Assist. Prof. Dr. Shuja Ansari | Virtual Power Plants | Best Sensor for Energy Management Award

Assist. Prof. Dr. Shuja Ansari, University of Glasgow, United Kingdom

Dr. Shuja S. Ansari is a Chartered Engineer and accomplished academic based at the University of Glasgow, UK, currently serving as Lecturer in Autonomous Systems and Connectivity at the James Watt School of Engineering. With over a decade of research and professional experience, Dr. Ansari has played leading roles in high-impact projects exceeding £30 million in value, directly securing over £5 million in competitive funding since 2020. He has authored over 70 research papers, co-edited two books on beyond-5G networks, and holds two patents resulting from innovative RFID-based asset management work. His research spans 5G/6G wireless systems, IoT, electromagnetic warfare, and AI-driven network solutions. He serves as the Glasgow Lead and Principal Investigator for the UK Electromagnetic Environment Hub (EME), collaborating with the UK Ministry of Defence, and has contributed to the development of the UK’s first 5G USB-C modem and 3D video calling technologies. A Senior Member of IEEE and Fellow of the Royal Economic Society of Technology, Dr. Ansari’s academic leadership includes supervising Ph.D. students, contributing to national-level O-RAN testbeds, and pioneering cross-sector collaborations in wireless and intelligent systems.

Professional Profile:

SCOPUS

ORCID

GOOGLE SCHOLAR

Summary of Suitability for the Best Sensor for Energy Management Award:

Dr. Shuja S. Ansari is a highly accomplished Chartered Engineer and academic researcher whose contributions to wireless systems, RFID-based asset management, and IoT-based infrastructure uniquely position him as an exceptional candidate for the Best Sensor for Energy Management Award. His interdisciplinary expertise spans telecommunications, power electronics, electromagnetic compatibility, and sensor-driven system optimization—core areas relevant to next-generation energy management technologies.

🎓 Education

📍 Ph.D. in Engineering
Glasgow Caledonian University, UK (Sep 2015 – Jan 2019)
Scholarship recipient
📍 M.Sc. in Telecommunications Engineering
Glasgow Caledonian University, UK (Jan 2014 – Jun 2015)
Graduated with Distinction
📍 B.Sc. (Hons) in Electrical (Telecoms) Engineering
COMSATS University, Pakistan (Sep 2009 – Jun 2013)

💼 Work Experience

🏛️ University of Glasgow

Lecturer / Assistant Professor in Autonomous Systems and Connectivity (Jan 2022 – Present)
- 📡 Lead Investigator for UK Electromagnetic Environment Hub (Dstl/MoD)
- 📶 Co-Investigator for multiple 5G/IoT and Open RAN projects
- 🧠 Supervised 15+ PhD students and participated in 13 examinations
- 👨‍🏫 Taught subjects like Power Electronics, mmWave Design, EMC

🔬 Postdoctoral Research Associate (Sep 2019 – Jan 2022)

🛜 Led 5G/IoT use case development
📡 Senior DevOps for 4G/5G, NB-IoT, LoRaWAN
🧑‍💼 Industry liaison and spectrum coordinator

🏫 Glasgow Caledonian University (Feb 2019 – Jul 2019)

📘 Associate Lecturer in Electrical and Wireless Communications
💪 Contributed to Athena Swan Charter for inclusion in STEM

💻 Freelance Network Engineer (2015 – 2019)

🔧 Led network infrastructure deployments and troubleshooting

🇵🇰 Internships – Jazz & Ufone (Pakistan)

📶 RF Optimization, Drive Testing, Network Monitoring (2010–2013)

🏆 Achievements & Awards

💷 Secured £5M+ in funding for R&D projects since 2020
📚 Co-edited 2 books on Beyond 5G technologies
✍️ Published 70+ research papers
🔬 Led to 2 patents and spin-off company “RFIoT”
📡 Developed UK’s first 5G USB-C modem
🏁 Hosted UK’s first Summer School on Electromagnetic Warfare
📽️ Delivered UK’s first 3D video call over 5G
🛠️ Inventor of CoMP algorithms for Open RAN fronthaul enhancement

Publication Top Notes:

Next-Generation IoT: Harnessing AI for Enhanced Localization and Energy Harvesting in Backscatter Communications

