Workshops
Workshop 1: Collaborative Learning in Computer Science Higher Education: Tips, Tricks, and Tools
Description:
Collaborative assignments have become a cornerstone of computer science education—reflecting both the realities of modern-day work in
data science and software engineering and offering a practical solution for managing large student cohorts. However, the implementation
of group work often proves challenging, particularly in international settings. Differences in project prioritization,
unequal participation (e.g., "low-riders" relying on more engaged peers), and difficulties in fairly assessing individual
contributions frequently emerge.
In this interactive workshop, we will explore various models of collaboration in student assignments, examine their effects on
learning outcomes, and share strategies for transparent and fair assessment. We will also discuss how software tools can support
and enhance the collaborative learning experience, making it more effective and equitable for all students.
Resource Person: Prof. Alexandra Blank - Leiden University, The Netherlands
M.Ed. Alexandra Blank supports MSc Computer Science students and PhD candidates at the Leiden Institute of Advanced Computer Science (LIACS) of Leiden University, The Netherlands, in their personal and professional development. In addition to her coaching role, she coordinates the Master Class, a course designed to prepare master's students for successfully completing their thesis research projects.
Date & Time: Friday, 21st November 2025, 15.00 - 16.30 hours.
Venue: JRDC
Workshop Fee: TBD
Workshop 2: Computational Biology: Predicting Structure and Function of Proteins
Description:
Since the sequencing of the human genome, biology has become a source of challenging inference problems. Several recent advances in
machine learning and computational modelling have been applied to biological problems at the levels of molecular sequences,
three-dimensional structures and in the prediction of their functions. This workshop will introduce participants to some of the
challenging problems to which machine learning techniques have been applied, with the focus on predicting the structure and function
of proteins.
Content:
- Introduction to Neural Networks and Machine Learning
- Proteins: Sequence, structure and function
- Predicting protein structures — architecture of AlphaFold
- Hands-on work on predicting protein secondary structure
- Discussion
Resource Persons: Prof. Mahasen Niranjan, University of Southampton, UK & Dr. Rupika Wijesinghe, University of Colombo School of Computing, Sri Lanka.
Mahesan Niranjan is professor in the School of Electronics and Computer Science at the University of Southampton, UK. Prior to this appointment he has served as Lecturer in Information Engineering at the University of Cambridge and as Professor of Computer Science at the University of Sheffield. At Sheffield, he has also served as Head of the Department of Computer Science and as Dean of the Faculty of Engineering. He graduated from the University of Peradeniya, Sri Lanka, holds a Masters’ degree from Eindhoven, The Netherlands, both in Electronics Engineering, and a PhD from The University of Cambridge, England in Information Engineering. His research interests are in the field of Machine Learning, in recent times he has focused on biological and medical applications.
Dr. Rupika Wijesinghe is a Senior Lecturer at the University of Colombo School of Computing (UCSC), Sri Lanka, and Programme Co-Coordinator of the M.Sc. in Bioinformatics. Her research focuses on bioinformatics workflows, semantic technologies, and tools that make complex computational analyses more accessible to domain scientists. She has contributed to advancing bioinformatics education in Sri Lanka through curriculum development and postgraduate training. In addition, she collaborates on interdisciplinary projects connecting computing with molecular biology and biotechnology.