Mr. Md. Humayun Kabir | Transfer Learning Awards | Best Researcher Award

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Mr. Md. Humayun Kabir | Transfer Learning Awards | Best Researcher Award

Mr. Md. Humayun Kabir, International Islamic University Chittagong, Bangladesh

MD. Humayun Kabir is a dedicated educator and professional in the field of computer and communication engineering, currently serving as a Lecturer at the International Islamic University Chittagong, Bangladesh. With a strong foundation in Electronic and Telecommunication Engineering, he is pursuing his M.Sc. at Chittagong University of Engineering and Technology. Humayun’s professional experience includes roles as a Course Instructor at CSL Academy, Senior Technical Trainer at New Vision Information Technology Limited, and Course Instructor at ERevo Technologies Limited, where he specializes in Cisco Certified Network Associate (CCNA) training and various IT certifications. His career is marked by a commitment to enhancing students’ practical skills and preparing them for the rapidly evolving technology landscape. Humayun’s research interests encompass a broad range of topics, including computer networking, cybersecurity, artificial intelligence, machine learning, and the Internet of Things (IoT). With a passion for teaching and mentoring, he aims to inspire the next generation of technology professionals.

Professional Profile:

ORCID

Summary of Suitability for the Research for Best Researcher Award: 

MD. Humayun Kabir emerges as an outstanding candidate for the Research for Best Researcher Award due to his impressive combination of academic achievements, professional experience, and dedicated research interests. Here are the key reasons that support his suitability

📚 Education

  • M.Sc. (Engineering)
    Chittagong University of Engineering and Technology
    Studying since 2020
    GPA: 3.5/4.0
    Field: Electronic and Telecommunication Engineering
  • B.Sc. (Engineering)
    International Islamic University Chittagong
    Graduated in 2018
    GPA: 3.863/4.0
    Field: Electronic and Telecommunication Engineering
  • Higher Secondary Certificate (H.S.C)
    Chittagong Model School & College
    Graduated in 2013
    GPA: 4.90/5.0
    Field: Science
  • Secondary School Certificate (S.S.C)
    Agrasar Bouddya Anathalay High School
    Graduated in 2011
    GPA: 4.44/5.0
    Field: Science

💼 Professional Experience

  • Lecturer
    Department of Computer & Communication Engineering (CCE)
    International Islamic University Chittagong, Bangladesh
    January 2023 – Present
  • Course Instructor
    CSL Training (CSL Academy)
    Cisco Certified Network Associate Routing & Switching (CCNA)
    August 2024 – Present
  • Senior Technical Trainer
    New Vision Information Technology Limited (NVIT / New Horizons)
    Cisco Certified Network Associate Routing & Switching (CCNA)
    May 2023 – Present
  • Course Instructor
    ERevo Technologies Limited
    CCNA, MikroTik Certified Network Associate (MTCNA), RedHat Certified System Administrator (RHCSA), CompTIA A+, IT Essential Certification and Training, Microsoft Office Professionals
    February 2019 – Present
  • Assistant Proctor
    International Islamic University Chittagong, Bangladesh
    March 2023 – August 2024
  • Assistant Lecturer
    Department of Computer & Communication Engineering (CCE)
    International Islamic University Chittagong, Bangladesh
    January 2022 – December 2022
  • Adjunct Lecturer
    Department of Electronic & Telecommunication Engineering (ETE)
    International Islamic University Chittagong, Bangladesh
    November 2018 – December 2021
  • Teaching Assistant
    Department of Electronic & Telecommunication Engineering
    International Islamic University Chittagong, Bangladesh
    May 2018 – September 2018

🏆 Achievements and Awards

  • Cisco Certified Network Associate (CCNA) certification
  • MikroTik Certified Network Associate (MTCNA) certification
  • RedHat Certified System Administrator (RHCSA) certification
  • CompTIA A+ certification
  • IT Essentials Certification and Training
  • Microsoft Office Professionals certification

Publication Top Notes:

Design and Simulation of AI-Enabled Digital Twin Model for Smart Industry 4.0

Enhancing Insider Malware Detection Accuracy with Machine Learning Algorithms

Transfer Learning-Based Anomaly Detection System for Autonomous Vehicle

Design and Implement IoT-Based Intelligent Manageable Smart Street Lighting Systems for Future Smart City

Design and Analysis of Multiband Microstrip Patch Antenna Array for 5G Communications

 

Prof. Yuguo Yu | Artificial Neural Awards | Best Researcher Award

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Prof. Yuguo Yu | Artificial Neural Awards | Best Researcher Award  

