Topic:
《Key Note: The Future of Big Data Infrastructure》

Speaker Biography:
Debashis Saha is a creative and innovative technology leader with a passion for customer focus, strong drive for results, excellent broad based domain knowledge and hands-on experience across full life cycle of product development, with the expertise in developing and delivering cutting-edge large scale distributed software and computer systems that solve real world problems. His current main focus is on developing highly scalable and distributed big data analytics infrastructure to support today's ever growing business need.

Topic:
《Hadoop Platform Monitoring, Alerting and Automation》

Abstract:
Hadoop, an open source software framework for storage and large scale processing of data sets on clusters of commodity hardware. It is the most popular open source framework to support Big Data processing and analytics. Ensuring the availability, efficiency and reliability of Hadoop platform is becoming a critical task in order to support ever-growing Big Data Analytics business needs. This topic will cover how Hadoop platform monitoring, alerting and automation should be done through the Hadoop Eagle open source project.

Speaker Biography:
Sami Ben-romdhane is an entrepreneur and founder of two startups in the Silicon Valley that were both successfully acquired by Symantec Corp and BEA Systems (now part of Oracle). He is currently a Distinguished Architect of Global Platform Infrastructure at eBay. Sami has long experience of designing and implementing high performance/reliable and highly scalable systems. Prior to eBay, Sami was a Chief Architect in Oracle and BEA Systems.

Topic:
《The Practice of Data Science》

Abstract:
Amidst the proliferous definitions of data science, there is common understanding of the practice of data science. To be a disciplined practice of data science that truly deserves the hyped social and economical attention, and more importantly that will scale and maximize to new potentials, a set of principles should be established to guide the practice. We present the principles for the practice, enumerate the current states of practices through examples, anticipate an optimal future for which the practitioners of data science should be prepared for and contribute to. A case study concludes with zeal for data science.

Speaker Biography:
Dr. Ying Li is the Chief Data Scientist at EV Analysis Corporation, which she founded after her career at Microsoft where she initiated and successfully built services, products, and teams with strong expertise in data mining and machines learning. Dr. Li is the winner of the 2012 ACM SIGKDD award for her outstanding contributions to the data mining field. She has filed and holds over 70 patent applications in the area of data mining, machine learning, computational advertising, software performance optimization, computer program tracing, profiling, and analysis. Dr. Li holds a Ph.D. degree in Computer Science from University of British Columbia, Canada, and MS and BS degrees in Applied Mathematics from Beijing University, China.

Topic:
《Large Scale Distributed Machine Learning》

Abstract:
Big-data machine learning has emerged as an important research topic because data larger than a machine's capacity are now very common. However, there are many challenges in applying big-data machine learning. First, most traditional machine learning algorithms were designed to run on a single computer. Second, sub-sampling data to one machine for analysis is always an option, so when distributed machine learning is preferable is an issue. After an overview of these challenges, we discuss distributed machine learning methods. In particular, a Newton method for large-scale logistic regression is given as an example. We also briefly review other existing developments and show how they are used in practical applications. Finally, we argue that big-data machine learning involves many issues other than algorithms. Programming environments, systems, and the application workflow must be considered together for a successful big-data machine learning project.

Speaker Biography:
Professor Chih-Jen Lin obtained his B.S. degree from National Taiwan University and Ph.D. degree from University of Michigan. His major research areas include machine learning, data mining, and numerical optimization. He is best known for his work on support vector machines (SVM) for data classification. His software LIBSVM is one of the most widely used SVM packages. For his research work he has received many awards, including the ACM KDD 2010 and ACM RecSys 2013 best paper awards. He is an IEEE fellow, a AAAI fellow, and an ACM distinguished scientist for his contribution to machine learning algorithms and software design. More information about him can be found at http://www.csie.ntu.edu.tw/~cjlin.