Palantir Technologies Inc.

Matthew Elkherj

Daniel Erenrich

Lawrence Manning

Xavier Falco

Mitchell Beard

Rahul Kumar Agarwal

Ashley Einspahr

Eli Bingham

Ryan Beiermeister

Yifei Huang

Roger Hu

Daniel Fernandez

Aleksandr Zamoshchin

Guillem Palou Visa

Rahul Mehta

Jeremy Elser

Nicholas Latourette

Chris Hammett

Rowan Gilmore

Joules Nahas

William Ayers

Jason Prestinario

Clayton Sader

Peter Wilczynski

John Antonio Carrino

Sharon Hao

Daniel Zangri

Ernest Zeidman

David Montague

Benjamin Funk

Anthony Bak

Breanna Bunge

Seth Robinson

Erika Crawford Tom

Seelig Sinton

Christian Burchhardt

Allen Chang

Lucas Lemanowicz

Gary Lin

David Lisuk

Guodong Xu

Clare Adrien

Jakob Juelich

Jeffrey Bagdis

Andrew Pettit

Christopher Brahms

Jeremy Liu

Luis Voloch

Daniel Spangenberger

Tinlok Pang

Yehonatan Steinmetz

Pierre Cholet

Ashwin Sreenivas

Alexander Sparrow

Ryan Smith

Rafael Cosman

Arvind Raju

Natasha Armbrust

Tomer Kremerman

Giulio D'Ali' Aula

Andrew Ehrich

Tatyana Gordeeva

Duncan Robertson

Mike Lewin

Adam Schexnayder

Meline Von Brentano

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

Systems and methods are provided for investigation network activities. Network activity information may be accessed. The network activity information may describe for an individual (1) respective relationship with one or more persons; and (2) respective activity status information indicating whether a given person has engaged in a particular activity. A network activity graph may be generated based on the network activity information. The network activity graph may include two or more nodes representing the individual and the one or more persons. Connections between the nodes may represent the respective relationships between the individual and the one or more persons. Data corresponding to the network activity graph may be presented through an interface.

Framework for exposing network activities

Systems, methods, and non-transitory computer readable media are provided for labeling depictions of objects within images. An image may be obtained. The image may include a depiction of an object. A user's marking of a set of dots within the image may be received. The set of dots may include one or more dots. The set of dots may be positioned within or near the depiction of the object. The depiction of the object within the image may be labeled based on the set of dots.

Machine learning assistant for image analysis

Systems and methods are provided for performing context-based keyword searching using a search bar. Based on search terms received via the search bar, the system may be configured to provide suggested search parameters to associate with that search term. The suggested search parameters may each include a type of data and/or a filter to associate with the search term (e.g., name, phone number, date of birth, etc.). The one or more suggested search parameters may be identified based on the search term itself, a list of possible types of data or filters, a preliminary search of one or more datasets, a record of one or more previous searches performed, requirements associated with one or more searchable datasets, the format of user input received via the search bar, and/or one or more other factors.

Systems and methods for context-based keyword searching

Systems and methods are provided for identifying relevant information for an entity, referred to as a seed entity. A plurality of search queries can be generated each comprising a property of a seed entity or one of the entities associated with the seed entity (seed-linked entities). Preferably, a collection of search queries includes ones representing different properties of the seed entity and properties of different seed-linked entities. Optionally, the collection of search queries is optimized to reduce search burden. Searches can then be conducted with the search queries in one or more data sources to obtain a plurality of search results, wherein each search result comprises a hit entity and one or more entities associated with the hit entity (hit-linked entity). For each of the search results, a score can be determined taking as input (a) likelihood of match between the seed entity and the hit entity or between a seed-linked entity and a hit-linked entity, (b) presence of a new entity in the search result not present in the search queries or a difference between the new entity and an entity present in the search queries, and (c) characteristic of the new entity in the search result. Based on the scores, high priority search results can be presented a user for further analysis.

Automated assistance for generating relevant and valuable search results for an entity of interest

Systems and methods are disclosed for systems and user interfaces for rapid analysis of viewership information. One of the methods includes accessing databases storing viewership information associated with segments, with each segment being associated with common features of viewers. Measures of association between the segment and content items are maintained for each segment. An interactive user interface is presented via a user device, the interactive user interface enabling creation of a customized viewing audience. The interactive user interface receives user input indicating a segment, identifies similar segments based on associations between features of the segment and of other segments, and presents the identified segments. Analysis information associated with the segments is presented for at least one of the one or more segments, with the segments being included in the customized viewing audience.

System and user interfaces for rapid analysis of viewership information

Systems and methods are provided for identifying and compiling information relating to an entity for investigative analysis. The system may comprise one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to search, in one or more data sources, with a plurality of known characteristics of an entity to obtain a first plurality of records, identify from the first plurality of records a subset of records that match the known characteristics with a substantial confidence, compile the subset of records to form a unified record representing the entity and conduct a second search with information from the unified record to obtain a second plurality of search results.

Identification and compiling of information relating to an entity

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Menlo Park, CA, United States of America

Matthew Elkherj