Sunday, March 10 to Tuesday, March 12, 2019
This three day conference will include workshops, poster presentations, parallel sessions and plenary talks. In addition to the traditional open exchange of ideas of an academic conference, a key outcome of this conference is the forging of new collaborations across the traditional divide between industry and academia.
Preliminary Schedule
Sunday, March 10, 2019
11:00am-12:00pm Registration
12:00pm Workshop Session 1
Deep Learning
Augmenting Data Analysis with Data from the US Census
2:30pm Workshop Session 2
Machine Learning in Python
Natural Language Processing
4:30pm Check-in and Registration
5:30pm Opening Plenary and Reception
Monday, March 11, 2019
8:30 - 9:00am Registration and Coffee & Pastries
9:00 - 11:00am Concurrent Sessions
11:00-11:15am Coffee and Refreshments
11:15-11:30am Welcome Remarks
11:30-12:30pm Plenary
12:30-2pm Lunch
2:00-4:00pm Concurrent Sessions
4:30-6:30pm Poster Session and Reception
7:00pm Off-site Reception
Tuesday, March 12, 2019
8:30 - 9:00am Registration and Coffee & Pastries
9:00 - 11:00am Concurrent Sessions
11:00-11:30am Coffee and Refreshments
11:30-12:30pm Plenary
12:30-2pm Lunch
2:00-4:00pm Concurrent Sessions
4:00-6:30pm Closing Panel and Reception
Tracks and Sessions
Deep Learning
From advances in computer vision such as recognizing street signs, 3D objects, to image classification, to advances in sentiment and NLP processing, deep learning (DL) or deep machine learning is rapidly being developed and applied to many areas of scientific and industry application. DL is largely considered a collection of machine learning algorithms largely based on neural networks to model nonlinear and hierarchical features in a data set. The primary family of DL architectures include Convolution Neural Networks, Recurrent Neural networks and Deep Belief networks. This session will bring together academics and industry practitioners to discuss recent advances and uses of DL.
Network Analysis
Network data arises from diverse systems including online social platforms, biological ecosystems, genetic regulation, consumer economies, and transportation infrastructures, and network-data analytics is pervasive throughout industry and academic research efforts. Because of the inherent high-dimensionality and complex correlation structure of networks, network analysis remains an active area of research and development. This field is extremely interdisciplinary with different communities focusing on various aspects including the statistical and mathematical rigor of analyses, the scalability of algorithms for networks that can potentially contain millions of nodes, and the development of domain-specific techniques catering to the uniques demands of diverse applications. Drawing from an interdisciplinary community, this track brings together ten academic and industry researchers to discuss state-of-the-art methods for network science. Talks will cover a variety of topics including general theory, methodology development, algorithm design, and best-practices for diverse applications. The speakers represent a healthy mix of established names and early-career researchers and hail from statistics, biostatistics, computer science, and applied mathematics backgrounds.
High Dimensional Signal Processing and Machine Learning
The challenge of Big Data is not simply that it is big. Many modern datasets are characterized by high-dimensionality, including data from texts, videos, images, and tweets. Dimensionality poses a serious challenge because the amount of data needed to support classical methods of inference grows very rapidly with the number of variables involved - the so-called “curse of dimensionality.” Over the last few decades, many innovative statistical methods have been proposed for high dimensional settings. For example, many binary classification problems involve data that are not linearly separable. However, a mapping to a higher dimensional space can induce separability. This track will bring together leaders in high dimensional analysis to describe novel ideas from signal processing to tackle the challenges of high dimensional data in machine learning.
Design of Experiments
Amidst the present data revolution decisions are increasingly becoming data-driven. However, much of the insight drawn from this influx of data is correlational, when what is often desired is causal inference. Designed experiments provide a framework for the collection and analysis of data that facilitate this type of inference. In the field of data science, experimentation -- colloquially referred to as A/B testing -- is an extremely effective tool for the development and optimization of products and processes. With speakers from both industry and academia, this session will explore modern problems and methodological advances in the design and analysis of experiments with applications to data science.
Technology and Data Science - Accelerants of the Sustainable Development Goals (SDGs)
In this track we will hear from academics from Stanford and Berkeley, tech industry leaders and other thought leaders who are bringing transformative change in advancing the 17 UN Sustainable Development Goals - global goals which are tackling the biggest challenges of our time and aiming to tackle poverty, hunger, access to quality education, healthcare, water, energy for all, sustainable development, gender equality and social justice as well as conserve our lands and forests. For the first time we have a global normative framework that most governments and the UN have adopted and committed to for comprehensive positive change - these ambitious goals recognize the linkages between the various goals and have the spirit of leaving no one behind. Technology, innovation and data science could be powerful tools for advancing and scaling solutions advancing the SDGs. On the other hand, technology can also make things worse if not designed and deployed in a thoughtful manner.
Data Science in Marketing
Big data, the Internet of Things, and social media have all forever changed the art and science of marketing. Today's marketing decision-making happens in real time, every day. Data science give marketers a hands-free way to quickly and effectively respond to data from a customer or potential buyer and tailor fit a product and buying experiences. Buyers on the fence can automatically receive special incentives to buy, and other customers can be directed to related services based on data-driven insight. This track brings together academic and industry researchers to discuss state-of-the-art methods for data science in marketing. A variety of topics include social media marketing, digital product customer service, market research analytics, networked experiment and multichannel customer analytics.
