个人信息

联系方式:010-62798365
电子邮箱:

学科:大气科学

教育经历

工学博士,清华大学计算机科学与技术系计算机网络专业,2003年9月~2007年6月
工学硕士,华中科技大学水电暨数字化工程学院水利水电工程专业,2000年9月~2003年6月
工学学士,武汉大学电子信息学院电子工程专业,1996年9月~2000年6月

工作经历

2022年8月至今 教授,清华大学地球系统科学系,地球系统值模拟教育部重点实验室主任
2011年11月~2022年7月 副教授,清华大学地球系统科学系,地球系统值模拟教育部重点实验室常务副主任
2010年10月~2011年11月 助理教授, 清华大学地球系统科学研究中心
2009年6月~2010年10月 助理研究员, 清华大学计算机科学与技术系高性能计算研究所
2007年7月~2009年5月 博士后,清华大学计算机科学与技术系高性能计算研究所

获得2021年国家杰出青年科学基金

获得2016年戈登贝尔奖(ACM Gordon Bell Prize)奖提名

获得2014年清华大学—浪潮集团计算地球科学青年人才奖

获得2013年清华大学地学中心先进工作者

获得2012年清华大学地学中心先进工作者

获得2011年中兴通讯产学研优秀成果一等奖

[1] Tao, F., Huang, Y., Hungate, B.A. ... , Huang, X(*), Luo YX(*). (2023). Microbial carbon use efficiency promotes global soil carbon storage. Nature. https://doi.org/10.1038/s41586-023-06042-3

[2] Wang, M., Wang, D., Xiang, Y., Liang, Y., Xia, R., Yang, J., ... & Huang, X(*). (2023). Fusion of ocean data from multiple sources using deep learning: Utilizing sea temperature as an example. Frontiers in Marine Science.

[3]   Chen, Y., Huang, X.(*), Luo, J. J., Lin, Y., Wright, J. S., Lu, Y., ... & Lin, P. (2023). Prediction of ENSO using multivariable deep learning. Atmospheric and Oceanic Science Letters, 100350.

[4] Zhang, B., Xiong, W., Ma, M., Wang, M., Wang, D., Huang, X., ... & Huang, X(*). (2022). Super-resolution reconstruction of a 3 arc-second global DEM dataset. Science Bulletin.

[5] Zhang, B., Ma, M., Wang, M., Hong, D., Yu, L., Wang, J., ... & Huang, X.(*) (2022). Enhanced Resolution of FY4 Remote Sensing Visible Spectrum Images Utilizing Super-Resolution and Transfer Learning Techniques. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 7391-7399.

[6]   Huang, P., Chen, Q., Wang, D., Wang, M., Wu, X., & Huang, X.(*). (2022). TripleConvTransformer: A deep learning vessel trajectory prediction method fusing discretized meteorological data. Frontiers in Environmental Science, 1720.

[7] Chen, Q., Ding, W., Huang, X.(*), & Wang, H. (2022). Generalized interval type ii fuzzy rough model based feature discretization for mixed pixels. IEEE Transactions on Fuzzy Systems.

[8]   Liao, C., Huang, W., Wells, J., Zhao, R., Allen, K., Hou, E., …, Huang, X. & Luo, Y. (2022). Microbe-iron interactions control lignin decomposition in soil. Soil Biology and Biochemistry, 173, 108803.

[9] Liao, C., Chen, Y., Wang, J., Liang, Y., Huang, Y., Lin, Z., ... Huang, X. & Luo, Y. (2022). Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP). Ecological Processes, 11(1), 1-16.

[10] Lei Lin, Hao Liu, Xiaomeng Huang, Qingjun Fu, and Xinyu Guo. Effect of tides on river water behavior over the eastern shelf seas of China. Hydrol. Earth Syst. Sci., 26, 5207–5225, 2022

[11]   Jin, W., Luo, Y., Wu, T., Huang, X., Xue, W., & Yu, C. (2022). Deep Learning for Seasonal Precipitation Prediction over China. Journal of Meteorological Research, 36(2), 271-281.

