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-rw-r--r--content/_index.md3
-rw-r--r--content/cv.md89
-rw-r--r--static/style.css30
3 files changed, 55 insertions, 67 deletions
diff --git a/content/_index.md b/content/_index.md
index c57b808..7fbe911 100644
--- a/content/_index.md
+++ b/content/_index.md
@@ -1,6 +1,6 @@
---
title: "Gustaf Rydholm's Webpage"
-description: "tbc"
+description: "Landing Page"
---
## Introduction
@@ -14,6 +14,7 @@ book reviews/notes, cv, and digital contact information.
### On this website...
- [Projects](/projects)
+- [Notes](/notes)
- [CV](/cv)
- [Contact](/contact)
diff --git a/content/cv.md b/content/cv.md
index bf01d14..812cbf8 100644
--- a/content/cv.md
+++ b/content/cv.md
@@ -134,51 +134,50 @@ tbc...
*Keywords: Microservices, Kubernetes, Infrastructure, Helm, CI/CD, Kotlin,
Spring, AWS, Terraform*
-- Machine Learning Engineer. Saab AB. Aug 2018 -- Aug 2021
- - Intelligence Application. Aug 2020 -- Aug 2021
- - Created data mining pipelines for extracting, cleaning, and creating
- datasets for machine learning models, i.e. train/test sets.
- - Was a driving force in making the system more asynchronous using
- message passing between microservices, by deploying and maintaining a
- Kafka instance. This improve the architecture by allowing multiple
- services to subscribe to incoming sensor data and process the
- information, instead of fetching the data from databases in batches.
- - Developed a modular pipeline for training and evaluating deep learning
- models with different architectures and/or losses. Automatic extraction
- of the best model based on user defined metric, ready for serving.
- - Built and deployed deep learning models for multi-modal trajectory
- predictions in production.
- - Took initiative and deployed and maintained a private Python Package
- Index (PyPI) for all developers. Greatly improving the development
- workflow, e.g. forcing versioning, and reducing/eliminating cross
- dependencies between locally developed Python packages.
- - Developed a graph algorithm for sensor fusion. Deployed it as a
- microservice listing to incoming sensor data. This enabled more complex
- pattern analysis in downstream services.
- - Reduced the docker image size of the Python microservices from ~2 GB
- to ~73 MB by utilizing multistage builds and alpine base images.
- - Built pipelines for CI/CD and packages deployment in Tekton.
- - With my docker images and pipelines we where able to reduce the
- average build times from ~10-30 minutes down to seconds, mostly thanks
- to improved caching capabilities.
-
- *Keywords: Deep Learning, Python, Microservices, Kubernetes,
- Infrastructure, Helm, CI/CD*
-
- - Radar Warning Receiver. Aug 2018 -- Aug 2020
- - Built simulation software for generating realistic signal
- environments with both radar and/or communication signals. Implemented
- the most common signal encoding for communications, as well as
- basic to SOTA radar modulations. This enabled the team to develop and
- evaluate different machine learning models and ideas.
- - Researched machine learning models in different stages of the radar
- warning receiver, with regards to compute and data limitations.
- - Held in several presentations of machine learning papers in a company
- reading group.
- - Shared the knowledge of implementing and using machine learning to
- multiple business areas within Saab.
-
- *Keywords: Deep Learning, Signal Processing, Python, VHDL*
+- Machine Learning Engineer (Cyber Security). Saab AB. Aug 2020 -- Aug 2021
+ - Created data mining pipelines for extracting, cleaning, and creating
+ datasets for machine learning models, i.e. train/test sets.
+ - Was a driving force in making the system more asynchronous using
+ message passing between microservices, by deploying and maintaining a
+ Kafka instance. This improve the architecture by allowing multiple
+ services to subscribe to incoming sensor data and process the
+ information, instead of fetching the data from databases in batches.
+ - Developed a modular pipeline for training and evaluating deep learning
+ models with different architectures and/or losses. Automatic extraction
+ of the best model based on user defined metric, ready for serving.
+ - Built and deployed deep learning models for multi-modal trajectory
+ predictions in production.
+ - Took initiative and deployed and maintained a private Python Package
+ Index (PyPI) for all developers. Greatly improving the development
+ workflow, e.g. forcing versioning, and reducing/eliminating cross
+ dependencies between locally developed Python packages.
+ - Developed a graph algorithm for sensor fusion. Deployed it as a
+ microservice listing to incoming sensor data. This enabled more complex
+ pattern analysis in downstream services.
+ - Reduced the docker image size of the Python microservices from ~2 GB
+ to ~73 MB by utilizing multistage builds and alpine base images.
+ - Built pipelines for CI/CD and packages deployment in Tekton.
+ - With my docker images and pipelines we where able to reduce the
+ average build times from ~10-30 minutes down to seconds, mostly thanks
+ to improved caching capabilities.
+
+ *Keywords: Deep Learning, Python, Microservices, Kubernetes,
+ Infrastructure, Helm, CI/CD*
+
+- Machine Learning Engineer (Surveillance). Saab AB. Aug 2018 -- Aug 2020
+ - Built simulation software for generating realistic signal
+ environments with both radar and/or communication signals. Implemented
+ the most common signal encoding for communications, as well as
+ basic to SOTA radar modulations. This enabled the team to develop and
+ evaluate different machine learning models and ideas.
+ - Researched machine learning models in different stages of the radar
+ warning receiver, with regards to compute and data limitations.
+ - Held in several presentations of machine learning papers in a company
+ reading group.
+ - Shared the knowledge of implementing and using machine learning to
+ multiple business areas within Saab.
+
+ *Keywords: Deep Learning, Signal Processing, Python, VHDL*
### Institutions
diff --git a/static/style.css b/static/style.css
index 87b1b38..d2df580 100644
--- a/static/style.css
+++ b/static/style.css
@@ -1,7 +1,7 @@
body {
- background: #073642 ;
- color: brown ;
- max-width: 950px ;
+ background: #151515 ;
+ color: #E1E1E1 ;
+ max-width: 750px ;
margin: auto ;
padding: 0 16px ;
margin-bottom: 500px ;
@@ -10,23 +10,15 @@ body {
}
main {
- max-width: 950px ;
+ max-width: 750px ;
margin: auto ;
}
main > article {
- background: #EEE8D5 ;
- color: #151515 ;
+ background: #151515 ;
+ color: #E1E1E1 ;
margin-bottom: 0 ;
- padding-top: 4em ;
- padding-left: 8em ;
- padding-right: 8em ;
- padding-bottom: 4em ;
- border-color: #1e4e76 ;
- border-width: 0.25em 0.25em 0.25em .25em ;
- border-style: solid ;
text-align: justify;
- border-radius: 0 ;
margin: auto ;
}
@@ -57,20 +49,16 @@ ul {
footer {
max-width: 300px ;
- background: #EEE8D5 ;
+ background: #151515 ;
margin-left: auto ;
margin-right: auto ;
- margin-top: -4px ;
+ margin-top: 6px ;
text-align: center ;
clear: both ;
- border-color: #1e4e76 ;
- border-width: 0em 0.25em 0.25em 0.25em ;
- border-style: solid ;
- border-radius: 0 0 0 0 ;
}
code {
- color: #373737 ;
+ color: #F4BF75 ;
overflow-wrap: break-word ;
font-size: 10pt ;
}