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is pleased to announce the publication of his 15th book "Brain Computations: What and How" by Oxford University Press.

The aim of this book is to elucidate what is computed in different brain systems; and to describe current computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions.

This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed. Details can be found . Professor Rolls notes that the research described in this book has high impact in terms of citations (see for more information).

Adam Shephard has just joined the department as a Research Fellow and is currently working in the Tissue Image Analytics (TIA) Lab on the ANTICIPATE project funded by Cancer Research UK. He has recently submitted his thesis on the application of deep learning to paediatric MRI at Aston University, under the supervision of Prof. Amanda Wood and Dr. Jan Novak. His role in the ANTICIPATE project will be concerned with the development and application of deep learning techniques to digitized histology slides to aid in the more efficient grading of head and neck tumours, to ultimately provide more accurate patient prognoses.

We are delighted to report that from the Theory and Foundations (FoCS) research theme at the Computer Science Department has received a prestigious EPSRC New Investigator Award. The approximately 拢264K project titled "New frontiers in Parameterizing Away From Triviality鈥� aims to develop novel notions of graph edit distance and investigate their connections to efficient solvability of computationally hard problems.

the proposal identifies research questions that are novel, has the potential to have a broader impact both within and outside academia and it is an exciting project that will break new ground.

We're happy to announce that has joined the department as a Research Fellow. He is currently funded by the project "New approaches to unconditional computational lower bounds", with support from the Royal Society.

Zhenjian Lu will soon defend a PhD thesis in computational complexity at Simon Fraser University under the supervision of Prof. Valentine Kabanets and Prof. Andrei Bulatov.

He is primarily interested in Computational Complexity, Circuit Lower Bounds, Algorithms, Pseudorandomness, Analysis of Boolean Functions, and Meta-Complexity.

has joined the department to work as a Research Fellow on the "Foundations of classical and quantum verifiable computing" project, which is led by .

Sathya completed his PhD in quantum computing at the University of Cambridge under the supervision of Prof. Richard Jozsa. His primary interests are quantum algorithms and computational complexity theory.

We congratulate all A-level students on their recent achievements. The quality of our intake in recent years has been outstanding and we are delighted to report that we will be welcoming another exceptional cohort of first year students. We look forward to getting to know you all in the coming term, and hope you will enjoy taking your next steps in your development as Computer Scientists.

Due to this year's special circumstances, this cohort will be by far the largest in the history of the department. While there will be challenges for students and staff alike, we will work hard, together with our students, to ensure that everybody can fully realize their considerable potential. We are excited and are looking forward to meeting this new talented group of students

Dr Dmitry Chistikov and Professor Mike Paterson, together with physicists Olga Goulko (Boise State University) and Adrian Kent (Cambridge), have published an interdisciplinary paper , solving a probabilistic puzzle on the sphere that has applications to quantum information theory.

Suppose a lawn must cover exactly half the area of a sphere. A grasshopper starts from a random position on the lawn and jumps a fixed distance in a random direction. What shape of lawn maximizes the chance that the grasshopper lands back on the lawn? A natural guess would be that a hemispherical lawn is best. It turns out, however, that this is nearly never the case — there are only a few exceptional jump sizes.

This work involving spherical geometry, probability theory, basic number theory, and theoretical physics appears in the Proceedings of the Royal Society A and shows, apart from concern for the well-being of grasshoppers, that there are previously unknown types of Bell inequalities. The Bell inequality, devised by physicist John Stewart Bell in 1964, demonstrated that no combination of classical theories with Einstein's special relativity is able to explain the predictions (and later actual experimental observations) of quantum theory.

A University press release can be found here.