Agenda in Review

The HKUST Big Data and AI Day 2018 was successfuly held on 12 April 2018. At the event, HKUST Big Data Institute (BDI) announced its partnership with NAVER/LINE and established joint research laboratory of “HKUST-NAVER/LINE AI Laboratory”. This joint lab aims to develop a comprehensive set of research and talent-development programs to pursue cutting-edge research for advancement of AI technology and enrich the learning experience for both undergraduate and postgraduate students. For details, please refer to here.

Thursday, 12 April 2018, 9:00am – 5:30pm

Venue: HKUST Jockey Club Institute for Advanced Study ​Lecture Theater, HKUST

Morning session

08:30 - 09:00      Registration and Networking
09:00 - 09:10      Opening Remarks
By
Prof Tim Kwang Ting CHENG, Dean of Engineering, HKUST

Prof Tim Kwang Ting Cheng became the Dean of Engineering in May 2016 in concurrence with his appointment as Chair Professor jointly in the Department of Electronic and Computer Engineering and in the Department of Computer Science and Engineering.

He graduated from University of California, Berkeley in 1988 with a PhD in Electrical Engineering and Computer Sciences. Before joining HKUST, he was a Professor of Electrical and Computer Engineering (ECE) at the University of California, Santa Barbara (UCSB), where he served since 1993. Prior to teaching at UC Santa Barbara, he spent five years at AT&T Bell Laboratories.

At UC Santa Barbara, Prof Cheng had taken up various important academic leadership roles, such as Founding Director of the Computer Engineering Program from 1999 to 2002, Chair of Department of ECE from 2005 to 2008, Acting Associate Vice-Chancellor for Research in 2013 and Associate Vice-Chancellor for Research from 2014 to 2016 where he helped oversee the research development, infrastructure, and compliance of UCSB’s research enterprise with over US$200 million extramural research funding.

A highly respected teacher-scholar and internationally leading researcher with excellent experience in fostering cross-disciplinary research collaboration, Prof Cheng is a world authority in the field of VLSI testing and design verification, as well as an impactful contributor across a wide range of research areas including design automation of electronic and photonic systems, mobile computer vision, and learning-based multimedia computing. He had previously served as Director of the US Department of Defense Multidisciplinary University Research Initiative (MURI) Center for 3D Hybrid Circuits which integrated CMOS and nano-memristors for future computing systems. He has published more than 400 technical papers, co-authored five books, held 12 US patents, and transferred several of his inventions into successful commercial products. He is a Fellow of IEEE and his works are of high impact with due recognition from the field, including 11 best paper awards and one Distinguished Paper Citation in major conferences and journals. He was also recognized as the Top 10 Author in Fourth Decade Award and Design Automation Conference (DAC) Prolific Author Award at the 50th DAC 2013.

Prof Cheng has been very active in providing professional services to the IEEE and to the academic community at large. Having served as the editor-in-chief of IEEE Design & Test of Computers, on the boards of IEEE Council on Electronic Design Automation’s Board of Governors and IEEE Computer Society’s Publications Board, and on various technology advisory or working groups including the International Technology Roadmap for Semiconductors (ITRS), Prof Cheng has been internationally known as an eminent member of the field.

09:10 - 09:20      Welcome Remarks
By
Prof Lei CHEN​, Acting Director, HKUST Big Data Institute & Professor, Department of Computer Science and Engineering, 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 crowdsourcing over social media, 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.

09:20 - 09:55      Data Science as A Vehicle for Digital Transformation of Academic Research and Education
By
Prof Sang Kyun CHA, Founding Director, SNU Big Data Institute, Seoul National University

Prof Sang Kyun Cha is a professor, an innovator, and an entrepreneur. He developed three generations of in-memory transactional and analytical data platforms since he joined Seoul National University (SNU) in 1992. Since 2014, he has been into the next phase of his career, pursuing scalable education of young digital innovators while researching on the next-generation digitalization platform technology.

