Agenda in Review
The Big Data and AI Day is inaugurated by the HKUST School of Engineering and Big Data Institute (BDI), to provide a platform to showcase BDI’s research and educational efforts, and to facilitate interaction and strengthen exchange among industries and researchers.
Friday, 26 May 2017, 9:00am – 5:30pm
Venue: Lecture Theatre J, HKUST
|09:00 - 09:10am||
|09:15 - 09:45am||
Big Visual Data Analysis
Dr. Tieniu Tan, Vice Minister of Liaison Office of the Central People’s Government in the Hong Kong S.A.R.
Prof Tieniu Tan is an expert on image processing, computer vision and pattern recognition.
|09:45 - 10:15am||
Big Data at DiDi Chuxing
Didi Chuxing is the world’s leading mobile transportation platform that offers a full range of mobile tech-based mobility options for nearly 400 million users across more than 400 Chinese cities. Every day, Didi's platform generates over 70TB worth of data, processes more than 9 billion routing requests, and produces over 13 billion location points. This talk is about how AI technologies have been applied to analyze such big transportation data to improve the travel experience for millions of people in China.
Dr. Jieping Ye, Vice President of Didi Research, Didi Chuxing
Dr. Jieping Ye is the Vice President of DiDi Research, and is also an associate professor of University of Michigan. His research interests include big data, machine learning, and data mining with applications in transportation and biomedicine. He has served as a Senior Program Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS, ICML, KDD, IJCAI, ICDM, SDM, ACML, and PAKDD. He serves as an Associate Editor of Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He won the NSF CAREER Award in 2010. His papers have been selected for the outstanding student paper at ICML in 2004, the KDD best research paper honorable mention in 2010, the KDD best research paper nomination in 2011 and 2012, the SDM best research paper runner up in 2013, the KDD best research paper runner up in 2013, and the KDD best student paper award in 2014.
|10:45 - 11:15am||
Big Data Software: What’s Next?
The Big Data revolution has been enabled in part by a wealth of innovation in software
platforms for data storage, analytics, and machine learning. The first wave of Big Data platforms such as Hadoop and
Spark focused on scalability, fault-tolerance and performance. As these and other systems increasingly become part of
the mainstream, the next set of challenges are becoming clearer. Requirements for performance are changing as workloads
evolve to include techniques such as hardware-accelerated deep learning. But more fundamentally, other issues are
moving to the forefront. These include ease of use for a wide range of users, security, concerns about privacy and
potential bias in results, and the perennial problem of data integration from heterogeneous sources.
Prof Michael Franklin, Liew Family Chair of Computer Science, University of Chicago
MICHAEL J. FRANKLIN is the Liew Family Chair of Computer Science and Sr. Advisor to the Provost for Computation and Data at the University of Chicago where his research focuses on database systems, data analytics, data management and distributed computing systems. Franklin previously was the Thomas M. Siebel Professor and Chair of the Computer Science Division of the EECS Department at the University of California, Berkeley. He co-founded and directed Berkeley’s Algorithms, Machines and People Laboratory (AMPLab), which created industry- changing open source Big Data software such as Apache Spark and BDAS, the Berkeley Data Analytics Stack. At Berkeley he also served as an executive committee member for the Berkeley Institute for Data Science. He currently serves as a Board Member of the Computing Research Association and on the NSF CISE Advisory Committee. Franklin is an ACM Fellow, a two-time recipient of the ACM SIGMOD “Test of Time” award and received the Outstanding Advisor award from Berkeley’s Computer Science Graduate Student Association.
|11:15 - 11:40am||
Overview and achievements of Big Data Institute
|11:40am - 12:00nn||
Speech and Language for AI and Data Analytics
Prof Pascale Fung, Professor of Department of Electronic and Computer Engineering, HKUST
Pascale Fung is a professor in the Department of Electronic and Computer Engineering at HKUST and the founding director of InterACT@HKUST, a joint research and education center with Carnegie Mellon University. Pascale is a leading researcher in the fields of statistical speech, language, and music processing. She cofounded a company that launched the first Chinese natural language search engine in 2000 and her entrepreneur story was featured in the Wall Street Journal, CNBC, and other magazines. Her second company launched the first Chinese virtual personal assistant on a smartphone in 2009 and the first Chinese language automobile infotainment system with a 3G connection in 2010. Her company also invented a world-leading intelligent music technology engine that powers the digital music services to more than fifty million Chinese users. She has published more than 130 papers and book chapters and holds fifteen world-wide and Chinese patents and software copyrights. In addition to core engineering subjects, Pascale also teaches technology entrepreneurship to engineering students at HKUST. In 2011 she cofounded the Women Faculty Association at HKUST to push for diversity in academia. Pascale received her PhD in computer science from Columbia University in 1997.
|12:00nn - 12:40pm||
MSc BDT Students
|2:00 – 2:20pm||
Human-Powered Machine Learning
Recently, machine learning becomes quite popular and attractive, not only to academia but also to the industry. The successful stories of machine learning on Alpha-go and Texas hold 'em games raise significant interests on machine learning. The question is whether machine learning can do everything perfect? In this talk, I will first give several examples that current machine learning techniques have difficulty to perform well. Then, I will show by putting human in the machine-learning loop, the results can be significantly improved. After that, I will discuss the challenges and opportunities for this human-powered machine learning paradigm.
