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Amir Hossein Hormati

from Seattle, WA
Age ~40
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Also known as:
Amir H Hormati, Amirhossein Hormati
Related to:
Mona HormatiMona N, 50
Connected to:
Jhon F Hormaza, 53Natasha Hormazabal, 32Yolanda Hormaza, 92Sally I Hormberg, 60Ruth G Hormeister, 98Audrey D Hormel, 83Deborah L Hormel, 71Juanita L Hormel, 104Kyle C Hormel, 49Marcia S Hormel, 67

Amir Hormati Phones & Addresses

  • Seattle, WA
  • Redmond, WA
  • Ann Arbor, MI
  • Kirkland, WA
  • Bellevue, WA
  • 516 10Th Ave, Kirkland, WA 98033

Work

Company: Google Apr 2013 Address: Seattle Position: Software engineer

Education

Degree: PhD School / High School: University of Michigan 2005 to 2010 Specialities: Computer Science and Engineering

Skills

Computer Architecture • Parallel Computing • Distributed Systems • Compilers • Algorithms • Processors • Fpga • Cloud Workload Acceleration

Interests

Reconfigurable Hardware Design • High Level Compilers • Parallel Systems • Cloud Workload Acceleration • Optimizations For Gpus

Industries

Computer Software

Resumes

Resumes

Amir Hormati Photo 1

Staff Software Engineer

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Location:
Seattle, WA
Industry:
Computer Software
Work:
Google - Seattle since Apr 2013
Software Engineer
Microsoft Research since Jun 2011
Senior Research Hardware Design Engineer
University of Michigan Sep 2005 - Apr 2011
Graduate Research Assistant
IBM Jun 2008 - Sep 2008
Research Intern
IBM May 2007 - Sep 2007
Research Intern
Education:
University of Michigan 2005 - 2010
PhD, Computer Science and Engineering Sharif University of Technology 2001 - 2005
B.Sc., Computer Science and Engineering
Skills:
Computer Architecture
Parallel Computing
Distributed Systems
Compilers
Algorithms
Processors
Fpga
Cloud Workload Acceleration
Interests:
Reconfigurable Hardware Design
High Level Compilers
Parallel Systems
Cloud Workload Acceleration
Optimizations For Gpus

Publications

Us Patents

Translation Of Simd Instructions In A Data Processing System

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US Patent:
20080141012, Jun 12, 2008
Filed:
Sep 27, 2007
Appl. No.:
11/905160
Inventors:
Sami Yehia - Boulognr Billancourt, FR
Krisztian Flautner - Cambridge, GB
Nathan Clark - Ann Arbor MI, US
Amir Hormati - Ann Arbor MI, US
Scott Mahlke - Ann Arbor MI, US
Assignee:
ARM LIMITED - Cambridge
The Regents of the University of Michigan - Ann Arbor MI
International Classification:
G06F 9/318
US Classification:
712226, 712E09037
Abstract:
A data processing system is provided having a processor and analysing circuitry for identifying a SIMD instruction associated with a first SIMD instruction set and replacing it by a functionally-equivalent scalar representation and marking that functionally-equivalent scalar representation. The marked functionally-equivalent scalar representation is dynamically translated using translation circuitry upon execution of the program to generate one or more corresponding translated instructions corresponding to a instruction set architecture different from the first SIMD architecture corresponding to the identified SIMD instruction.

Explainable Artificial Intelligence In Computing Environment

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US Patent:
20220405623, Dec 22, 2022
Filed:
Jun 22, 2021
Appl. No.:
17/354392
Inventors:
- Mountain View CA, US
Lisa Yin - Redmond WA, US
Amir H. Hormati - Kirkland WA, US
Mingge Deng - Kirkland WA, US
Christopher Avery Meyers - Kirkland WA, US
International Classification:
G06N 5/04
G06K 9/62
G06F 16/245
G06N 20/00
Abstract:
The disclosure is directed to a query-driven machine learning platform for generating feature attributions and other data for interpreting the relationship between inputs and outputs of a machine learning model. The platform can receive query statements for selecting data, training a machine learning model, and generating model explanation data for the model. The platform can distribute processing for generating the model explanation data to scale in response to requests to process selected data, including multiple records with a variety of different feature values. The interface between a user device and the machine learning platform can streamline deployment of different model explainability approaches across a variety of different machine learning models.

