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Christopher Agustin Serrano, 31Los Angeles, CA

Christopher Serrano Phones & Addresses

Los Angeles, CA   

208 Laurel St, Broomfield, CO 80020   

Superior, CO   

Wheat Ridge, CO   

Superior, CO   

Mentions for Christopher Agustin Serrano

Career records & work history

Medicine Doctors

Christopher W. Serrano

Specialties:
Obstetrics & Gynecology
Work:
Serrano Obstetrics & Gynecology
20726 Stone Oak Pkwy UNIT 101, San Antonio, TX 78258
210-5457700 (phone) 210-5457705 (fax)
Education:
Medical School
University of Texas Medical School at San Antonio
Graduated: 1990
Procedures:
Cesarean Section (C-Section), Cystoscopy, Hysterectomy, Ovarian Surgery, Tubal Surgery, Vaccine Administration, Vaginal Repair
Conditions:
Abnormal Vaginal Bleeding, Conditions of Pregnancy and Delivery, Genital HPV, Breast Disorders, Candidiasis of Vulva and Vagina, Complicating Pregnancy or Childbirth, Diabetes Mellitus Complicating Pregnancy or Birth, Endometriosis, Female Infertility, Hemorrhoids, Herpes Genitalis, Hypertension (HTN), Menopausal and Postmenopausal Disorders, Polycystic Ovarian Syndrome (PCOS), Pregnancy-Induced Hypertension, Premenstrual Syndrome (PMS), Spontaneous Abortion, Uncomplicated or Low Risk Pregnancy and Delivery
Languages:
English, Spanish
Description:
Dr. Serrano graduated from the University of Texas Medical School at San Antonio in 1990. He works in San Antonio, TX and specializes in Obstetrics & Gynecology. Dr. Serrano is affiliated with Methodist Hospital and North Central Baptist Hospital.
Christopher Serrano Photo 1

Christopher W Serrano

Specialties:
Obstetrics & Gynecology
Education:
The University of Texas at Austin

Christopher Serrano resumes & CV records

Resumes

Christopher Serrano Photo 47

Post Masters Research Scientist

Location:
6528 Pico Vista Rd, Pico Rivera, CA 90660
Industry:
Research
Work:
Georgia Institute of Technology
Reinforcement Learning Teaching Assistant
Hrl Laboratories, Llc
Post Masters Research Scientist
Education:
Georgia Institute of Technology 2014 - 2017
Masters, Computer Science
Uc Santa Barbara 2001 - 2004
Skills:
Machine Learning, Probabilistic Models, Python, Weka, Scikit Learn, Entrepreneurship, Start Ups, Online Advertising, Online Marketing, Reinforcement Learning, Artificial Neural Networks, Data Analysis, R, Neural Networks
Languages:
English
Spanish
Italian
Christopher Serrano Photo 48

Manager - Software Engineering

Location:
24674 Brighton Dr, Valencia, CA 91355
Industry:
Computer Software
Work:
Ticketmaster
Manager - Software Engineering
Live Nation Entertainment
Lead Software Engineer
Ibm Sep 2001 - Jun 2008
Systems Engineer Specialist
Techempower 1999 - Sep 2001
Systems Architect
Israel Electric Company 1996 - 1999
Systems Architect
Hughes Aircraft Company Jun 1995 - Mar 1996
Systems Engineer
Education:
Loyola Marymount University 1991 - 1995
Bachelors, Bachelor of Science, Computer Science
Skills:
Web Services, Java, Scrum, Agile Methodologies, Software Development, Xml, .Net, Javascript, Ios Development
Interests:
Kids
Cooking
Exercise
Medicine
Electronics
Home Improvement
Reading
Crafts
Fitness
Home Decoration
Health
Languages:
English
Christopher Serrano Photo 49

Los Angeles Film School

Location:
Los Angeles, CA
Work:

Los Angeles Film School
Education:
Los Angeles Film School
Christopher Serrano Photo 50

None

Location:
Los Angeles, CA
Industry:
Entertainment
Work:

None
Christopher Serrano Photo 51

Christopher Serrano

Location:
7818 Geyser Ave, Reseda, CA 91335
Education:
Bonanza High School
Christopher Serrano Photo 52

Los Angeles Film School

Location:
Los Angeles, CA
Work:

Los Angeles Film School
Christopher Serrano Photo 53

Cyber Threat Investigator

Work:

Cyber Threat Investigator
Christopher Serrano Photo 54

Christopher Serrano

Publications & IP owners

Us Patents

Deep Reinforcement Learning Method For Generation Of Environmental Features For Vulnerability Analysis And Improved Performance Of Computer Vision Systems

US Patent:
2021031, Oct 14, 2021
Filed:
Dec 8, 2020
Appl. No.:
17/115646
Inventors:
- Malibu CA, US
Christopher Serrano - Whittier CA, US
International Classification:
G06N 3/08
G06N 3/04
Abstract:
Described is a system for generating environmental features using deep reinforcement learning. The system receives a policy network architecture, initialization parameters, and a simulation environment that models a trajectory of a target system through a physical environment. Landmark features sampled from the policy network are initialized, and a trained policy network is generated by training the policy network using a reinforcement learning algorithm. A set of environmental features are generated using the trained policy network and displayed on a display device.

Method For Proving Or Identifying Counter-Examples In Neural Network Systems That Process Point Cloud Data

US Patent:
2021027, Sep 9, 2021
Filed:
Oct 22, 2020
Appl. No.:
17/078079
Inventors:
- Malibu CA, US
Christopher Serrano - Whittier CA, US
Aleksey Nogin - Fresno CA, US
International Classification:
G06N 3/08
Abstract:
Described is a system for proving correctness properties of a neural network for providing estimates for point cloud data. The system receives as input a description of a neural network for generating estimates from a set of point cloud data. The description of the neural network is parsed to obtain a symbolic representation. Based on a combination of the symbolic representation and a set of analysis parameters, the system generates an analysis output indicating whether the neural network satisfies a correctness property in generating the estimates from the set of point cloud data. The analysis output is a mathematical proof artifact proving that the set of analysis parameters is satisfied, a list of one or more point clouds for which the set of analysis parameters is violated, or a report that progress could not be made by the analysis.

Deep Reinforcement Learning Based Method For Surreptitiously Generating Signals To Fool A Recurrent Neural Network

US Patent:
2021008, Mar 25, 2021
Filed:
Jul 23, 2020
Appl. No.:
16/937503
Inventors:
- Malibu CA, US
Christopher Serrano - Whittier CA, US
Pape Sylla - Thousand Oaks CA, US
International Classification:
G06N 3/08
G06N 3/04
G06F 17/18
Abstract:
Described is an attack system for generating perturbations of input signals in a recurrent neural network (RNN) based target system using a deep reinforcement learning agent to generate the perturbations. The attack system trains a reinforcement learning agent to determine a magnitude of a perturbation with which to attack the RNN based target system. A perturbed input sensor signal having the determined magnitude is generated and presented to the RNN based target system such that the RNN based target system produces an altered output in response to the perturbed input sensor signal. The system identifies a failure mode of the RNN based target system using the altered output.

Solving Based Introspection To Augment The Training Of Reinforcement Learning Agents For Control And Planning On Robots And Autonomous Vehicles

US Patent:
2020022, Jul 16, 2020
Filed:
Nov 21, 2019
Appl. No.:
16/691446
Inventors:
- Malibu CA, US
Christopher Serrano - Glendora CA, US
International Classification:
G06N 3/08
Abstract:
Described is a system for controlling a mobile platform. A neural network that runs on the mobile platform is trained based on a current state of the mobile platform. A Satisfiability Modulo Theories (SMT) solver capable of reasoning over non-linear activation functions is periodically queried to obtain examples of states satisfying specified constraints of the mobile platform. The neural network is then trained on the examples of states. Following training on the examples of states, the neural network selects an action to be performed by the mobile platform in its environment. Finally, the system causes the mobile platform to perform the selected action in its environment.

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