Prof Lei Chen, Professor of Department of Computer Science & Engineering, Associate Director of Big Data Institute, HKUST
Lei Chen received the BS degree in computer science and engineering from Tianjin University, Tianjin, China, in 1994, the MA degree from Asian Institute of Technology, Bangkok, Thailand, in 1997, and the PhD degree in computer science from the University of Waterloo, Canada, in 2005. He is currently a full professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. His research interests include human-powered machine learning, crowdsourcing , social media analysis, probabilistic and uncertain databases, and privacy-preserved data publishing. The system developed by his team won the excellent demonstration award in VLDB 2014. He got the SIGMOD Test-of-Time Award in 2015. He is PC Track chairs for SIGMOD 2014, VLDB 2014, ICDE 2012, CIKM 2012, SIGMM 2011. He has served as PC members for SIGMOD, VLDB, ICDE, SIGMM, and WWW. Currently, he serves as Editor- in-Chief of VLDB Journal and an associate editor-in-chief of IEEE Transaction on Data and Knowledge Engineering. He is a member of the VLDB endowment. http://www.cs.ust.hk/~leichen/
|2:20 – 2:50pm||
Anomaly detection in large graphs
Given a large graph, like who-calls-whom, or who-likes-whom, what behavior is normal
and what should be surprising, possibly due to fraudulent activity? How do graphs evolve over time?
Prof Christos Faloutsos, Professor in Department of Computer Science, Carnegie Mellon University
Christos Faloutsos is a Professor at Carnegie Mellon University. He has received the
Presidential Young Investigator Award by the National Science Foundation (1989), the Research Contributions Award
in ICDM 2006, the SIGKDD Innovations Award (2010), 24 ``best paper'' awards (including 5 ``test of time'' awards),
and four teaching awards.
|2:50 – 3:20pm||
AI-Powered Information Creation, Distribution and Interaction
In the mobile era, we are being presented an exciting opportunity to shape the way people acquire and consume information. We believe that AI will fundamentally change the way people connect with information, and we can use AI to improve the effectiveness and efficiency in the entire process of content creation, moderation, dissemination, consumption, and interaction. By closing human feedback loop in this entire process, we can also enable human and AI algorithms collaboratively evolve and improve. Based on this vision, Toutiao was started 5 years ago and it recommends information tailored to users’ likes and interests. To date, it serves 100M daily active users and their average use time is over 76 minutes per day. In this talk, we will introduce the roles of AI technologies in information consumption platforms. We will share several recent research results at Toutiao AI Lab towards more efficient information creation and interaction. We will introduce a robot writer, Xiaomingbot, which has produced 5000 articles since August 2016. We will present a deep-learning based system that answers factoid questions with the state-of-the-art accuracy.
Dr. Hongjiang Zhang (on behalf of Dr. Lei Li at Toutiao AI Lab)
Dr. HongJiang ZHANG, retired on Dec 1, 2016, was an Executive Director and the chief
executive officer (“CEO”) of Kingsoft (a Hong Kong listed public company), from November 2011 to November 2016. He
was also a director and the CEO of Kingsoft Cloud, and a director of Cheetah Mobile Inc. (NYSE: CMCM) a subsidiary
of Kingsoft. Dr. ZHANG was also a director of Xunlei Limited (NASDAQ: XNET) and a director of 21Vianet Group, Inc.
|3:40 – 4:00pm||
Impact of Social Media and AI on the Financial Market
Prof Michael Zhang, Associate Professor, Department of Information Systems, Business Statistics and Operations Management, HKUST
Professor Michael Zhang is an Associate Professor of Information Systems, Business
Statistics and Operations Management at the Hong Kong University of Science and Technology, and an affiliated
faculty at MIT Center for Digital Business. He holds a PhD in Management from MIT Sloan School of Management, an
MSc in Management, a BE in Computer Science and a BA in English from Tsinghua University. Before joining the
academia, he worked as an analyst for an investment bank, and as an international marketing manager for a high-tech
company. He holds a US patent, and cofounded a social-network company.
|4:00 – 4:20pm||
Looking for something that leads exponential growth with big data and AI
While Moore's Law is coming to an end, can big data and AI support sustained economic development next? To do that, you need to create something that grows exponentially with big data and AI.
Dr. Masayuki Mizuno, Deputy General Manager, Data Science Research Laboratories, NEC Corp.
20-year R&D career in the semiconductor industry of NEC and Renesas Electronics. He
has created an array of products based on R&D, e.g. high-speed, low-power, highly reliable, and safety IPs for
vector supercomputer, world's first MPEG-2 encoder chips, ADAS image recognition chips and HEV/EV isolation driver
chips for automotive.
|4:20 – 5:20pm||Panel Discussion|
|5:20 - 5:30pm||Closing Remarks by Prof Yang Wang|