Machine Learning Time Series Anomaly Detection

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US Patent:
20220382857, Dec 1, 2022
Filed:
May 24, 2022
Appl. No.:
17/664865
Inventors:
- Mountain View CA, US
Xi CHENG - Kirkland WA, US
Amir HORMATI - Mountain View CA, US
Weijie SHEN - Mountain View CA, US
Assignee:
Google LLC - Mountain View CA
International Classification:
G06F 21/55
G06N 20/00
G06F 16/242
Abstract:
A method includes receiving a time series anomaly detection query from a user and training one or more models using a set of time series data values. For each respective time series data value in the set, the method includes determining, using the trained models, an expected data value for the respective time series data value and determining a difference between the expected data value and the respective time series data value. The method also includes determining that the difference between the expected data value and the respective time series data value satisfies a threshold. In response to determining that the difference between the expected data value and the respective time series data value satisfies the threshold, the method includes determining that the respective time series data value is anomalous and reporting the anomalous respective time series data value to the user.

Machine Learning Hyperparameter Tuning

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US Patent:
20220366318, Nov 17, 2022
Filed:
May 15, 2022
Appl. No.:
17/663430
Inventors:
- Mountain View CA, US
Mingge Deng - Kirkland WA, US
Amir Hormati - Mountain View CA, US
Assignee:
Google LLC - Mountain View CA
International Classification:
G06N 20/20
G06F 16/242
G06F 16/27
Abstract:
A method, when executed by data processing hardware, causes the data processing hardware to perform operations including receiving, from a user device, a hyperparameter optimization request requesting optimization of one or more hyperparameters of a machine learning model. The operations include obtaining training data for training the machine learning model and determining a set of hyperparameter permutations of the one or more hyperparameters. For each respective hyperparameter permutation in the set of hyperparameter permutations, the operations include training a unique machine learning model using the training data and the respective hyperparameter permutation and determining a performance of the trained model. The operations include selecting, based on the performance of each of the trained unique machine learning models of the user device, one of the trained unique machine learning models. The operations include generating one or more predictions using the selected one of the trained unique machine learning models.

Machine Learning Regression Analysis

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US Patent:
20230094479, Mar 30, 2023
Filed:
Sep 30, 2021
Appl. No.:
17/449660
Inventors:
- Mountain View CA, US
Lisa Yin - Redmond WA, US
Mingge Deng - Kirkland WA, US
Amir Hormati - Mountain View CA, US
Umar Ali Syed - Edison NJ, US
Assignee:
Google LLC - Mountain View CA
International Classification:
G06N 7/00
G06K 9/62
Abstract:
A method includes receiving a model analysis request from a user. The model analysis requests requesting the data processing hardware to provide one or more statistics of a model trained on a dataset. The method also includes obtaining the trained model. The trained model includes a plurality of weights. Each weight is assigned to a feature of the trained model. The model also includes determining, using the dataset and the plurality of weights, the one or more statistics of the trained model based on a linear regression of the trained model. The method includes reporting the one or more statistics of the trained model to the user.

Principal Component Analysis

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US Patent:
20230045139, Feb 9, 2023
Filed:
Jul 29, 2022
Appl. No.:
17/816288
Inventors:
- Mountain View CA, US
Mingge Deng - Kirkland WA, US
Amir Hossein Hormati - Seattle WA, US
Assignee:
Google LLC - Mountain View CA
International Classification:
G06K 9/62
G06F 16/242
Abstract:
A method for principal component analysis includes receiving a principal component analysis (PCA) request from a user requesting data processing hardware to perform PCA on a dataset, the dataset including a plurality of input features. The method further includes training a PCA model on the plurality of input features of the dataset. The method includes determining, using the trained PCA model, one or more principal components of the dataset. The method also includes generating, based on the plurality of input features and the one or more principal components, one or more embedded features of the dataset. The method includes returning the one or more embedded features to the user.

Time Series Forecasting

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US Patent:
20210357402, Nov 18, 2021
Filed:
Aug 6, 2020
Appl. No.:
16/986861
Inventors:
- Mountain View CA, US
Amir H. Hormati - Seattle WA, US
Lisa Yin - Redmond WA, US
Umar Syed - Edison NJ, US
Assignee:
Google LLC - Mountain View CA
International Classification:
G06F 16/2458
G06F 16/22
Abstract:
A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

Creating A Machine Learning Model With K-Means Clustering

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US Patent:
20200320413, Oct 8, 2020
Filed:
Apr 8, 2020
Appl. No.:
16/843371
Inventors:
- Mountain View CA, US
Amir H. Hormati - Kirkland WA, US
Xi Cheng - Mountain View CA, US
International Classification:
G06N 5/04
G06F 16/29
G06F 7/14
G06N 20/00
Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.
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Amir Hossein Hormati from Seattle, WA, age ~40 Get Report