I am Hisham Ihshaish, Senior Lecturer at the Department of Computer Science and Creative Technologies, and member of the CS Research Centre (CSRC). I currently lead the MSc Programme in FinTech, ...read more
I am member of the the Computer Science Research Centre (CSRC), and I am interested in exploring and developing application-inspired approaches to Machine Learning, large-scale data analysis and AI, to better understand systems and applications in climate dynamics, automation, environment, finance, engineering, health and social sciences.
I am also interested in computer science education, learner-centric pedagogy and constructivism in computer science education. Come and try one of my courses - see here.
I lead the supervision of doctoral research as well as MSc projects, and currently involved in a number of funded research collaborations into applications of AI, ML, NLP, Automation, etc — read more here.
Between 2019 and 20121, I co-developed our exciting MSc Programmes in Data Science, HealthTech and FinTech. I lead the latter in collaboration with our Business School and partnership with Deloitte UK.
Activities and affilliation
"If you look at the statistics, people spend most of their time in the kitchen. Aside from the backyard, it's one of my favorite places to renovate." — Vanilla Ice
Edsger Dijkstra's Evaluation of Programming Languages (c. 1982)
"FORTRAN, "the infantile disorder", by now nearly 20 years old, is hopelessly inadequate for whatever computer application you have in mind today: it is now too clumsy, too risky, and too expensive to use.
PL/I -- "the fatal disease" -- belongs more to the problem set than the solution set.
It is practically impossible to teach good programming to students that have had a prior exposure to BASIC: "as potential programmers they are mentally mutilated beyond hope of regeneration."
"The use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offense."
I completed a PhD in High-Performance Computing (cum laude, 2012) and MSc in Advanced Informatics, Parallel Computing (distinction, 2008), both from the School of Engineering, Autonomous University of Barcelona 'Universitat Autònoma de Barcelona', where I also taught courses in operating systems.
Later in 2012, towards late 2014, I worked as a post-doctoral research fellow at VORtech BV and the University of Utrecht in the Netherlands (IMAU with Professor Henk Dijkstra) within an EU Marie-Curie ITN project, LINC where I developed Par@Graph, a software package of parallel algorithmic solutions for the construction and analysis of large-scale complex networks.
I hold the PGCert in higher education and I am FHEA since 2018.
Find out more on my PhD work here:
Later work on Large-scale timeseries analysis:
"In the network approach to the Earth’s climate, the vertices of the network are
identified with the spatial grid points of an underlying global climate data set.
Edges are added between pairs of vertices depending on the degree of statistical
interdependence between the corresponding pairs of time series taken from the
climate data sets (pressure, temperatures, precipitations, etc.).
A crucial step for understanding the characteristic behavior of such systems consists in inferring the connection topology. When studying the connectivity structure, a main challenge is due to multiple spatial scales and temporal scales in the climate system. A basic tool for identifying connectivity is causality. There are various techniques to quantify causality, ranging from classic cross-correlation, to information theory measures, such as coarse-grained entropy or transfer entropy, to Granger causality, to recurrence-based measures. However, there is a need to develop appropriate statistical tests for several of these measures."
See my contribution - chapter 5: Computational Tools for Network Analysis.
Cambridge University Press
28 Feb. 2019
ISBN-10 : 1107111234
ISBN-13 : 978-1107111233