11:00am-12:00pm Registration
12:00pm Workshop Session 1
Deep Learning
Augmenting Data Analysis with Data from the US Census
2:30pm Workshop Session 2
Machine Learning in Python
Natural Language Processing
4:30pm Check-in and Registration
5:30pm Opening Plenary and Reception
Monday, March 11, 2019
8:30 - 9:00am Registration and Coffee & Pastries
9:00 - 11:00am Concurrent Sessions
11:00-11:15am Coffee and Refreshments
11:15-11:30am Welcome Remarks
11:30-12:30pm Plenary
12:30-2pm Lunch
2:00-4:00pm Concurrent Sessions
4:30-6:30pm Poster Session and Reception
7:00pm Off-site Reception
Tuesday, March 12, 2019
8:30 - 9:00am Registration and Coffee & Pastries
9:00 - 11:00am Concurrent Sessions
11:00-11:30am Coffee and Refreshments
11:30-12:30pm Plenary
12:30-2pm Lunch
2:00-4:00pm Concurrent Sessions
4:00-6:30pm Closing Panel and Reception
Tracks and Sessions
Deep Learning
From advances in computer vision such as recognizing street signs, 3D objects, to image classification, to advances in sentiment and NLP processing, deep learning (DL) or deep machine learning is rapidly being developed and applied to many areas of scientific and industry application. DL is largely considered a collection of machine learning algorithms largely based on neural networks to model nonlinear and hierarchical features in a data set. The primary family of DL architectures include Convolution Neural Networks, Recurrent Neural networks and Deep Belief networks. This session will bring together academics and industry practitioners to discuss recent advances and uses of DL.
Network Analysis
Network data arises from diverse systems including online social platforms, biological ecosystems, genetic regulation, consumer economies, and transportation infrastructures, and network-data analytics is pervasive throughout industry and academic research efforts. Because of the inherent high-dimensionality and complex correlation structure of networks, network analysis remains an active area of research and development. This field is extremely interdisciplinary with different communities focusing on various aspects including the statistical and mathematical rigor of analyses, the scalability of algorithms for networks that can potentially contain millions of nodes, and the development of domain-specific techniques catering to the uniques demands of diverse applications. Drawing from an interdisciplinary community, this track brings together ten academic and industry researchers to discuss state-of-the-art methods for network science. Talks will cover a variety of topics including general theory, methodology development, algorithm design, and best-practices for diverse applications. The speakers represent a healthy mix of established names and early-career researchers and hail from statistics, biostatistics, computer science, and applied mathematics backgrounds.
High Dimensional Signal Processing and Machine Learning
The challenge of Big Data is not simply that it is big. Many modern datasets are characterized by high-dimensionality, including data from texts, videos, images, and tweets. Dimensionality poses a serious challenge because the amount of data needed to support classical methods of inference grows very rapidly with the number of variables involved - the so-called “curse of dimensionality.” Over the last few decades, many innovative statistical methods have been proposed for high dimensional settings. For example, many binary classification problems involve data that are not linearly separable. However, a mapping to a higher dimensional space can induce separability. This track will bring together leaders in high dimensional analysis to describe novel ideas from signal processing to tackle the challenges of high dimensional data in machine learning.
Design of Experiments
Amidst the present data revolution decisions are increasingly becoming data-driven. However, much of the insight drawn from this influx of data is correlational, when what is often desired is causal inference. Designed experiments provide a framework for the collection and analysis of data that facilitate this type of inference. In the field of data science, experimentation -- colloquially referred to as A/B testing -- is an extremely effective tool for the development and optimization of products and processes. With speakers from both industry and academia, this session will explore modern problems and methodological advances in the design and analysis of experiments with applications to data science.
Technology and Data Science - Accelerants of the Sustainable Development Goals (SDGs)
In this track we will hear from academics from Stanford and Berkeley, tech industry leaders and other thought leaders who are bringing transformative change in advancing the 17 UN Sustainable Development Goals - global goals which are tackling the biggest challenges of our time and aiming to tackle poverty, hunger, access to quality education, healthcare, water, energy for all, sustainable development, gender equality and social justice as well as conserve our lands and forests. For the first time we have a global normative framework that most governments and the UN have adopted and committed to for comprehensive positive change - these ambitious goals recognize the linkages between the various goals and have the spirit of leaving no one behind. Technology, innovation and data science could be powerful tools for advancing and scaling solutions advancing the SDGs. On the other hand, technology can also make things worse if not designed and deployed in a thoughtful manner.
Data Science in Marketing
Big data, the Internet of Things, and social media have all forever changed the art and science of marketing. Today's marketing decision-making happens in real time, every day. Data science give marketers a hands-free way to quickly and effectively respond to data from a customer or potential buyer and tailor fit a product and buying experiences. Buyers on the fence can automatically receive special incentives to buy, and other customers can be directed to related services based on data-driven insight. This track brings together academic and industry researchers to discuss state-of-the-art methods for data science in marketing. A variety of topics include social media marketing, digital product customer service, market research analytics, networked experiment and multichannel customer analytics.