The two most widely considered software development models in modern project management are, without any doubt, the Waterfall Methodology and […]
Author: Francesco Gadaleta
Director at Amethix Technologies amethix.com
Co-founder of fitchain.io the decentralised machine learning factory
Former Chief Data Officer at Abe.ai (acquired by Envestnet Yodlee Inc.)
Former member of the Advanced Analytics Team of Johnson&JohnsonMy interests range from applied math, machine learning, cryptography and blockchain technology.
I have worked in domains such as healthcare and pharma, R&D in bioinformatics, and personal finance. For additional details, contact me directly.Experience in academic research (PhD in Computer Science)
• designed web browser security mechanisms against heap-spraying attacks
• implemented compiler level techniques against buffer overflows
• developed novel hypervisor-based approaches to computer securityExperience in R&D healthcare and bioinformatics
• designed solutions to integrate heterogeneous genetic data (OMICS) to predict the risk of genetic diseases in a pre-symptomatic fashion
• provided analytics solutions for the Non-Invasive-Prenatal-Testing (NIPT) research group
• designed and implemented the most accurate predictive models for disease interception and drug recommendation (Johnson&Johnson)
• advised a number of big data technologies and strategies essential to the organisation on a global scaleSpecialties
• Applied mathematics and statistics
• Programming in multiple languages from proof-of-concept to production
• Game theory, protocol designHosting podcast datascienceathome.com, have a weakness for remote working, highly-skilled individuals, long distance running, and startups
How businesses can scale Artificial Intelligence & Machine Learning
Scaling technology and business processes are not equal, here is a framework for the business side of artificial intelligence. Since […]
No data, no honey: the grim reality of machine learning in medicine (and other domains)
The increasing amount of data collected by the numerous internet services people use every day has opened doors to a […]
Entropy in machine learning
It all starts from physics. The entropy of an isolated system never decreases… Everyone at school, at some point of […]
The physics of optimization algorithms
In a previous post, we mentioned that one reason for the successes of deep learning methods in the last decade […]
Episode 47: Are you ready for AI winter?
Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research […]
Episode 46: why do machine learning models fail? (Part 2)
In this episode I continue the conversation from the previous one, about failing machine learning models. When data scientists have […]
Episode 45: why do machine learning models fail?
The success of a machine learning model depends on several factors and events. True generalization to data that the […]
Episode 50: Decentralized machine learning in the data marketplace
In this episode we briefly explain how two massive technologies have been merged in 2018, one providing secure machine learning […]
Episode 53: Estimating uncertainty with neural networks
Have you ever wanted to get an estimate of the uncertainty of your neural network? Clearly Bayesian modelling provides a […]
