Palantir Technologies Inc.

Daniel Erenrich

Anirvan Mukherjee

Rahul Kumar Agarwal

Aleksandr Zamoshchin

Matthew Elkherj

Eli Bingham

Lawrence Manning

Xavier Falco

Yifei Huang

Roger Hu

Daniel Fernandez

Guillem Palou Visa

Rahul Mehta

Jeremy Elser

William Dwyer

Rowan Gilmore

Jason Prestinario

Clayton Sader

Nicholas Latourette

Andre Frederico Cavalheiro Menck

Guodong Xu

David Lisuk

Simon Slowik

Luis Voloch

Daniel Chin

Di Wu

Parvathy Menon

John Kenneth Garrod

Ankit Shankar

Allen Cai

Geoff Stowe

Jacob Scott

Zachary Bush

Brian Ngo

Feridun Arda Kara

Andrew Ash

Greg Martin

Diane Wu

Sean Kelley

Winnie Chai

Engin Ural

Christian Tessier-Lavigne

Ted Mabrey

Drausin Wulsin

Gilad Gray

Greg Cohan

Chris Dorsey

Dennis Kwon

Chris Lewis

Asli Ozyar

Mike Reilly

Matt Sills

Spencer Stamats

Samir Talwar

Holt Wilkins

Joyce Yu-Hsin Chen

Bar Kaya

Jack Grossman

Arseny Bogomolov

Cooper Bills

Nathaniel Klein

Eric Anderson

Ryan Beiermeister

Eric Raboin

Matthew Levan

Matthew Gerhardt

Kevin Simons

Patricio Jones Velez

Yu-Hsin Joyce Chen

Baris Kaya

P J Valez

Pj Valez

James Rosswog

Tim O'Brien

Rahul Agarwal

Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Systems and methods for automatic clustering and canonical designation of related data in various data structures

Systems and methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process.

Systems and methods for selecting machine learning training data

A model management system provides a centralized repository for storing and accessing models. The model management system receives an input to store a model object in a first model state generated based on a first set of known variables. The model management system generates a first file including a first set of functions defining the first model state and associates the first file with a model key identifying the model object. The model management system receives an input to store the model object in a second model state having been generated based on the first model state and a second set of known variables. The model management system generates a second file including a second set of functions defining the second model state and associates the second file with the model key. The model management system identifies available versions of the model object based on the model key.

Model object management and storage system

Systems and methods are disclosed for determining a propensity of an entity to take a specified action. In accordance with one implementation, a method is provided for determining the propensity. The method includes, for example, accessing one or more data sources, the one or more data sources including information associated with the entity, forming a record associated with the entity by integrating the information from the one or more data sources, generating, based on the record, one or more features associated with the entity, processing the one or more features to determine the propensity of the entity to take the specified action, and outputting the propensity.

System and method for determining a propensity of entity to take a specified action

In various example embodiments, a vector modeling system is configured to access a set of data distributed across client devices and stored in a structured format. The vector modeling system determines vector parameters and vector templates suitable for the set of data and transforms the set of data from the structured format into a second format including one or more vectors based on one or more transformation strategies. The vector modeling system stores the transformed data and performs machine learning analysis on the vector.

Vector generation for distributed data sets

Computer implemented systems and methods are disclosed for associating records across lists, wherein the lists include a plurality of records and the plurality of records is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise grouping one or more records from a first list into a first group based on fields of the records in the first list, grouping one or more records from a second list into a second group based on fields of the records in the second list, pairing a record from the first group with a record from the second group, assessing each pair of records based on an evaluation of the respective pair according to fields of the pair, and associating records from the first group and records of the second group with an entity based on the assessment.

System and method for associating related records to common entities across multiple lists

Growing community of inventors

Mountain View, CA, United States of America

Daniel Erenrich