Person

Dipl.-Inform.

Matthias Rüdiger

M.Sc.

Research Associate

Matthias Rüdiger
Institute for Technology and Innovation Management (TIM/ISO)

Address

Building: 3011

Room: B342

Kackertstraße 7

52072 Aachen

Contact

workPhone
Phone: +49 241 80 99187
Fax: +49 241 80 699197

Consultation Hours

By appointment only
 

Responsibilities

  • Research at the Interface of Data Mining/Text Mining, Bibliometrics and Science Studies as well as Innovation Research
  • BMBF-supported Research Project InnoMetrics - Development of innovation metrics for measuring novelty based on real-time text mining methods​
  • BMBF-supported Research Project IMPRO - Fully automated content analysis of citation environments bringing bibliometrics and text mining together.
  • Coaching and supervision of students
  • Courses: Quantitative Innovation Research (QIR), Introduction to Business Administration (IBA)

Profile

  • Research Associate at the Institute for Technology and Innovation Management (TIM) since October 2014
  • Doctoral Studies at the Faculty of Business and Economics (Dr. rer. pol.) at RWTH Aachen University. Dissertation title: "Essays on Text Mining: Methodological Advances and Practical Applications to Scientific Texts"
  • Studies of Management, Business and Economics (M.Sc.)
  • Studies of Computer Science (Dipl.-Inform.) with majors in Datamining and Dataexploration, Networks and Communication, Graphtheory and Efficient Algorithms at RWTH Aachen University
  • Working Experience as a Founder in the Field of Application Development & Network Services
  • Student Assistant at the Technology and Innovation Management Group and the Data Management and Data Exploration Group at RWTH Aachen University

Publications

  • Matthias Rüdiger, David Antons, and Torsten-Oliver Salge (2018): Unpacking Impact in Science: The Explanatory Power of Citations. Proceedings of the International Conference on Information Systems (ICIS), San Francisco
  • Matthias Rüdiger, David Antons, and Torsten-Oliver Salge (2017): From Text to Data: On the Role and Effect of Text Pre-Processing in Text Mining Research. Accepted for Presentation at the Academy of Management Meeting (AOM), Atlanta
  • Stephan Günnemann, Ines Färber, Matthias Rüdiger and Thomas Seidl (2014): SMVC - Semi-Supervised Multi-View Clustering in Subspace Projections. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), New York

Research Interests

  • Information Systems
  • Data Mining and Text Mining
  • Natural Language Processing (NLP) and Computational Linguistics

Research Methodology

  • Analytical Technics: Quantitative Data Analysis, Text Mining, NLP
  • Programming languages, Libraries and Tools: Python, Scikit-Learn, NumPy, SciPy, Matplotlib/Seaborn, TensorFlow, Keras, Gensim, Java, PHP, JavaScript, XML, SQL, Stata