By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Cookie Policy and Privacy Policy for more information.
PreferencesDenyAccept
HomeArticles
Another great year at the Rice Data Science Conference

Another great year at the Rice Data Science Conference

Another great year at the Rice Data Science Conference

For the third year in a row, Arundo attended the Rice Data Science Conference. Read more about the themes and trends this year.

For the third year in a row, Arundo attended the Rice Data Science Conference this month. The Rice Data Science Conference is an annual conference put on by the Ken Kennedy Institute at Rice University in Houston. The conference is organized with help from members of the wider Houston data science community and I had the opportunity to join the organizing committee once again this year.

Houston is a world capital of energy, health care, and space technology, among other things. The Rice Data Science Conference has brought together people from those industries, along with academics, students, and others beyond Houston to share their latest data science related work, cross pollinate, and build and even stronger community. It’s always interesting to see people from cancer care discussing the similarity of their problems with someone from oil and gas or someone talking to a professor of computer science to see if the latest academic developments are applicable to some real-world problems in urban engineering.

photograph of Rice University and the Texas Medical Center by David Daniel Turner under the CC BY-SA 3.0 license
Photograph of Rice University and the Texas Medical Center by David Daniel Turner under the CC BY-SA 3.0 license.

THEMES AND TRENDS THIS YEAR

As it was the past two years, the 2019 conference was held at the BioScience Research Collaborative Building, just off of Rice Campus, in the Texas Medical Center, the largest medical complex in the world. This year there were several tracks during the two day event schedule. Some of the themes we saw this year included:

  • The application of machine learning to natural disaster related problems
  • Deep learning applied to all manner of medical and healthcare related problems
  • Fundamental issues in improving reinforcement learning
  • Improving large scale training of deep neural networks from both an efficiency and resources point of view
  • Combining physics-based models with learning models

arundo-poster-Rice-DS-conf-2019

We were happy to present a poster based on the work of our summer intern, Conrad Goffinet from MIT, about creating an on-prem, specialized optical character recognition system using synthetic training data, and had several good discussions with others working on related problems.

The conference also offered us a chance to meet some new (and old) Houston-area startups and teams from established companies that are working in data science and related areas. Seeing the emergence of these teams and the growing Houston data science community is always nice.

RICE IS GREAT

This was the third year of the conference and the third year that Arundo both attended and sponsored the conference at Rice. Being a Houston (and Oslo) based company, Arundo has close ties to Rice, including many Rice graduates (go Owls) and are always happy to have a chance to work with Rice.

We are looking forward to another strong showing next year at the 2020 Rice Data Science Conference. Hope to see you there as well...

Related articles

Show all articles
Building an Industrial AI Companion using Arundo Foundation

Building an Industrial AI Companion using Arundo Foundation

While large language models (LLMs) may struggle to generate insights directly from time-series data, their strong pattern-recognition capabilities make them well-suited for identifying and synthesizing already existing insights. We therefore argue that an architecture featuring a separate graph-based domain model and time-series data storage is the ideal architecture for a generalized AI companion serving the heavy asset industry: An agentic AI can first identify relevant nodes (assets, sensors, and a large variety of pre-computed models) for answering a user prompt using a mixture of APIs, query language generation, semantic search, and graph traversal. Only after this filtering step is time-series data accessed and retrieved to synthesize an answer.
Revolutionizing Heavy Asset Maintenance: The Power of LLM-based Virtual Assistants

Revolutionizing Heavy Asset Maintenance: The Power of LLM-based Virtual Assistants

How Large Language Models (LLMs) can power virtual maintenance assistants for enhanced equipment issue diagnosis and root-cause analysis.
Navigating Sustainability: The Path to Scope 3 Emissions Management for Maritime Leaders

Navigating Sustainability: The Path to Scope 3 Emissions Management for Maritime Leaders

In the race to combat climate change, the maritime industry stands at a pivotal crossroads. As one of the most energy-efficient modes of transport, maritime shipping is crucial to the global economy, yet it remains a significant and growing source of greenhouse gas (GHG) emissions.