Seminars

Every month, we welcome you to attend the RMDS Lab Seminar series in which we explore the most innovative developments and applications within data science and artificial intelligence. At these events, you can expect to hear from leaders across data science and AI, representing companies and organizations from IBM to Caltech. Beyond that, the RMDS Lab Seminars allow attendees to network with these experts and the RMDS Lab community.

Sample topics include:

  • Understanding the Applications of Facial Recognition Technology
  • Intersections of Artificial Intelligence and Healthcare
  • Love & Machine Learning: How Data Analytics Impact Online Dating
  • Developing Skills for Success in Data Science​

Training Workshop: Skills with Big Data and AI in Media and Entertainment

This February, RMDS Lab will provide training for individuals who have an interest in the intersection between media and data science/artificial intelligence. A group of experts will help our RMDS community to meet a variety of learning objectives, such as understanding the importance of business insights, creating analytic workflows, and looking into future career paths in entertainment, media, and data science. In order to cover instructor service fees, this event requires a ticket which can be purchased here. All ticket holders will also receive access to a recorded version of this event so that they can return to review specific information when necessary. If you would like to learn more about this learning opportunity or other RMDS Lab learning opportunities, please contact Training Manager Yuki Ma via email at yukima@rmdslab.com

Applications of Big Data and AI in Media and Entertainment​

After the success of the IM DATA Conference and the previous year of RMDS Lab events, we are preparing for an even more productive and exciting 2020! We’re kicking this off with TWO events in the next few weeks, relating to the applications of big data and AI. First, we will host a roundtable discussion with experts from the entertainment and media industry, followed by a chance to network and interact. This event will take place on Jan. 22 at Pasadena City College and you can RSVP to attend by clicking the button on this page. Second, we will host a training seminar where attendees can receive a hands-on learning experience towards developing their knowledge of data science and AI within entertainment and media. This event will take place in early February at Pasadena City College with more details coming soon. We hope to see you at both events! Make sure to bring along your friends and colleagues too! Everyone is welcome in the RMDS Community. Learn more about RMDS Lab and our AI ecosystem platform by visiting GRMDS.org.

IM DATA Conference 2019 Innovative Methods with Big Data and AI​

The 1st ever IM DATA Conference drew over 2,000 attendees across two days filled with enlightening lectures and engaging demos in the most innovative areas of data science and artificial intelligence today 

Here are some highlights from the conference:

• Alex Liu discussed how the RMDS Platform at GRMDS.org is working to resolve pertinent issues in data science such as the Replication Crisis.

• The Correlation to Causality panel featuring students of Turing Award recipient Judea Pearl explained the importance of asking “why?” in the development of machine learning.

• George Djorgovski delivered a fascinating presentation on human cognition and AI and his company Virtualitics was present to give demos of their VR analytics technology. To view speaker presentations or PowerPoints, click here.

Developing Skills for Success in Data Science​

The RMDS Lab November MeetUp event took place in collaboration with the Robert G. Freeman Career Center of Pasadena City College and provided valuable information for community members and local students to learn about gathering skills for success in data science. This event saw a panel of highly accomplished individuals across fields such as AI and data science discuss what aspiring data scientists ought to learn about, the practices that helped them in this industry, and what they might change if they had an opportunity to go back and restart at the beginning of their careers. Daniel Chen of RMDS Lab moderated a panel consisting of Brian Dolan, Phil Bangayan, Nicholas Beaudoin, and Anuj Saini. Event slides can be  downloaded here.

Intersections of Artificial Intelligence and Healthcare​

In October, RMDS Lab gathered a group of experts from the fields of healthcare and artificial intelligence to discuss the ways in which these two fields are rapidly becoming more interrelated. Moderator Martin Devon posed challenging questions to Dr. Anna Farzindar, Dr. Priyanka Mathur, and Dr. C Charles Lin and attendees were given the opportunity to interact directly with the panel after their individual presentations. Dr. Mathur’s slides can be downloaded here and you can learn more about her AI-powered healthcare app MediPocket by visiting this link.

Evolution of Machine Learning​

In August, RMDS Lab partnered with the growing business machine learning company Big Squid to discuss the evolution of machine learning and view a demo of their Kraken platform. Matt McCoy and Kyle Jourdan of Big Squid provided insightful explanations of how the Kraken platform improves a company’s efficiency and ability to effectively interpret analytics through the use of machine learning algorithms. Matt and Kyle’s slides

Newest Developments of Open City Data​

In July, Dr. Jeanne Holm, Senior Technology to Mayor Garcetti of Los Angeles, delivered an enlightening presentation on all of the ways that cities are beginning to use data to improve safety, efficiency, economics, and the lives of citizens.

NASA Earth- Observing Satellite Data Analysis​

In August, RMDS Lab partnered with the growing business machine learning company Big Squid to discuss the evolution of machine learning and view a demo of their Kraken platform. Matt McCoy and Kyle Jourdan of Big Squid provided insightful explanations of how the Kraken platform improves a company’s efficiency and ability to effectively interpret analytics through the use of machine learning algorithms. Matt and Kyle’s slides