Speaker: Amparo Güemes
Area of career: Academia
Research interest: Bioelectronic medicine for metabolic control (bioelectronics, neurotechnology and electrophysiology)
Talk topic: The talk will explore new technology and algorithms for interfacing with the nervous system to improve type 1 diabetes control. In particular, I will present my research in the Bioelectronics Lab at University of Cambridge, where we aim to create a fully autonomous system that will combine blood glucose measurements and neural signals decoded from peripheral nerves to determine the optimal insulin dose and the characteristics of the electrical stimulation to modulate glucose metabolism as desired. We expect this closed-loop platform will enable patient-tailored and fully autonomous treatment to eventually address one of the greatest challenges of medicine.
Biography: Dr Amparo Güemes is a postdoctoral 1851 Research Fellow at the Bioelectronic Lab at the University of Cambridge. She received the B.S. degree in Biomedical Engineering from the Universidad Politécnica de Madrid (UPM), Madrid, Spain, in 2016, and the M.S. degree in Biomedical Engineering from Imperial College London (ICL), London, UK, in 2017, both with 1st Class Honours.
She received her PhD in Bioelectronic Medicine from the Department of Electrical and Electronic Engineering at Imperial College London in 2021. Amparo’s interdisciplinary research combines signal processing, modelling, electronics and electrophysiology to develop advanced algorithms and neurotechnology to be integrated into a closed-loop platform aiming to improve to improve the management of autoimmune diseases, including type 1 diabetes and systemic lupus erythematosus.
Speaker: Btissam Er-Rahmadi
Area of career: Industry
Research interests: Mobile Communications, Distributed Systems, Operations Research, and Artificial Intelligence (i.e. Knowledge Graph, Machine/deep learning).
Talk topic: Failure detectors (FDs) are fundamental building blocks for distributed systems in which it is crucial to know whether a process crashed or not based on the reception of its heartbeats messages.
A key challenge of FDs is to tune their parameters to achieve optimal performance which satisfies the desired system requirements under complex large-scale networks. In this talk, I will discuss a new Mixed Integer Linear Programming (MILP) optimisation-based FD algorithm. To adapt to real-time network changes, the FD fits the probability distribution of heartbeats’ inter-arrivals. Amazon Cloud experiments show consistent improvement of overall FD performance and scalability.
Biography: Btissam Er-Rahmadi is a Senior Researcher at Huawei Edinburgh Research Centre, United Kingdom, in the Knowledge Graph Lab. Prior to that, she was a Research Fellow in Network Systems at the University of Southampton, United Kingdom, for 21 months, where she worked on optimized failure detection performance for resilient networks/distributed systems. She received her Ph.D. degree in Computer Science and Master degree in Electronics and Telecommunications from University Rennes 1, France, in 2016 and 2012, respectively. She also holds an Engineering degree in Telecommunications and Information Technologies from INPT-Rabat, Morocco jointly with Telecom SudParis Evry, France, dating back to 2011.
Her research interests include distributed systems, network systems and resiliency, distributed ledgers, knowledge graphs, machine/deep learning, performance analysis/evaluation, and mobile network architectures and protocols.
Speaker: Jennifer Williams
Area of career: My career area is currently both academia and industry. I just started my first full-time postdoctoral fellowship that will go for three years. I also hold a position in industry part-time as a senior speech scientist at a startup.
Talk topic: As consumers, we use our voice openly in many contexts from posting to YouTube and TikTok to chatting casually on Facetime. Usually we do not think much about our voice as a form of personally identifiable information. New advances in speech technology have led to the commercialization of voice as a form of biometric identity using a voiceprint, like a fingerprint. This talk discusses what a voiceprint is and how it is created. I will also talk about how voiceprints can be used to build secure passwords and how voiceprints can be used to create audio deepfakes.
Biography: Dr Jennifer Williams recently finished her PhD in speech technology from the University of Edinburgh, Scotland. She has joined the University of Southampton as a postdoctoral research fellow in Citizen-Centric AI Systems where she works on audio-based room occupancy detection for smart buildings and smart energy management. She also works on content-based privacy for the UKRI Trustworthy Autonomous Systems Hub.
Dr Williams holds a part-time position in industry as a senior speech scientist at MyVoice AI where she leads the company intellectual property strategy for voice-based security solutions embedded on chips.
Speaker: Waqar Asif
Area of career: Academia
Talk topic: The changing dynamics of our power distribution systems: A Smart Grid Case Study
This talk will help highlight how the change in power requirements have pushed the need to adapt/update our power distribution and power generation systems.
Biography: Waqar is a Senior Lecture in Cyber Security at the School of Computing and Engineering, University of West London. Prior to this, he was involved in tech start-ups and did his post doc at City University, London.
Waqar is actively participating in research directed towards graph theory, sensor networks, network performance metrics, Blockchain, network privacy, network security, power networks and The Internet of Things. He is a full stack developer, an ethical hacker and soon to be an AWS Cloud instructor.