[12] Tang, Q., Huang, X.(*), Lin, L., Xiong, W., Wang, D., Wang, M. and Huang, X.(2021). MERF v3.0, a highly computationally efficient non-hydrostatic ocean model with implicit parallelism: algorithms and validation experiments. Ocean Modelling. https://doi.org/10.1016/j.ocemod.2021.101877.

[13] Wang J.M., Li W., Ciais P., Li L., Chang J., Goll D., Gasser T., Huang X., Devaraju N., Bouche O. (2021). Global cooling induced by biophysical effects of bioenergy crop cultivation. Nature Communications. 12(1):7255. doi: 10.1038/s41467-021-27520-0.

[14] Huang X.(*), Lin Lin, Yuwen Chen, Yue Chen, Xing Huang, Mingqing Wang, Jonathon S. Wright.(2021).  Improving machine learning-based weather forecast post-processing with clustering and transfer learning. CLIVAR Exchanges (Special Issue: Advances in Climate Prediction Using Artificial Intelligence ). No.81, Nov. 2021 

[15] Hu J. , Tian C. , Ge B., Huang X.(*), Wu X.(*)(2021). A climate downscaling method based on deep back-projection neural network. CLIVAR Exchanges (Special Issue: Advances in Climate Prediction Using Artificial Intelligence ). No.81, Nov. 2021

[16] Li X., Zhang L., Wang J., Huang X., Zhong Q., Wu X.(2021). Forecasting of Time Series Significant Wave Height based on ConvLSTM. CLIVAR Exchanges (Special Issue: Advances in Climate Prediction Using Artificial Intelligence ). No.81, Nov. 2021

[17] Li L., Zhang H,, Wang J. , Huang X.(*), Zhong Q. , Wu X.(*). (2021). Forecasting Significant Wave Height based on Transformer. CLIVAR Exchanges (Special Issue: Advances in Climate Prediction Using Artificial Intelligence ). No.81, Nov. 2021

[18] Shi, P., Lu, H., Leung, L.R., He, Y., Wang, B., Yang, K., Yu, L., Liu, L., Huang, W., Xu, S. , Liu, J., Huang, X., Li, L. and Lin, Y.(2021). Significant land contributions to interannual predictability of East Asian summer monsoon rainfall. Earth's Future, 9(2), p.e2020EF001762.

[19] Dou, X., Liao, C., Wang, H., Huang, Y., Tu, Y., Huang, X., Peng, Y., Zhu, B., Tan, J., Deng, Z. and Wu, N.(2021). Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means. Advances in Applied Energy, 2, p.100017.

[20] Xiao, Q., Geng, G., Cheng, J., Liang, F., Li, R., Meng, X., Xue, T., Huang, X., Kan, H., Zhang, Q. and He, K.(2021). Evaluation of gap-filling approaches in satellite-based daily PM2. 5 prediction models. Atmospheric Environment, 244, p.117921.

[21] Tao, F., Huang, Y., Hungate, B. A., Lu, X., Hocking, T. D., Mishra, U., Hugelius, G., Huang, X., and Luo, Y.(2021). PROcess-guided deep learning and DAta-driven modelling (PRODA) uncovers key mechanisms underlying global soil carbon storage, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-10.

[22] Xiao, Q., Zheng, Y., Geng, G., Chen, C., Huang, X., Che, H., Zhang, X., He, K. and Zhang, Q. (2021). Separating emission and meteorological contributions to long-term PM 2.5 trends over eastern China during 2000–2018. Atmospheric Chemistry and Physics, 21(12), pp.9475-9496.

[23] Wang, J., Li, W., Ciais, P., Ballantyne, A., Goll, D., Huang, X., Zhao, Z. and Zhu, L.(2021). Changes in biomass turnover times in tropical forests and their environmental drivers from 2001 to 2012. Earth's Future, 9(1).

