I have used quite a bit of advanced math, especially to solve problems in experimental mathematics using data science methods. For instance, solving stochastic integral equations. You can find the…Continue
Do you think that one day, humans will find a way to not work and enjoy the life, relying on robots to help them with their needs, just like dogs who don't need to spend their time finding food and…Continue
Hi,I am looking for a tool (online app if possible) that measures the overlap in the number of users between two websites A and B, a tool that would offers statistics such asWebsite A had x visitors…Continue
It is well know that office furniture / computer equipment, as well as renting / leasing or purchasing real estate to host your employees, is very expensive for corporations. Are there any companies…Continue
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.
You will find here our selection of featured articles and resources posted since Monday.
Resources and TutorialsContinue
Updated on March 24. See new sections on Fibonacci numbers [3.2.(b)], comparing stochastic processes [4.1.(b)], connection with Brownian motions [4.1.(c)], new material in section 4.3, and new addition in the Appendix [5.4].
I present here some innovative results from my most recent research on stochastic processes. chaos modeling, and dynamical systems, with applications to Fintech, cryptography, number theory, and random number…Continue