Guy Lebanon, Ph.D. - US grants
Affiliations: | 2005 | Carnegie Mellon University, Pittsburgh, PA |
Area:
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The funding information displayed below comes from the NIH Research Portfolio Online Reporting Tools and the NSF Award Database.The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, Guy Lebanon is the likely recipient of the following grants.| Years | Recipients | Code | Title / Keywords | Matching score |
|---|---|---|---|---|
| 2006 — 2008 | Lebanon, Guy | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Assessing the Readability of Documents and Statistical Tools For Non-Euclidean Data @ Purdue University Documents are written with a specific audience in mind that varies across several dimensions. One such dimension is the readability level, which may vary from elementary child readability to adult readability. The investigator developd statistical models for readability prediction and experiment with different alternatives. As most standard representations of documents are not well described using Euclidean geometry, the investigator directd his research at non-Euclidean modeling of the word histogram or term-frequency representation. Specifically, the task is that of non-linear regression where the covariates are points in the simplex, but do not obey Euclidean geometry. |
0.961 |
| 2007 — 2011 | Lebanon, Guy | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
"Ips: Decision Theoretic Approaches to Measuring and Minimizing Customized Privacy Risk" @ Purdue University The goal of this project is to provide a principled way of quantitatively characterizing the effect of disclosing private data. Based on statistical decision theory, the proposed framework incorporates user-defined sensitivity information and identification model into a personalized risk function. The risk is intuitive and interpretable as it is based only on a user-specified loss function and elementary laws of probability and statistics. The proposed framework leads to a more accurate measure of the consequences of popular disclosure policies such as k-anonymity as well as efficient search for novel optimal policies. |
0.961 |
| 2008 — 2014 | Lebanon, Guy | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Career: Multiresolution Representations of Documents @ Purdue University An effective document representation is a crucial text processing component and without it, even the most sophisticated methods and models perform poorly. Current document representations such as the bag of words or Markov n-gram models ignore nearly all sequential information and focus instead on the histogram of words or short phrases. The proposed work develops sequential representations for documents that go beyond bag of words and Markov models and effectively capture a wide range of sequential information. The main idea behind these representations is to use smoothing techniques to transform the word sequence into smooth curves representing sequential content through changes in the local word histogram. By varying the amount of smoothing, the proposed representations interpolate between different sequential resolutions, thus conveniently capturing sequential details at varying levels of granularity. The proposed work provides improved document analysis, including the classification, segmentation, and summarization of documents. Furthermore, it enables visualizing the sequential trends in documents thus leading to the emergence of computer-assisted document browsing technology. In addition to computer experiments validating improved modeling accuracy, the project involves a series of user studies thus demonstrating the wide applicability of the project. |
0.961 |
| 2009 — 2012 | Lebanon, Guy Mei, Yajun (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Statistical Inference For Censored Preference Data @ Georgia Tech Research Corporation Ranked data arises from m raters ordering by some mechanism n items to express their preferences for the item. Such data can represent election voting, psychological and medical surveys, book and movie recommendation, and web-site ranking system such as search engines. In this proposal the investigators develop the theory and methodology of statistical inference in the case where n and m tend to infinity, and each rater provides an increasingly censored or partial preference information. Under this scenario, they demonstrate how to obtain consistent non-parametric estimators and develop efficient computational procedures for their use. Another aspect that is examined is visualizing preference data by embedding it in a low dimensional space, and designing appropriate surveys for preference data. |
0.906 |