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

Prof Dr. Ersin Elbasi | Machine learning Award | Excellence in Research

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Prof Dr. Ersin Elbasi | Machine learning Award | Excellence in Research

Prof Dr. Ersin Elbasi, American University of the Middle East, Kuwait 

Ersin Elbasi, Ph.D., is a distinguished professor specializing in Computer Science and Engineering, currently serving at the American University of the Middle East in Kuwait. He earned his Ph.D. in Computer Science from the Graduate Center, CUNY, with a dissertation on robust video watermarking schemes, following a Master of Philosophy in the same field from the same institution. He holds a Master of Science in Electrical Engineering and Computer Science from Syracuse University and a Bachelor of Science in Industrial Engineering from Sakarya University, Turkey. Dr. Elbasi’s extensive academic career includes previous positions as Associate Professor at the American University of the Middle East and faculty roles at the Higher Colleges of Technology in the UAE and Çankaya University in Turkey. His research focuses on machine learning, multimedia security, and data mining, with notable projects in digital image and video watermarking and event mining in video sequences. He has also held significant roles in research and development at TÜBİTAK, contributed to SQL application development in New York City, and engaged in various international research activities. Dr. Elbasi’s technical expertise spans Visual C++, SQL programming, and Java, with a notable scholarship record and recognition for his contributions to the field.

Professional Profile:

Summary of Suitability for Excellence in Research 

Dr. Ersin Elbasi holds a Ph.D. in Computer Science from the Graduate Center, CUNY, with a specialization in robust video watermarking schemes in transform domains. His advanced degrees in computer science, electrical engineering, and industrial engineering demonstrate a strong interdisciplinary foundation.

Education

  • Ph.D. in Computer Science
    Graduate Center, CUNY, New York City, NY
    Graduated: April 2007
    Dissertation: “Robust Video Watermarking Scheme in Transform Domains”
  • Master of Philosophy in Computer Science
    Graduate Center, CUNY, New York City, NY
    Graduated: May 2006
  • Master of Science in Electrical Engineering & Computer Science
    Syracuse University, Syracuse, NY
    Graduated: May 2001
  • Bachelor of Science in Industrial Engineering
    Sakarya University, Sakarya, Turkey
    Graduated: June 1997

Work Experience

  • Professor
    American University of the Middle East (QS ranking 500-600), Kuwait
    June 2022 – Current
  • Associate Professor
    American University of the Middle East (QS ranking 500-600), Kuwait
    October 2016 – June 2022

    • Taught courses including CNIT 180, CNIT 280, CNIT 380, CNIT 315, CS 159, CNIT 480, CNIT 372, CNIT 399/499, TECH 330, and TECH 320.
  • Faculty Member
    Computer and Information Science, Higher Colleges of Technology, Al Ain, Abu Dhabi, UAE
    August 2015 – July 2016

    • Taught courses including Introduction to Multimedia, Research Methods in Emerging Technologies, Statistics and Probability, and Information Systems in Organizations and Society.
  • Instructor/Associate Professor
    Çankaya University (400-500 by Times ranking), Department of Computer Engineering, Ankara
    September 2007 – June 2015

    • Taught courses including Data Mining, Multimedia Security, Object-Oriented Languages, Database Management, Multimedia and Internet, Data Management and File Structures, and Formal Languages and Automata.
  • Expert/Chief Expert of Scientific Programs
    TÜBİTAK, Ankara, Turkey
    August 2007 – July 2014

    • Served as Executive Secretary to the Electrical, Electronics, and Informatics Research Grant Committee, National Scientific Expert in COST Information and Communication Domain, and National Delegate in COST (FP 7) Trans Domain Proposals.
  • SQL Application Developer
    Bureau of Revenue Enhancement and Automation, Finance Office, New York City Government
    November 2004 – July 2007

    • Developed SQL applications, performed ad-hoc queries, and managed staff training in SQL and related software tools.
  • Instructor
    The City University of New York (CUNY)
    September 2004 – May 2007

    • Taught courses at Brooklyn College, Borough Manhattan Community College, and Lehman College, including Operations Management, Introduction to Computer Applications, Database Management, Discrete Structures, and GMAT Math.
  • Research Assistant
    Electrical Engineering and Computer Science, Syracuse University
    January 2003 – May 2004

    • Worked on Automated Scenario Recognition in Video Sequences and implemented data mining and machine learning techniques.
  • Engineer
    Calik Textile, Istanbul, Turkey
    January 1999 – August 1999

    • Focused on Production Planning.
  • Engineer
    HES Machine, Kayseri, Turkey
    June 1997 – March 1998

    • Focused on Production Planning and Quality Control.

Publication top Notes:

Transformer Based Hierarchical Model for Non-Small Cell Lung Cancer Detection and Classification

Anticipate Movie Theme from Subtitle: A Deep Learning Approach

Robust and Secure Watermarking Algorithm Based on High Frequencies of Integer Wavelet Transform

Fortifying Integrity and Privacy in Medical Imaging: Discrete Shearlet and Radon Transform-Based Watermarking Approach

Machine Learning-Based Analysis and Prediction of Liver Cirrhosis