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Satya Varaprasad Allumallu

from San Diego, CA
Age ~46
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Also known as:
Satya V Allumallu, Varaprasad S Allumallu, Allumallu Satye Varaprasad, A U
Related to:
Ranga P Allumallu
Connected to:
Shaun M Allumbaugh, 45Aaron T Allums, 29Calvin C Allums, 45Christian Allums, 61Debbie S Allums, 58Denise J Allums, 69Gary S Allums, 70Gwinelle P Allums, 79James K Allums, 64John W Allums, 85

Satya Allumallu Phones & Addresses

  • San Diego, CA
  • 9500 Declaration Dr, Chanhassen, MN 55317
  • Eden Prairie, MN
  • Plymouth, MN

Work

Company: Bd Mar 2018 Position: Staff data scientist - team lead, software technology solutions

Education

School / High School: Stanford University 2010 to 2012 Specialities: Mining, Statistics

Skills

Machine Learning • Statistics • R • Sql • Python • Apache Spark • Design of Experiments • Data Analysis • Algorithms • Matlab • Failure Analysis • Jmp • Simulink • Simulations • Testing • Minitab • Reliability Analysis • Spc

Ranks

Certificate: Machine Learning

Industries

Medical Devices

Resumes

Resumes

Satya Allumallu Photo 1

Staff Data Scientist - Team Lead

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Location:
10366 Reserve Dr, San Diego, CA 92127
Industry:
Medical Devices
Work:
Bd
Staff Data Scientist - Team Lead, Software Technology Solutions
Seagate Technology Aug 1, 2015 - Mar 2018
Senior Staff Data Scientist, Operations and Technology Advanced Analytics
Seagate Technology Aug 2010 - Jul 2015
Staff Engineer
Seagate Technology Sep 2007 - Jul 2010
Senior Development Engineer
Seagate Technology Jan 2006 - Aug 2007
Development Engineer
Education:
Stanford University 2010 - 2012
University of Minnesota 2004 - 2006
Master of Science, Masters, Statistics University of Minnesota 2000 - 2003
Master of Science, Masters, Mechanical Engineering Mvsr Engineering College 1996 - 2000
Bachelors, Bachelor of Science, Mechanical Engineering
Skills:
Machine Learning
Statistics
R
Sql
Python
Apache Spark
Design of Experiments
Data Analysis
Algorithms
Matlab
Failure Analysis
Jmp
Simulink
Simulations
Testing
Minitab
Reliability Analysis
Spc
Certifications:
Machine Learning
Computing For Data Analysis
Exploratory Data Analysis
Intro To Computer Science
Coursera
Udacity

Publications

Us Patents

Fault Detection System And Method Using Multiway Principal Component Analysis

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US Patent:
20070124113, May 31, 2007
Filed:
Nov 28, 2005
Appl. No.:
11/288818
Inventors:
Wendy Foslien - Minneapolis MN, US
Satya Allumallu - Edina MN, US
International Classification:
G06F 17/18
US Classification:
702185000, 702115000, 702182000, 702183000, 701099000
Abstract:
A fault detection system and method is provided that facilitates detection of faults that are manifest over a plurality of different operational phases. The fault detection system and method use multiway principal component analysis (MPCA) to detect fault from turbine engine sensor data. Specifically, the fault detection system uses a plurality of load vectors, each of the plurality of load vectors representing a principal component in the turbine engine sensor data from the multiple operational phases. The load vectors are preferably developed using sets of historical sensor data. When developed using historical data covering multiple operational phases, the load vectors can be used to detect likely faults in turbine engines. Specifically, new sensor data from the multiple operational phases is projected on to the load vectors, generating a plurality of statistical measures that can be classified to determine if a fault is manifest in the new sensor data.

Rare Instance Analytics For Diversion Detection

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US Patent:
20220375581, Nov 24, 2022
Filed:
Sep 25, 2020
Appl. No.:
17/763622
Inventors:
- San Diego CA, US
Abhikesh Nag - San Diego CA, US
Satya Varaprasad Allumallu - San Diego CA, US
Dennis Tribble - Ormond Beach FL, US
International Classification:
G16H 40/20
G16H 20/10
Abstract:
A method for detecting diversion may include identifying an activity pattern associated with a clinician as being an infrequent activity pattern that occurs below a threshold frequency. Whether the infrequent activity pattern corresponds to an anomalous behavior may be determined based at least on one or more data models. The infrequent activity pattern may include a series of transaction records, which may be matched to the reference transaction values included in each of the one or more data models. An investigative workflow may be triggered in response to the infrequent activity pattern being determined to correspond to the anomalous behavior. Related methods and articles of manufacture are also disclosed.

Dosage Normalization For Detection Of Anomalous Behavior

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US Patent:
20220277838, Sep 1, 2022
Filed:
Feb 26, 2021
Appl. No.:
17/187609
Inventors:
- San Diego CA, US
Abhikesh Nag - San Diego CA, US
Satya Varaprasad Allumallu - San Diego CA, US
Dennis Tribble - Ormond Beach FL, US
International Classification:
G16H 40/20
G16H 20/10
G16H 50/70
G06N 20/20
Abstract:
A method may include receiving a first transaction record indicating a first interaction with a first raw quantity of a first medication and a second transaction record indicating a second interaction with a second raw quantity of a second medication. The first transaction record and the second transaction record may be normalized by generating, based on an equivalent unit, a first normalized quantity of the first medication and a second normalized quantity of the second medication. A machine learning model may be applied to the normalized first transaction record and second transaction record to detect, based on the first transaction record and the second transaction record, an anomalous behavior. An investigative workflow may be triggered in response to the machine learning model detecting the anomalous behavior. Related systems and articles of manufacture, including computer program products, are also provided.

Adaptive Failure Prediction Modeling For Detection Of Data Storage Device Failures

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US Patent:
20180060192, Mar 1, 2018
Filed:
Aug 31, 2016
Appl. No.:
15/253103
Inventors:
- Cupertino CA, US
Wenjuan Wang - Edina MN, US
Sailaja Venkata Chilaka - Edina MN, US
Satya V. Allumallu - Chanhassen MN, US
Jason M. Feist - Shakopee MN, US
Bruce D. Emo - Longmont CO, US
International Classification:
G06F 11/16
G06F 11/30
G06F 11/34
G06F 3/06
Abstract:
Method and apparatus for predicting data storage device failures using adaptive failure prediction modeling. In some embodiments, monitored parameters are used to predict a potential imminent failure of a first data storage device using a first copy of a first failure prediction model (FPM). Data associated with the predicted potential imminent failure are transferred by the device across a computer network to a host device. The host device generates an updated, second FPM responsive to the transferred data as well as from data from at least a second data storage device transmitted across the computer network having a second copy of the first FPM. A first copy of the updated, second FPM is transferred, via the network, for use by the second data storage device.
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Satya Varaprasad Allumallu from San Diego, CA, age ~46 Get Report