Keynote Speakers
Latifur Khan
Professor
The University of Texas at Dallas
Stream Data Mining: A Big Data Perspective
Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e.,
volume, velocity, variety and veracity) and add challenges to data stream mining. In this talk we will present an organized picture on how to handle various data mining techniques in data streams.
Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class
detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence
of concept-drift, when the underlying data distributions evolve in streams. In this talk we will show how to make fast and correct classification decisions under this constraint with limited labeled training data and apply them
to real benchmark data. In addition, we will present a number of stream classification applications such as adaptive malicious code detection, website fingerprinting, evolving insider threat detection and textual stream classification.
This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR) and Raytheon.
Bio
Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received
his Ph.D. and M.S. degrees in Computer Science from the University of Southern California in August of 2000, and December of 1996 respectively. Dr. Khan is an ACM Distinguished Scientist. He has received prestigious awards including
the IEEE Technical Achievement Award for Intelligence and Security Informatics.
Dr. Khan has published over 200 papers in prestigious journals, and in peer reviewed conference proceedings. Currently, his research area focuses on big data management and analytics, data mining, complex data management including
geo-spatial data and multimedia data. More details can be found at: www.utdallas.edu/~lkhan/
Le Song
Assistant Professor
Georgia Institute of Technology
Dynamic Processes over Information Networks Representation, Modeling, Learning and Inference
Nowadays, large-scale human activity data from online social platforms, such as Twitter, Facebook, Reddit, Stackoverflow, Wikipedia and Yelp, are becoming increasing available and in increasing spatial and temporal resolutions. Such data provide great
opportunities for understanding and modeling both macroscopic (network level) and microscopic (node-level) patterns in human dynamics. Such data have also fueled the increasing efforts on developing realistic representations and
models as well as learning, inference and control algorithms to understand, predict, control and distill knowledge from these dynamic processes over networks.
It has emerged as a trend to take a bottom-up approach which starts by considering the stochastic mechanism driving the behavior of each node in a network to later produce global, macroscopic patterns at a network level. However,
this bottom-up approach also raises significant modeling, algorithmic and computational challenges. In this talk, I will present machine learning framework for representing, modeling, and performing learning and inference for human
activity data. The framework leverage methods from temporal point process theory, probabilistic graphical models and optimization, and often produce state-of-the-art results on various modeling and time-sensitive inference tasks.
Bio
Le Song is an assistant professor in the Department of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. He received his Ph.D. in Machine Learning from University of Sydney
and NICTA in 2008, and then conducted his post-doctoral research in the Department of Machine Learning, Carnegie Mellon University, between 2008 and 2011. Before he joined Georgia Institute of Technology, he was a research scientist
at Google. His principal research direction is machine learning, especially nonlinear methods and probabilistic graphical models for large scale and complex problems, arising from artificial intelligence, social network analysis,
healthcare analytics, and other interdisciplinary domains. He is the recipient of the NSF CAREER Award’14, AISTATS ’16 Best Student Paper Award, IPDPS ’15 Best Paper Award, NIPS ’13 Outstanding Paper Award, and ICML ’10 Best Paper
Award. He has also served as the area chair for leading machine learning conferences such as ICML , NIPS and AISTATS , and the action editor for JMLR .
Naren Ramakrishnan
Thomas L. Phillips Professor
Virginia Tech
New Time Series Methods for Flu Forecasting
There has been recent concerted interest in computational methods for forecasting the flu, spurred by competitions organized by agencies like the CDC and IARPA. The CDC competition aimed to forecast flu seasonal characteristics in the US and the IARPA
Open Source Indicators (OSI) forecasting tournament was focused on disease forecasting (flu and rare diseases) in countries of Latin America. The speaker's team was declared the winner in the IARPA OSI competition and this task
will communicate our lessons learned about what goes into a successful flu forecasting engine, how to evaluate its performance, and how best to ensure its relevance to public health policy and planning purposes.
Bio
Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering at Virginia Tech. He directs the Discovery Analytics Center, a university-wide effort that brings together researchers from computer science,
statistics, mathematics, and electrical and computer engineering to tackle knowledge discovery problems in important areas of national interest. His work has been featured in the Wall Street Journal, Newsweek, Smithsonian Magazine,
PBS/NoVA Next, Chronicle of Higher Education, and Popular Science, among other venues. Ramakrishnan serves on the editorial boards of IEEE Computer, ACM Transactions on Knowledge Discovery from Data, Data Mining and Knowledge Discovery,
IEEE Transactions on Knowledge and Data Engineering, and other journals. He received his PhD in Computer Sciences from Purdue University.
