Rice Data Science Conference
Data science is rapidly evolving as an essential interdisciplinary field, where advances often result from combinations of ideas from several disciplines. This conference, to be hosted annually by Rice University, is for professionals and practitioners working in machine learning, deep learning, data mining, artificial intelligence, or big data problems broadly. Recognizing that discovery and innovation happens at interfaces of disciplines and communities, the conference aims to bring together a diverse set of people from multiple communities spanning academia and industry.
The conference will be a research, development, and innovation (RD&I) gathering, bringing together university and research labs (technology developers), key industry verticals (technology consumers), and IT industry (technology providers) that are looking at opportunities created by advances in AI, data analytics, machine learning and deep learning.
Building on the model we have used very successfully in our annual Oil & Gas HPC Conference, the Data Science Conference will strike a balance between applied and fundamental data science, and academia and industry. This combination of research and application discussion will create a venue for networking, collaboration and partnership building.
With Houston being the fourth-largest city in the U.S., and recognized as a hub for energy, health, space, finance and transportation the conference will help foster collaboration and networking. In order to best capitalize on this dynamic industry landscape, technical themes will evolve based on local, regional and national needs.
Associate Professor, Statistics
Interactive and Dynamic Visualization for Clustering
*NEW* Eric Berger
Editor, Space City Weather
What Did the Public Really Know About Harvey, and How Can We Better Inform Them?
The Walter E. Koss Professor and Distinguished Professor of Mathematics
Texas A&M University
Optimal Data Assimilation Algorithms
Director of Data Intensive Computing, Texas Advanced Computing Center
University of Texas at Austin
Wrangling Data at Texas Advanced Computing Center
Justin Gosses & Yulan Lin
Practical Considerations for Data Science Consulting and Innovation in a Large Organization
Associate Professor, Computer Science
University of Texas at Austin
Learning Discrete Markov Random Fields with Optimal Runtime and Sample Complexity
Engineer, Two Sigma (author of the “pandas” python data analysis library)
Shared Infrastructure for Data Science
Associate Laboratory Director for Computing, Environment and Life Sciences
Argonne National Laboratory
Integrating Simulation Data Analysis and Deep Learning in Science and Engineering Applications
Professor, Computer Science
Using big data and machine learning to build spatially fine-grained prediction models of wind and flood damage risk
For more information and registration, visit: http://dsconference.rice.edu/
BioScience Research Collaborative Building
6500 Main Street
Houston, TX 77005
Jan Erik Odegard