Some of my key learnings from this week include the following:
- Any official scientific research, such as our Master's thesis, requires a complete understanding of why it's being done, what it will change in the world around us and how (i.e. what's new?), what the risks are and how we can mitigate them, as well as financial costs and whether there are any competitors in the market. Whatever research is performed for a business purpose, for example, must have a solid foundation because of resource limits (both financial and people). And, since businesses care a lot about success, the research process has to follow a time-stamped plan whereby regular evaluations are performed and certain milestones are set to be met. In other words, there must be a goal and timeline to be reached.
- Our Master's thesis should not be some mandated topic communicated to us from top down, but instead, something we feel passionate about and something that can make a change in the world, i.e. leave an impact, even if it's a minuscule one. Because at the end, it's all about our purpose in life and career.
- The thesis is not just some theoretical paper but mostly a practical application of an end-to-end system that solves a particular problem. In other words, there must be a problem statement and our thesis is the solution. (A quote I remembered: "Don't start writing until you know the solution.").
- Data collection comes with a lot of challenges - not only in terms of finding its sources but mostly in terms of making sure that ethics are complied with. E.g., when it comes to private data, such as patient health data. The rules around the collection of such data must be researched beforehand and appropriate measures taken.
- Documentation is important for posterity as well as for yourself and your colleagues (you might forget why you coded things up this or the other way, as time passes by!).
- Visuals matter a lot when you're trying to relay the important information about your paper to the audience. People are into pictures more, not into plain text and numbers. Therefore, good to consider adding extra visual elements.
- There are two types of research types to choose from: (a) making experiments, e.g. trying out different machine learning techniques not tried before, and (b) creating your own system or using new data. Seems like our research topic is more an experimental one but does have a creative side to it - professor Pless suggested adding an extra application layer that will make it possible to tell which part of an InChi string, for example, relates to a particular part of the chemical structure.