|Speaker:||Xiang Li, University of Florida|
|When:||April 12, 2018, 11:00 am - 12:00 pm|
|Where:||Engineering Building, Room 4801|
Modern networked systems such as Internet of Things or Online Social Networks have growth to billion-scale with billions of nodes and edges. At the same time, most network analyses are conducted on incomplete information due to 1) collecting network data is expensive and time-consuming; and 2) security and privacy concerns have made users limit the information being exposed publicly. With such big and incomplete data, it becomes very challenging to mine the billion-scale networks. In this talk, I address the above problems via two primary approaches: 1) Develop novel dynamic sampling techniques with the performance bound guarantee to exactly perform the big data mining in billion-scale networks; and 2) Develop active learning methods to support more accurate decisions where decisions are made based on the outcomes of previous decisions. To confirm the practical uses of these mathematical techniques, I develop the first almost exact solution to an NP-complete problem, Maximum-Influence,which can run on Twitter within 3 hours. The results of active learnings are used to not only unveil the near-optimal attack strategies of APTO, one of the most persistent attacks in OSNs, but also advance the research front of adaptive stochastic optimization, including: active learning with batch, adaptive theory under the matroids intersection, and providing the first tool to analyze greedy algorithms for optimization problems where the objective function is non-submodular. Towards the end of the talk, I will summarize my other research results in the field of Cyber Physical Systems and discuss about my future research agenda.
Xiang Li is currently a UF Informatics Institute Fellow and a Ph.D. candidate in the Computer and Information Science and Engineering department, University of Florida, expected to graduate in May 2018. Her research interests at the intersection of cyber-security, data science, and highly scalable algorithms with applications in complex networking systems. More specifically, the focus of her research is to develop models, theories, and approximation algorithms for many computationally hard problems in Online Social Networks, Device-to-Device Networks, Internet of Things, and Smart Grids. She has published 21 articles in various prestigious journals and conferences such as IEEE Transactions on Mobile Computing, IEEE Transactions on Smart Grids, IEEE INFOCOM, IEEE ICDM, including one Best Paper Award at IEEE MSN 2014, and Best Paper Nominee at IEEE ICDCS 2017. Xiang has served as a reviewer for several journals such as Journal of Combinatorial Optimization, and IEEE Transactions on Networks Science and Engineering. She is a recipient of many awards, including UF Graduate School Fellowship, UF Informatics Institute Fellowship, UF Outstanding Merits Award, Travel Grant Awards at IEEE INFOCOM, IEEE ICDCS, IEEE ICDM, and IEEE INFOCOM Best-in-Section-Presentation Award.