RIS-Enabled Secret Key Generation for Secured Vehicular Communication in the Presence of Denial-of-Service Attacks

An Overview of Emergency Communication Networks

Crowd Control, Planning, and Prediction Using Sentiment Analysis: An Alert System for City Authorities

Towards the Digital Twin (DT) of Narrow-Band Internet of Things (NBIoT) Wireless Communication in Industrial Indoor Environment

Coverage Extension for the UK Smart Meter Implementation Programme Using Mesh Connectivity

Capacity Optimization of Next-Generation UAV Communication Involving Non-Orthogonal Multiple Access

Elnaz Yaghoubi | Power System Analysis | Best Researcher Award

Dr. Elnaz Yaghoubi, karabuk university, Turkey.

Elnaz Yaghoubi is a dedicated Ph.D. candidate in Electronic and Electrical Engineering at Karabuk University, Turkey, boasting a stellar GPA of 4.0. Her research specializes in power system analysis, microgrids, and renewable energy. Elnaz holds an M.Sc. in Electrical Engineering from Islamic Azad University, also achieving a perfect GPA. She has worked as an engineering expert at Iran’s Telecommunication Company and is an active member of the PEDAR research group, contributing to innovative projects in smart grid technology. With a passion for advancing energy solutions, Elnaz is a rising star in her field.

Education and Experience:

Ph.D. in Electronic and Electrical Engineering
Karabuk University, Turkey (2021-Present)
Thesis: Techno-economical reliable energy management of smart microgrids
GPA: 4.0

M.Sc. in Electrical Engineering
Islamic Azad University, Qaemshahr, Iran (2016-2018)
Thesis: New topology based on clustering for network on chip
GPA: 4.0

B.Sc. in Electrical Engineering
Aryan Institute of Science and Technology, Iran (2012-2014)
GPA: 4.0

Associate’s Degree in Electrical Engineering
University College of Rouzbahan, Iran (2010-2012)
GPA: 4.0

Principle Researcher
PEDAR Group (2023-Present) 🧑‍🔬

Expert in Traffic Monitoring and Data Support
Telecommunication Company, Iran (2017-2021)

Data Network Design
Telecommunication Company, Iran (2015-2017)

Suitability for Best Researcher Award:

Dr. Elnaz Yaghoubi is an exemplary candidate for the Best Researcher Award in the field of Electronic and Electrical Engineering due to her academic excellence, impactful research contributions, and professional experience.

Professional Development:

Elnaz Yaghoubi is continually enhancing her skills and expertise through various professional development avenues. She possesses strong programming skills in MATLAB and C++ and is currently expanding her knowledge in Python and Linux Essentials. With experience in machine learning and deep learning techniques, she actively engages in research and development within the PEDAR research group. Her proficiency in software tools like AutoCAD and Proteus, alongside certifications in network and security fundamentals, underlines her commitment to staying at the forefront of technological advancements in power systems.

Research Focus:

Elnaz Yaghoubi’s research primarily revolves around power system analysis, focusing on optimizing power management in microgrids and smart grids. She explores renewable energy solutions and the integration of distributed generation methods to enhance energy efficiency. Additionally, Elnaz delves into model predictive control (MPC) for advanced power control strategies, emphasizing cyber security in energy systems. Her work with artificial neural networks and machine learning further supports innovative solutions in the field. Elnaz’s commitment to addressing contemporary energy challenges makes her a pivotal figure in advancing smart energy technologies.

Publication top Notes:

State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques

Cited by: 85 | Year: 2023

The role of mechanical energy storage systems based on artificial intelligence techniques in future sustainable energy systems

Cited by: 15 | Year: 2023

Triple-channel glasses-shape nanoplasmonic demultiplexer based on multi nanodisk resonators in MIM waveguide

Cited by: 12 | Year: 2021

Reducing the vulnerability in microgrid power systems

Cited by: 11 | Year: 2023

Electric vehicles in China, Europe, and the United States: Current trend and market comparison

Cited by: 10 | Year: 2024

Tunable band-pass plasmonic filter and wavelength triple-channel demultiplexer based on square nanodisk resonator in MIM waveguide

Cited by: 10 | Year: 2022

A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior

Cited by: 9 | Year: 2024