Zulfiqar Ahmad Khan

I am a fresh PhD graduate specialized in time series data analysis, computer vision, and IoT programming. I worked as the Coordinator of the Intelligent Media Laboratory (IMLab), where I led a research group of 8 members and supervised diverse projects. My research focuses on time-series forecasting for smart grid management using statistical, machine learning, and hybrid deep learning models. In computer vision, I have worked on fire detection, object detection, anomaly recognition, etc., using advanced deep learning techniques. I have also implemented active, incremental, and federated learning for adaptive, scalable solutions, and employ Explainable AI (XAI) for model interpretability

Education

  • Ph.D

    Sep 2019 ~ Feb 2025:       Sejong University, Seoul, South Korea

    Thesis Title: A Study of Spatiotemporal Feature Analysis using Deep Hybrid Models for Power Forecasting.

    MS.c

    Aug 2016 ~ Aug 2018:       Islamia College Peshawar, KPK, Pakistan

    Final Year Project: Towards smart home automation using IoT-enabled edge-computing paradigm. Watch the demo here:

    BS.c

    Aug 2014 ~ Aug 2016:       Govt. Post Graduate College, Timergara, Dir Lower, KPK, Pakistan

Work

  • Research Assistant

    June 2017 ~ Aug 2019:       DIPLab, Islamia College Peshawar, KPK, Pakistan

    Participated in various projects including face recognition, image classification, object detection, Time series data analysis, IoT, an Samrt home

    Research Assistant

    Sep 2019 ~ Present:       IMLab, Sejong University, Seoul, South Korea

    Since September 2019, I have been actively involved in several key research projects funded by the National Research Foundation of Korea (NRF), where my contributions have covered a variety of critical tasks. These include projects management, implementation of advanced algorithms and methodologies, drafting and publishing research articles, and developing patents based on innovative findings. Additionally, I have been responsible for preparing comprehensive yearly reports, ensuring that project progress is accurately documented and communicated to stakeholders.

Selected Publications

  • Optimized Cross-Module Attention Network and Medium-Scale Dataset for Effective Fire Detection

    Authors: Zulfiqar Ahmad Khan, Fath U Min Ullah, Hikmat Yar, Waseem Ullah, Noman Khan, Min Je Kim, and Sung Wook Baik

    Accepted, 9 Dec 2024

    Publisher: Elsevier

    Journal: Pattern Recognition

    DSPM: Dual sequence prediction model for efficient energy management in micro-grid

    Authors: Zulfiqar Ahmad Khan, Shabbir Ahmad Khan, Tanveer Hussain, Sung Wook Baik

    Volume: 356

    Publisher: Elsevier

    Journal: Applied Energy

    A Trapezoid Attention Mechanism for Power Generation and Consumption Forecasting

    Authors: Zulfiqar Ahmad Khan, Tanveer Hussain, Waseem Ullah, Sung Wook Baik

    Volume: 20

    Publisher: IEEE

    Journal: IEEE Transactions on Industrial Informatics

    Dual stream network with attention mechanism for photovoltaic power forecasting

    Authors: Zulfiqar Ahmad Khan, Tanveer Hussain, Sung Wook Baik

    Volume: 338

    Publisher: Elsevier

    Journal: Applied Energy

    Intelligent dual stream CNN and echo state network for anomaly detection

    Authors: Waseem Ullah, Tanveer Hussain, Zulfiqar Ahmad Khan, Umair Haroon, Sung Wook Baik

    Volume: 253

    Publisher: Elsevier

    Journal: Knowledge-Based Systems

    Randomly initialized CNN with densely connected stacked autoencoder for efficient fire detection

    Authors: Zulfiqar Ahmad Khan, Tanveer Hussain, Fath U Min Ullah, Suneet Kumar Gupta, Mi Young Lee, Sung Wook Baik

    Volume: 116

    Publisher: Elsevier

    Journal: Engineering Applications of Artificial Intelligence

    Optimized dual fire attention network and medium-scale fire classification benchmark

    Authors: Hikmat Yar, Tanveer Hussain, Mohit Agarwal, Zulfiqar Ahmad Khan, Suneet Kumar Gupta, Sung Wook Baik

    Volume: 31

    Publisher: IEEE

    Journal: IEEE Transactions on Image Processing

Languages

  • Pashto          Urdu          English

    Native               Fluent          Fluent

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