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Our paper "Bid Optimizing and Inventory Scoring in Targeted Online Advertising" got the best paper award in the industry track.
Our paper "Leakage in Data Mining: Formulation, Detection, and Avoidance" got the best research paper award.
Our work on "“Evaluating and Optimizing Online Advertising: Forget the click, but there are good proxies” was voted a runner up in Whartons workshop on "What Works in the New Age of Advertising & Marketing".
The KDD Cup 2009 offers the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling).
The challenge is to beat the in-house system developed by Orange Labs. It is an opportunity to prove that you can deal with a very large database, including heterogeneous noisy data (numerical and categorical variables), and unbalanced class distributions. Time efficiency is often a crucial point. Therefore part of the competition will be time-constrained to test the ability of the participants to deliver solutions quickly.
Project on Wallet Estimation for Optimal Sales Allocation
The KDD Cup 2008 challenge focuses on the problem of early detection of breast cancer from X-ray images of the breast based on data provided by Siemens Medical. In a screening population, a small fraction of cancerous patients have more than one malignant lesion. To simplify the problem, we only consider one type of cancer - cancerous masses - and only include cancer patients with at most one cancerous mass per patient. The challenge consists of two parts, each of which is related to the development of algorithms for Computer Aided Detection (CAD) of early stage breast cancer from X-ray images.
Identifying Pneumonia Patients
This year's KDD Cup focuses on predicting aspects of movie rating behavior. There are two tasks. The tasks, developed in conjunction with Netflix, have been selected to be interesting to participants from both academia and industry You can choose to compete in either or both of the tasks.
Predictive modeling for marketing
Genetic classification of the Yeast Genome
This year's competition focuses on problems motivated by network mining and the analysis of usage logs. Complex networks have emerged as a central theme in data mining applications, appearing in domains that range from communication networks and the Web, to biological interaction networks, to social networks and homeland security. At the same time, the difficulty in obtaining complete and accurate representations of large networks has been an obstacle to research in this area.
The first task involves predicting the future; contestants predict how many citations each paper will receive during the three months leading up to the KDD 2003 conference.
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Adjunct Professor of Data Science at NYU Stern School of Business
Vice President, Analytics at Dstillery
CTO at Dstillery
Senior Data Scientist at Dstillery
VP of Data at Jawbone
Founder at Fast Forward Labs