What I learned from my Computer Science research experience

Computer Science is a very practical field and I like the emphasis my university places on teaching practical computer science skills. However, I recommend every computer science student planning on higher education to try to get some research experience during their undergraduate studies. First, it is helpful to know already at undergraduate level whether you enjoy research, for example to be able to choose appropriate higher education. Second, doing research is a great learning experience that is very useful for future career in both the industry and academia.

This semester I worked on an independent research project in the field of Multimedia, Data Science and Machine Learning, with my friend Þórhildur. I want to share both the Computer Science knowledge and the soft skills I learned from the process, so that I can hopefully inspire others to take on a research project. (The idea of the research can be found in this post)

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MULTIMEDIA ANALYTICS – The whys, wherefores and challenges.

Multimedia is a data that consists of a combination of different content forms such as images, video, text, audio and interactive content. Multimedia collections are becoming a central information resource for a growing number of domains, which increases the need for fast and insightful multimedia analysis tools. Since today’s multimedia collections are very large and ever-growing, the tools also need to be applicable to large-scale data. For example, the data obtained from social media platforms is almost all multimedia, the largest publicly available multimedia collection compromises 100 million images from Yahoo Flickr, called YFCC100M. However, there are many much larger multimedia collections that are not publicly available, like Facebook’s over hundred billion images.

But what is the best way to extract knowledge and insight from multimedia collections? The dominant approach revolves around search. Search is suitable only for cases when the user has a clear information need and is able to formulate it as a precise query. However, often the analyst wants to explore the collection, looking for the question to ask, and structure or categorize the data herself. Thus, multimedia systems should support interactive, open-ended tasks where the objective is the analyst’s knowledge gain. Below is example of few domains where this kind of interactive multimedia learning is important: