All PhD candidates around the world know about the thesis. You always knew about the thesis. It marks the beginning of the end for your career as a PhD and if you actually do it, you can have that cool “Dr.” title that you always wanted in your business card. What is the problem then? Why it seems so frustrating when you are sitting down to do it? The following is based on a true story, actually my story. How I managed to write it down and track my progress. Continue reading
During recent experiments for a research paper, my research group observed very strange symptoms from our Google Glass. Most of our experiments were done to study the impact of latency on cognitive assistance applications such as programs designed to remind you who is in front of you, or notify you that it is safe to cross the street. We observed a large variation in latency which was unexplainable by the usual culprits such as poorly performing WiFi networks. We had isolated all the possible sources outside of the Google Glass, but the unknown source of latency jitter was still ruining our experimental results. At this point, we knew we had to figure out what was going on inside the Google Glass itself.
As a follow-up to my previous post on the discussion of where theory and experimentation meet in the study of large-scale networks, I would like to discuss in more detail one of the empirically best-performing algorithms which also has a sound theoretical background: spectral partitioning. In this post I will examine the history of the problem, outline some key results, and present some future ideas for the problem. Continue reading
The quintessence of an e-voting transaction is to be secure. In the e-voting context, security issues are very subtle. This is because there are features that clash with each other. For example, guaranteeing anonymity makes it harder to track election fraud. In addition, security in e-voting is highly related to the type of the technology used during the process. In distance e-voting, the voter can cast his vote from his personal computer by sending it to a central server via the Internet. The electronic, network-based nature of the latter makes it susceptible to a wide range of attacks. Continue reading
As a PhD student who does research on theory and algorithms for massive data analysis, I am interested in exploring current and future challenges in this area, which I’d like to share it here. There are two major points of view when we talk about big data problems:
One is more focused on industry and business aspects of big data, and includes many IT companies who work on analytics. These companies believe that the potential of big data lies in its ability to solve business problems and provide new business opportunities. To get the most from big data investments, they focus on questions which companies would like to answer. They view big data not as a technological problem but as a business solution, and their main goals are to visualize, explore, discover and predict.