Coming from an Information Science background, my research interests have been interdisciplinary in nature. In 2012 I completed a Bachelor of Applied Science (Hons) in Software Engineering at the University of Otago with my dissertation focusing on Genetic Programming. Following this, in 2015 I began my PhD with the Department of Information Science and started to take a significant interdisciplinary perspective by drawing on theory from Marketing (i.e., S-D Logic) and combining this with grounded approaches in Information/Computer Science (e.g., Data mining). My interest in application development led to the deployment of software which demonstrated the intersection between these two fields. I defended my PhD in 2019 and had the opportunity to present my work at the European Conference on Information Systems (ECIS) at the University of Stockholm.
In 2020 I become a Postdoctoral Researcher in Text Mining, Big Data, and Databases within the Department of Computer Science at Edge Hill University in the United Kingdom. I conducted research within the Horizon 2020 (H2020) TYPHON project which delivered a scalable data analytics ecosystem for internationally recognized academic (e.g., Centrum Wiskunde & Informatica) and industry partners (e.g., Volkswagen, Alpha Bank). My experience in the project developed my appreciation for distributed systems, big data technologies, Natural Language Processing, and Machine Learning. In addition, the project was successfully accepted in its review by the European Commission.
More recently, I have attained the position of Postdoctoral Research Fellow within the Department of Informatics at the University of Oslo. The focus of my research has been to both investigate how Machine Learning can be used to unearth insights within epidemiology and also to understand how Causal Learning can build robustness within Machine Learning. This naturally has led me to the PharmaTox initiative and expanded my area of research into the scientific fields of pharmacology and epigenetics. I have been fortunate to have worked within such an incredibly diverse group of researchers and am excited about contributing to the state-of-the-art in epidemiological research.
Academic Interests
Big Data, Machine Learning, Causal Learning, Epigenetics
Publications
- Alwash, M., Savarimuthu, B. T. R., & Parackal, M. (2019). Shallow vs. Deep customer engagement: A study of brand value propositions in Twitter. Proceedings of the 27th European Conference on Information Systems (ECIS). (pp. 1-12). Link – https://aisel.aisnet.org/ecis2019_rp/96
- Alwash, M., Savarimuthu, B. T. R., & Parackal, M. (2016). Identifying and classifying value propositions in brand tweets: A study of Top-10 coffee brands. Proceedings of the 20th Pacific Asia Conference on Information Systems (PACIS). (pp. 168-181). Link – http://aisel.aisnet.org/pacis2016/168