Ms. Geetika Aggarwal | Next Generation Awards | Women Researcher Award

Ms. Geetika Aggarwal | Next Generation Awards | Women Researcher Award 

Ms. Geetika Aggarwal, teesside university, United Kingdom

Dr. Geetika Aggarwal is a Lecturer in the School of Computing, Engineering, and Digital Technologies at Teesside University. She completed her PhD in Electronics and Communication Engineering at Northumbria University, Newcastle Upon Tyne, UK, where she designed a real-time wireless communication system for Electroencephalography (EEG) monitoring. Prior to her current role, Dr. Aggarwal was a Post-Doctoral Researcher at Nottingham Trent University, working on the Horizon 2020 Clean Sky EU project, focusing on Instrumentation and Control, Digital Signal Processing, Data Acquisition, Embedded Systems, Virtual Reality, and Artificial Intelligence using tools such as MATLAB/SIMULINK and LabVIEW. Dr. Aggarwal has received the Outstanding Staff Member Award in 2019 from the Department of Electrical and Electronic Engineering at Northumbria University. Her teaching responsibilities encompass various modules, including Sustainable Systems, Robust Control Systems, and Digital Signal Processing. She is a Fellow of the Higher Education Academy (FHEA), a member of the IEEE and IET, and serves as an Executive Committee Member of the IET Teesside Local Network. Dr. Aggarwal actively engages in research, with an academic profile that includes over 250 citations and a strong commitment to enhancing student learning experiences.

Professional Profile:

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Suitability of Dr. Geetika Aggarwal for the Research for Women Researcher Award

Dr. Geetika Aggarwal is an exemplary candidate for the Research for Women Researcher Award due to her impressive academic background, diverse research experience, and significant contributions to the field of engineering and technology, particularly in sustainable systems and digital technologies. Her career trajectory reflects a strong commitment to both teaching and research, evidenced by her role as a Lecturer in the School of Computing, Engineering, and Digital Technologies at Teesside University.

๐ŸŽ“ Education

  • PhD in Electronics and Communication Engineering
    Northumbria University, Newcastle Upon Tyne, England, UK
    (Oct 2015 โ€“ Oct 2019)

    • Research Focus: Designed a real-time wireless communication system for Electroencephalography (EEG) monitoring.

๐Ÿ’ผ Work Experience

  • Lecturer
    School of Computing, Engineering, and Digital Technologies, Teesside University
    (Nov 1, 2022 โ€“ Present)
  • Post-Doctoral Researcher
    Nottingham Trent University, England, UK
    (Nov 2019 โ€“ Oct 2022)

    • Worked on Horizon 2020 Clean Sky EU project involving:
      • Instrumentation and Control ๐Ÿ› ๏ธ
      • Digital Signal Processing ๐Ÿ“Š
      • Data Acquisition ๐Ÿ’ป
      • Embedded Systems ๐Ÿ“ก
      • Virtual Reality ๐Ÿ•ถ๏ธ
      • Artificial Intelligence ๐Ÿค–
      • Tools used: MATLAB/SIMULINK, LabVIEW, Audacity, Adobe Audition, Fusion 360.

๐Ÿ† Achievements

  • Outstanding Staff Member Award 2019
    Department of Electrical and Electronic Engineering, Northumbria University, UK ๐ŸŒŸ
  • Fellow of Higher Education Academy (FHEA)
    Awarded on October 26, 2023 ๐ŸŽ“

๐Ÿ… Professional Affiliations

  • Member of the Institute of Electrical and Electronic Engineers (IEEE) โšก
  • Member of the Institute of Engineering and Technology (IET) โš™๏ธ
  • Executive Committee Member of IET Teesside Local Network and University Liaison ๐Ÿค

๐Ÿ“š Teaching Responsibilities

  • Modules Taught:
    1. Sustainable Systems and Industry 4.0 ๐ŸŒฑ
    2. Sustainability ๐ŸŒ
    3. Robust Control Systems (RCS) โš™๏ธ
    4. Digital Control and Digital Signal Processing (DSP) ๐Ÿ“Š
    5. Embedded Systems ๐Ÿ’ป
    6. Linear Systems and Control ๐Ÿ“
    7. Electronic Principles โšก
    8. Instrumentation ๐Ÿ“
    9. Design Implementation ๐Ÿ› ๏ธ

Publication Top Notes:

An Approach to Control Electric Automotive Water Pumps Deploying Artificial Neural Networks

 

Distributed Feature matching for Robust Object Localization in Robotic Manipulation

 

Intelligent Energy Management across Smart Grids Deploying 6G IoT, AI, and Blockchain in Sustainable Smart Cities

 

Parallel Implementation of Fuzzy-PID Controllers for Pitch Control of Offshore Wind Turbines

 

Integrated Operational Planning For Smart Electric Networks in Middlesbrough, UK

 

PLC & Scada Based Automation for Smart Juice Manufacturing Process