Class Time: Mondays, 12PM to 1PM. Room: https://ucsd.zoom.us/j/99067937524. Piazza: TBD.
Due to the COVID-19, this course will be delivered over Zoom. All lectures will be recorded.
This seminar course mainly focuses on discussing the state-of-the-art methods in AI-related fields. We will invite researchers to talk about their most recent works.
Recording Note: Please download the recording video for the full length. Dropbox website will only show you the first one hour.
|1||10/05||Jian Pei||Practicing the Art of Data Science||CS@Simon Fraser University|
|7||11/16||Giorgio Quer||Scripps Research|
|9||11/30||Cong Yu||Google Research|
Week 1: Practicing the Art of Data Science
Data science embraces interdisciplinary methodologies and tools, such as those in statistics, artificial intelligence/machine learning, data management, algorithms, computation and economics. Practicing data science to empower innovative applications, however, remains an art due to many factors beyond technology, such as sophistication of application scenarios, business demands, and the central role of human being in the loop. In this talk, I share with the audience some experience and lessons I learned from my practice of data science research and development. First, I illustrate the core value of building domain-oriented, end-to-end data science solutions that can help people gain new interpretable domain knowledge. Second, using network embedding as an example, I demonstrate that the nature of data science practice is to connect challenges in vertical applications with general scientific principles and tools. I also discuss some future directions, particularly about data strategies for enterprises and organizations on data as assets, privacy, fairness, accountability, and transparency.
Dr. Jian Pei is a Professor at the School of Computing Science and an associate member of the Department of Statistics and Actuarial Science, Simon Fraser University, Canada. His expertise is in developing effective and efficient data analysis techniques for novel data intensive applications. He is a research leader in the general areas of data science, big data, data mining, and database systems. He is recognized as a fellow of Royal Society of Canada (RSC) (i.e., the national academy of Canada), the Canadian Academy of Engineering (CAE), ACM and IEEE. He is one of the most cited authors in data mining, database systems, and information retrieval. His research has generated remarkable impact substantially beyond academia. His algorithms have been adopted by industry in production and popular open source software suites. He is responsible for several commercial systems of record-breaking large scale. As a renowned professional leader, he has played important roles in many academic organizations and activities. He is the Chair of ACM SIGKDD and was the Editor-in-Chief of IEEE TKDE. He received many prestigious awards, including the 2017 ACM SIGKDD Innovation Award and the 2015 ACM SIGKDD Service Award. In his last leave-of-absence from the university, he took the executive roles of two Fortune Global 500 companies. He is a mentor of Creative Destruction Lab (CDL).