Keynote Speakers


Lise Getoor
Professor,
CS Department,
Director,
D3 Data Science Center,
University of California, Santa Cruz, USA


Ramanathan Guha
Founder and Lead,
DataCommons.org,
Google, USA


Ling Liu
Professor,
School of Computer Science,
Georgia Institute of Technology, USA


Judea Pearl
Chancellor Professor,
Departments of Computer Science and Statistics,
University of California, Los Angeles, USA


Yang Qiang
New Bright Professor of Engineering,
Chair Professor and Head,
Department of Computer Science and Engineering,
Hong Kong University of Science and Technology, China

TBD

Lise Getoor, Professor in Computer Science Department, Director of the UC Santa Cruz D3 Data Science Center, University of California, Santa Cruz, USA

Abstract: TBD

Prof. Lise Getoor is a professor in the Computer Science Department at UC Santa Cruz and the director of the UC Santa Cruz D3 Data Science Center. Her research areas include machine learning and reasoning under uncertainty; in addition she works in data management, visual analytics and social network analysis. She has over 200 publications and extensive experience with machine learning and probabilistic modeling methods for graph and network data. She is a Fellow of the Association for Artificial Intelligence, an elected board member of the International Machine Learning Society, serves on the board of the Computing Research Association (CRA), has served as Machine Learning Journal Action Editor, Associate Editor for the ACM Transactions of Knowledge Discovery from Data, JAIR Associate Editor, and on the AAAI Council. She was co-chair for ICML 2011, and has served on the PC of many conferences including the senior PC of AAAI, ICML, KDD, UAI, WSDM and the PC of SIGMOD, VLDB, and WWW. She is a recipient of an NSF Career Award and eleven best paper and best student paper awards. In 2014, she was recognized as one of the top ten emerging researchers leaders in data mining and data science based on citation and impact according to KDD Nuggets. She is on the external advisory board the San Diego Super Computer Center, and the scientific advisory board for the Max Planck Institute for Software Systems, and has served on the advisory board for companies including Sentient Technologies. She received her PhD from Stanford University in 2001, her MS from UC Berkeley, and her BS from UC Santa Barbara, and was a professor at the University of Maryland, College Park from 2001-2013.

TBD

Ramanathan Guha, Founder and Lead, DataCommons.org, Google, USA

Abstract: TBD

Dr. Ramanathan Guha is the founder and lead for DataCommons.org, a platform which synthesizes a wide range of data sets into a single knowledge graph, for use by students and researchers. He is the creator of widely used web standards such as RSS, RDF and Schema.org, and products such as Google Custom Search, and co-founder of Epinions.com and Alpiri. He is currently a Google Fellow and Vice President at Google. He has a Ph.D. in Computer Science from Stanford University, a Master of Science from University of California, Berkeley and a Bachelor of Technology in Mechanical Engineering from IIT Chennai.

Deception, Robustness and Trust in Big Data Fueled Deep Learning Systems

Ling Liu, Professor, School of Computer Science, Georgia Institute of Technology, USA

Abstract: We are entering an exciting era where human intelligence is being enhanced by machine intelligence through big data fueled artificial intelligence (AI) and machine learning (ML). However, recent work shows that DNN models trained privately are vulnerable to adversarial inputs. Such adversarial inputs inject small amount of perturbations to the input data to fool machine learning models to misbehave, turning a deep neural network against itself. As new defense methods are proposed, more sophisticated attack algorithms are surfaced. This arms race has been ongoing since the rise of adversarial machine learning. This keynote provides a comprehensive analysis and characterization of the most representative attacks and their defenses. As more and more mission critical systems are incorporating machine learning and AI as an essential component in their real-world big data applications and their big data service provisioning platforms or products, understanding and ensuring the verifiable robustness of deep learning becomes a pressing challenge in the presence of adversarial attacks. This includes (1) the development of formal metrics to quantitatively evaluate and measure the robustness of a DNN prediction with respect of intentional and unintentional artifacts and deceptions, (2) the comprehensive understanding of the blind spots and the invariants in the DNN trained models and the DNN training process, and (3) the statistical measurement of trust and distrust that we can place on a deep learning algorithm to perform reliably and truthfully. In this keynote talk, I will use empirical analysis and evaluation of our cross-layer strategic teaming defense framework and techniques to illustrate the feasibility of ensuring robust deep learning.

Prof. Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large-scale data intensive systems. Prof. Liu is an internationally recognized expert in the areas of Big Data Systems and Analytics, Distributed Systems, Database and Storage Systems, Internet Computing, Privacy, Security and Trust. Prof. Liu has published over 300 international journal and conference articles, and is a recipient of the best paper award from a number of top venues, including ICDCS 2003, WWW 2004, 2005 Pat Goldberg Memorial Best Paper Award, IEEE CLOUD 2012, IEEE ICWS 2013, ACM/IEEE CCGrid 2015, IEEE Edge 2017. Prof. Liu is an elected IEEE Fellow and a recipient of IEEE Computer Society Technical Achievement Award. Prof. Liu has served as general chair and PC chairs of numerous IEEE and ACM conferences in the fields of big data, cloud computing, data engineering, distributed computing, very large databases, World Wide Web, and served as the editor in chief of IEEE Transactions on Services Computing from 2013-2016. Currently Prof. Liu is co-PC chair of The Web 2019 (WWW 2019) and the Editor in Chief of ACM Transactions on Internet Technology (TOIT). Prof. Liu’s research is primarily sponsored by NSF, IBM and Intel.

The new science of cause and effect, with reflections on data science and artificial intelligence

Judea Pearl, Chancellor Professor, Departments of Computer Science and Statistics, University of California, Los Angeles, USA

Abstract: The past three decades have seen the development of powerful tools for modeling and computing causal relationships which may have major impact on data science. My talk will illustrate how these tools work in seven tasks:
1. Encoding causal assumptions in transparent and testable way
2. Predicting the effects of actions and policies
3. Computing counterfactuals and finding causes of effects
4. Computing direct and indirect effects (Mediation)
5. Integrating data from diverse sources.
6. Recovering from missing data
7. Discovering causal relations from data
A friendly, non technical account of these ideas is available in: "The Book of Why: the new science of cause and effect," Judea Pearl and Dana MacKenzie,(Basic Books, 2018). http://bayes.cs.ucla.edu/WHY/

Prof. Judea Pearl is Chancellor professor of computer science and statistics at UCLA, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, human reasoning, and philosophy of science. He has authored hundreds of researche papers and three books: Heuristics (1983), Probabilistic Reasoning (1988) and Causality (2000, 2009) which won of the London School of Economics Lakatos Award in 2002. More recently, he co-authored Causal Inference in Statistics (2016, with M. Glymour and N. Jewell) and "The Book of Why" (2018, with Dana Mackenzie) which introduces causal analysis to a general audience. Pearl is a member of the National Academy of Sciences the National Academy of Engineering, a fellow of the IEEE, the Cognitive Science Society and the Association for the Advancement of Artificial Intelligence. In 2012, he won the Technion's Harvey Prize and the ACM Alan Turing Award "for fundamental contribution to artificial intelligence through the development of a calculus for probabilistic and causal reasoning."

TBD

Yang Qiang, New Bright Professor of Engineering, Chair Professor and Head of Department of Computer Science and Engineering, Hong Kong University of Science and Technology, China

Abstract: TBD

Prof. Yang Qiang