Data science pioneer, founder, author, CEO, investor, with broad spectrum of domain expertise, technical knowledge, and proven success in bringing measurable added value to companies ranging from startups to fortune 100, across multiple industries (finance, Internet, media, IT, security) and domains (data science, operations research, machine learning, computer science, business intelligence, statistics, applied mathematics, growth hacking, IoT).
Vincent developed and deployed new techniques such as hidden decision trees (for scoring and fraud detection), automated tagging, indexing and clustering of large document repositories, black-box, scalable, simple, noise-resistant regression known as the Jackknife Regression (fit for black-box, real-time or automated data processing), model-free confidence intervals, bucketisation, combinatorial feature selection algorithms, detecting causation not correlations, and generally speaking, the invention of a set of consistent robust statistical / machine learning techniques that can be understood, implemented, interpreted, leveraged and fine-tuned by the non-expert. Vincent also invented many synthetic metrics (for instance, predictive power and L1 goodness-of-fit) that work better than old-fashioned stats, especially on badly-behaved sparse big data. Some of these techniques have been implemented in a Map-Reduce Hadoop-like environment. Some are concerned with identifying true signal in an ocean of noisy data.
Vincent is a former post-doctorate of Cambridge University and the National Institute of Statistical Sciences. He was among the finalists at the Wharton School Business Plan Competition and at the Belgian Mathematical Olympiads. Vincent has published 40 papers in statistical journals and is an invited speaker at international conferences. Vincent also created the first IoT platform to automate growth and content generation for digital publishers, using a system of API's for machine-to-machine communications, involving Hootsuite, Twitter, and Google Analytics.
Vincent's profile is accessible at http://bit.ly/1jWEfMP and includes top publications, presentations, and work experience with Visa, Microsoft, eBay, NBC, Wells Fargo, and other organisations.
Convolution is a concept well known to machine learning and signal processing professionals. In this article, we explain in simple English how a moving average is actually a discrete convolution, and we use this fact to build weighted moving averages with natural weights that at the limit, have a Gaussian behavior guaranteed by the Central Limit Theorem. Moving averages are nothing more than blurring filters for signal processing experts, with a Gaussian-like kernel in the case discussed…Continue
Here I provide translations for various important terms, to help professionals from related backgrounds better understand each other. In particular, machine learning professionals versus statisticians.
Source for picture:…Continue
This is the second part of my article Spectacular Visualization: The Eye of the Riemann Zeta Function, focusing on the most infamous unsolved mathematical conjecture, one that has a $1 million dollar price attached to it. I used the word deep not in the sense of deep neural networks, but because the implications of these…Continue
We discuss here one of the most famous unsolved mathematical conjectures of all times, one among seven that has a $1 million award attached to it, see here. It is known as the Riemann Hypothesis and abbreviated as RH. Of course I did not solve it (yet), but the material presented here offers a new…Continue