Hazel Health, Inc.
Manager of Clinical Innovation
Juma Ventures Mar 2018 - Jul 2018
Innovation Fellow
Hopelab Mar 2018 - Jul 2018
Strategy Fellow
Gsr Ventures Aug 2017 - Sep 2017
Summer Associate
Redf Jun 2017 - Aug 2017
Farber Intern
Education:
Stanford School of Medicine 2012 - 2018
Doctor of Medicine, Doctorates, Medicine
Stanford University Graduate School of Business 2016 - 2018
Master of Business Administration, Masters
Stanford University Graduate School of Business 2010 - 2011
Master of Business Administration, Masters
University of Pennsylvania 2006 - 2010
Bachelors, Bachelor of Science, Bioengineering
Senior Director Of Program Management, Cal-Comp Usa
New Kinpo Group
Senior Director of Program Management, Cal-Comp Usa
Asteelflash Aug 2012 - Jul 2013
Plant Manager, Mexico Operations
Asteelflash Apr 2009 - Aug 2012
Director of Program Management
Asteelflash 2006 - Apr 2009
Manager, Program Management
Flash Electronics 2000 - 2006
Program Manager
Education:
University of California, Davis 1992 - 1996
Bachelors, Bachelor of Arts, Economics
Skills:
Program Management Contract Manufacturing Cross Functional Team Leadership Supply Chain Management Lean Manufacturing Continuous Improvement Manufacturing Six Sigma Mrp Supply Chain Strategic Sourcing Process Improvement Product Development Kaizen Materials Management Supply Management Erp Procurement Product Management Project Planning Supply Chain Management and Execution Revenue Forecast and Gap Analysis Strategic Planning and Mapping Cost Control and Efficiency New Business Development Inventory Control Management Team Building and Career Development Volume Price Agreement Management Highly Organized and Strong Analytical Abilities Inventory Management Management Start Ups 5S Value Stream Mapping Kanban
City of Menlo Park
Senior Civil Engineer
City of Menlo Park May 2016 - Jun 2019
Associate Engineer
Bkf Engineers May 2015 - May 2016
Project Engineer
Sherwood Design Engineers Nov 2011 - Apr 2015
Design Engineer
Bkf Engineers Dec 2008 - Apr 2011
Engineer
Education:
University of California, Davis
Bachelors, Bachelor of Science, Civil Engineering
Skills:
Civil Engineering Stormwater Management Drainage Autocad Water Resources Hydrology Autocad Civil 3D Grading Highways Water Resource Management Site Plans Microsoft Office Engineering Road Land Development Watercad Construction Management Project Management
Dec 2011 to May 2013 Senior Director, Investor Relations and Corporate DevelopmentMarcus Food & Beverage Mgmt Co
Mar 2010 to Nov 2011 Vice President, Finance and Real EstateThe Bear Stearns Companies, Inc New York, NY Jun 2006 to Aug 2008 Senior Associate, Real Estate and Lodging GroupBank of America Corporation San Francisco, CA Jul 2005 to May 2006 Associate, Real Estate and Lodging GroupSignal Hill Capital Group LLC Baltimore, MD Jul 2002 to May 2004 Associate, Mergers and AcquisitionsDeutsche Bank AG Baltimore, MD Jul 2000 to Jun 2002 Analyst, Media and Telecommunications Group
Education:
Northwestern University - Kellogg School of Management Evanston, IL Jun 2004 to Jun 2005 MBA in Finance and EntrepreneurshipUniversity of Michigan - Ross School of Business Ann Arbor, MI Sep 1996 to Jun 2000 Bachelor of Business Administration in Finance and Accounting
Jul 2013 to 2000 Curriculum Writer/DeveloperGoogle Inc Mountain View, CA Oct 2012 to Oct 2013 Enterprise Product Fulfillment SpecialistWestlake Middle School Oakland, CA Aug 2011 to Jun 2012 Middle School Science InstructorMaking Waves Academy Richmond, CA Jul 2010 to Jun 2011 Middle School Math Instructor
Education:
UNIVERSITY OF CALIFORNIA, BERKELEY Berkeley, CA May 2009 M.A. in Science and Math Education
Isbn (Books And Publications)
Conditional Monte Carlo: Gradient Estimation and Optimization Applications
D'Ambrosio Eye Care IncDambrosio Eye Care Inc 479 Old Un Tpke, Lancaster, MA 01523 9785373900 (phone), 9785376030 (fax)
Dambrosio Eye Care 100 Powdermill Rd, Acton, MA 01720 9788977212 (phone), 9784610345 (fax)
Dambrosio Eye Care Inc 74 Main St, Gardner, MA 01440 9786323930 (phone), 9785376030 (fax)
Procedures:
Ophthalmological Exam
Languages:
English
Description:
Dr. Fu works in Acton, MA and 2 other locations and specializes in Optometry. Dr. Fu is affiliated with Emerson Hospital, HealthAlliance Hospital Leominster, Henry Heywood Memorial Hospital and St Vincent Hospital.