His pioneering outlier life began with founding Transact In Memory, Inc. in Silicon Valley in early 2000’s to materialize his group’s innovative research publications in SIGMOD, VLDB, ICDE into his second-generation system called P*TIME. In late 2005, German software company SAP secretly bought his start up as well as his vision of real-time in-memory platform paradigm.

In the first quarter of 2006, P*TIME was integrated into SAP’s ERP stack as the world’s first modern enterprise-scale in-memory DBMS. This historical achievement, although eight years ahead of the next comparable product, was unfortunately known to only a small circle of SAP’s top management and developers because of non-disclosure agreement. P*TIME’s pioneering features included in-memory-optimized MVCC, a novel compressed B+-tree with cache-conscious optimistic index concurrency control, parallel logging for scalable update performance, and parallel recovery of large in-memory data volume.

After leading this initial phase of SAP’s R&D toward the new in-memory paradigm, he took charge of SAP’s internal research for integrated in-memory transactional and analytical platform. Based on this research, from 2009 to early 2014, he co-led the development of SAP HANA, which triggered the enterprise software industry to shift to the in-memory paradigm. With the success of HANA, SAP has now become the most valuable German company with ~120 billion Euro market value.

After his dream of in-memory paradigm shift achieved with SAP HANA’s support for operational enterprise database such as ERP, in 2014, he fully returned to SNU and founded Big Data Institute to lead trans-disciplinary digital transformation research and education across all academic disciplines. In 2017, he founded SNU urban data science laboratory in the downtown Seoul with funding from Seoul city government, and the fourth industrial revolution academy transforming young people into digital innovators.

With his past outlier experience, he served on the board of trustees of SNU from December 2014 to 2016. He is the longest-serving board member of Korea Telecom since March 2012. From June 2017, he serves as the chair of digitalization of Korea Electric Power Company. In his spare time, he advises various Korean government ministries and companies.

Prof. Cha received his BS and MS from Seoul National University in 1980 and 1982, respectively, and his Ph.D. from Stanford University in 1991. He was on the editorial board of VLDB Journal from 2009 to 2015. He was a general co-chair of 2015 IEEE International Conference on Data Engineering in Seoul, which drew 630 participants, a historical record in the past ten years. Since then he became a member of IEEE ICDE Steering committee. In 2017, Professor Cha was offered an invitation of chief scientist in one of Chinese research institutions under Thousand Talents Program.

09:55 - 10:30      Data Intelligence and Analytics at Alibaba
By
Dr Jingren ZHOU, Vice President, ‎Alibaba Group

Jingren Zhou is Vice President at Alibaba Group. He is responsible for driving Big Data and AI infrastructure development at Alibaba. Specifically, he manages the cloud engineering team to develop cloud-scale distributed computing platform, data analytic products, and various business solutions. He is also Head of the Search Division at Alibaba, leading the search engineering team to develop advanced techniques for personalized e-commerce and multimedia search, and provide best-in-class shopping experience at Alibaba's e-commerce platforms, including Taobao and Tmall. His research interests include cloud-computing, distributed systems, databases and large scale machine learning. Dr. Zhou received his PhD in Computer Science from Columbia University.

10:30 - 10:50      Networking Coffee Break
10:50 - 11:25      How is Big Data Science Changing the Face of Retail?
By
Dr Jian PEI, Vice President, JD.com

Always eager to meet new challenges and opportunities, Jian Pei is currently a Vice President of JD.com, China’s largest online retailer and its biggest overall retailer, as well as the country’s biggest Internet company by revenue. He is currently on leave from Simon Fraser University and holding the position of Canada Research Chair (Tier 1) in Big Data Science. Recognized as an ACM Fellow and an IEEE Fellow, he published over 200 technical publications, which have been cited by 77000+ times, 34000+ in the last 5 years. His research has generated remarkable impact substantially beyond academia.

