Inaugural Lectures
All new professors, whether they have been internally promoted or appointed externally, are given the opportunity to give an inaugural lecture. Inaugural lectures are organised by the relevant academic school and provide an opportunity to celebrate these achievements with each lecture representing a significant milestone in an academic's career.
Lectures are open to all University staff and students as well as to members of the public.
Upcoming events
The unreasonable effectiveness of communication complexity
Professor Or Meir | Professor of Algorithms |School of Computer Science
Wednesday 6 May 2026, 17:00, Lecture Theatre 2, The Diamond
- Abstract and bio
Abstract: Communication complexity is an area of theoretical computer science that studies the amount of communication needed to solve various problems. Quite surprisingly, this area turned out to have applications to many other theoretical areas, including the data structures and algorithms, distributed computing, algorithmic game theory and computational economics, and most recently, understanding the limitations of transformers and deep networks. In this lecture, I will give a brief introduction to communication, and demonstrate its applicability to various other areas.
Bio: Prof. Meir completed his Phd at the Weizmann Institute of Science under the supervision of Oded Goldreich. He was then a postdoc at Stanford (hosted by Luca Trevisan), the Institute for Advanced Study (hosted by Avi Wigderson), and the Weizmann Institute of Science (hosted by Irit Dinur). In 2015 he became a faculty member at the University of Haifa, before joining the University of Sheffield in September 2025. Prof Meir's research area is theoretical computer science, and specifically complexity theory. These days he is particularly interested in circuit complexity, communication complexity, and derandomization. In the past, he also worked on probabilistic proof systems and error-correcting codes.
From brain signals to real-world impact: My journey in AI-powered neurotechnology
Professor Mahnaz Arvaneh | Professor of Intelligent Human-Machine Interfaces | School of Electrical and Electronic Engineering
Tuesday 12th May, 17:00-18:30, Lecture Theatre 6, The Diamond
- Abstract and bio
Over the past years, my research journey has been shaped by a simple but powerful question: how can we make neurotechnology more useful in real life? From my early work on brain signals and brain–computer interfaces to more recent research in AI-driven rehabilitation, brain stimulation, and inclusive design, I have been motivated by the belief that neurotechnology should not remain confined to laboratories and specialist clinics. In this inaugural lecture, I will reflect on that journey and share how artificial intelligence is helping us move neurotechnology closer to everyday use.
I will discuss the opportunities and challenges of building systems that can adapt to individuals, cope with variability in brain signals, reduce lengthy calibration, and support practical applications such as stroke rehabilitation. Drawing on examples from my work in closed-loop neurotechnology, transfer learning, generative AI, and home-based rehabilitation, I will highlight both the scientific progress we have made and the barriers that still remain. Above all, this talk is about people as much as technology. It is about designing neurotechnology that is not only intelligent, but also accessible, inclusive, and grounded in real human needs. Through this journey, I hope to share a vision for a future in which AI-powered neurotechnology can make a meaningful difference beyond the lab.
Biography
Mahnaz Arvaneh is Professor of Intelligent Human–Machine Interfaces at the University of Sheffield. Her research combines artificial intelligence, brain–computer interfaces, neurostimulation, and rehabilitation engineering to develop personalised, user-centred closed-loop neurotechnologies for real-world use. She leads AI-driven research in stroke rehabilitation, brain signal processing, and equitable neurotechnology design, including high-TRL projects such as TeleRegain and StimDose. Mahnaz also contributes to shaping the policy and standards landscape for neurotechnology through initiatives including the Royal Society, the British Standards Institution, ISO/IEC working groups, and the UK Regulatory Horizons Council. She has published more than 90 peer-reviewed papers, secured major funding from UK and international funders, and is recognised for her interdisciplinary leadership, including as a finalist in the UK Women in Neuroscience interdisciplinary research award in 2025.
Past lectures
Computational Microscopy: Imaging atoms, mapping magnets and surveying cells
Professor Andy Maiden | Professor of Computational Imaging | School of Electrical and Electronic Engineering
Tuesday 21st April, 17:00-18:30, Lecture Theatre 6, The Diamond
- Abstract and bio
Abstract: Traditional microscopy depends exclusively on lenses to form an image - whether using glass to focus visible light, gold nanostructures to diffract X-rays or magnetic fields to steer electrons. However, these physical optics introduce aberrations that fundamentally limit resolution, contrast and accuracy. Computational microscopy replaces optical hardware with advanced algorithmic methods that (in theory!) completely avoid aberrations and produce perfectly accurate images.
“Ptychography” (developed here in Sheffield and pronounced without the “P”) is one particular implementation of this computational approach to microscopy. In my talk, I will describe how it is fundamentally expanding what we can observe and measure. I will highlight how we are using this technique to solve practical imaging challenges across a diverse range of fields, allowing us to analyse atomic structures in advanced materials, map magnetic fields at nanoscale resolutions, and non-destructively survey living biological cells. Finally, I will discuss the future trajectory of computational imaging, exploring how ongoing algorithmic developments are driving new applications across the physical and life sciences.
Bio: Andy Maiden studied Electronic and Electrical Engineering at the Universities of Birmingham and Melbourne, before moving to Durham University to complete his PhD on the use of computer-generated holograms for lithography. After a stint as a barman in the Lake District and attempts to become an outdoor pursuits instructor, he began his long-standing collaboration with Prof. John Rodenburg as a PDRA at Sheffield, working to develop novel instruments and algorithms for optical, x-ray and electron microscopy. He went on to commercialise this work through a University spinout, before returning to academia to establish his own Research Group. Andy is now Professor of Computational Imaging in the School of EEE and a Research Fellow at the Diamond Light Source synchrotron. His current research focuses on 3D imaging of brains, semiconductors, nanomagnets and cells.