Log in to bookmark your favorites and sync them to your phone or calendar.

Room C [clear filter]
Sunday, October 22

9:45am PDT

Implementing Artificial Intelligence with Big Data
Human beings have been doing research on Artificial Intelligence for decades, and the algorithms that are commonly used today to implement AI products are not new. It is with the large volume of data available today, along with the computing power, we are finally able to implement the methodology to achieve more accurate results, and build AI products. This talk will go over the basics on machine learning and deep learning for the purpose of AI, and how Big Data can improve the performance.

avatar for Raymond Fu

Raymond Fu

Big Data Architect, Trace3
Raymond Fu is a seasoned IT professional specializing in big data, artificial intelligence, and enterprise architecture. As an innovative technology builder balanced by business acumen, his ten-year corporate career with Bank of America was highlighted by leading many data integrations... Read More →

Sunday October 22, 2017 9:45am - 10:15am PDT
Room C Room C

10:20am PDT

Deep Learning in Spark with BigDL
As Deep Learning architectures grow in popularity, companies are beginning to use it with their existing data to gain deeper insights. BigDL enables companies with existing Spark clusters to run Deep Learning jobs where the data sits. You no longer have to export your data outside of Spark. Real-time deep learning pipelines are now possible too! In this talk, Dave will explore the common Neural Network models used by companies who already have Hadoop or Spark clusters.

avatar for Dave Nielsen

Dave Nielsen

Sr. Developer Advocate - Trust Analytics Platform, Intel Corporation
As a Technical Program Manager in the Big Data Technologies group at Intel, Dave oversees partnerships and programs that help customers find out about and get started with Deep Learning in Spark. Prior to Intel, Dave ran Developer Relations at companies like Redis Labs, Strikeiron... Read More →

Sunday October 22, 2017 10:20am - 10:50am PDT
Room C Room C

10:55am PDT

Persuasion Modeling
Political campaigns in the U.S. have made extensive use of persuasion modeling in recent election cycles. These same modeling approaches are now making their way into commercial applications. In this presentation, I discuss political persuasion modeling, and present case studies from recent political campaigns. I'll then turn to potential commercial applications, and discuss best practices for data security, modeling, validation, and evaluation.

avatar for Michael Alvarez

Michael Alvarez

Professor, Caltech
A professor of political science at Caltech, R. Michael Alvarez has extensive research and professional experience in statistics, research methodology, and data science. His research has focused on data science as applied to survey methodology, political behavior, electoral campaigns... Read More →

Sunday October 22, 2017 10:55am - 11:25am PDT
Room C Room C

11:30am PDT

Operationalizing your Data Lake: Get Ready for Advanced Analytics
Data without analytics are wasted resources. Analytics without a modern data architecture is useless. With next-gen capabilities like machine learning and automation emerging, how do you set yourself up for success?

Many enterprises are finding success with an architecture that has a data lake at the core because a data lake offers the agility and scalability that is required for big data. However, not all data lakes are able to deliver analytics at the speed needed to make a disruptive difference among their competition.

With a majority of Hadoop implementations failing to make it to production, it is critical to add a big data management platform to operationalize, automate and optimize your data lake for success.


Parth Patel

Big Data Solutions Engineer, Zaloni
He has extensive experience in architecting analytics-ready next-generation data lakes incorporating on-premise or public cloud (AWS, Azure, GCP) infrastructure to meet enterprise needs. Previous experience in network solutions architecting for Orologic, and Vitesse; and technology... Read More →

Sunday October 22, 2017 11:30am - 12:00pm PDT
Room C Room C

12:00pm PDT

Lunch Break
Sunday October 22, 2017 12:00pm - 1:00pm PDT
Room C Room C

1:00pm PDT

Machine Learning in Healthcare and Life Science
AI and ML opportunities and challenges in Pharmaceutical and Life Sciences, focus on use cases in drug discovery, clinical development, and real-world evidence.

avatar for Andrew Zhang

Andrew Zhang

Big Data Analytics Solution Architect, IBM
Andrew Zhang is a solution architect with IBM Analytics, his specialty is data science, machine learning and open source technologies such as Apache Spark and Hadoop. He consults clients in healthcare, life sciences, and public sector and provides cloud analytics solutions with IBM... Read More →

Sunday October 22, 2017 1:00pm - 1:50pm PDT
Room C Room C

2:00pm PDT

MCL Clustering of Sparse Graphs
The increasing need for clustering in several scientific domains has inevitably driven the creation of innovative algorithms, each designed to perform more efficiently in certain applications. More specifically, in many applications, the data entities involved can be portrayed effectively by a graph as a collection of nodes and edges. One of the most established algorithms for graph clustering problems is the Markov Cluster Algorithm (MCL).

When dealing with large and complex datasets, the underlying graphs can easily reach proportions that independent computing systems are inadequate to deal with. Additionally, the graphs encountered are typically sparse: the number of edges is far smaller than might be possible in a fully-connected graph. Consequently, there is a concrete need for algorithms that are designed to handle sparse graph clustering utilizing distributed computing resources.

Our motivation was the development of a distributed architecture, able to accommodate large and sparse graphs, to actualize the MCL and R-MCL algorithm. The Apache Spark framework was chosen due to its ability to utilize distributed resources and its proven track record.

Although Spark is a framework capable of handling massive datasets, it currently does not provide rich support for computation with sparse matrices and sparse graphs. Hence, methods have been implemented to enable the exploitation of sparse adjacency matrices in distributed sparse matrix multiplication, a critical component of MCL. The proposed solution can handle arbitrarily large inputs, provide almost linear speed-up with the addition of computational resources and output results directly comparable to the non-distributed reference MCL implementation.

avatar for Athanassios Kintsakis

Athanassios Kintsakis

Machine Learning Engineer, Capital One Financial
Athanassios Kintsakis is an ECE BSc/MSc graduate, and Ph.D. Candidate in the field of statistics and machine learning applied in bioinformatics at the Aristotle University of Thessaloniki. He has co-authored numerous journal publications, presented at international conferences and... Read More →

Sunday October 22, 2017 2:00pm - 2:30pm PDT
Room C Room C

2:40pm PDT

Data Science at City Scale
As a data scientist at the City of Los Angeles, we see a novel form of big data- that is, a novel variety of data.This talk will analyze three projects done at the city.
1) Predicting Displacement
2) Easing Traffic
3) Targeting Audits

avatar for Hunter Owens

Hunter Owens

Data Scientist, City of Los Angeles
Hunter Owens is a Data Scientist for the City of Los Angeles. Prior to joining the City, he worked for the Center for Data Science and Public Policy, KIPP NJ and Obama for America. He spends his weekends biking around Los Angeles and making maps.

Sunday October 22, 2017 2:40pm - 3:10pm PDT
Room C Room C

3:20pm PDT

AI-Powered Future Space Exploration
Space is the final frontier whose exploration requires disrupting technology advancement in many areas including AI. In this talk we will present a historical flashback of AI's application in the past and current space exploration, explain its unique technical challenges, and describe some viable use cases and emerging opportunities of future space exploration enabled by AI as a game-changing technology.

avatar for Yutao He

Yutao He

Sr. Research Technologist, NASA/JPL
Dr. Yutao He is currently Senior Research Technologist at NASA/Jet Propulsion Laboratory (JPL), leading the research and development of advanced computing technology for future NASA space missions. He is also an adjunct faculty member at UCLA and CSULA. His current research interests... Read More →

Sunday October 22, 2017 3:20pm - 3:50pm PDT
Room C Room C
Filter sessions
Apply filters to sessions.