11:25 - 12:00      New Era of Information Dissemination - Toutiao’s Vision and Practice
By
Dr Hang LI​, Director, Toutiao AI Lab & Adjunct Professor, Peking University and Nanjing University

Dr Hang Li is director of Toutiao AI Lab, adjunct professors of Peking University and Nanjing University. He is an IEEE Fellow and an ACM Distinguished Scientist. His research areas include information retrieval, natural language processing, machine learning, and data mining. Hang graduated from Kyoto University in 1988 and earned his PhD from the University of Tokyo in 1998. He worked at NEC Research as researcher from 1990 to 2001, Microsoft Research Asia as senior researcher and research manager from 2001 to 2012, and chief scientist and director of Huawei Noah’s Ark from 2012 to 2017. He joined Toutiao in 2017. Hang has published three technical books, and more than 120 technical papers at top international conferences including SIGIR, WWW, WSDM, ACL, EMNLP, ICML, NIPS, SIGKDD, AAAI, IJCAI, and top international journals including CL, NLE, JMLR, TOIS, IRJ, IPM, TKDE, TWEB, TIST. He and his colleagues’ papers received the SIGKDD’08 best application paper award, the SIGIR’08 best student paper award, the ACL’12 best student paper award. Hang worked on the development of several products such as Microsoft SQL Server 2005, Office 2007, Live Search 2008, Bing 2009, Office 2010, Bing 2010, Office 2012, Huawei smartphones 2014 and Huawei smartphones 2017. He has 42 granted US patents. Hang is also very active in the research communities and has served or is serving top international conferences as PC chair, Senior PC member, or PC member, including SIGIR, WWW, WSDM, ACL, NACL, EMNLP, NIPS, SIGKDD, ICDM, IJCAI, ACML, and top international journals as associate editor or editorial board member, including CL, IRJ, TIST, JASIST, JCST.

Afternoon session

13:30 - 14:00      Opening Ceremony of HKUST-NAVER/LINE AI Laboratory
14:00 - 14:35       Big Data or Big Garbage? A Tale of a Quest for Insights from Social Data
By
Dr Rakesh AGRAWAL, Founder & President, Data Insights Laboratories, San Jose, USA

Dr Rakesh Agrawal is the President and Founder of the Data Insights Laboratories, San Jose, USA and a Visiting Professor at the Kyoto University, Japan. He is a member of the National Academy of Engineering, both USA and India, a Fellow of ACM, and a Fellow of IEEE. He has been both an IBM Fellow and a Microsoft Fellow. He has also been the Rukmini Visiting Chair Professor at the Indian Institute of Science, Bangalore, India, and a Visiting Professor at EPFL, Lausanne, Switzerland. ACM SIGKDD awarded him its inaugural Innovations Award and ACM SIGMOD the Edgar F. Codd Award. He was named to the Scientific American’s First list of top 50 Scientists. Rakesh has been granted 80+ patents and published 200+ papers, including the 1st and 2nd highest cited in databases and data mining. Five of his papers have received “test-of-time” awards. His papers have received 100,000+ citations. His research formed the nucleus of IBM Intelligent Miner that led the creation of data mining as a new software category. Besides Intelligent Miner, several other commercial products incorporate his work, including IBM DB2 and WebSphere and Microsoft Bing.

14:35 - 15:10        The Rise of Conversational AI - Challenges and Opportunities
By
Dr Inho KANG​, Executive Director, NAVER Corp.

Dr Inho Kang is the director of Naver Search NLP team, and it is a technology that uses deep learning to understand various input forms such as text, voice, video and provide improved search result.

He said that Naver search developers have been continuously participating in international conferences and have continued to share and enhance Naver's various search technologies. We will continue to strengthen our AI-based new search technologies and try to make the service actually available.

