About me

I'm a Research Mathematician and Senior Data Scientist/AI Technical Product Owner, leveraging my expertise to push the boundaries of AI and its real-world applications.

Currently, I lead Data Science teams and collaborate with Product Management teams on cutting-edge AI initiatives. My background spans quantitative modelling, data analysis, automation, and more, honed through experience with diverse organizations and government departments.

During the COVID pandemic, I played a key role in developing AI-driven response strategies for government agencies. This work has even extended to the USA, with NASA and the CDC requesting my expertise in graph theory to develop novel network approaches for detection algorithms.

Beyond the industry, I've held positions at top academic institutions like Columbia University, leading research projects that resulted in publications in prestigious international journals. My contributions haven't gone unnoticed - I've been a speaker at MIT & Caltech conferences, received recognition from the Australian Research Society and the United Nations, and earned a spot on the Forbes 30 Under 30 list.

My academic journey includes a Mathematics degree from the University of Athens, a Master's by Research in Decision Making Under Risk & Uncertainty from the University of Liverpool, and a Ph.D. in Mathematics from Monash University, where I was recognized as a Prestigious Award Recipient for my doctoral research.

Actively involved in the research community, I serve on the Editorial Advisory Group for the Australian Research Society’s journal and lead the Quant Lab Research Centre as its founder and director. The Greek government recognised my achievements by including me on their list of Distinguished Scientists.

I'm passionate about using data science and AI to create positive change. Let's connect and discuss how we can collaborate!

Research

Regarding my publications see my CV and information below:
Links to free versions can be found at  

∪   Citations: 50+    h-index: 5

On the limitations of the Wiener path integral most probable path technique for solving nonlinear Itô stochastic differential equations

Examples and Counterexamples

Implicit analytic solutions for a nonlinear fractional partial differential beam equation

Communications in Nonlinear Science and Numerical Simulation

Closed-form approximate solutions for a class of coupled nonlinear stochastic differential equations

Applied Mathematics and Computation

An approximate technique for determining in closed form the response transition probability density function of diverse nonlinear/hysteretic oscillators

Nonlinear Dynamics

Approximate transition probability density functions for a class of coupled nonlinear stochastic differential equations

CSM8 conference proceedings

Implicit analytic solutions for the linear stochastic partial differential beam equation with fractional derivative terms

Systems & Control Letters

Approximate analytical solutions for a class of nonlinear stochastic differential equations

European Journal of Applied Mathematics

A closed form approximation and error quantification for the response transition probability density function of a class of stochastic differential equations

Probabilistic Engineering Mechanics

Some observations on the approximations of the Wiener path integral technique

Meccanica dei Materiali e delle Strutture

FEATURES IN THE MEDIA

A Brief History of Randomness: From divination and gambling to modern Probability Theory & Statistics
English version link 1
English version link 2
Greek version link 1
Greek version link 2

Decisions, decisions: Dealing with uncertainty from antiquity to modern times – and coronavirus is no exception
English version
Greek version

We asked experts for their opinion on the COVIDSafe App: Is it a game-changer?
English version

Meet Antonis and Vasileios; Two young academics that make us proud in Australia
English version
Greek version