RVA's leading Data Conference Returns March 2023.
Join the technology community March 2023, for our next conference full of innovation, in-depth workshops, and incredible presentations given by a diverse line-up of expert speakers + thought leaders at rvatech/DataSummit. Get immersed in the world of Data & discover the trends, and innovations shaping the future of technology!
About the Event
Tech is continuously evolving and innovating, making it essential to stay ahead of the curve and explore the year’s trending technology. In 2023, artificial intelligence and data science are continuing to move from the realm of university research to be a critical part of a software developers’ toolkit and a key differentiator for forward-leaning companies.
9:30 AM – 5:oo PM
Discount Rate – The special discounted rate for student tickets is 25% off. That’s $50 off the regular ticket price!
Discount code: DATA23STUDENT
**All student tickets must provide a student .edu email address when registering**
Call for Proposals
Interested in Speaking at the 2023 DataSummit?
Our conference Call for Proposals is now open! The deadline to submit is February 3rd.
Science Museum of Virginia – Dewey Gottwald Center
2301 W Leigh Street Richmond, VA 23220
Where exactly am I going?
The Dewey Gottwald Center is located behind the museum. Parking lot entrance located off of West Leigh Street or DMV Drive.
Where do I park?
Science Museum parking is ample and free. Spaces near the front of the building are designated for guests with mobility challenges. All other guests should park in the lot located directly behind the Science Museum off West Leigh Street or DMV Drive.
You may also view a detailed parking map here.
Call for Speakers
What we're looking for:
This is and has always been a practitioner-focused event. We want to provide as much learning and education as possible. The focus on this is making learning accessible and helpful. This event is NOT about product showcases or hand-wavy leadership talks. Potential talks ought to be applicable to building models or managing teams of real data scientists and engineers with real-world problems. All talks submitted will be viewed from that perspective.
Who should submit:
Everyone is encouraged to submit a proposal. Our primary goal is to have excellent sessions that inform and engage our attendees. Thus, the speaker selection committee will be favoring speakers who have a record of delivering excellent talks, but we also understand that everyone has to start somewhere. If you are a first-time speaker, please make sure your proposal is compelling and communicates why you are the right person to deliver it.
We are hoping to have speaker pool that is representative of our local developer population and showcases some regional talent, but we welcome submissions from anyone anywhere. If you have an idea for a session, please submit it!
Submit your Speaker Proposal:
Machine Learning and Advanced Analytics
Description: This track will focus primarily on applied machine learning and advanced analytics applications. Topics of interest would include: * Applied machine learning and deep learning, natural language processing, computer vision, applied predictive modeling and inferential statistics, and operations optimization. * Data visualization and exploratory analysis * Common packages and frameworks for machine learning, data science, and advanced analytics
Data Engineering, Operations, and Management
Description: This track will focus primarily on data engineering, data and data science operations, and data platforms and management. Topics of interest would include: * Machine learning and data science operations * Resilient pipelines for data-intensive and analytic workloads * Data platforms to support data-intensive and analytic workloads
Data Leadership and Organization
Description: This track will focus primarily on leadership, organizational structure, team management, and workforce development for data-focused organizations. Topics of interest would include: * Building and managing data-centric organizations * Implementing sound data and analytics strategies at various stages of organizational maturity * Building skills and developing an existing workforce to support modern data and analytics techniques
Description: This track will focus primarily on any topics not covered in the above tracks, including any novel/emergent focal areas for data and analytics practitioners and leaders. Topics of interest could include: * Analytics explainability and bias * Societal impacts of algorithmic-driven products * Environmental impacts of machine learning research
Vice President Machine Learning Experience Design and Responsible Artificial Intelligence
Dr. Scott Penberthy
Director, Applied AI, Office of the CTO
Dr. Timothy Haas
University of Wisconsin
Head of Developer Relations
Dr. Santona Tuli
Staff Data Scientist
Associate Professor of Information Systems
VCU + Anything Awesome
Machine Learning Engineer
Machine Learning Engineer
Lead multi-cloud Developer Advocate
Engineering Analytics & Modeling
Deputy Chief Data Officer
Virginia Office of Data Governance and Analytics
Senior Vice President of Engineering
Director, Office of Data Quality and Visualization
at Commonwealth of Virginia
Data Engineer, Office of Data Quality and Visualization
at Commonwealth of Virginia