Angelica Hjelm Gardner

Simple [A]

Software Developer Intern • Jan 2019 — Jun 2019

• Remote project-based internship for higher education diploma thesis.
• Developed a content-based recommender module using GraphQL API in ASP.NET with a DotnetSpider crawler collecting data from website articles, a BoW model extracting information and storing it in a Neo4j graph database.
• The module supported a chatbot system during fallback intent to improve the user experience.

Mjukhud.se

Junior Web Developer • Jan 2015 — Dec 2016

• Developed a classic WordPress blog theme using HTML5, CSS3, JavaScript and PHP according to WordPress coding standards. Responsible for monitoring theme and plugin updates.
• Improved the responsiveness of the Magento 1 e-commerce theme using CSS media queries, as large parts of the website were still desktop-only.
• Implemented Magento security patches by replacing the changed files via FTP.
• Identified UX and performance issues through user feedback and PageSpeed Insights reports.

Linnaeus University

Master of Science - MS, Software Technology • 2020 — 2022

• Degree Project (Grade A) - Conducted ML experiments in Jupyter notebooks to recognise pain, estimate intensity, and classify painful body areas in adults based on body movements and facial expressions. I used a hybrid CNN-BidirectionalLSTM and a recurrent CNN (RCNN) with Keras Functional API (Tensorflow 2).

Linnaeus University

Bachelor of Science - BS, Computer Science • 2019 — 2020

• Degree Project (Grade A) - Conducted ML experiments with Sacred to compare the performance in detecting deepfakes between ensembles of CNNs and individual models. I used the Deepfake Detection Challenge dataset from Kaggle and the following CNN architectures in PyTorch; VGG16, VGG19, ResNet50 and Xception.

Mid Sweden University

Higher Education Diploma with specialization in Computer Engineering • 2017 — 2019

• Degree Project (Grade A) - Developed a content-based recommendation module with four components; (1) web spider using DotnetSpider, (2) bag-of-words model using ML.NET, (3) Neo4j graph database and (4) GraphQL API to which the chatbot agent connects via Dialogflow's webhook service.

Visual Explorer

Python, Flask, SQLite, React.js, Plotly.js, Docker Compose

Visual Analytics course project using a dataset from VAST challenge 2019. This tool allows a human user to analyse messages from a social media network to detect signs of an earthquake. I was responsible for the ML backend, but also worked on some frontend tasks, such as implementing the message table.

Languages, libraries, frameworks

HTML, CSS, JavaScript, React.js, Python, Flask

Technologies and tools

MySQL, Git

Other

Data structures and algorithms