Prof. Yuguo Yu, Fudan University, China

Yuguo Yu, Ph.D., is a distinguished professor in Brain-inspired Artificial Intelligence and Computational Neuroscience at Fudan University, where he has been a faculty member since 2011. He currently serves as a professor at both the Research Institute of Intelligent Complex Systems and the National Key Laboratory of Medical Neurobiology. Yu obtained his Bachelor’s degree in Physics from Lanzhou University in 1995 and completed his Ph.D. in Condensed Matter Physics at Nanjing University in 2001. He pursued postdoctoral training in Computational/Behavior Neuroscience at Carnegie Mellon University from 2001 to 2004 and was an Associate Research Scientist at Yale University from 2005 to 2011, where he continues to contribute as a visiting Research Scientist since 2021. Yu has been recognized for his academic excellence through prestigious awards, including the Shanghai Eastern Scholar Professorship in 2013 and the Shanghai Excellent Academic Leader award in 2021. He is an active member of the Chinese Society of Computational Neuroscience and serves as an associate editor for several prominent journals, including IEEE Transactions on Cognitive and Developmental Systems and Frontiers in Computational Neuroscience. His research interests encompass brain-inspired neural networks, cellular mechanisms of energy-efficient cortical dynamics, synaptic learning mechanisms, and large-scale cortical network modeling, with over 100 publications in leading journals such as Nature and Neuron. Yu has also led or participated in numerous national foundation projects, advancing the field of computational neuroscience.

Professional Profile:

GOOGLE SCHOLAR

Research for Best Researcher Award

Candidate Overview: Dr. Yuguo Yu is a prominent researcher and professor in Brain-inspired artificial intelligence and computational neuroscience at Fudan University. With extensive academic and research experience, he is a strong candidate for the Best Researcher Award due to his significant contributions to the field, impactful publications, and leadership roles.

Education

  • B.Sc. in Physics
    Lanzhou University, 1995
  • Ph.D. in Condensed Matter Physics
    Nanjing University, 2001
  • Postdoctoral Researcher in Computational Neuroscience
    Carnegie Mellon University, 2001–2004
  • Research Scientist in Neurobiology
    Yale University, 2005–2011

Work Experience

  • Professor
    Research Institute of Intelligent Complex Systems, Fudan University, 2020–Present
  • Professor
    National Key Laboratory of Medical Neurobiology, Fudan University, 2013–Present
  • Visiting Research Scientist
    Yale University School of Medicine, 2021–Present
  • Associate Research Scientist
    Department of Neuroscience, Yale University, 2005–2011

Research Interests:

  • Brain-inspired Intelligence and Computational Neuroscience
  • Neural Computation Model
  • Neural Coding Theory
  • Network Topology Analysis
  • Sensory Fusion Mechanism
  • Brain Connectome Atlas
  • Self-organizing Learning Algorithm
  • Multi-sensory Fusion Model
  • Low-power Mechanism of the Human Brain 🔍

Publication Top Notes

CITED:1904
CITED:444
CITED:300
CITED:238
CITED:219

CITED:216

Ms. Rachel Stephen Mollel | Machine Learning Awards | Best Scholar Award

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Ms. Rachel Stephen Mollel | Machine Learning Awards | Best Scholar Award

Ms. Rachel Stephen Mollel, University of Strathclyde, United Kingdom

Rachel Stephen Mollel is a Ph.D. student in Electrical and Electronic Engineering at the University of Strathclyde, UK. Her research focuses on machine learning, explainable AI, energy demand-side management, smart metering, and non-intrusive load monitoring (NILM). She holds a Master of Engineering from Arkansas Tech University, USA, and a Bachelor’s degree in Telecommunication Engineering from Visvesvaraya Technological University, India. Rachel has contributed significantly to the energy sector, exploring the role of smart meters in reducing energy costs and enhancing communication between energy providers and consumers. Her recent work, which investigates the potential of NILM to reveal hidden demand flexibility in residential energy consumption, has been published in various peer-reviewed journals and conferences. Additionally, she is actively involved in improving the interpretability of NILM models to enhance algorithm performance. Her contributions have been recognized with a Commonwealth Scholarship in 2020.

Professional Profile:

ORCID

Summary of Suitability for the Best Scholar Award:

Rachel Stephen Mollel is a highly suitable candidate for the Best Research Scholar Award based on her significant contributions to the fields of machine learning, explainable AI, and energy demand-side management. As a PhD student at the University of Strathclyde, her research aims to address critical energy issues through innovative approaches like Non-Intrusive Load Monitoring (NILM), which helps uncover hidden demand flexibility in residential energy consumption.

Education:

  • 2021 – Present: PhD in Electrical and Electronic Engineering, University of Strathclyde, UK
  • 2010 – 2012: Master of Engineering, Arkansas Tech University, USA (GPA: 3.75/4.0)
  • 2006 – 2010: Bachelor’s degree in Telecommunication Engineering, Visvesvaraya Technological University, India (First Class)

Work Experience:

  • 2011 – 2012: Graduate Assistant, Arkansas Tech University, USA
    Assisted in the Digital Logic and Robotics Course & Lab; delivered tutorials, graded lab reports and exams, and supported the development of course materials under faculty supervision.
  • 2014 – 2020: Assistant Lecturer, University of Dar es Salaam, Tanzania
    Delivered lectures, prepared and graded exams in Control Systems Engineering and Fundamentals of Electrical Engineering. Supervised undergraduate student projects, practical training, and fieldwork. Managed various administrative duties, such as student registration and coordination of departmental examinations.

Publication top Notes:

Explainability-Informed Feature Selection and Performance Prediction for Nonintrusive Load Monitoring

Using explainability tools to inform non-intrusive load monitoring algorithm performance

Using explainability tools to inform NILM algorithm performance