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Yao H Morin

from San Francisco, CA
Age ~42
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Also known as:
Yao Huang Morin, Yao Andrew Morin, Yao Huang, Morin Yh
Phone and address:
1221 Harrison St APT 23, San Francisco, CA 94103
Related to:
Zachary Morin
Connected to:
Abraham Morin, 52Adriana L Morin, 57Aldo V Morin, 46Alejandra A Morin, 42Alexa V Morin, 35Allyson Morin, 31Andre P Morin, 53Ariana A Morin, 33Barry E Morin, 66Bert Morin, 83

Yao Morin Phones & Addresses

  • 1221 Harrison St APT 23, San Francisco, CA 94103
  • San Diego, CA
  • Oceanside, CA
  • 9714 Hillside Trl S, Cottage Grove, MN 55016 (952) 459-1257
  • Minneapolis, MN
  • Mt Pleasant, SC
  • Saint Paul, MN

Resumes

Resumes

Yao Morin Photo 1

Chief Data Officer

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Location:
Urbana, IL
Industry:
Computer Software
Work:
University of Minnesota May 2008 - May 2009
Research Assistant
University of Minnesota May 2006 - May 2008
Laboratory Instructor
College of Liberal Arts, University of Minnesota Jun 2006 - Sep 2006
Research Programmer
Siemens Sep 2004 - Dec 2004
Technical Assistant
Associated Press Television News (APTN), Beijing, China Sep 2004 - Dec 2004
Office Assitant Intern
Education:
University of Minnesota-Twin Cities 2005 - 2009
Doctorate, Electrical EngineeringRamy H. Gohary, Yao Huang, Zhi-Quan Luo, and Jong-Shi Pang, “A Generalized Iterative Water-filling Algorithm for Distributed Power Control in the Presence of a Jammer” accepted to IEEE Transaction of Signal Processing, Apr 2008 Yao Huang, Ramy H. Gohary, and Zhi-Quan Luo, “Structured Spectrum Balancing in DSL Multiuser Communications” accepted to IEEE International Conference on Acoustic, Speech and Signal Processing 2009. Yao Huang, Ramy H. Gohary, and Zhi-Quan Luo, “Approaching User Capacity in a DSL System via Harmonic Mean-Rate Optimization” accepted to IEEE International Conference on Acoustic, Speech and Signal Processing 2009. Yao Huang “Resource Allocation in Digital Subscriber Lines System”, presented in Communication Seminar, University of Minnesota, Sep 2008 Yao Huang “Nash Game for Multi-User Communication in the Presence of a Jammer”, presented in Communication Seminar, Department of Electrical and Computer Engineering, University of Minnesota, Nov 2007 University of Minnesota-Twin Cities 2005 - 2008
Master's, Electrical Engineering Beijing University of Post and Telecommunications 2001 - 2005
Bachelor, Information Engineering The affiliated high school of south china normal university
Skills:
Matlab
Algorithms
Python
C
C++
Java
Programming
Signal Processing
Data Mining
Latex
Machine Learning
Optimization
Simulink
Data Analysis
Agile Methodologies
Distributed Systems
Mathematical Modeling
Statistics
Simulations
Pattern Recognition
Interests:
Programming
Civil Rights and Social Action
Politics
Environment
Reading
Science and Technology
Human Rights
Animal Welfare
Movies
Video Games
Yao Morin Photo 2

Yao Morin

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Publications

Us Patents

Domain Specific Natural Language Understanding Of Customer Intent In Self-Help

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US Patent:
20200250247, Aug 6, 2020
Filed:
Apr 22, 2020
Appl. No.:
16/855675
Inventors:
- Mountain View CA, US
Yao MORIN - San Diego CA, US
Jonathan LUNT - San Diego CA, US
Joseph B. CESSNA - San Diego CA, US
International Classification:
G06F 16/9535
G06N 5/02
G06F 16/33
G06F 16/338
G06F 9/451
G06F 16/9032
G06F 40/284
G06F 40/40
Abstract:
Method and apparatus for providing a personalized self-support service to a user of an online application coupled with an online community forum. Embodiments include obtaining a plurality of questions from the online community forum and obtaining historical user data. Embodiments further include identifying one or more part-of-speech words in the plurality of questions and generating a high-dimensional vector for each question of the plurality of questions based on a frequency of the one or more part-of-speech words. Embodiments further include identifying one or more user features of the plurality of users based on the historical user data and establishing, based on the historical user data, one or more statistical correlations between user features and part-of-speech words. Embodiments further include training a predictive model based on the one or more statistical correlations. Embodiments further include using the predictive model to predict to provide one or more relevant questions to the user.

