CUBRC
4455 Genesee Street, Suite 106, Buffalo, NY, 14225, United States
Overview
CUBRC is a people in data but still starving for information. As the world continues to become more networked, the availability and access to data can overwhelm people and obscure the information they are seeking. CUBRC sits at the forefront of Multisource Information Fusion RDT&E which encompasses a class of technologies which address these issues of turning massive amounts of data into streamlined information for an end user in the civilian and military domains. Personnel CUBRC's Information Exploitation Group consists of skilled computer scientists, engineers, and subject matter experts who specialize in the development of software and hardware systems supporting: * Modeling and Simulation * Cyber Network Defense * Maritime Domain Awareness * Intelligence Analysis * Healthcare IT Facilities The Center for Multisource Information Fusion (CMIF) was jointly established in 1997 by CUBRC and the University at Buffalo as the first Information Fusion center of its kind. CMIF focuses on advanced RDT&E in multiple-source information processing environments, such as those seen frequently in defense applications for advanced surveillance and reconnaissance systems but also in robotics, civil infrastructure systems, medical monitoring systems, intrusion detection systems, intelligent transportation systems, and in environmental monitoring applications, among others. Work in the center is performed in two separate CMIF facilities. One located at the University at Buffalo, where basic research is performed in an unclassified, academic environment and in a second facility, where applied research is performed to support long-term technology transition.
- Total investments
- 2
- Lead investments
- 2
- Investments · 12mo
- 0
- Active investors
- 0
Sector focus
- Health Care
- Information Technology
- Robotics
Investment portfolio
- TROVE Predictive Data Science
Led · Equity · Aug 2013
TROVE offers a Sunstone Platform that fuses multi-source client data with over 2,000 external attributes to process massive volumes of structured, semi-structured, unstructured, and high-velocity streaming data. Its proprietary algorithms and self-learning models identify correlations and predict individual customer behaviors, enabling clients to act on data-driven insights. TROVE’s technology has reportedly increased energy-efficiency program response rates by nearly 300% and delivered energy reduction per customer of 37%. The company’s solutions enable benchmarking, forecasting, and optimization of individual energy assets to reduce total building energy consumption. TROVE’s platform is positioned for use by utilities and commercial clients to better manage resources and assets across large portfolios. TROVE Predictive Data Science (formerly GridGlo) fuses internal customer data with external third-party attributes and applies proprietary predictive science through its TFX platform. The platform ingests structured, semi-structured, unstructured, and high-velocity streaming data and leverages over 2,000 external attributes. TROVE’s analytics and algorithms power applications including program optimization, fraud identification, demand forecasting, and customer segmentation and are delivered through web-interface tools. Today the platform is helping utility companies optimize programs, identify fraud, forecast demand, and segment customers. The company raised $1.1M in new capital to develop new applications for the platform, with funding from new strategic investors and existing investor/partner CUBRC, Inc. CUBRC is described as a research organization with deep multi-source data fusion expertise initially developed for the U.S. Department of Defense and other government agencies. TROVE was founded in 2010 and is headquartered in Delray Beach, FL.
- GridGlo
Led · Seed · May 2011
GridGlo sells software and services that help utilities see how and why customers—primarily homeowners—use electricity in real time and provides an Energy People Scoring Mechanism (EPM) intended to benchmark customer energy behavior. The EPM aggregates four criteria—consumption, efficiency, engagement and predictability—and GridGlo says it starts with roughly 1,300 data criteria to model customers. The company focuses strictly on serving utilities rather than offering consumer-facing apps, and its models infer household attributes (e.g., household size, square footage, rooftop solar, vehicle type) to explain usage patterns. GridGlo is still in a proof-of-concept phase and the CEO, Isais Sudit, acknowledged the company cannot yet provide firm evidence that its EPM will change customer behavior. Financially, GridGlo recently received a $1.2 million seed investment from New York research firm CUBRC and established a strategic partnership with that investor. The company hopes the EPM will become a standard metric for utilities akin to a FICO-style score for energy.
Team
No current team members are available.