Dr. Kim graduated from the UMDNJ School of Osteopathic Medicine in 2006. He works in Brooklyn, NY and 2 other locations and specializes in Internal Medicine. Dr. Kim is affiliated with New York Methodist Hospital, The Brooklyn Hospital Center and University Hospital Of Brooklyn.
Dr. Kim graduated from the Univ Estadual De Campinas, Fac De Cien Med, Campinas, Sp, Brazil in 1987. He works in Van Nuys, CA and specializes in Pediatrics. Dr. Kim is affiliated with Northridge Hospital Medical Center, Providence Holy Cross Medical Center and Valley Presbyterian Hospital.
Salem Radiology ConsultantsSalem Radiology Consultants PC 2925 Ryan Dr SE, Salem, OR 97301 (503)3991262 (phone), (503)3710777 (fax)
Education:
Medical School Wayne State University School of Medicine Graduated: 2003
Languages:
English Spanish
Description:
Dr. Kim graduated from the Wayne State University School of Medicine in 2003. He works in Salem, OR and specializes in Diagnostic Radiology. Dr. Kim is affiliated with Salem Health West Valley and Salem Hospital.
Rheumatology Consultants PC 1351 Main St STE 1, Brockton, MA 02301 (508)5874112 (phone), (508)5836810 (fax)
Education:
Medical School Tulane University School of Medicine Graduated: 2008
Languages:
English
Description:
Dr. Kim graduated from the Tulane University School of Medicine in 2008. He works in Brockton, MA and specializes in Rheumatology. Dr. Kim is affiliated with Good Samaritan Medical Center and Morton Hospital.
2013 to 2000 Strategic Planning Manager/Master SchedulerKLA-Tencor Milpitas, CA 2012 to 2013 Business Program ManagerKLA-Tencor Milpitas, CA 2005 to 2012 Senior Business AnalystE-Book Systems, Inc. Santa Clara, CA 2003 to 2005 Manager, Enterprise SolutionsNexxIT, INC Santa Clara, CA 2000 to 2003 Manager, Strategic planning and marketing
Education:
San Jose State University San Jose, CA 1995 to 2000 Economics
Skills:
SAP R3 module, SAP CRM, MCA SPO/Tactics, Extensive experience in MS Excel, MS Access , MS PowerPoint, Fluent in Korean.
Us Patents
Automatically Determining Items To Include In A Variant Group
- Bentonville AR, US Swagata Chakraborty - Santa Clara CA, US Abhinandan Krishnan - Sunnyvale CA, US Abon Chaudhuri - Sunnyvale CA, US Aakash Mayur Mehta - San Francisco CA, US Edison Mingtao Zhang - San Francisco CA, US Kyu Bin Kim - Mountain View CA, US
A method including obtaining image data and attribute information of a first item in an item catalog. The method also can include generating candidate variant items from the item catalog for the first item using a combination of (a) a k-nearest neighbors approach to search for first candidate variant items based on text embeddings for the attribute information of the first item, and (b) an elastic search approach to search for second candidate variant items based on image embeddings for the image data of the first item. The method additionally can include performing respective classifications based on respective pairs comprising the first item and each of the candidate variant items to filter the candidate variant items. The method further can include determining a respective distance between the first item and each of the candidate variant items, as filtered. The method additionally can include determining one or more items in the candidate variant items, as filtered, to include in a variant group for the first item, based on a decision function using a predetermined threshold and the respective distance for the each of the candidate variant items, as filtered. Other embodiments are described.
- Bentonville AR, US Swagata Chakraborty - Santa Clara CA, US Abhinandan Krishnan - Sunnyvale CA, US Abon Chaudhuri - Sunnyvale CA, US Aakash Mayur Mehta - San Francisco CA, US Edison Mingtao Zhang - San Francisco CA, US Kyu Bin Kim - Mountain View CA, US
Assignee:
Walmart Apollo, LLC - Bentonville AR
International Classification:
G06N 3/04 G06Q 30/06
Abstract:
A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform obtaining a set of items that have been grouped together as matching items in a group; performing an ensemble mismatch detection; performing multiple detection models on the set of items to generate respective outputs regarding mismatches; combining the respective outputs to determine whether a quantity of detected mismatches is at least a predetermined threshold; when the quantity of detected mismatches is at least the predetermined threshold, the acts also can include separating at least one of the set of items from the group; and when the quantity of detected mismatches is not at least the predetermined threshold, the acts additionally can include maintaining each item of the set of items in the group. Other embodiments are disclosed.
