MLtechniques.com

Privately funded Machine Learning research lab, creating and disseminating open source intellectual property. Developing explainable AI, synthetic data, computer vision, NLP and generic ML technologies. Check out our eBooks, articles, courses and podcasts, including our best seller "Intuitive Machine Learning and Explainable AI". Our main offering is described at https://mltblog.com/3NVyxLQ. I also offer a class on synthetic data and generative AI, in partnership with LearnCrunch.com.

Executive Machine Learning Scientist

Data Science Central

The company was acquired in 2020.  I am still a contributing author.

Executive Data Scientist, Co-Founder, Managing Partner

LookSmart

Optimizing advertising campaigns, Ad arbitrage. Projects and technology include:

• Created list of top commercial keywords accounting for 85% of all pay-per-click revenue
• Leveraged Google API's to automatically find millions of high yield keywords 
• Automated taxonomy creation (NLP)  
• Click fraud detection (click jacking and Botnet detection)
• Internet traffic quality scoring using hidden decision trees 
• Automation of keyword bidding 
• Reverse engineering Google keyword pricing algorithms, for ad arbitrage

Chief Scientist

Microsoft

Developed a strategy to collect the right data and detect minuscule change points in time series associated with KPI's (when the changes occur, their amplitudes and directions). Goal: assessing the impact of events influencing core metrics. Example: changing the definition of a website user resulting in shift in user count, or changing some features on Bing.com resulting in increase / decrease in pageviews. These were blind tests: I did not know when the website changes were made. But when I detected a change, it was matched against known events that took place at the time flagged by my algorithm as "change point". I designed and then helped automate this algorithm.

Data Science Consultant

eBay

Keyword pricing optimization. Improving keyword bid pricing algorithm to update bids on millions of keywords purchased on Google AdWords. Automated daily updates. Setting bids for large tail of keywords with no conversion history, based on aggregation methods (NLP). Detection of new keywords.

Adometry (acquired by Google)

Built scoring system to measure traffic quality associated to keywords and publishers, for keyword ad networks, search engines, or advertisers (the customers). Scores were used for fraud detection, removal of bad publishers and keyword pricing. Detected major Botnets. Developed proofs of concept, attracting Looksmart and eBay as customers.

Authenticlick

VC-funded startup. Prototyped automated bidding and click/keyword scoring solutions for search engines, ad networks and advertisers. Worked with the engineering team to implement HDT (hidden decision tree) algorithms. Worked on score standardization and  IP blacklist/whitelist architecture. Developed patent-pending statistical technologies and other intellectual properties.  Presented at Ad-Tech and the American Statistical Association conferences.  Helped win several deals (Microsoft, Turn.com) against large competitors (including Fair Isaac), through proof-of-concept blind tests. Identified major Botnet.

Co-Founder and Chief Science Officer

InfoSpace

Click scoring technology, query intelligence, web analytics, business intelligence related to mobile search. Designed click fraud detection algorithms to process billions of clicks. Created rule selection and rule discovery system for fraud detection, based on machine learning (unsupervised clustering), design of experiments, robust cross validation and linkage with external data sources (Google search results) to discover additional fraud patterns. Enhanced keyword taxonomies using data driven algorithms to detect keyword associations. Defined and tested metrics for keyword correlations. Developed multi-threaded web crawler to feed text mining algorithms with rich, targeted data sources related to local search and / or yellow pages.

Data Mining, and Click Fraud Expert

Wells Fargo

Detected abnormalities in traffic monitoring systems resulting in a major fix in the Tealeaf reporting system. The bug resulted in visitors and web session statistics to be wrong for all big vendors relying on multiple servers, with visitor session data collected across multiple servers broken down by error in multiple sessions / multiple users.

Data Mining Consultant

Visa

Developed proprietary feature selection system (200x faster than SAS Enterprise Miner) to detect first instances of fraud and horizontal (single ping) fraud in real time. Production of US zip code maps showing fraud simultaneously in the time, location, recency and volume dimensions. SAS, Perl, C, R, Splus.

Senior Fraud Consultant

Author of the book "Intuitive Machine Learning and Explainable AI".  Founder and Executive Scientist at MLTechniques.com. Machine learning expert, mathematician, patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay.  Vincent also founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target).

18 Differences Between Good and Great Data Scientists

Vincent Granville

Open article: 18 Differences Between Good and Great Data Scientists - DataScienceCentral.com by bit.ly

How to Choose the Best Machine Learning Technique: Comparison Table

Open article: How to Choose the Best Machine Learning Technique: Comparison Table - DataScienceCentral.com by bit.ly

Book Review: Probabilistic Machine Learning

Open article: Book Review: Probabilistic Machine Learning - Machine Learning Techniques by bit.ly

Open article: The Riemann Hypothesis in One Picture - DataScienceCentral.com by bit.ly

Open article: Video: Introduction to Machine Learning - Machine Learning Techniques by bit.ly

Weird Random Walks: Synthetizing, Testing and Leveraging Quasi-randomness

Open article: Weird Random Walks: Synthetizing, Testing and Leveraging Quasi-randomness - Machine Learning Techniques by bit.ly

Dynamic Clouds and Landscape Generation: Morphing and Evolutionary Processes. With full Python code, details about the model with connection to random walks and Brownian motions.

Open article: Dynamic Clouds and Landscape Generation: Morphing and Evolutionary Processes - Machine Learning Techniques by bit.ly

Open article: Video: Introduction to Machine Learning - DataScienceCentral.com by bit.ly

Open article: Synthetic Data - Machine Learning Techniques by bit.ly

Video: Original Supervised Classification Method to Deal with Massive Cluster Overlapping. Click on picture to watch video.

Open video: Points Sampled on the Orbit of the Riemann Zeta Function on bit.ly

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