Us Patents
Multiple Pass Optimization For Automatic Electronic Circuit Placement
Ross A. Donelly - Sunnyvale CA William C. Naylor - San Jose CA Michael Fu - San Jose CA
Assignee:
Synopsys, Inc. - Mountain View CA
International Classification:
G06F 1750
US Classification:
716 10, 716 13, 716 14, 716 12, 716 1
Abstract:
A computer implemented process for the automatic creation of integrated circuit (IC) geometry including a multiple pass process flow using multiple passes of direct timing driven placement after a first pass of non-direct timing driven placement. First, a high level description of the circuit design may be synthesized. Next, a non-direct timing driven placement process may place the design. Then the placed design may be routed. Alternatively, routability may be estimated. After routing, a modified design may be resynthesized. The resynthesized design may then be placed according to a direct timing driven placement process. This sequence may be repeated several times.
System For Storing, Displaying, And Navigating Content Data Regarding Market Driven Industries
Michael Fu - San Jose CA, US Harold Sun - Tigard OR, US Unni Narayanan - Sunnyvale CA, US William Ward Carey - Hillsborough CA, US Phani Saripella - Santa Clara CA, US
Assignee:
PRIMARY GLOBAL RESEARCH, LLC - Mountain View CA
International Classification:
G06F 7/00
US Classification:
7071041
Abstract:
A model and system employing the model provides an organized structure for storing, displaying, and navigating content data regarding instruments for market driven industries (i.e., securities). A Market Research Model (MRM) paradigm is used to represent elemental concepts, a plurality of specific classes of entities form the MRM, and an interface is used to assemble, maintain, and interact with the model. Information may be added to the model by a research provider and provided to an end user on a subscription basis. The user is provided with an interconnected, navigable model of an item of interest for research and decision-making support.
Streaming Synchronized Media Content To Separate Devices
- San Jose CA, US Michael Chin-Ming FU - Cupertino CA, US
International Classification:
H04L 29/08 H04L 29/06 H04N 21/4363 H04N 21/43
Abstract:
Described are system, apparatus, article of manufacture, method, or computer program product embodiments for controlling streaming of media content. An embodiment operates by halting a presentation of future content from a buffer upon determining that the buffer is exhausted of content to present. The embodiment includes receiving one or more packets over a network connection, the one or more packets including media information corresponding to a first portion of streaming media content, in which the first portion corresponds to a second portion of the streaming media content. The one or more packets in a buffer are stored as buffered content. Responsive to determining that the network connection is not experiencing a burst condition, the buffer is trimmed. Then, presentation of buffered content is resumed and the first portion is caused to be presented in sync with the second portion.
System And Method For Capture And Adaptive Data Generation For Training For Machine Vision
A computer-implemented method of performing machine vision prediction of digital images using synthetically generated training assets comprises digitally capturing a plurality of assets; configuring each of the assets in the plurality of assets with a plurality of asset attributes; under computer program control, selecting a plurality of different combinations of parameters from among the plurality of asset attributes, and creating a plurality of sets of different synthetic dataset parameters; using computer graphics software, and example parameter values from among the synthetic dataset parameters, creating a synthetic dataset by compiling from a plurality of example images and metadata; configuring a plurality of machine learning trials and executing the trials to train a machine vision model, resulting in creating and storing a trained machine vision model; executing a validation of the trained machine vision model; and inferring a prediction using the trained machine vision model. Trained models are scored against success criteria and re-trained using pseudo-random sampling of different parameters clustered around failure points. As a result, machine vision models may be trained with high accuracy using large datasets of synthesized digital images that are richly parameterized, rather than human captured digital images.
Disclosed herein are system, method, and computer program product embodiments for flexible output of streaming media. An embodiment operates by receiving, at a media server, media over a network. Output media, corresponding to the received media, is streamed to a display device. The output media includes a video component and an audio component. A command is received. Responsive to the command, the streaming of the video component to the display device is discontinued. A streaming of the video component is begun to a mobile device at a point in the video component corresponding where the video was discontinued.
Streaming Synchronized Media Content To Separate Devices
- Saratoga CA, US Michael Chin-Ming FU - Cupertino CA, US
International Classification:
H04L 29/08 H04L 29/06
Abstract:
Described are system, apparatus, article of manufacture, method, or computer program product embodiments for controlling streaming of media content. An embodiment operates by halting a presentation of future content from a buffer upon determining that the buffer is exhausted of content to present. The embodiment includes receiving one or more packets over a network connection, the one or more packets including media information corresponding to a first portion of streaming media content, in which the first portion corresponds to a second portion of the streaming media content. The one or more packets in a buffer are stored as buffered content. Responsive to determining that the network connection is not experiencing a burst condition, the buffer is trimmed. Then, presentation of buffered content is resumed and the first portion is caused to be presented in sync with the second portion.