Staff Software Engineer | Computer Vision and Machine Learning
2017 - 2020Lyft- Applied deep learning and computer vision algorithms to solve autonomous driving problems.
- Led the design and development of custom deep learning-based multi-object trackers used by multiple teams to track cars, cyclists, pedestrians, and traffic control elements.
- Designed the initial production release of the active learning data pipeline for continuous machine learning model improvement.
Technologies: Amazon Web Services (AWS), Object Detection, Deep Learning, AWS, OpenCV, Python, TensorFlow, PyTorchArtificial Intelligence Fellow
2017 - 2017Insight Data Science- Developed a Computer Vision system based on a convolutional neural network that automatically annotates post-hurricane flooded roads on satellite imagery.
- Developed an open-source project on GitHub.
- Researched and published a blog post about the use of deep learning for disaster recovery.
Technologies: Keras, Semantics, Deep LearningStaff Software Engineer
2015 - 2017LinkedIn- Helped build LinkedIn's video platform from scratch.
- Wrote a technical blog post on building a Native video player library for Android.
Technologies: Devices, Mobile, Video Codecs, JavaSoftware Architect
2011 - 2015Trulia- Scaled our mobile platform to over a dozen apps across iOS, and Android. This grew the audience over ten times. Trulia mobile apps are top-rated by users, and frequently selected for prestigious editorial features by Apple, Google Android (Editors' Choice, and Staff Pick), Amazon, and Samsung.
- Developed and built projects for iOS and Android involving geofencing and geospatial data, user data synchronization, cross-platform communication, and natural language processing.
Technologies: REST APIs, iOS, AndroidTechnical Leader
2006 - 2011Cisco- Led the architectural designs of Cisco Jabber (an enterprise VoIP app) on Android. Worked with product managers to translate business needs into technical specifications. Worked with cross-functional teams to define requirements, interfaces, and implementation approaches, and led a group of six talented Android developers to build out layered components of the application from scratch.
- Extended the Blackberry client with innovative voice features, coordinated development activities, and mentored junior engineers.
Technologies: CODE, VoIP, AndroidSenior Software Engineer
2004 - 2006Weathernews Americas- Developed the prototype, architecture, design, and implementation of Weathernews LiveLocal. LiveLocal is the US’s first streaming video application bringing videos from local broadcast TV stations to cell phones.
Technologies: Videos, Mobile
Jack Kwok
Deep Learning Developer in San Francisco, CA, United States
Member since May 23, 2019
Jack has over a dozen years of experience developing complex software systems. He is experienced in applying deep learning techniques to solve Computer Vision problems such as object detection, multi-object tracking, and ND semantic segmentation, to name a few. Jack has worked on state-of-the-art Computer Vision models to solve autonomous driving problems. He is also fluent in traditional ML techniques and has an expert rating at Kaggle.
Jack is now available for hire
Portfolio
- LyftAmazon Web Services (AWS), Object Detection, Deep Learning, AWS, OpenCV...
- Insight Data ScienceKeras, Semantics, Deep Learning
- LinkedInDevices, Mobile, Video Codecs, Java
Experience
- Python 7 years
- Machine Learning 5 years
- Deep Learning 5 years
- Computer Vision 4 years
- PyTorch 3 years
- Object Detection 3 years
- Object Tracking 2 years
- Natural Language Processing (NLP) 1 year
Availability
Part-time
Preferred Environment
Amazon SageMaker, Google Cloud ML, Amazon Web Services (AWS), Scikit-learn, AWS, Windows, Linux, PyTorch, Python
The most amazing...
...project I have worked on involved building a neural network from scratch to recognize handwritten digits. It led me to pursue a career in deep learning.
Employment
Experience
- Deep Learning for Disaster Recoveryhttps://github.com/jackkwok/neural-road-inspector
I attempted to automatically annotate flooded, washed out, or otherwise severely damaged roads using state-of-the-art deep learning methods. My goal was to create a tool to help detect and visualize anomalous roads in a simple user interface.
For details, see the blog post:
https://blog.insightdatascience.com/deep-learning-for-disaster-recovery-45c8cd174d7a
Skills
Libraries/APIs
PyTorch, OpenCV, Scikit-learn, NumPy, Keras, Pandas, SciPy, REST APIs, TensorFlowTools
Scikit-image, Amazon SageMakerOther
Computer Vision Algorithms, Deep Learning, Computer Vision, Object Detection, Object Tracking, Image Processing, Machine Learning, Linear Algebra, Convolutional Neural Networks, AWS, Devices, Videos, Google Cloud ML, Video Codecs, Recurrent Neural Networks, Natural Language Processing (NLP)Languages
Python, JavaPlatforms
Google Cloud Platform (GCP), Linux, Windows, Mobile, Android, iOS, Amazon Web Services (AWS), Kubernetes
Education
- Master of Engineering Degree in Computer Science2001 - 2002Massachusetts Institute of Technology - Cambridge, MA
- Bachelor of Science Degree in Computer Science1997 - 2001Massachusetts Institute of Technology - Cambridge, MA
- Bachelor of Science Degree in Mathematics1997 - 2001Massachusetts Institute of Technology - Cambridge, MA
Certifications
- Google Cloud Platform Professional Machine Learning EngineerMARCH 2021 - PRESENTGoogle