Domain Specific Natural Language Understanding Of Customer Intent In Self-Help

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US Patent:
20190188326, Jun 20, 2019
Filed:
Dec 15, 2017
Appl. No.:
15/844475
Inventors:
- Mountain View CA, US
Yao MORIN - San Diego CA, US
Jonathan LUNT - San Diego CA, US
Joseph B. CESSNA - San Diego CA, US
Assignee:
INTUIT INC. - Mountain View CA
International Classification:
G06F 17/30
G06F 9/44
G06N 5/02
G06F 17/28
Abstract:
Method and apparatus for providing a personalized self-support service to a user of an online application coupled with an online community forum. Embodiments include obtaining a plurality of questions from the online community forum and obtaining historical user data. Embodiments further include identifying one or more part-of-speech words in the plurality of questions and generating a high-dimensional vector for each question of the plurality of questions based on a frequency of the one or more part-of-speech words. Embodiments further include identifying one or more user features of the plurality of users based on the historical user data and establishing, based on the historical user data, one or more statistical correlations between user features and part-of-speech words. Embodiments further include training a predictive model based on the one or more statistical correlations. Embodiments further include using the predictive model to predict to provide one or more relevant questions to the user.

Method And Apparatus For Providing Personalized Self-Help Experience

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US Patent:
20190163500, May 30, 2019
Filed:
Nov 28, 2017
Appl. No.:
15/824883
Inventors:
- Mountain View CA, US
Yao MORIN - San Diego CA, US
Ling Feng WEI - San Diego CA, US
Chris PETERS - San Diego CA, US
Itai JECZMIEN - San Diego CA, US
International Classification:
G06F 9/44
G06N 3/08
G06Q 40/00
G06F 3/048
H04L 29/08
H04L 12/58
Abstract:
Method and apparatus for providing personalized self-help experience in online application. A predictive model is trained to learn a relationship between one or more user features and one or more tags using historical user feature data. High-dimensional vectors representing each of a plurality of questions are generated and stored in the lookup table. The trained predictive model outputs tags probabilities from the incoming user data, using the learned relationship. A user high-dimensional vector is formed based on the tags probabilities. Similarity metrics are calculated between the high-dimensional vector for the respective question and the user high dimensional vector. One or more of the most relevant question titles are returned to a client device for presentation to a user.

System And Method For Generating Aggregated Statistics Over Sets Of User Data While Enforcing Data Governance Policy

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US Patent:
20190163790, May 30, 2019
Filed:
Nov 29, 2017
Appl. No.:
15/825832
Inventors:
- Mountain View CA, US
Yao H. Morin - San Diego CA, US
Mustafa Iqbal - San Diego CA, US
Deepen Prashant Mehta - San Diego CA, US
Ralph Tice - San Diego CA, US
Ravindra Kulkarni - San Diego CA, US
Ganesh Kannappan - Bangalore, IN
Assignee:
Intuit Inc. - Mountain View CA
International Classification:
G06F 17/30
G06F 21/62
G06N 99/00
Abstract:
A system and method for use with a data management service provides aggregated statistics derived from a large amount of user data extracted from one or more transaction management systems. The aggregated statistics are based on client queries from client systems. The queries request statistical information about a queried user grouping. An input interpreter module uses machine learning to modify the queried user grouping into a plurality of improved user groupings. A statistics calculator module performs a set of calculations on the user data based on the improved user groupings, and returns the results to an output preparer module. The output preparer module uses machine learning to determine which aggregated statistic to return to the client system.

