Data Scientist 2 reviews. Raluca-Mihaela Gordan At Duke, research in this area has focused on four broad directions: Duke Computer Science professors Kartik Nayak, Ashwin Machanavajjhala, and Jun Yang are collaborating with Lavanya Vasudevan from Duke FMCH in an NSF-funded COVID-19 exposure detection project, Poirot. The online content may be viewed in the context of self-learning, but the principal objective of the online +DS modules as previewing prior to … Researchers: Mary Knox (Pratt School of Engineering), Bohao Huang (Pratt School of Engineering), Leslie Collins (Pratt School of Engineering), Kyle Bradbury (Managing Director of the Energy Data Analytics Lab, Energy Initiative), Richard Newell (in his former role as Energy Initiative Director). Much of this research spans multiple disciplines and is collaborative in nature.

This includes work in healthcare, criminal justice, fake news, and in other areas. Projects connected with Duke’s unique Data+ and Bass Connections programs accomplish lab research objectives while deepening undergraduate and graduate students’ research, project management, and communications skills.

Modern water heaters come in many forms ranging from conventional storage water heaters to tankless and heat pump water heaters.

Duke's Energy Data Analytics Lab is also creating a pipeline of talented innovators. These techniques may allow building owners to cheaply automate building energy audits, identify energy efficiency improvements, predict equipment failure, and maximize cost savings by using less power or using it at a time when the cost is lower. Duke Computer Science professors Jun Yang and Ashwin Machanavajjhala are collaborating with Lavanya Vasudevan from Duke Community and Family Medicine and Global Health and also a multi-institution, multi-disciplinary research team in an NSF ~$1M project award to help combat misinformation. Associate Professor. The team’s work could inform other researchers’ efforts to tackle a range of topics beyond energy, identifying and measuring nearly any physical object that’s visible from above— like monitoring, assessing, managing, and predicting urban development or changes in agricultural production. ), Duke WiSE: Women in Science Virtual Career Panel and Discussion, Vinson & Elkins: Energy Transactions & Projects, Watch: A Wider Lens on Energy - Adapting Deep Learning Techniques to Inform Energy Access Decisions, Energy Data Analytics Symposium abstracts due 2/21.

As a fellow, Conitzer joins a select group of the top 1% of 2019 ACM professional members recognized for far-reaching accomplishments that define the digital age. Job Title. Horizontal drilling and hydraulic fracturing have fundamentally changed the oil and gas industry, with a significant impact on natural gas markets and pricing. Associate Professor. 140 Science Drive Their methods can be used to improve solar PV estimates and aid government agencies and power grid independent system operators (ISOs) in evaluating the state of distributed PV deployment and use that information for planning purposes to increase system reliability and resilience. Faculty Director Dr. Billy Pizer, 919-613-9286. Electric meter data, advanced thermostats and other components of modern smart grid and systems connected in an "internet-of-things" have the potential to enable significant insights and energy automation in buildings. The Energy Data Analytics Lab is exploring methods and applications for non-intrusive load monitoring (NILM), which breaks down aggregate energy consumption data from a building’s smart electric meter to provide feedback on each type of device that is consuming energy. Big data offers big opportunities to solve our world’s most daunting and complex energy challenges. One of the lab's long-term objectives is to create a map of global energy infrastructure that can be automatically updated. Now a 2017-2018 project team is working to pilot and apply automated algorithms for generating spatially-disaggregated data on electricity access in developing countries using aerial imagery. Machine learning algorithms allow computers to learn automatically from data to perform complicated tasks in vision, natural language processing and many other fields. Researchers: Matthew Harding (former faculty member, Sanford School of Public Policy). Water heating in the residential building sector is the second-highest energy end use after space heating. Harding was looking in particular at how consumers respond to incentives to improve energy conservation or adopt green power. Durham, NC 27708. At Duke, we use data to solve many real-world problems, with an emphasis on problems that impact social good. Reviews from Duke Energy employees about Duke Energy culture, salaries, benefits, work-life balance, management, job security, and more. Researchers are looking at ways to improve NILM techniques and applications, and how data from the growing number of connected devices in a building may be leveraged to optimize building energy consumption through automation.