficiency) • Korean (Very Basic) • Urdu(Native)

Selected Works

PEGAT: Prediction Error-Guided Adversarial Training to Enhance Robustness of Deep Learning Models in Autonomous Vehicles

Adaptive Precision Layering for Efficient Adversarial Training of Deep Learning Models in Intelligent Vehicles

Evaluating and Improving Adversarial Robustness of Deep Learning Models for Intelligent Vehicle Safety

DeepGuard: a framework for safeguarding autonomous driving systems from inconsistent behaviour

Vision beyond the Field-of-View: A Collaborative Perception System to Improve Safety of Intelligent Cyber-Physical Systems

Reconstruction-Based Adversarial Attack Detection in Vision-Based Autonomous Driving Systems

Analyzing Safety of Collaborative Cyber-Physical Systems Considering Variability

Researcher's Profiles

Google Scholar Profile

Research Gate Profile

ORCID

Web of Science (WoS) Profile

Affiliation:

Post Doctoral Researcher

Mechatronics Division, Dept. Engineering Design, KTH Royal Institute of Technology, Stockholm, Sweden

Email: manzoorh@kth.se

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