Dr. Ho graduated from the University of Southern California Keck School of Medicine in 1992. He works in Torrance, CA and 1 other location and specializes in Dermatology. Dr. Ho is affiliated with St Vincent Medical Center.
Medical School Temple University School of Medicine Graduated: 2001
Conditions:
Hemophilia A or B Iron Deficiency Anemia Leukemia Malignant Neoplasm of Female Breast Multiple Myeloma
Languages:
English Spanish
Description:
Dr. Ho graduated from the Temple University School of Medicine in 2001. He works in Tarzana, CA and 1 other location and specializes in Hematology/Oncology. Dr. Ho is affiliated with Providence Tarzana Medical Center, Ronald Reagan UCLA Medical Center, Tarzana Treatment Centers, Valley Presbyterian Hospital and West Hills Hospital & Medical Center.
Emory ClinicEmory Breast Imaging Center 1701 Upper Gate Dr NE, Atlanta, GA 30322 4047784446 (phone), 4047781901 (fax)
Emory ClinicEmory Breast Imaging Center 1365 Clifton Rd NE, Atlanta, GA 30322 4047787465 (phone), 4047783095 (fax)
Emory ClinicEmory University Hospital Radiology 1364 Clifton Rd NE, Atlanta, GA 30322 4047782650 (phone), 4047782145 (fax)
Education:
Medical School University of Virginia School of Medicine Graduated: 2004
Languages:
Chinese English Spanish
Description:
Dr. Ho graduated from the University of Virginia School of Medicine in 2004. He works in Atlanta, GA and 2 other locations and specializes in Diagnostic Radiology and Radiation Oncology. Dr. Ho is affiliated with Emory Johns Creek Hospital, Emory University Hospital, Emory University Hospital Midtown and Grady Memorial Hospital.
Wikipedia References
Christopher Ho
Name / Title
Company / Classification
Phones & Addresses
Christopher T. Ho
Dfined Studios, LLC
673 Adagio Way, San Jose, CA 95111
Christopher Ho Principal
East Coast Volleyball Store, LLC Ret Misc Merchandise
272 Lauriston St, Philadelphia, PA 19128
Resumes
International Product Coordinator Intern At Ubisoft
Amazon Studios
Content Strategy, Original Movies
Paramount Pictures
Director - Strategic Planning and Business Operations
The Walt Disney Company 2013 - 2016
Strategy and Business Development
Strategy& (Formerly Booz & Company) 2012 - 2013
Management Consultant
Deloitte 2010 - 2011
Business Analyst Intern
Education:
The Wharton School 2016 - 2018
Master of Business Administration, Masters, Marketing, Management, Business
University of Southern California 2016
The University of British Columbia 2007 - 2012
Bachelor of Commerce, Bachelors, Finance
Skills:
Media and Entertainment Financial Modeling Business Development Strategic Planning
Interests:
Mobile Digital Media E Commerce Entertainment Health and Wellness Internet of Things Consumer Internet Food and Beverages Travel
Space Systems Loral Mar 2007 - Aug 2014
Principal Systems Engineer
Space Systems Loral Mar 2007 - Aug 2014
Senior Manager, Systems Architecture at Ssl
Applied Signal Technology Aug 2005 - Mar 2007
Senior Systems Engineer
Bae Systems Feb 2004 - Aug 2005
Systems Engineer
Boeing Jun 2000 - Aug 2002
Member of Technical Staff
Education:
Oakland Senior High School
Skills:
Space Systems Systems Engineering Satellite Systems Design Aerospace System Design Requirements Management Security Clearance Dod Telelogic Doors C4Isr
Apex.ai, Inc.
Principal Software Engineer
Faraday Future Nov 2016 - Sep 2017
Software Engineer
A9.Com Jun 2016 - Sep 2016
Software Development Intern
Stanford University Oct 2014 - Jun 2016
Research Assistant
Nissan Motor Corporation Jun 2015 - Sep 2015
Intern - Enterprise Architecture
Education:
Stanford University 2015 - 2018
Doctorates, Doctor of Philosophy, Engineering, Philosophy
Stanford University 2013 - 2015
Master of Science, Masters, Engineering
Uc Santa Barbara 2009 - 2013
Bachelors, Bachelor of Science, Mechanical Engineering
Skills:
Machine Learning Artificial Intelligence Programming Algorithms Research Data Science Optimization Markov Decision Processes Computer Science Python Reinforcement Learning Deep Learning C++ Natural Language Processing Statistics Mathematics R Matlab Latex Control Systems Design Aerospace Engineering Mechanical Engineering Modeling C Java Hadoop Hive Big Data Analytics Apache Pig Microsoft Office Software Development Dds Iso26262
Interests:
Mathematics Artificial Intelligence Programming Sustainability Writing Fiction Language System Design Machine Learning Brazilian Jiu Jitsu Autonomy Economics Efficiency Food History
Punahou School
Bloomsburg University of Pennsylvania
Bachelors, Medicine
Villanova University
Doctor of Jurisprudence, Doctorates
Skills:
Event Management Coaching Sports Microsoft Office Public Speaking Social Networking Customer Service Strategic Planning Team Building Marketing Strategy Social Media Social Media Marketing Event Planning Powerpoint Entrepreneurship Microsoft Word Marketing Microsoft Excel
Efficient and scalable three-dimensional point cloud segmentation. In an embodiment, a three-dimensional point cloud is segmented by adding points to a spatial hash. For each unseen point, a cluster is generated, the unseen point is added to the cluster and marked as seen, and, for each point that is added to the cluster, the point is set as a reference, a reference threshold metric is computed, all unseen neighbors are identified based on the reference threshold metric, and, for each identified unseen neighbor, the unseen neighbor is marked as seen, a neighbor threshold metric is computed, and the neighbor is added or not added to the cluster based on the neighbor threshold metric. When the cluster reaches a threshold size, it may be added to a cluster list. Objects may be identified based on the cluster list and used to control autonomous system(s).
