My Academic Journey
My journey into glaciology had a "rocky start," in a sedimentology lab surrounded by muddy beakers, vintage microscopes, and rock samples taken deep inside the sea floors around the Antarctic margins. At Wesleyan Universiy, my involvement as a sophomore research assistant in sedimentary paleoclimatology introduced me to the icy world that is our cryosphere. I used geochronological methods, those that track the passing of deep geologic time by counting isotope quantities via radioactive decay, to understand how expansive the incipient Antarctic ice sheetw as 34 million years ago. Later on, I realized I am more passionate about quantitative analysis that combines geosciences, math, and physics, and decided to pursue a master's degree in Computational Earth Sciences at University at Buffalo, working with computational glaciologists Kristin Poinar and Sophie Nowicki.
Working with my mentors at UB taught me all the skills to excel as a glacier and ice sheet modeler (as well as the stamina to remain sedentary and patient for hours before a numerical experiment completes). The experience also revealed to me the new trend that is geospatial data sciences. I took the liberty over a summer to intern at Sust Global, a start-up specializing in harvesting the risk insights from large climate model ensembles. Despite an interesting emerging space, the internship made me realize the importance of fundamental scientific investigation (which start-ups are not at a position to take a lead on) to better bridge observational data and numerical models. This means better initialization of our climate and ice sheet numerical models that match the observations well, which requires both developing advanced data assimilation techniques and integrating geophysical data that has never been used for large-scale modeling before.
This took me to Georgia Tech and to Dr. Winnie Chu's lab. Here I explore the intersection of numerical ice sheet modeling, ice-penetrating radar physics, and scientific machine learning to improve our understanding of the dynamical state of the polar ice sheets and model initialization.
Research Interest
Modeling dynamic thinning I am collaborating with Kristin Poinar and Beata Csatho (University at Buffalo) on modeling the dynamic thickness change of Helheim glacier, one of the fastest flowing outlet glaciers in Greenland. This is a companion study of my M.S. thesis which characterises the dynamic thinning across various synthetic glacier geometry and basal conditions (figure left).
Figure on the right shows my preliminary results, where I use conditional Normalizing Flows - a generative AI model - to invert for the basal temperature from discrete tracks of depth-averaged temperature estimates (derived from ice-penetrating radar).
AI-powered inversion of thermodynamics: My first Ph.D. project experiments with noval AI-based methods to tackle glaciological inverse problem. Currently I am primarily concerned with the internal thermal state of Antarctic ice sheet: how warm is the base of the glacier? Is there melt water? Does it faciliate sliding? What does it mean for numerical modeling? Can our AI-enabled predictions match with other independent observational evidences?
Figure below shows my preliminary results, where I use conditional Normalizing Flows - a generative AI model - to invert for the basal temperature from discrete tracks of depth-averaged temperature estimates (derived from ice-penetrating radar).
Latest News
For more up-to-date news, please visit my personal website: donglaiyang.org
Interest Beyond Research
I enjoy listening to and performing Jazz,(neo-)Soul, RnB, and Funk music, and I am a Jazz guitarist performing with Georgia Tech Jazz Combo; recently started my journey into electric bass playing. I boulder regularly at CRC and Central Rock Gym, and I enjoy occasional long road biking trip. I also dabble in (phone) photography whenever I travel. You can find some of my works on my personal website.
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