LinkedInClaudia Perlich

Claudia Perlich

Greater New York City Area
Information Technology and Services
  1. Dstillery,
  2. NYU Stern School of Business,
  3. Big Data Journal
  1. SIGKDD 2014,
  2. IBM Research,
  3. New York University
  1. New York University - Leonard N. Stern School of Business
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Chief Scientist

– Present

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Chief Scientist

– Present (4 years 10 months)

Adjunct Professor

NYU Stern School of Business
– Present (4 years 10 months)New York

Board Member

Big Data Journal
– Present (1 year 4 months)NYC

General Chair

(1 year 1 month)New York City

Research Staff Member

IBM Research
(5 years 4 months)

PhD Student

New York University
(6 years)

Volunteer Experience & Causes


Milbitz Vaulting Team
Social Services

Opportunities Claudia is looking for:

Causes Claudia cares about:

Organizations Claudia supports:



General Chair of KDD 2014
– Present

Honors & Awards

100 Most Ceative People 2014


2014 Executive Management Award


Smart List

Wired UK

AMA's "4 under 40" Emerging Leaders Award

American Marketing Association

PopTech Fellow at Rockefeller Foundation

PopTech/Rockefeller Foundation

Grand Winner (Innovation) Great Mind Awards 2013

Advertising Research Foundation

Crain's 40 under 40


Best Industry Paper

KDD 2012

Our paper "Bid Optimizing and Inventory Scoring in Targeted Online Advertising" got the best paper award in the industry track.

Best Research Paper

KDD 2011

Our paper "Leakage in Data Mining: Formulation, Detection, and Avoidance" got the best research paper award.

People's Choice Award Runner Up

Wharton's EMPGENS2

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".

Winner Task 1 & 2

KDD CUP 2009

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.

Finalist at the Edelman Competition


Project on Wallet Estimation for Optimal Sales Allocation

Winner Task 1 & 2

KDD CUP 2008

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.

Winner Task 1

INFORMS 2008 Data Mining Challenge

Identifying Pneumonia Patients

Winner Task 2

KDD CUP 2007

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.

Second Place

Data Mining Prectice Prize KDD 2007

Predictive modeling for marketing


ILP Challenge 2005

Genetic classification of the Yeast Genome

Second Place Task 1

KDD CUP 2003

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.


  • Data Analysis
  • Predictive Modeling
  • Data Mining
  • Machine Learning
  • Statistics
  • R
  • Perl
  • Matlab
  • SQL
  • Analytics
  • Business Intelligence
  • Statistical Modeling
  • Predictive Analytics
  • High Performance...
  • Hadoop
  • C++
  • Algorithms
  • Optimization
  • Artificial Intelligence
  • LaTeX
  • Text Mining
  • Big Data
  • Information Retrieval
  • Natural Language...
  • Distributed Systems
  • Python
  • MapReduce
  • Computer Science
  • Pattern Recognition
  • Data Science
  • Data Visualization
  • Recommender Systems
  • Text Analytics
  • Information Extraction
  • Weka
  • Computer Vision
  • Semantic Web
  • Mathematical Modeling
  • Neural Networks
  • Time Series Analysis
  • Organizational...
  • Entrepreneurship
  • Critical Thinking
  • See 28+  See less


New York University


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