15:10 - 15:30        Networking Coffee Break
15:30 - 16:05        Artificial Intelligence at Alibaba
By
Dr Zaiqing NIE, Researcher & Senior Director, Alibaba AI Labs

Zaiqing Nie is a researcher and senior director at Alibaba AI Labs focusing on natural language understanding and knowledge mining. Before joining Alibaba in 2017, Nie was a Principal Researcher and led the Big Data Mining group at Microsoft Research. During the 13 and half years there, he led several critical projects for Microsoft on building entity knowledge graphs to enable novel search experiences (Microsoft Academic Search, Renlifang/EntityCube, and Q20) and to enhance machine’s natural language understanding capability (EDI and LUIS). Before that, Nie received a Ph.D. in Computer Science from Arizona State University in 2004, a Master of Engineering degree in Computer Applications from Tsinghua University in 1998, and a Bachelor of Engineering degree in Computer Science and Technology from Tsinghua University from in 1996. His research interests include natural language understanding, data mining, and machine learning. Nie has publications in conferences and journals including SIGKDD, ACL, WWW, AAAI, IJCAI, ICML, CIDR, ICDE, and JMLR. His recent academic activities include PC member of SIGMOD 2018, KDD 2017, CIKM 2017, KDD 2016, PC co-chair of IIWeb 2014, senior PC of IJCAI 2013, SDM 2013, and KDD 2012.

16:05 - 16:40        StarGAN with NAVER Smart Machine Learning (NSML)
By
Dr Jung-Woo HA, Clova AI Research Director & Tech Lead, Asset Management Intelligence, NAVER Corp.

Dr Jung-Woo HA is the AI research director and leader of machine learning team of Clova, an advanced AI-assistant platform, developed by NAVER and LINE. He got his BS and PhD degrees from the department of computer science, Seoul National University. He is interested in machine learning, deep learning, computer vision, natural language processing, multimodal data analysis, and recommendation. He has published his work on many top-tier conferences including NIPS, AAAI, KDD, CVPR, ILCR, and so on. He focuses on innovative machine learning technologies for real-world applications to make users be happy rather than pure research.

16:40 - 17:15        One Approach, Two Applications: Sequence-to-Sequence Learning for Weather Forecasting and Online Education
By
Prof Dit-Yan YEUNG​, Acting Head & Professor, Department of Computer Science and Engineering, HKUST

Dit-Yan Yeung is a Professor in the Department of Computer Science and Engineering of the Hong Kong University of Science and Technology (HKUST), with joint appointment in the Department of Electronic and Computer Engineering. His main research interests have been in computational and statistical approaches to machine learning and artificial intelligence, beginning with his doctoral thesis research in neural networks and robotics when he was in the University of Southern California (USC). On the development of machine learning models and algorithms, he has done research on different machine learning approaches including kernel methods, probabilistic graphical models, and neural network models as well as their theoretical connections. Apart from pursuing theoretical research, he has also been interested in developing machine learning models for various applications particularly in computer vision, recommender systems, and, more recently, learning analytics. He publishes frequently in top conferences in machine learning, artificial intelligence, and computer vision.

17:15 - 17:30        Closing Remarks
By
Prof Dit-Yan YEUNG​, Acting Head & Professor, Department of Computer Science and Engineering, HKUST

Dit-Yan Yeung is a Professor in the Department of Computer Science and Engineering of the Hong Kong University of Science and Technology (HKUST), with joint appointment in the Department of Electronic and Computer Engineering. His main research interests have been in computational and statistical approaches to machine learning and artificial intelligence, beginning with his doctoral thesis research in neural networks and robotics when he was in the University of Southern California (USC). On the development of machine learning models and algorithms, he has done research on different machine learning approaches including kernel methods, probabilistic graphical models, and neural network models as well as their theoretical connections. Apart from pursuing theoretical research, he has also been interested in developing machine learning models for various applications particularly in computer vision, recommender systems, and, more recently, learning analytics. He publishes frequently in top conferences in machine learning, artificial intelligence, and computer vision.