I try to use science to solve real problems. I am involved simultaneously in a variety of projects and circle myself with some amazing people. I have been very fortunate to work with a number of researchers, professionals and academics but above all inspirational thinkers.
On my spare time I am working on a predictive algorithm for stock movements based on crowd sourced data. This work grew out of my team's final project for the Machine Learning in Finance class at the Graduate Center, CUNY. We hope to show our first results in NYC Media Lab this fall.
I am a second year student at the Graduate Center of the City University of New York, supervised by Professor Rosario Gennaro. My office is located in the CAISS Institute. I wrote my Masters thesis on Homomorphic Signature Schemes with Yevgeniy Dodis and the incalculably important guidance of Dario Fiore. I am interested in Cryptography, Data Science and more broadly in Applied Science. I hold an MSCi degree from Imperial College and a Masters from NYU both in Mathematics. My graduate studies concentrated in Applications of Mathematics in Finance and Computer Science. In particular I studied Stochastic Calculus, Derivatives, Pricing theory, and Risk Management as well as Algorithms, Cryptography and Cloud Computing.
I have previously produced original work in Bio-Engineering, Applied Cryptography and Mathematical Modeling. Working with a collaborative team of Imperial College and University of Oxford we developed new probes for measuring blood flood in human arteries. At NYU I worked on Digital Signature Schemes as well as securing 'the Cloud'. I have also completed various unpublished scientific projects. Before finishing high school I constructed an inverted pendulum, a highly sensitive device that demonstrates the extraordinary properties of Mathiee equations. As part of different thesis requirements, among others, I wrote reports on Algebraic Topology, Model Theory and Cryptographic properties of various mathematical functions.
I worked towards an innovative solution for a longstanding problem in online markets, Click Fraud. Deploying Data Science I examined the current state of the art in catching fraudulent activity in the biggest online advertisement networks in existence. I worked closely with the Machine Learning and Big Data group to be able to extract user trends on the go and stay up to date with behavior shifts. This way we will automatically be able to filter out anomalous activity. This is ongoing research ultimately aiming at revising the internal anomaly detection methods of the company. My work was featured in Microsoft Research's Facebook page .
In January 2015 I joined the famed team at Applied Communication Sciences, formerly known as Bell Labs, in the Data Analytics department. So far I have worked in information extraction from large data sets and machine learning on text data.
In 2014 I was Research Assistant at the Graduate Center of CUNY. I worked with Prof. Zhang's team towards new methods for oil detection underwater. Before that I have worked in Applied Cryptography with Dario Fiore at NYU to create the first digital signatures that can be combined to sign collectively packets in a Network. I have also worked towards better devices that can measure vascular flow, at Imperial College and Oxford.
During 2011-13 I taught an graded two courses at NYU Mathematics department. I also led student problem classes at Imeprial College London during 2008-10. In addition, I served as a teaching associate for Lilian Baylis Technology school in South London in 2008.
During 2011-13 I worked at the Residential Life Office of NYU, first as an office assistant and then as a Graduate Resident Assistant. For this position, among other tasks, I managed $1000+ budget for the graduate halls, mediated conflict situations, demonstrated and enforced university policies and served as an emergency/crisis first responder, including the period following hurricane Sandy.