My name is Konstantinos Pelechrinis and I joined the faculty of Information Sciences at the University of Pittsburgh in Fall 2010, where I lead the Network Data Science Lab. Prior I received my Phd degree from the Computer Science department at the University of California at Riverside, under the supervision of Prof. Srikanth V. Krishnamurthy. Before joining UCR, I obtained my Diploma degree from the Electrical and Computer Engineering department of the National Techincal University of Athens, where I worked with Prof. Vasileios Maglaris at the Network Management and Optimal Design Laboratory.
My research interest include:
In more detail, my research interestes include network science and social computing and in particular, location based social networks (LBSNs) and urban informatics. I am involved in: (a) understanding the social, spatial, temporal and network dynamics of the behavior of people as captured through LBSNs, (b) developing models and algorithms for intelligent urban services, (c) studying the effect of LBSNs on bussinesses and (d) securing the new bussiness model paradigm introduced by these systems. More details can be found here. In the past, I have been involved in research in computer networking and, in particular, wireless and mobile networks: (a) protocol design, (b) real world experimentation, and (c) performance analysis for wireless network systems. I am extremely interested in the desing and implementation of practical systems, based on analytical frameworks.
I have taught the following classes (material is available only through the University's blackboard):
MST students that want advising please see here before contacting me. Furthermore, topics for MST students' independent studies will be posted here. Hence, if there are no topics it means that I cannot take any student for independent studies.
If you want a reference letter from me, please contact me first! If I receive a notification for a reference letter without having talked first, chances are that I will not respond to the request.
Here I will be posting some interesting (IMO always!) - but rather basic - data analysis. My goal is to showcase through these posts either some fundamental methodologies that can be useful in many different problems or some generic idea that could be useful in various problems. For those interested in sports analytics I am having a separate blog for that that you can visit.
With this post I want to discuss the small-world network searchability using Kleinberg's model and explore a potential connection with the fractal dimesnion.
With this post I want to discuss how aggregate counters can be biased indicators of the quality of an entity (in this case Foursquare restaurants). This can be found in many different areas, such as, bibliometrics (older papers have more time to accumulate citations), online markets (older products accumulate more reviews) etc.