Real-Time Wireless Video Delivery System Using A Multi-Channel Communications Link
A system described herein provides real-time wireless video delivery using a multi-channel communications link. A method of employing elements of the system includes generating a first set of video data and generating a second set of video data. Further, encoding the first set of video data such that the second set of video data is a higher resolution version of the first set of video data and the encoded first set of video data is to supplement the second set of video data in response to a data drop.
- Houston TX, US Kyu Han KIM - Palo Alto CA, US Puneet JAIN - Palo Alto CA, US Xiaochen LIU - Palo Alto CA, US
International Classification:
H04W 4/80 H04W 4/02
Abstract:
An example system comprising: a processing resource; and a memory resource storing machine readable instructions executable to cause the processing resource to: receive a Bluetooth Low Energy (BLE) signal transmitted from a user device; generate, from the BLE signal, a BLE moving pattern of the user device, wherein the BLE moving pattern is generated at a different entity than an entity that transmits the BLE signal; track an object carrying the user device via visual information of the object such that a visual moving pattern of the object is generated from the tracking; determine the visual moving pattern matches the BLE moving pattern; and assign, responsive to the determination, an identity obtained from the user device to the object being tracked via the visual information.
- Houston TX, US Kyu Han Kim - Palo Alto CA, US Puneet Jain - Palo Alto CA, US Xiaochen Liu - Palo Alto CA, US
International Classification:
H04W 4/00 H04W 4/02
Abstract:
An example system comprising: a processing resource; and a memory resource storing machine readable instructions executable to cause the processing resource to: receive a Bluetooth Low Energy (BLE) signal transmitted from a user device; generate, from the BLE signal, a BLE moving pattern of the user device, wherein the BLE moving pattern is generated at a different entity than an entity that transmits the BLE signal; track an object carrying the user device via visual information of the object such that a visual moving pattern of the object is generated from the tracking; determine the visual moving pattern matches the BLE moving pattern; and assign, responsive to the determination, an identity obtained from the user device to the object being tracked via the visual information.
Examples provided herein describe a method for real-time processing of IoT data. For example, a first physical processor of an edge computing device may receive a set of data from a first IoT device communicably coupled to the edge device. The first physical processor may split the set of data into a set of individual data packets. A second physical processor of the edge device process the set of individual data packets by: concurrently applying, by a plurality of instances of the second physical processor of the edge computing device, a learning model to each of a corresponding plurality of data packets from the set of individual data packets; and annotating, by a subset of the plurality of instances of the second physical processor, a corresponding subset of the plurality of data packets with a corresponding output from the concurrent application of the learning model.
- HOUSTON TX, US KYU HAN KIM - PALO ALTO CA, US JEREMY GUMMESON - PALO ALTO CA, US DAN GELB - PALO ALTO CA, US
International Classification:
G06F 3/0346 G06F 3/038 G06F 3/01 G06F 3/042
Abstract:
Examples relate to determining finger movements. In one example, a computing device may: receive input from at least one of: a first proximity sensor coupled to the frame at a first position and facing a first direction; or a second proximity sensor coupled to the frame at a second position and facing a second direction; determine, based on the input, that a finger action occurred, the finger action being one of: a first movement of a first finger, the first movement being detected by the first proximity sensor; a second movement of a second finger, the second movement being detected by the second proximity sensor; generate, based on the finger action, output that includes data defining an event that corresponds to the finger action; and provide the output to another computing device.
Supplying Power To A Computer Accessory From A Captured Wifi Signal
- HOUSTON TX, US JEREMY GUMMESON - PALO ALTO CA, US DAVID LEE - PALO ALTO CA, US MARTIN R FINK - PALO ALTO CA, US KYU HAN KIM - PALO ALTO CA, US
International Classification:
G06F 1/26 H02J 50/20 G06F 3/0354 G06F 3/038
Abstract:
Examples herein disclose capturing a wifi signal from a computing device corresponding to a computing accessory and harvesting energy from the captured wifi signal. The examples power the computing accessory based on the harvested energy.