Dr. Palle Villesen

Assistant Professor

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Biography

Palle Villesen is an Associate Professor at Aarhus University with over 20 years of experience in bioinformatics. His research primarily focuses on applying machine learning techniques to medical fields, particularly through large-scale methods like metabolomics. He has a strong background in population biology, genetics, and genome evolution analysis. Villesen also collaborates with various universities, including Sheffield and Linköping, as well as industry partners on student projects, emphasizing the importance of data-driven research to improve societal outcomes. He is dedicated to teaching at the Bachelor, Master, and PhD levels, focusing on high-performance computing, data science, and machine learning. Villesen is actively engaged in developing university courses and continuously seeks to enhance educational experiences by incorporating new tools such as generative AI. His work spans bioinformatics, statistics, AI, and data science, with significant contributions to understanding molecular evolution, gene expression analysis, and evolutionary networks.

Research Interests

Experience

Associate Professor

2001-01-01 — Present

Aarhus University • Aarhus C, Denmark

Involved in bioinformatics and applied statistics, focusing on large-scale data analysis related to medicine.

Awards

#

Student Competition Triennial IOFOS Congress Forensic Odonto-Stomatology

2023-09-01

Courses

High Performance Computing Data Science Machine Learning

Requirements for Aarhus University

Master Program
Requirements
GPA Requirement
Required:3
IELTS
Overall
Required:6.5
TOEFL
Total
Required:83
Prerequisites
Bachelor's degree in a relevant field Specific credit requirements in programming, algorithms, and computer systems
Application Checklist
  • Official Bachelor's degree certificate
  • Official transcripts of records
  • Course descriptions
  • Curriculum Vitae (CV)
  • Documentation of English language proficiency
Specialization Notes

Department of Computer Science offers tracks in Software Efficiency, Cryptography, and Data Science.