Feng Yan: New analysis from the Natural Resources Defense Council | Undergraduate research



New analysis from the Natural Resources Defense Council


Feng yan


IT and Engineering


Feng Yan is an Assistant Professor in the Department of Computer Science and Engineering at the University of Nevada, Reno. He obtained a master’s degree and a doctorate. degrees from the Computer Science Department of the College of William and Mary. He received his science degree from Northeastern University. He was an associate researcher at Microsoft Research in 2014 and at HP Labs in 2013.

Dr Yan has a broad research interest in big data and is the Director of the Intelligent Data and Systems Lab (IDS Lab). Dr Yan is the recipient of the NSF BIGDATA Award (Principal PI), NSF CRII Award (unique-PI) and Amazon Web Services (AWS) Research Award (unique-PI). He and his student received the IEEE CLOUD 2018 Best Student Paper Award and the CLOUD 2019 Best Paper Award. He and his students actively publish in the most prestigious venues in the fields of machine learning and computer systems.

Dr Yan actively collaborates with industry partners (such as Amazon, Microsoft Research, Google Brain, Google Research, IBM Research, Bell Labs, HP Labs, Facebook, Baidu Research USA, NetApp ATG, EMC, etc.) to solve important but difficult problems. problems to generate impact in the real world.

Project overview

A new analysis from the Natural Resources Defense Council shows that about two-thirds of the United States, or nearly 212 million people, live in counties affected by smoke from wildfires. Exposure to smoke from forest fires can cause serious health problems, especially in vulnerable people with respiratory problems, including asthma and heart disease. State-of-the-art smoke prediction models can only perform infrequent updates and give predictions on very limited spatial resolution due to low resolution of spatiotemporal data and slow processing speed Datas. However, smoke can be transported very quickly and cause a sudden drop in air quality. Therefore, there is an urgent need to develop methodologies for predicting real-time smoke transport and air quality with better spatial resolution. This research project aims to develop fine-grained real-time forest fire smoke prediction approaches using camera vision data collected from ALERTWildfire camera arrays. This project will use both big data and machine learning techniques to develop a new approach to predicting air quality using computer vision data. This project requires some basic programming experience. Students interested in interdisciplinary research are particularly welcome.

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