Alexantrou Serb has graduated from Imperial college in 2009 as a biomedical engineer and later obtained his PhD from Imperial college in 2013 in electronics engineering. Since then he has been a research fellow in prof. Prodromakis' group, working on memristors technology characterisation and application development. His research interests include circuit design, computational neuroscience and biology-machine interfaces.
One of the greatest challenges of current research is how to embody the level of intelligence found in the brain into a physical computer that operates with the energy efficiency of the brain. To that end, algorithmics, computer architecture design, nanotechnology and ultimately also bio-interfacing must work together, as no isolated approach can deliver this vision. Throughout project FORTE this researcher will be involved in: 1) developing new applications enabled by enmeshing memristors into existing electronics technologies and 2) understanding how the availability of memristive technologies can unlock new capabilities in artificial intelligence systems.The applications developed in this programme will span from simple, modular micro-circuits employing several memristors to larger scale systems utilising thousands of devices. Examples of the former would include reconfigurable logic (digital) and filter (analogue) blocks that can be aggregated into much larger computational systems much like Lego pieces can be used to construct complicated superstructures. Examples of the latter would include functional blocks that can carry out probabilistic inference, biosignal processing or neutral network-type inference.The algorithmics part of the research is oriented towards the 'problem of multiplication': multiplication is expensive to perform accurately in hardware. Memristive technologies, however, offer an opportunity to implement not only multiplication, but other basic mathematical operations such as scalar matching in hardware at very low energy. Furthermore, reducing energy budgets for such operations opens the way towards more powerful, more intelligent AI.
Ioulia Tzouvadaki received her B.Sc. degree in Physics, from National and Kapodistrian University of Athens (U.O.A) and the M.Sc. degree in Microsystems and Nanodevices from National Technical University of Athens (N.T.U.A). Her M.Sc. thesis concerned the computational study and simulation of polymer nanocomposite materials, within the Computational Materials Science and Engineering (CoMSE) research group, of the School of Chemical Engineering at the NTUA. She received her PhD in Microsystems and Microelectronics at École Polytechnique Fédérale de Lausanne (EPFL). In her PhD research at the Integrated System Laboratory (LSI) she focused on the fabrication and characterization of nanostructures and their implementation as ultrasensitive nano-bio-sensors in both diagnostics and therapeutics. She joined Stanford University as a postdoctoral fellow working on the design of an electronic platform for integration with wearable sweat biomarker sensors for multi-panel, continuous monitoring to enhance human health and performance. Currently she is a Marie Curie Research Fellow in the Electronic Materials and Devices group.
Inflammatory markers consist a pivotal tool in clinical practice since they allow detection of acute inflammation that might be an indicator of specific diseases, or to enable signalizing the response of a patient to a specific medical treatment. However, the detection of an inflammation is still performed only in vitro, while the overall testing procedure requires a long waiting time for the clinical results that can be crucial for instance in a case of serious injuries in a contaminated environment or after a rejection of an organ transplant. Moreover, the status quo of the clinical practice does not take into consideration the aspect of continuous monitoring of the inflammatory markers.
My research interests include the development of disposable, implantable sensing devices that give the possibility to perform reliable and robust continuous, in-blood, sensing of critical inflammatory markers directly from the patient’s body. I target to develop a flexible, low-cost, miniaturized sensing platform implementing memristive nanoscale devices as intelligent minimally invasive bio-interfaces, allowing reliable, continuous and real-time monitoring of inflammatory markers.
Spyros obtained his Applied Physics diploma in 2009 and his MSc in Microelectronics and Nanotechnology in 2011, both from the National Technical University of Athens (NTUA). In 2015 he received his PhD from the Department of Physics, NTUA for his work on the effects of infrared laser annealing in the electrical characteristics of silicon and germanium. He joined the University of Southampton, UK in 2016.
Spyros is currently working on the fabrication, characterisation and application of metal oxide memristive devices.
Firman is developing hard-rad memristor devices for nuclear and space applications. The response towards various irradiations is exploited to design a novel radiation sensing technology and adaptive electronic circuit
Neeraj is a Research Fellow in the Electronic Materials and Devices research group at the Zepler Institute of Photonics and Nanoelectronics at the University of Southampton. He received his Ph.D. in Microelectronics from IIT Bombay, India. He did his MTech in Solid State Electronic Materials from IIT Roorkee, India and MSc in Physics from Janta Vedic College Baraut, India.
His research interests are Resistive Switching Devices for NVM and Neuromorphic Engineering applications; RF devices; and 3D integration of Memristor with CMOS.
Sachin Maheshwari is a Research Fellow at the Centre for Electronics Frontiers in the Zepler Institute at the University of Southampton. He did his PhD in the Applied DSP and VLSI Research Group at the University of Westminster, London, UK. His doctoral thesis was focussed on Adiabatic Approach for Low-Power Passive NFC systems. He did his Master of Engineering Degree in Microelectronics from Birla Institute of Technology and Science (BITS), Pilani, India and Bachelor of Technology Degree in Electrical and Electronics Engineering from ICFAI Tech, Hyderabad, India. In the past, he was also a Lecturer in the EEE Department at BITS Pilani for over 4 years.
His research interest includes Design of Ultra-low-Power Digital Circuits, Energy and Area Efficient Integrated Circuit Design and Energy Recovery Logic.
Georgios received his M.Eng. degree in electrical and computer engineering (ECE) from the Democritus University of Thrace (DUTh), Greece, in 2015, and his M.Sc. degree in ECE from DUTh, Greece, in 2017. He is currently pursuing his Ph.D. studies at University of Southampton in Electronic Materials & Devices research group, working towards the implementation of reconfigurable hybrid CMOS/memristor circuits, systems and computer architectures.
Georgios currently participates in the research of novel nano-electronic circuits and systems design for reconfigurable mixed-signal CMOS/memristor computer architectures towards developing a post-von-Neumann computing paradigm.
Jiaqi received her Bachelor of Science in Microelectronic Science and Engineering from Shenzhen University, China, in 2017. Then she obtained her Master of Science in Microelectronic Systems Design from University of Southampton, in 2018. Currently, she is pursuing her PhD in Electronic Materials & Devices Research Group, University of Southampton.
She is working towards the memristor-based analogue & mixed-signal integrated circuit (IC) design. In neural recording system, we intend to design a front-end system outputting directly a digital, back-end processed waveform corresponding to neural spikes and demonstrate extremely low power dissipation in the process. The front-end contains an integrating amplifier and a dynamic latch comparator (DLC). Instead of purely amplifying the minute signal, we opt for integrating the micron-volt level signal. The integration process not only filters out noise, but also provides amplification. With memristors implanted along the differential current path, the offset can be tuneable precisely, making the systems operate with power efficiency and high precision.
Yihan's PhD is studying on hardware development for AI at a high level design. She works on memory architectures in hyperdimensional computing designed for semantic representations.