What I do
At GESIS, I’m part of the Designed Digital Data team, focusing on providing an easy to use, transparent and secure infrastructure for collecting mobile survey and smartphone usage data for social science research.
As a PhD student at Ulm University, I am part of the molecular psychology lab and investigate how interpersonal relationships can be quantified using chat logs, specifically donated WhatsApp chat logs. I am developing interactive methods for transparent data donations, and investigate how social relationships are expressed through different communication patterns.
Download my Curriculum Vitae.
Interests
- Computational Social Science
- Interpersonal Relationships
- Data Science
- Text as Data
- Group Dynamics
- Social Networks
Education
PhD in Psychology, in progress
Ulm University
M.Sc in Social & Organizational Psychology, 2016
University of Groningen
B.Sc in Psychology, 2014
University of Groningen
My Research
and other projects
Skills
Social Psychology
Expert
R
Expert
Statistics
Advanced
Shiny
Advanced
Machine Learning
Enthusiast
Git
Enthusiast
Experience
and previous positions
Scientific Staff Member
Establishing the GESIS AppKit as a new Infrastructure for collecting mobile phone survey and sensor data.
PhD Student
In my PhD project, I am investigating how interpersonal relationships can be quantified using chat logs, specifically donated WhatsApp chat logs. I am developing interactive methods for transparent, ethical and secure data donation, and investigate how social relationships are expressed through different communication patterns.
Scientific Advisor for Digial Behavioral Data
Coordination of our institute wide efforts to expand the GESIS service portfolio to digital
behavioral data, conceptualization of new services and acquisition of third-party funding. The position includes a component for research in data and web science.
Computational Social Science Internship
Analysis of text and metadata on token-level on a dataset of 1.3 million Wikipedia revisions.
The goal was to quantify the learning curves of editors.
Scientific Traineeship
Evaluation of the „Buurkracht“ project by Enexis, a quantitative long-term study of a bottom-up energy conservation initiative.
Research Assistant
Questionnaire design and maintenance in Qualtrics, statistical analyses in R and SPSS
Certificates
and professional development
Introduction to Deep Learning in R
Workshop Contents:
- Deep Learning and Logit Regression
- Hyperparameter tuning
- Deep learning applictions with Keras in R
- Recurrent Neural Networks and LSTM Neural Networks
- Convolutional Neural Networks
Summer Institute in Computational Social Science
Workshop Contents:
- Lectures, group problem sets, participant-led research projects
- Collection of Digital Trace Data
- Automated Text Analysis
- Transparent and reproducible Research
- Ethics
GDPR - In-depth Workshop
Workshop Contents:
- Research Priviliges in Data Protection Law
- Deletion of Data
- Pseudonimization & Anonimization
- Secondary Use
- Research Data Management
- Practical Examples
GDPR - Basics for dealing with research data
Workshop Contents:
- Overview of GPDR Guidelines
- Informed Consent
- Priviliges of Research Projects
- Personal, anonymous and pseudonomous data
Project Manager (IHK)
Workshop Contents:
- Project management according to DIN 69901
- Types of project organization
- Cost and resource management
- Working with specification sheets
- Time management
Big Data - Introduction to Data Science with Python
Workshop Contents:
- The Python Data Science Stack
- Data Exploration and Preprocessing
- Web Data Acquisition
- Data visualization
- Machine Learning
LaTeX
Workshop Contents:
- Scientific Papers in LaTeX
- Bibliographies
- Tables
- Equations
- LaTeX with R and Stata
- LaTeX for posters and presentations
Practical Introduction to Text Mining
Workshop Contents:
- Computer-assisted text coding
- Metadata evaluation
- Linguistic preprocessing
- Lexicometrics
- Topic Models
- Methodological Integration
An Introduction to R Markdown
Workshop Contents:
- Introduction to R Markdown
- Writing scienctific papers in R Markdown
- Citations in R Markdown
The Data Scientists Toolbox
In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
See certificateMachine Learning
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
See certificateContact
- julian.kohne[at]gesis.org
- +49 (0221) 47694-222
- Tweet me