Loading Events

IEEE WIE UKI Ambassadors’ Scheme | Early Career Talks 8 – An Inspirational and Empowering Webinar for Women in Engineering

About this Event

IEEE WIE in Engineering is an initiative with the goal to facilitate the recruitment and retention of women in technical disciplines globally. We envisage a vibrant community of IEEE women and men collectively using their diverse talents to innovate for the benefit of humanity.

Early Career Talks aim to expose the participants to novel areas and aspects of engineering for solving real world problems. They also aim to establish a link for networking, mentorship and to create a connection for research opportunities.

Speaker: Nour Aburaed

Area of career: Academia

Research interest: Remote sensing, image processing

Talk title: Single Image Super Resolution of Hyperspectral Remote Sensing Imagery: Advances and Challenges

Talk topic: Hyperspectral Imaging is an imaging technique that acquires hundreds of narrow, adjacent spectral bands within a wide range of wavelengths. The aim is to obtain the spectrum for each pixel in the image of a scene, which makes HSI a powerful imaging modality for object recognition, identifying materials, or detecting processes.

Hyperspectral Images (HSI) have high spectral resolution, but they suffer from low spatial resolution. This occurs due to the inherent sensors trade-off that prevents them from capturing images with high spectral resolution and high spatial resolution simultaneously. This limitation prevents HSI from being used to their maximum potential. Hence, the spatial enhancement of HSI is an important research problem in remote sensing community that has rapidly gained attention over the past two decades. This talk demonstrates some of the advances in the field of HSI enhancement using Single Image Super Resolution techniques, and highlights the challenges encountered in this area of research.

Biography: Nour Aburaed is a holder of BSc degree in Computer Engineering from Khalifa University of Science and Technology (Abu Dhabi – UAE), and MSc in Electrical and Computer Engineering from the same University. Her MSc thesis is specialized in “High-ISO Image De-noising and Quantum Image Processing”. She was a Teaching Assistant at Khalifa University of Science and Technology from 2016-2018 for theoretical and laboratory-based courses, including Calculus, Physics, and Introduction to Programming.

Since June 2018, Nour has been working as a Research Assistant at Mohammed Bin Rashid Space Centre (MBRSC) Lab based at the University of Dubai (Dubai – UAE). Her responsibilities include applying Image Processing and Artificial Intelligence tools within the context of remote sensing to achieve various industrial tasks, including autonomous object detection, semantic segmentation, classification, image enhancement, and satellite calibration/validation activities. Concurrently, Nour is an Industrial PhD student at the University of Strathclyde, where she studies the spatial enhancement of Hyperspectral Imagery using Single Image Super Resolution techniques. Nour has authored multiple papers in reputable conferences and journals. She has also assisted on various occasions as a reviewer, conference organizer, and session chair. Nour was the recipient of the President’s Scholarship and Master Research Teaching Scholarship (MRTS) from Khalifa University of Science and Technology for International Students.

Speaker: Dr Gabriella Pizzuto

Area of career: Academia

Research interests: Robotic Systems/Robot Learning/Robotics for Laboratory Automation

Talk title: The Role of Intelligent Robotic Systems in Laboratory Environments

Talk topic: As a result of the COVID-19 pandemic and the current climate crisis, there is strong demand to accelerate material discovery for industrial applications e.g. healthcare and energy production. Whilst our robotic scientist has outperformed human-level performance in finding clean sustainable fuels, there are still open problems when it comes to having intelligent robots working in laboratories designed for humans.

This talk will highlight developments on robotics and artificial intelligence in our Autonomous Chemistry Lab. It will also discuss some of the additional challenges and lessons learned from having robots in this environment.

Biography: Dr Gabriella Pizzuto is an interdisciplinary early career researcher in robot learning, currently working at the University of Liverpool as the lead research associate on the ERC Synergy Grant ‘Autonomous Discovery of Advanced Materials’ (ADAM). Previously, she was a research associate at the Edinburgh Centre for Robotics working on physics-constrained learning and uncertainty estimation in robot dynamics model learning. She obtained her PhD in Computer Science from the University of Manchester, where she was also a Marie Sklodowska Curie early stage researcher and a visiting scholar at the University of Edinburgh and Italian Institute of Technology.

She is broadly interested in different topics within the intersection of robot learning, vision and control, focusing on generalisation and safe human-robot collaboration, particularly for real-world environments such as physical sciences laboratories.

Speaker: Dr Tabia Ahmad

Area of career: Academia

Research interests: Power system dynamics, data analytics and signal processing techniques for power system analytics, interpretable ML for power system studies.

Talk title: Advanced Data-driven Techniques for Power System Analytics

Talk topic: The electric power system is witnessing significant transformations towards an integrated, active, and ubiquitously sensed cyber-physical system. An abundance of multi-scaled field data offers opportunities as well as scientific challenges to infer the state of the power grid. Building on mathematical and statistical foundations of random processes and graph theory, this talk aims to provide an overview of data analytic tools which may be useful for situational awareness of modern power systems.

The key highlights of this talk are two-fold: revisiting the statistical nature of uncertainty encountered in power system measurement data and discussing its effect on the efficacy of power system analytics; and leveraging the inherent structure present in spatio-temporal power system measurements to develop interpretable and physics aware learning-based models to predict high impact low probability events such as cascading failures.

Biography: Tabia Ahmad completed her PhD thesis (with doctoral thesis distinction award) in electric power systems from the Indian Institute of technology Delhi, India. Prior to this she did her B. Tech degree in Electrical Engineering and the MTech degree in Instrumentation and Control Engineering from Aligarh Muslim University, India, in 2014 and 2016, respectively.

Since 2021, she is working as a post-doctoral researcher at the EEE department of University of Strathclyde, Glasgow (UK) working on, “Addressing the complexity of future power systems dynamic behavior” as a part of UKRI Future Leaders Fellowship. Her research interests include power system dynamics, WAMS based analytics, signal processing techniques in power systems and interpretable machine learning for power system applications.

Speaker: Amair Anwar

Area of career: Industry (5 years), currently academia (4 years)

Research interests: AI in Education, Sentiment Analysis, Cognitive Systems Design and Development, Software Process Improvement

Talk title: Role of AI in Education – Intelligent Tutoring Systems and Sustainable Education

Talk topic: This talk will consider the role of AI in education to facilitate students through effective learning platforms and teaching techniques.

Biography: Aamir Anwar is a Lecturer in ICT at Harrow College, Harrow on the Hill campus, West London. He is also currently working as Research Assistant while studying PhD (CS) at the University of West London. He obtained his Master of Science (MS) degree in Software Engineering with the specialisation in “AI in Education” from the Faculty of Engineering at Bahria University, Islamabad Pakistan, and Bachelor of Science (BS) degree in Software Engineering degree from City University of Science & IT, Peshawar, Pakistan.

His research interests are mainly in the areas of Machine and Deep Learning applications in Education, Sentiment Analysis, IoT systems security and Data Sciences. He has around 9 years of experience in teaching at College/University level and software development projects in cross platform tools and techniques. He is the author and co-author of more than 20 journals and peer-reviewed conferences publications.

Share This Story, Choose Your Platform!

Go to Top