Invited Speakers

• Leman Akoglu (Stony Brook University)
  Keynote Talk: Fraud Detection with Networks and Beyond
  
  
• David A. Bader (Georgia Tech)
  Invited Talk: Massive-Scale Streaming Analytics
  
  
• Tanya Y. Berger-Wolf (University of Illinois)
  Invited Talk: Dynamic Interaction Networks: from Inference to Insight
  
  

Abstracts and Biographies of the Speakers

• Leman Akoglu (Stony Brook University)
  
  Keynote Talk: Fraud Detection with Networks and Beyond
  
  Abstract: Fraud is a prevalent problem -- almost wherever there is finances involved, there is fraud (e.g., tax fraud, identity theft, credit card fraud, auction fraud, bankruptcy fraud, etc.). Annual monetary loss to fraud can find tens of billions in any given domain. Networks, especially the power of "guilt-by-association", is essential for this problem domain, as most fraud is often opportunistic and/or organized. Beyond networks, there is often additional information about the entities or the environment involved, where leveraging all data sources effectively and collectively becomes a key and a challenge. In this talk, I will dive into two of our representative work: (i) bankruptcy fraud and (ii) opinion fraud. The key elements of (i) are supervised nature, available meta-data, and aspect of time, while those of (ii) include semi-/un-supervised nature as well as linguistic and behavioral aspects. I will show how networks play a key role in thinking about and formulating both of these problems. I will also show how data beyond networks can be leveraged for improved performance. I will highlight/summarize even more examples in the literature where networks have been successfully used for fraud detection (e.g., insider trading (Li), insider threat (Holder), auction fraud (Chau), corporate residence fraud (Provost), and securities fraud (Neville)). I will finish with open challenges in this problem domain, including network construction, transience, cost, adversary, and systems issues.
  
  Biography: Leman Akoglu is an Assistant Professor in the Department of Computer Science at Stony Brook University. She received her Ph.D. from the Computer Science Department at Carnegie Mellon University in 2012. She also spent summers at IBM T. J. Watson Research Labs and Microsoft Research at Redmond. Her research interests span a wide range of data mining and machine learning topics with a focus on algorithmic problems arising in graph mining, pattern discovery, social and information networks, and especially anomaly mining; outlier, fraud, and event detection. Dr. Akoglu's research has won 4 publication awards; Best Research Paper at SIAM SDM 2015, Best Paper at ADC 2014, Best Paper at PAKDD 2010, and Best Knowledge Discovery Paper at ECML/PKDD 2009. She also holds 3 U.S. patents filed by IBM T. J. Watson Research Labs. Dr. Akoglu is a recipient of the NSF CAREER award (2015) and Army Research Office Young Investigator award (2013). Her research is currently supported by the National Science Foundation, the US Army Research Office, DARPA, a gift from Northrop Grumman Aerospace Systems, and a gift from Facebook.
  
  
  
• David A. Bader (Georgia Tech)
  
  Invited Talk: Massive-Scale Streaming Analytics
  
  Abstract: Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in computational science and engineering, solving these problems at scale often raises new challenges because of the sparsity and lack of locality in the data, the need for additional research on scalable algorithms and development of frameworks for solving these problems on high performance computers, and the need for improved models that also capture the noise and bias inherent in the torrential data streams. In this talk, the speaker will discuss the opportunities and challenges in massive data-intensive computing for applications in computational science and engineering.
  
  Biography: David A. Bader is a Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology, and Executive Director of High Performance Computing. He received his Ph.D. in 1996 from The University of Maryland. Dr. Bader serves on the NSF Advisory Committee on Cyberinfrastructure, on the Council on Competitiveness High Performance Computing Advisory Committee, on the IEEE Computer Society Board of Governors, as the editor-in-chief of IEEE Transactions on Parallel and Distributed Systems, and on the Steering Committees of the IPDPS and HiPC conferences, and has served as a board member of the Computing Research Association (CRA). Dr. Bader's interests are at the intersection of high-performance computing and real-world applications, and he a leading expert on parallel computing for data-intensive applications such as those in massive-scale analytics. Prof. Bader is a Fellow of the IEEE and AAAS, a National Science Foundation CAREER Award recipient, and has received numerous industrial awards from IBM, NVIDIA, Intel, Cray, Oracle/Sun Microsystems, and Microsoft Research. Dr. Bader has served as a lead scientist in several DARPA programs including High Productivity Computing Systems (HPCS) with IBM PERCS, Ubiquitous High Performance Computing (UHPC) with NVIDIA ECHELON, Anomaly Detection at Multiple Scales (ADAMS) and Power Efficiency Revolution For Embedded Computing Technologies (PERFECT). Bader is a co-founder of the Graph500 List for benchmarking "Big Data" computing platforms, and is recognized as a "RockStar" of High Performance Computing by InsideHPC and as HPCwire's People to Watch in 2012 and 2014.
  
  
  
• Tanya Y. Berger-Wolf (University of Illinois)
  
  Invited Talk: Dynamic Interaction Networks: from Inference to Insight
  
  Abstract: From gene interactions and brain activity to cellphone calls and zebras grazing together, large, noisy, and highly dynamic networks of interactions are everywhere. Unfortunately, in this domain, our ability to analyze data lags substantially behind our ability to collect it. Moreover, we may be collecting the wrong data for the questions we want to answer in the first place. From collecting the data and inferring the networks to producing meaningful insight at scale, challenges are there every step of the way and computational approaches have been developed to meet those challenges. In this talk I will show computational approaches that address some of the questions about dynamic interaction networks: whom should we sample? how often? what is the "right" implicit network? what are the meaningful patterns and trends? and how can we use the network to gain insight into other aspects of the node behavior? The methods leverage the topological graph structure of the networks and the size of the available data to, somewhat counter-intuitively, to produce more accurate results faster. We will demonstrate the scientific implications of the computational analysis on networks of zebras, baboons, and interacting brains cells.
  
  Biography: Dr. Tanya Berger-Wolf is an Associate Professor in the Department of Computer Science at the University of Illinois at Chicago, where she heads the Computational Population Biology Lab. Her research interests are in applications of computational techniques to problems in population biology of plants, animals, and humans, from genetics to social interactions. As a legitimate part of her research she gets to fly in a super-light airplane over a nature preserve in Kenya, taking a hyper-stereo video of zebra populations. Dr. Berger-Wolf has received her Ph.D. in Computer Science from University of Illinois at Urbana-Champaign in 2002. After spending some time as a postdoctoral fellow working in computational phylogenetics and doing research in computational epidemiology, she returned to Illinois. She has received numerous awards for her research and mentoring, including the US National Science Foundation CAREER Award and the UIC Mentor of the Year and Graduate Mentor awards. Dr. Berger-Wolf is also a board director for the conservation software non-profit IBEIS.org.