Efficient And Scalable Three-Dimensional Point Cloud Segmentation
Efficient and scalable three-dimensional point cloud segmentation. In an embodiment, a three-dimensional point cloud is segmented by adding points to a spatial hash. For each unseen point, a cluster is generated, the unseen point is added to the cluster and marked as seen, and, for each point that is added to the cluster, the point is set as a reference, a reference threshold metric is computed, all unseen neighbors are identified based on the reference threshold metric, and, for each identified unseen neighbor, the unseen neighbor is marked as seen, a neighbor threshold metric is computed, and the neighbor is added or not added to the cluster based on the neighbor threshold metric. When the cluster reaches a threshold size, it may be added to a cluster list. Objects may be identified based on the cluster list and used to control autonomous system(s).
Efficient And Scalable Three-Dimensional Point Cloud Segmentation For Navigation In Autonomous Vehicles
- Palo Alto CA, US Christopher HO - San Francisco CA, US
International Classification:
G01S 17/93 G06T 7/136 G06T 7/187 G05D 1/02
Abstract:
Efficient and scalable three-dimensional point cloud segmentation. In an embodiment, a three-dimensional point cloud is segmented by adding points to a spatial hash. For each unseen point, a cluster is generated, the unseen point is added to the cluster and marked as seen, and, for each point that is added to the cluster, the point is set as a reference, a reference threshold metric is computed, all unseen neighbors are identified based on the reference threshold metric, and, for each identified unseen neighbor, the unseen neighbor is marked as seen, a neighbor threshold metric is computed, and the neighbor is added or not added to the cluster based on the neighbor threshold metric. When the cluster reaches a threshold size, it may be added to a cluster list. Objects may be identified based on the cluster list and used to control autonomous system(s).
Motion planning is described herein. Motion planning includes determining one or more trajectories and/or velocities. Trajectories and velocities are then provided to one or more controllers that cause a vehicle to travel to a location. By dynamically determining a motion path with various math equations, time may be saved by eliminating the need to choose between a plurality of motion plans.
- Tokyo, JP Jun Murakawa - Foster City CA, US Christopher Ho - Belmont CA, US
International Classification:
G06T 17/20 G06T 3/40
Abstract:
Systems, methods, and devices are disclosed for rendering computer graphics. In various embodiments, a displacement map is created for a plurality of surfaces and a tessellation process is initiated. It is determined that the tessellation density of a first set of surfaces and a second set of surfaces should be modified based on the displacement map. Based on the displacement map, a tessellation factor scale for each surface of the first set of surfaces is increased and a tessellation factor scale for each surface of the second set of surfaces is decreased, respectively.
- Tokyo, JP Osman Steven - Foster City CA, US Jun Murakawa - Foster City CA, US Christopher Ho - Foster City CA, US
International Classification:
G06T 1/20
US Classification:
345501
Abstract:
A system has a central processing unit (CPU) and a graphics processing unit (GPU) that includes one or more registers. The GPU can change a resource descriptor in one of the GPU's registers. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.
- Tokyo, JP Christopher Ho - Foster City CA, US Mark E. Cerny - Foster City CA, US Steven Osman - Foster City CA, US Jun Murakawa - Foster City CA, US Dmitri Shtilman - Foster City CA, US Jason Scanlin - Foster City CA, US
International Classification:
G06T 1/20
US Classification:
345522
Abstract:
A method for processing graphics for a GPU program, translating instructions from a shading language into an intermediate language with a front end of a GPU compiler; translating the instructions from the intermediate language into a GPU object language with a back end of the GPU compiler; wherein the instructions in the shading language include instructions defining a layout of resources for the GPU program.
Joon Ho Lee (1988-1992), Tom Bowman (1990-1994), Christopher Ho (1980-1984), Wiley Thomas (1977-1981), David Calica (1973-1977), Martin Flyxe (1975-1979)