My research interests include online social networks, security and privacy, and datacenter networks. Much of my work focuses on using measured data to analyze and understand social networks and the social web. My interests in security and privacy dovetail nicely with my work on social networks, since social networks have become major targets for malicious activity. Finally, I am interested in the datacenter networks and distributed systems that power social networks and other large-scale web services, with a focus on novel network protocols and datacenter-scale applications.
As a new professor, I am actively recruiting new students. If you are interested in social networks or security and privacy, read the prospective students section of my webpage, and then send me an email.
- Filter Bubbles: Many websites use personalization to fine-tune content for each user. For example, if you and a friend both search for identical keywords on Google, it is likely that you will each receive stightly different results. However, social scientists have begun to argue that this invisible personalization is harmful to users. In our work, we are focused on quantifying the degree to which major websites personalize content and what information they use to power their algorithms. Once we understand the underlying mechanisms behind filter bubbles, we can devise new techniques to overcome them. Our work on the Filter Bubble has appeared at WWW 2013, and the code/data from our experiments is available here. We are actively researching other areas of web personalization, as well as defense mechanisms to pop Filter Bubbles.
- Crowdturfing: There is a growing underground market on the Web for malicious crowdsourcing. For just a few cents, you can buy Facebook likes, Twitter followers, bulk social networking accounts, and fake reviews on Yelp. These types of social spam are extremely difficult for existing security systems to stop because the damage is caused by real people, not automated programs. In our work, we have measured malicious crowdturfuing systems, and we are actively engaged in devising new solutions to stop these insidious threats.
- Social Sybils: Fake accounts, otherwise known as Sybils, are a pervasive threat on the social web. Sybils generate a large portion of the spam on social networks, and steal personal information that is used to power targeted phishing attacks. Our research is focuses on using measured data to track Sybils, understand their attack strategies, and devise practical solutions to stop them.
|PhD in Computer Science||College of Engineering at UCSB||2008-2012|
|Masters in Computer Science||College of Engineering at UCSB||2006-2007|
|BS in Computer Science||College of Creative Studies at UCSB||2002-2006|
|Nominated for ACM Doctoral Dissertation Award||2012|
|Outstanding Dissertation Award from UCSB||2012|
|Best Paper Award: Honorable Mention at SIGCOMM||2011|
|Dean's Fellowship from UCSB||2010-2011|
|Distinguished Graduate Research Fellowship from UCSB||2008-2009|
|Best Poster Award at UCSB Graduate Student Workshop||2007|
|Nominated for Best Student Paper Award at IWQoS||2007|
|Microsoft Research Redmond||Cheng Huang and Jin Li||Summer 2011|
|Microsoft Research Cambridge||Thomas Karagiannis and Ant Rowstron||Summer 2010|