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  1. Dr. Nan Li is currently working on developing novel whole-brain imaging methods to integrate molecular and system neuroscience and solve brain science problems. She is specifically interested in understanding the neural mechanisms of reward, learning, and memory in rodent brains.

  2. Expectation driven firm dynamics and business cycles. N Li, MS Mehkariz. Department of Economics, The Ohio State University. , 2009. 19. 2009. Real sectoral spillovers: A dynamic factor analysis of the great recession. N Li, VL Martin. Journal of Monetary Economics 107, 77-95.

  3. Jan 5, 2024 · Li specializes in international macroeconomics, trade, firm innovations and economic growth. Her research has been published in top academic journals, including the Review of Economic Studies, American Economic Journal: Macroeconomics, Journal of Monetary Economics, Journal of International Economics and Journal of Development Economics.

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  5. 2010. Study of the spatial correlation between neuronal activity and BOLD fMRI responses evoked by sensory and channelrhodopsin-2 stimulation in the rat somatosensory cortex. N Li, P Van Zijl, N Thakor, G Pelled. Journal of Molecular Neuroscience 53, 553-561.

  6. A data-driven framework to evaluate the indirect economic impacts of transportation infrastructure disruptions. International Journal of Disaster Risk Reduction. 2022-06 | Journal article. DOI: 10.1016/j.ijdrr.2022.102946. Contributors : Fang Wei; Eyuphan Koc; Nan Li; Lucio Soibelman; Dan Wei.

  7. Summary. Dr Nan Lis research addresses one of the major challenges facing the transport industry world-wide: developing technological breakthroughs in the manufacturing and design of high-performance lightweight vehicles for a more environmentally-friendly footprint. Her current main research focuses are: Developing innovative manufacturing ...

  8. N Li. University of California, Santa Barbara. , 2013. 2013. Associating Financial Trading Volume Volatility and Information Volume based on Neural Network and Support Vector Machine. N Li, X Liang. Finding Top-k Topic-Dense Vertex Sets in Large Graphs CS290D Project Report. N Li, A Khan.

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