Systems And Methods For Intelligently Grouping Financial Product Users Into Cohesive Cohorts

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US Patent:
20190130016, May 2, 2019
Filed:
Oct 27, 2017
Appl. No.:
15/796594
Inventors:
- Mountain View CA, US
Yao MORIN - San Diego CA, US
Joseph B. Cessna - San Diego CA, US
International Classification:
G06F 17/30
Abstract:
Systems and methods are provided that, in some embodiments that extract user data from at least one data warehouse. The user data is sorted within each dimension, and partitions each dimension into bins. Clusters are defined as each bin that includes user data for a number of users that exceeds a threshold. Clusters are determined for every combination of dimensions. Each combination of clusters that exceed the threshold is defined as clusters that are formed from multiple dimensions. All clusters and other clusters are stored into a cluster definition table. The clusters are used to analyze the profile of specific users.

Harmonic Feature Processing For Reducing Noise

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US Patent:
20170148465, May 25, 2017
Filed:
Jan 9, 2017
Appl. No.:
15/401608
Inventors:
- San Diego CA, US
Yao Huang Morin - San Diego CA, US
Assignee:
KNUEDGE, INC. - San Diego CA
International Classification:
G10L 21/02
G10L 21/0388
G10L 21/0264
G10L 25/18
Abstract:
Devices, systems and methods are disclosed for reducing noise in input data by performing a hysteresis operation followed by a lateral excitation smoothing operation. For example, an audio signal may be represented as a sequence of feature vectors. A row of the sequence of feature vectors may, for example, be associated with the same harmonic of the audio signal at different points in time. To determine portions of the row that correspond to the harmonic being present, the system may compare an amplitude to a low threshold and a high threshold and select a series of data points that are above the low threshold and include at least one data point above the high threshold. The system may iteratively perform a spreading technique, spreading a center value of a center data point in a kernel to neighboring data points in the kernel, to further reduce noise.

Determining Features Of Harmonic Signals

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US Patent:
20160232906, Aug 11, 2016
Filed:
Dec 15, 2015
Appl. No.:
14/969029
Inventors:
- San Diego CA, US
Yao Huang Morin - San Diego CA, US
Massimo Mascaro - San Diego CA, US
Janis I. Intoy - San Diego CA, US
Sean O'Connor - San Diego CA, US
Ellisha Marongelli - San Diego CA, US
Nick Hilton - San Diego CA, US
Assignee:
The Intellisis Corporation - San Diego CA
International Classification:
G10L 19/038
G10L 17/26
G10L 25/45
G10L 17/02
Abstract:
Features that may be computed from a harmonic signal include a fractional chirp rate, a pitch, and amplitudes of the harmonics. A fractional chirp rate may be estimated, for example, by computing scores corresponding to different fractional chirp rates and selecting a highest score. A first pitch may be computed from a frequency representation that is computed using the estimated fractional chirp rate, for example, by using peak-to-peak distances in the frequency distribution. A second pitch may be computed using the first pitch, and a frequency representation of the signal, for example, by using correlations of portions of the frequency representation. Amplitudes of harmonics of the signal may be determined using the estimated fractional chirp rate and second pitch. Any of the estimated fractional chirp rate, second pitch, and harmonic amplitudes may be used for further processing, such as speech recognition, speaker verification, speaker identification, or signal reconstruction.

Harmonic Feature Processing For Reducing Noise

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US Patent:
20160232917, Aug 11, 2016
Filed:
Feb 5, 2016
Appl. No.:
15/016801
Inventors:
- San Diego CA, US
Yao Huang Morin - San Diego CA, US
International Classification:
G10L 21/0232
G10L 21/0388
G10L 25/90
G10L 21/0264
G10L 15/20
Abstract:
Devices, systems and methods are disclosed for reducing noise in input data by performing a hysteresis operation followed by a lateral excitation smoothing operation. For example, an audio signal may be represented as a sequence of feature vectors. A row of the sequence of feature vectors may, for example, be associated with the same harmonic of the audio signal at different points in time. To determine portions of the row that correspond to the harmonic being present, the system may compare an amplitude to a low threshold and a high threshold and select a series of data points that are above the low threshold and include at least one data point above the high threshold. The system may iteratively perform a spreading technique, spreading a center value of a center data point in a kernel to neighboring data points in the kernel, to further reduce noise.
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Yao H Morin from San Francisco, CA, age ~42 Get Report