Dr Kizito Salako

Overview

Dr Kizito Salako is an applied mathematician and software developer at the Centre for Software Reliability (CSR), City University London. He holds a first-class double honours degree in Mathematics and Statistics from the University of Lagos; a Master of Advanced Study in Mathematics degree from the University of Cambridge (where he was both a Shell Centenary Scholar and a Commonwealth Scholar); and a PhD in Computer Science from City, University of London.

Kizito is passionate about applications of probability theory, Bayesian statistics, geometry and machine-learning techniques, when trying to simulate, assess and forecast the (failure) behaviour of software-based systems. His doctoral thesis clarifies and significantly extends the applicability of a family of probabilistic models used to describe the failure behaviour of multi-version software. He also developed a novel geometric approach to extremising the expected system reliability of a class of fault-tolerant software-based systems; so-called 1-out-of-N systems. Currently, he is particularly interested in the assessment/forecasting challenges that arise when these systems rely on evolving machine-learning solutions.

Since joining CSR, Kizito has contributed to several projects (e.g. DISPO, DIRC, IRRIIS, PIA-FARA, AFTER, DIDERO-PC, DISIEM). He is experienced in building simulations of complex systems using C++, and was instrumental in developing the PIA-FARA simulation engine -- creating modular software that implements and analyses the probabilistic, functional and process relationships that may exist between the components of large-scale interdependent complex critical infrastructure.

Research Interests

Quantitative assessment of the dependability of software-based systems (in particular, systems that are complex, safety/security critical, and that may rely on evolving machine learning solutions):

• The development, validation and application of advanced statistical approaches for dependability assessment;


• Conservative Bayesian assessment methods, that take into account various forms of dependability evidence when assessing system dependability;


• Monte-Carlo methods for simulating complex systems (where these systems are modelled as Generalised Semi-Markov and Markov Regenerative Processes);


• The combined use of probabilistic conditional independence relations and physics models in modelling interdependencies between complex systems


• The efficacy (in terms of improved reliability) of using diverse-redundant software in fault-tolerant configurations

Statistical forecasting/analyses of system risk, dependability and cyber-security issues:

• Investigating the impact of software development strategies on system reliability;


• Studying the efficacy of mitigation strategies for a system under cyber-attack;


• Studying the efficacy of diverse redundant system architectures in mitigating cyber-attacks, and the implications of such strategies for the likelihood of confidentiality and integrity breaches;

Research Projects

Kizito has collaborated on several national and international research projects. Details of these projects can be found here. The projects include:

1. DISIEM (funded by the EU horizon-2020 program) Diversity Enhancements for Security Information and Events Management Systems, 2016–2019


2. CEDRICS (funded by the UK’s EPSRC) Communicating and Evaluating Cyber–Risks and Dependencies, 2014–2017


3. AFTER (EU funded FP7 project) A Framework for sysTems vulnerability identification, dEfence and Restoration, 2011–2014


4. DIDERO-PC (funded by the UK’s EPSRC) DIverse DatabasE ReplicatiOn Performance Comparison, 2013–2014


5. PIA-FARA (funded by the UK’s Technology Strategy Board) Probabilistic Interdependency Analysis: Framework/data-Analysis/Risk–Assessment, 2009–2010


6. CETIFS (funded by the UK’s Technology Strategy Board) A Critical Infrastructure Interdependency Modelling Feasibility Study, 2008


7. IRRIIS (EU funded FP6 Project) Integrated Risk–Reduction of Information-based Infrastructure Systems, 2006–2009


8. DIRC (funded by the UK’s EPSRC) Dependability Integrated Research Collaboration, 2000–2006

Mathematical Interests

Probability Theory and Mathematical Statistics:

1. measure theoretic probability – probability spaces, integration, conditional expectation, characteristic functions, Radon-Nikodym derivatives and change-of-measure;


2. stochastic models and processes – filtered probability spaces; stationary/non-stationary; time-series analyses; point processes; order-statistics models; Gaussian processes; Markov processes (Markov chains, Markov renewal processes, Markov reward processes, Markov decision processes, hidden Markov models); hybrid stochastic/deterministic models;


3. stochastic analysis – convergence of random variables, stochastic calculus/stochastic differential equations (Ito-calculus, jump-diffusion processes);


4. statistical (Bayesian) inference and modelling – parametric and non-parameteric; estimation theory; posterior predictive distributions; prequential/statistical forecasting systems; asymptotic theory (Slutsky’s theorem, delta-method, portmanteau theorem, weak/strong laws); hypothesis testing; goodness-of-fit analyses; ROC techniques;

Machine Learning Approaches and Considerations: supervised learning (Regression, GLMs, SVMs, ANNs, CNNs, LSTMs); deep reinforcement learning (approximate Q-learning, temporal difference methods)

Mathematical Finance: mean-variance portfolio theory; CAPM, option-pricing, Wiener processes/Brownian motion, Martingales, risk-neutral measures, the Black-Scholes model;

Mathematical Analysis and Vector-Space Theory: real and complex analysis; metric spaces; infinite-dimensional vector-spaces – functional analysis (Banach/Hilbert spaces); finite-dimensional vector-spaces – linear/multilinear algebra, differential geometry/calculus on manifolds, tensor calculus; applications of fixed-point theorems; non-linear dynamical systems theory;

Mathematical/Statistical Optimisation: linear programming, convex optimisation, stochastic gradient methods, dynamic programming, simulated annealing, steepest descent;

Publications by category