Through Data Science Implementation and Commercialization (DSIC) services, RMDS Lab has pioneered the development of data science ecosystems and is offering extensive project support to engineer solutions and to bring them to market. With resources and tools in our robust scientific community, we have the capability to advance your project impact through data impact consultation and AI-powered services that evaluate your needs, develop and operationalize solutions, and maximize value-add for company goals. The DSIC services expand your company’s reach to find resources and new technologies with the goal of reshaping how commercial clients, researchers, and industry data scientists work together to develop high-impact data science solutions. We have a long-standing ecosystem of data scientists, engineers, and subject matter experts who are eager to be your trusted partner, enhance your research, increase the success rate of your work, and leverage the power of analytics. Each of our DSIC consultant teams has experience delivering and commercializing E2E data science solutions coupled with industry insight and business consulting expertise.
|Application||10 business days||After your application is chosen, it will undergo additional screening and kickoff meeting||1-2 weeks|
|Phase 1||1-3 weeks||Problem Discovery||1-2 weeks|
|Phase 2||4-8 weeks||Proof of concept or prototype||1-2 weeks|
|Phase 3||4-8 weeks||Operationalize solution with testing||2-4 weeks|
How to Apply
1. Fill out the application form
2. We will take 10 business days to review your application. If you are qualified and your application is accepted, we will contact you as soon as we can to set up an additional screening meeting.
3. Kick off meeting with you/your team for the future collaboration.
Past Funded Opportunities
Client: Los Angeles City, Esri, SafeGraph
Challenge: The client seeks to create an innovative solution to determine the risk of exposure to COVID-19 in locations in and around the City of Los Angeles. As the city relaxes "Safer at Home" orders, it wants to help people assess the risks of going to various locations (like their local gym, or supermarket) in real time.
Solution: This solution is an ensemble of machine learning and statistical epidemiological models and uses data about infections, testing, mobility and social distancing, comorbidity, socioeconomic factors, and other relevant sources to estimate levels of risk. The resulting interactive map shows community-level and POI-level risk of exposure to COVID-19 in Los Angeles County as well as trend analysis and locations of test centers and hospitals.
Client: Center for Geographic Analysis at Harvard University and Future Data Lab
Challenge: The workflow-based data analysis project aims to provide a new approach for efficient data analysis and replicable, reproducible, and expandable research.
Solution: The implementation of KNIME data analysis workflows for selected case studies with government statistics of China, which include population and environment, urban and rural development, green energy transition, and county-level GDP estimates with nighttime light data.
Client: Research group from leading center for robotic exploration of space
Challenge: The client looked to create a more nuanced hurricane forecast solution that serves the needs of insurance companies and improves the existing model used by the National Hurricane Center.
Solution: Using various climatology features and other relevant spatial-temporal data, we developed an algorithm and interactive map visualization to predict changes in hurricane intensity in the next 24 hours as well as associated economic loss using machine learning and statistical models.
High Impact GRMDS Members
CEO, RMDS Lab
Dr. Alex Liu is one of the world’s top experts for big data analytics and machine learning as applied to business and social research, especially to produce positive social impacts. He is well-regarded as a thought leader and distinguished data scientist, certified by IBM and the Open Group. He is a pioneer and lead developer of data science ecosystem approaches as well as the RM4Es with AI.
Co-Director of MSBA program at Loyola Marymount University
Sijun Wang is a professor of marketing at Loyola Marymount University and chair of the Department of Marketing and Business Law. Her expertise includes relationship marketing and service marketing via data science.
Senior Data Engineer, DoorDash
Ari has helped develop a geospatial telemetry pipeline and data warehouse in 3 weeks that processed over 70 billion events per week using Kafka, Flink, and Presto hosted on Kubernetes and provisioned by Terraform and Helm.
Data Scientist, Grainger
Yen-Chen has built and standardized an ETL module for recommendation system and saved at least 50% data fetching time.