Theodoros S Salonidis

US Patent:

20220207444, Jun 30, 2022

Filed:

Dec 30, 2020

Appl. No.:

17/137930

Inventors:

- Armonk NY, US
Saket Sathe - Mohegan Lake NY, US
Long Vu - Chappaqua NY, US
Theodoros Salonidis - Wayne PA, US
Horst Cornelius Samulowitz - Armonk NY, US
Jean-François Puget - Saint Raphael, FR

International Classification:

G06Q 10/06
G06Q 30/02
G06N 5/02

Abstract:

A system and method for assessing Pay-As-You-Go (PAYG) Automatic machine learned (AutoML) model pipeline charge to a user on the basis of performance improvement achieved by configuring a model pipeline with performance enhancements relative to a performance obtained by a base model pipeline. The method performs a ranking of pipelines (customized models) based on a user-specified metric (for example, prediction accuracy, run time, F1 score) or combination of metrics. The price for ranked pipelines is specified based on a “surrogate” model where the surrogate model is fit to the base model price and the maximum price for a model. The base model price relates to use of a current cloud resource utilization-based pricing model. The pricing per model pipeline increments on the basis of performance metric(s) in a linear fashion, e.g., using a linear pricing model, or in an exponential fashion, e.g., using a fixed percentage hike price model.

US Patent:

20230042426, Feb 9, 2023

Filed:

Oct 19, 2022

Appl. No.:

17/968957

Inventors:

- Armonk NY, US
Theodoros Salonidis - Wayne PA, US
Rahul Urgaonkar - Shoreline WA, US
Dinesh C. Verma - New Castle NY, US

International Classification:

H04L 67/10
G06F 16/951
G06F 9/50

Abstract:

Methods are provided. A method includes announcing to a network meta information describing each of a plurality of distributed data sources. The method further includes propagating the meta information amongst routing elements in the network. The method also includes inserting into the network a description of distributed datasets that match a set of requirements of the analytics task. The method additionally includes delivering, by the routing elements, a copy of the analytics task to locations of respective ones of the plurality of distributed data sources that include the distributed datasets that match the set of requirements of the analytics task.

US Patent:

20230035687, Feb 2, 2023

Filed:

Oct 6, 2022

Appl. No.:

17/938405

Inventors:

- San Francisco CA, US
Keith Grueneberg - Stewart Manor NY, US
Bongjun Ko - Harrington NJ, US
Christian Makaya - Summit NJ, US
Jorge J. Ortiz - Rego Park NY, US
Swati Rallapalli - Ossining NY, US
Theodoros Salonidis - Wayne PA, US
Rahul Urgaonkar - Rye NY, US
Dinesh Verma - New Castle NY, US
Xiping Wang - Scarsdale NY, US

International Classification:

G06Q 30/02

Abstract:

A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

US Patent:

20210150269, May 20, 2021

Filed:

Nov 18, 2019

Appl. No.:

16/686522

Inventors:

- ARMONK NY, US
THEODOROS SALONIDIS - WAYNE PA, US
ISSA SYLLA - BOSTON MA, US

International Classification:

G06K 9/62
G06F 21/62
G06N 20/00
G06K 9/68

Abstract:

A computer-implemented method for training a global federated learning model using an aggregator server includes training multiple local models at respective local nodes. Each local node selects a set of attributes from its training dataset for training its local model. Each local node generates an anonymized training dataset by using a syntactic anonymization method, and by selecting quasi-identifying attributes from training attributes, and generalizing the quasi-identifying attributes using a syntactic algorithm. Further, each local node computes a syntactic mapping based on equivalence classes produced in the anonymized training dataset. The aggregator server computes a union of mappings received from all the local nodes. Further, federated learning includes training the global federated learning model by iteratively sending, by the local nodes to the aggregator server, parameter updates computed over the local models. The aggregator server aggregates the received parameter updates, and sends the aggregated parameters to the local nodes.

US Patent:

20200193478, Jun 18, 2020

Filed:

Feb 24, 2020

Appl. No.:

16/799395

Inventors:

- Armonk NY, US
Keith Grueneberg - Stewart Manor NY, US
Bongjun Ko - Harrington Park NJ, US
Christian Makaya - Summit NJ, US
Jorge J. Ortiz - Rego Park NY, US
Swati Rallapalli - Ossining NY, US
Theodoros Salonidis - Wayne PA, US
Rahul Urgaonkar - Rye NY, US
Dinesh Verma - New Castle NY, US
Xiping Wang - Scarsdale NY, US

International Classification:

G06Q 30/02

Abstract:

A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

US Patent:

20200050951, Feb 13, 2020

Filed:

Aug 9, 2018

Appl. No.:

16/100177

Inventors:

- Armonk NY, US
THEODOROS SALONIDIS - BOSTON MA, US

International Classification:

G06N 5/04
G06N 99/00

Abstract:

A model requester node, which is an edge node of a cloud computing network, generates a specification of a machine learning model, distributes the specification to a plurality of other edge nodes, and receives replies to the specification from the plurality of other edge nodes. In response to the replies, the model requester node identifies a set of participating edge nodes based on a learning utility and a cost estimate of each of the plurality of other edge nodes. The model requester node then trains the machine learning model, without exchanging training data among the model requester node and the participating edge nodes, by repeatedly: distributing most recent parameters of the machine learning model to the participating edge nodes; receiving updates to the most recent parameters from the participating edge nodes; and establishing new parameters for the machine learning model by aggregating the updates from the participating edge nodes.

US Patent:

20200028670, Jan 23, 2020

Filed:

Sep 27, 2019

Appl. No.:

16/585971

Inventors:

- Armonk NY, US
Wentao Huang - Pasadena CA, US
Jiyong Jang - Ossining NY, US
Theodoros Salonidis - Wayne PA, US
Marc Ph Stoecklin - White Plains NY, US
Ting Wang - White Plains NY, US

International Classification:

H04L 9/08
H04L 9/30
H04L 29/06

Abstract:

An encoder includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generate a key, estimate a network capacity, and encode each bit of the key using a random matrix of a selected rank and the estimated network capacity for secure transmission of the key through a network.

US Patent:

20190318268, Oct 17, 2019

Filed:

Apr 13, 2018

Appl. No.:

15/952625

Inventors:

- Armonk NY, US
Tiffany Tuor - London, GB
Theodoros Salonidis - Boston MA, US
Christian Makaya - Summit NJ, US
Bong Jun KO - Harrington Park NJ, US

International Classification:

G06N 99/00
H04L 29/08

Abstract:

A training process of a machine learning model is executed at the edge node for a number of iterations to generate a model parameter based at least in part on a local dataset and a global model parameter. A resource parameter set indicative of resources available at the edge node is estimated. The model parameter and the resource parameter set are sent to a synchronization node. Updates to the global model parameter and the number of iterations are received from the synchronization node based at least in part on the model parameter and the resource parameter set of edge nodes. The training process of the machine learning model is repeated at the edge node to determine an update to the model parameter based at least in part on the local dataset and updates to the global model parameter and the number of iterations from the synchronization node.