David Saad

Aston University, UK

In a nutshell

In 1995 I joined the Neural Computing Research Group at Aston as a lecturer and was promoted later on to a reader (1997) and subsequently to a professor (1999).  Between 2006-2012 and again between 2015-2019 I had been the Head of the Mathematics Group. My main research areas focus on statistical mechanics of disordered systems, advanced inference in complex systems, and learning from data and neural networks.

Societal Affiliations

Fellow of the Institute of Mathematics and its Application
Senior Fellow of the Higher Education Academy

Research Website

ORCID: 0000-0001-9821-2623

    50th Anniversary Chair of Complexity Physics

    Prof David Saad received a BA in Physics and a BSc in Electrical Engineering from the Technion, Haifa, Israel (1982), an MSc in Physics (1987) and a PhD in Electrical and Electronic Engineering (1993) from Tel-Aviv University. He joined the University of Edinburgh in 1992 and Aston in 1995. His research, published in around 200 journal/conference papers and presented over 200 conferences/workshops (in many by invitation), focusses on the application of statistical physics methods to a range of fields, which include error-correcting codes, multi-node communication, network optimisation, routing, noisy computation, spreading processes and advanced inference methods. He has attracted 26 grants from various sources, totalling about £5M, and played a leading role in major European research networks under FP4, FP5, FP6, FP7 and Horizon2020; he participated in an advisory capacity in other international consortia and is on the editorial board of Scientific Reports. Prof Saad also served as Head of the Mathematics Department for 10 years as well as in other senior capacities in the university.

    Selected Publications

    • A. Mozeika, B. Li, and D. Saad, “Space of functions computed by deep-layered machines”, Phys. Rev. Lett. 125, 168301, (2020)
    • B. Li and D. Saad, “Large Deviation Analysis of Function Sensitivity in Random Deep Neural Networks”, Jour. Phys. A, 53, 104002 (2020)
    • B. Li and D. Saad, “Exploring the function space of deep-learning machines”, Phys. Rev. Lett. 120, 248301, (2018)
    • H. Mahmoudi and D. Saad, “Generalized Mean Field Approximation for Parallel Dynamics of the Ising Model”, Jour. Stat. Mech., P07001 (2014)
    • A. Mozeika and D. Saad, “Dynamics of Boolean Networks -an Exact Solution”, Phys. Rev. Lett. 106, 214101 (2011)
    • A. Mozeika, D. Saad and J. Raymond, “Computing with Noise -Phase Transitions in Boolean Formulas”, Phys. Rev. Lett. 103, 248701 (2009)