Hi, I am Anna-Lena

Anna-Lena Popkes

Machine Learning Engineer at inovex

I am an enthusiastic learner who is always looking for new projects and challenges. I am deeply fascinated by machine learning and its applications in questions that affect and benefit many people.

ML
Research
Engineering
Teaching

Skills

Experiences

1
Machine Learning Engineer
inovex

02/2020 - Present, Cologne

Responsibilities:
  • Working on applied data science and data engineering projects
Voluntary work
KI macht Schule

05/2019 - Present, Everywhere in Germany

KI macht Schule offers free courses on AI and ML to German school kids. Our goal is to educate the next generation on these important topics, allowing them to understand and discuss new developments and challenges.

Responsibilities:
  • Being a course instructor
  • Coordinate school requests
  • Develop and prepare courses
2
3
Machine Learning Engineer (AI Resident)
Microsoft Research, Cambridge, UK

09/2018 - 10/2019, Cambridge, UK

Responsibilities:
  • One of the first participants in the Microsoft AI Residency program in Cambridge, UK.
  • The AI Residency is an educational program, focused on teaching skills necessary for becoming an excellent research/machine learning engineer.
  • Within the program I worked in the role of a full-time research engineer, applying machine learning to real-world problems.
Deep Learning Intern
Bosch Center for Artificial Intelligence

05/2018 - 10/2018, Palo Alto, California, USA

Responsibilities:
  • Work of deep learning projects, mostly focused on computer vision applications and image recognition tasks
4
5
Machine Learning Research Assistant
Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS)

09/2016 - 05/2018, Sankt Augustin, Germany

Responsibilities:
  • Research and industrial projects on machine learning, especially neural networks and reinforcement learning
Research assistant
Harvard Medical School

08/2014 - 11/2014, Cambridge, USA

Responsibilities:
  • Research assistant at Prof. Nadine Gaab’s laboratory for developmental cognitive neuroscience
  • Investigation of perceptual, cognitive and brain development in typically developing infants and children, as well as children diagnosed with or at risk for various developmental disorders
6

Education

University of Bonn
2015-2018
Master's Degree Computer Science
GPA: 4 out of 4
Description
  • The University of Bonn is one of eleven German Universities of Excellence, the only university with six Clusters of Excellence and more Nobel Prize winners and Fields Medalists have emerged from from it in the past decades than from any other German university.
  • I gained deep theoretical knowledge about Artificial Intelligence and Machine Learning via the intelligent systems focus track.
Osnabrück University
2012-2015
Bachelor's Degree Cognitive Science
GPA: 4 out of 4
Description
  • Focus modules were Neuroinformatics, Computer Science, Neuroscience, Cognitive (Neuro-) Psychology, Philosophy of Mind and Cognition

Projects

ML Basics
ML Basics
Owner Feb 2018 - Present

Plain python implementations of basic machine learning algorithms

Star
Magical Universe
Magical Universe
Owner August 2018 - Present

Awesome Python features explained using the world of magic

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Horseshoe Bayesian Neural Network
Horseshoe Bayesian Neural Network
Contributor July 2018

A Bayesian Neural Network with a horseshoe prior for improved interpretability

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Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Author May 2020

Clinical decision making is challenging because of pathological complexity, as well as large amounts of heterogeneousdata generated as part of routine clinical care. In recent years,machine learning tools have been developed to aid this process […]

Details
LSTM Language Model
LSTM Language Model
Owner Feb 2018

LSTM-based language model in TensorFlow

Star
Language Modeling with Recurrent Neural Networks - Using Transfer Learning to Perform Radiological Sentence Completion
Author March 2018

Motivated by the potential benefits of a system that accelerates the process of writing radiological reports, we present a Recurrent Neural Network Language Model for modeling radiological language […]

Details
Tensorflow deep learning models
Tensorflow deep learning models
Owner April 2017

TensorFlow implementations of several deep learning models (e.g. variational autoencoder, RNN)

Star
PyTorch deep learning models
PyTorch deep learning models
Owner July 2018

PyTorch implementations of deep learning models

Star

Talks & Podcasts

Testing without dependencies, mocking in Python
Testing without dependencies, mocking in Python
Talk Python To Me Oct 21, 2020

I’m a guest at “Talk Python to Me” to talk about TDD and mocking in Python.

Go to Episode
Finding Magic in Python - Keynote at PyCon Estonia
Finding Magic in Python - Keynote at PyCon Estonia
PyCon Estonia Oct 13, 2020

Some of Python’s exciting features explained using the world of magic.

Go to Talk
100 Days of Python in a Magical Universe
100 Days of Python in a Magical Universe
Talk Python To Me Nov 16, 2018

I’m joining Michael on “Talk Python to Me” to talk about my 100 days of code challenge for which I created a magical Python universe.

Go to Episode
Dropping by on the Python Bytes Podcast
Dropping by on the Python Bytes Podcast
Python Bytes September 11, 2020

I’m a guest at “Python Bytes” to talk about recent Python developer news.

Go to Episode
Mocking in Python
Mocking in Python
Test & Code September 28, 2020

I joined the “Test & Code” podcast to talk about TDD and mocking in Python.

Go to Episode

Recent Posts

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Support vector machines

I posted another notebook in my machine learning basics repository. This time, I took a detailed look at support vector machines. The blog post below contains the same content as the original notebook. You can run the notebook directly in your Browser using Binder. 1. What are support vector machines? Support vector machines (short: SVMs) are supervised machine learning models. They are the most prominent member of the class of kernel methods.

April 13, 2021 Read
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Personal reading list of non-fiction books

Books I am currently reading Atomic Habits by James Clear Link to book Books I have read A Promised Land by Barack Obama (Link to book) The Alchemy of Air by Thomas Hager (Link to book) Superintelligence: Paths, Dangers, Strategies by Nick Bostrom (Link to book) Beethoven, A Life by Jan Caeyers (Link to book) Deep Work: Rules for Focused Success in a Distracted World by Cal Newport (Link to book)

April 6, 2021 Read
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Bayesian linear regression

I finally found time to continue working on my machine learning basics repository which implements fundamental machine learning algorithms in plain Python. Especially, I took a detailed look at Bayesian linear regression. The blog post below contains the same content as the original notebook. You can run the notebook directly in your Browser using Binder. 1. What is Bayesian linear regression (BLR)? Bayesian linear regression is the Bayesian interpretation of linear regression.

February 20, 2021 Read