Radu joined UNM's School of Computer Science in October 2019. Previously he was a post-doctoral researcher in Software Engineering at Technical University Darmstadt, Germany. Radu obtained his Ph.D. in Computer Science in 2013 in the DistriNet Research Group at KU Leuven, Belgium. His PhD research focused on novel modelling language features for supporting software variability, software product lines, feature models and dynamic software updating. Radu obtained his M.Sc. in Computer Science from Victoria University of Wellington, New Zealand in 2009 and his B.Sc. from Hochschule München, Germany in 2006. He has worked as a software engineer in industry for several years.
My core research interest is in designing language concepts for programming, specification and modelling that aim at making the software developing process more efficient and the resulting software safe and trustworthy.
Applications of my research include development of advanced software engineering tools such as compilers and program analysers. These are used as part of the software development life cycle, but also in other engineering disciplines where simulation and analysis of complex behaviour is of paramount importance, such as railway operations and synthetic biology.
I am further invested in building international cooperation in higher education, e.g. through research and teaching exchanges and the development of joint degree programs.
In the current academic year (2021-2022) I am teaching:
- Software Quality Assurance (3rd year), Fall semester
- Compilers (3rd year), Fall Semester, jointly with Dr. Tomas Maul
- Software Engineering (1st year), Spring semester
In previous academic years I have also taught:
- Software Engineering Management (postgraduate module), 2019-2020
Final Year Project topics (academic year 2022-2023)
If you already have a well-thought-out project idea in mind, I would be happy to discuss a potential supervision with you. I am interested in topics in the following areas:
- Software Engineering
- Programming Languages
- Software Modelling and Simulation
- Computational Biology
- Applications of Machine Learning to above topics
I am looking for topics in these areas that have a research component and novelty factor. The outcome should have scientific value and should be potentially publishable. Conversely, I am not interested to supervise topics that are purely about developing a software prototype (web app, mobile app, etc.) that replicates or repackages already existing ideas.
Furthermore, I am currently looking for one motivated student to work on the topic described below. Note that this is challenging project at the intersection of informatics and genomics, and requires above-average dedication and commitment.
When a protein is needed by a cell, copies of the gene for that protein are made into mRNA and these "mRNA transcripts", as they are called, are sent to the ribosomes on the endoplasmic reticulum to be used as templates for the synthesis of the protein. However, the expression of the gene into mRNA copies involves the activation of the cell's "transcriptional machinery". That is to say, the activation of the cell's transcriptional machinery is what is responsible for the process of making mRNA copies of the gene. This transcriptional machinery primarily involves the binding of specific proteins called transcription factors (TFs) to a stretch of sequence adjacent to the gene called the "promoter sequence". The binding of the TFs to the promoter will set in motion a catalytic activity that churns out the mRNA copies of the gene. However, there are also other regulatory elements called "enhancers" and "silencers" that can, as their name suggest, either increase or decrease the number of copies of mRNA made. Enhancers and silencers are generally located a long way away from the gene that they control and therefore, it is often difficult to identify which enhancer controls which gene. Moreover, enhancers have no distinguishable features so that makes identifying their location in the genome difficult, let alone identifying which gene they control. In the past year, we have made an effort to identify the location of enhancers in the genome and now have a catalogue of more than 2 million candidate enhancers. The current project hopes to leverage on this success to identify the genes that these enhancers control.
A. C. ACHDA, A. AZURAT, R. MUSCHEVICI and M. R. A. SETYAUTAMI, 2017. Extending the automated feature model analysis capability of the abstract behavioral specification In: icacsis. 453-458
FERRUCCIO DAMIANI, MICHAEL LIENHARDT, RADU MUSCHEVICI and INA SCHAEFER, 2017. An Extension of the ABS Toolchain with a Mechanism for Type Checking SPLs In: ifm. 111-126
NAILY, MOH. AFIFUN, SETYAUTAMI, MAYA RETNO AYU, MUSCHEVICI, RADU and AZURAT, ADE, 2017. A Framework for Modelling Variable Microservices as Software Product Lines In: sefm. 246-261
HÄHNLE, REINER and MUSCHEVICI, RADU, 2016. Towards Incremental Validation of Railway Systems In: isola. 433-446