Virginia Institute of Marine Science
Gong Home Page

Donglai Gong

Assistant Professor

Email : [[gong]]
Phone : (804) 684-7529
Office : Andrews Hall 236
Lab : Andrews Hall 220
Department : Physical Sciences


B.S./B.A., Rutgers University, 2001

S.M., Massachusetts Instititute of Technology, 2004

Ph.D., Rutgers University, 2010

Research Interests

My research aims to better understand the physical processes that drive transport and mixing in coastal oceans and estuaries at mid- and high latitudes.  These processes can affect the transfer and storage of heat, freshwater, nutrients, organic material and pollutants in coastal marine environment.  I mainly apply an observational approach, employing a range of sampling platforms such as AUV/gliders, floats/drifters, satellites, HF-Radar, moorings, and ships.  Currently I am involved in research projects investigating (1) circulation and water mass transformation in the Chukchi Sea of the Arctic; (2) the along-current variability of the coastal boundary current in Barrow Canyon and the Beaufort Sea; (3) physical-biological interactions at the head of the Hudson Submarine Canyon.  For future projects, I plan to investigate the shelf-estuary exchange flow at the mouth of the Chesapeake Bay and the along-shelf evolution of the Mid-Atlantic Bight 'cold pool'.  I am also interested in investigating the atmospheric and oceanic teleconnections between polar and mid-latitude regions.

I am currently looking for undergraduate and graduate students to join my lab to pursue above mentioned research topics.  I am also looking for students with background in electrical engineering, computer science, computer graphics, web/mobile/social network application development.  The goal of this computational and engineering student team is to develop novel solutions to challenging technical in oceanographic and climate research, education, and outreach.  Examples of thses include AUV under-ice navigation, development of low-power oceanographic sensors, control system for hybrid glider-AUV's, application social networks for crowd-source data gathering/analysis and application of machine learning tools/methods to geophysical datasets as well as human-based datasets (i.e. data on social networks).