Julian Kohne

Julian Kohne

Scientific Advisor / PhD Student

GESIS

Ulm University

What I do

As a scientific advisor at GESIS, I am coordinating our efforts to establish new services for social scientists to collect, process, analyze, and get access to digital behavioral data. The term refers to all traces of human behavior that are created or can be made accessible through the use of digital technologies. This includes for example data from social media, webtracking, smartphones, contact sensors or other smart devices. In combination with high quality survey data, these types of data allow us to study the digitization of society, new digital phenomena, and established social science questions from new perspectives!

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, ethical and secure data donation, 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

Skills

Social Psychology

Expert

R

Expert

Statistics

Advanced

Shiny

Advanced

Machine Learning

Enthusiast

Git

Enthusiast

Experience

and previous positions

 
 
 
 
 
PhD Student
Sep 2020 – Present Cologne
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. The project is conducted in collaboration with the Stanford Social Media Lab
 
 
 
 
 
Scientific Advisor for Digial Behavioral Data
Oct 2017 – Present Cologne
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
Apr 2017 – Jul 2017 Cologne
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
Oct 2015 – Oct 2016 Groningen
Evaluation of the „Buurkracht“ project by Enexis, a quantitative long-term study of a bottom-up energy conservation initiative.
 
 
 
 
 
Research Assistant
Oct 2014 – Jun 2015 Groningen
Questionnaire design and maintenance in Qualtrics, statistical analyses in R and SPSS

Certificates

and professional development

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
See certificate
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 certificate
Machine 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 certificate

Contact

  • julian.kohne[at]gesis.org
  • +49 (0221) 47694-222
  • Tweet me