[24] Lin, Y., Huang, X., Liang, Y., Qin, Y., Xu, S., Huang, W., Xu, F., Liu, L., Wang, Y., Peng, Y., Wang, L., Xue, W., Fu, H., Zhang, G., Wang, B., Li, R., Zhang, C., Lu, H., Yang, K., Luo, Y., Bai, Y., Song, Z., Wang, M., Zhao, W., Zhang, F., Xu, J., Zhao, X., Lu, C., Chen, Y., Luo, Y., Hu, Y., Tang, Q., Chen, D., Yang, G. and Gong, P.(2020). Community Integrated Earth System Model (CIESM): Description and Evaluation. Journal of Advances in Modeling Earth Systems, 12(8), p.e2019MS002036.

[25] Han, Y., Zhang, G.J.(*), Huang, X(*). and Wang, Y.(2020). A moist physics parameterization based on deep learning. Journal of Advances in Modeling Earth Systems, 12(9), p.e2020MS002076. 

[26] Tao, F., Zhou, Z., Huang, Y., Li, Q., Lu, X., Ma, S., Huang, X., Liang, Y., Hugelius, G., Jiang, L. and Doughty, R.(2020). Deep learning optimizes data-driven representation of soil organic carbon in Earth system model over the conterminous United States. Frontiers in big Data, 3, p.17.

[27] He, Y., Wang, B., Liu, L., Huang, W., Xu, S., Liu, J., Wang, Y., Li, L., Huang, X., Peng, Y. and Lin, Y.(2020). A DRP‐4DVar‐based coupled data assimilation system with a simplified off‐line localization technique for decadal predictions. Journal of Advances in Modeling Earth Systems, 12(4).

[28] Yao, P., Lu, H., Wang, W., Shao, C., Yang, K., Gianotti, D., Liu, Z., Huang, X. and Entekhabi, D.(2020). Soil Moisture Retrieval Only Using Smap L-Band Radar Observations. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 4586-4589). IEEE.

[29] He, Q., Yue, S., Lu, H., Liu, Z., Huang, X. and Entekhabi, D.(2020). Identifying Terrestrial Vegetation-Soil Moisture Oscillation from Satellite Observations. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 4570-4573). IEEE.

[30] He, Y., Wang, B., Huang, W., Xu, S., Wang, Y., Liu, L., Li, L., Liu, J., Yu, Y., Lin, Y. and Huang, X.(2020). A new DRP-4DVar-based coupled data assimilation system for decadal predictions using a fast online localization technique. Climate Dynamics, 54(7), pp.3541-3559.

[31] Xiao, Q., Geng, G., Liang, F., Wang, X., Lv, Z., Lei, Y., Huang, X., Zhang, Q., Liu, Y. and He, K.(2020). Changes in spatial patterns of PM2. 5 pollution in China 2000–2018: Impact of clean air policies. Environment international, 141, p.105776.

[32] Zhao, J., Yu, L., Xu, Y., Li, X., Zhou, Y., Peng, D., Liu, H., Huang, X., Zhou, Z., Wang, D. and Ren, C.(2020). Exploring difference in land surface temperature between the city centres and urban expansion areas of China’s major cities. International Journal of Remote Sensing, 41(23), pp.8965-8985.

[33] Dong, W. H. , Lin, Y. L. , Zhang, M. H.  and Huang, X.(2020). Footprint of tropical mesoscale convective system variability on stratospheric water vapor. Geophysical Research Letters, 47(5), e2019GL086320.

[34] Chen, Y., Huang, X., Li, Y., Chen, Y., Xu, Z. and Huang X.(2020). Ensemble Learning for Bias Correction of Station Temperature Forecast Based on ECMWF Products. Journal of Applied Meteorological Science,31(4): 494-503. 

[35] Liu, X., Yu, L., Dong, Q., Peng, D., Wu, W., Yu, Q., Cheng, Y., Xu, Y., Huang, X., Zhou, Z. and Wang, D.(2020). Cropland heterogeneity changes on the Northeast China Plain in the last three decades (1980s–2010s). PeerJ, 8, p.e9835.

[36] Zhang Y., Li J., Yu R., Liu Z., Zhou Y., Li X. and Huang X. (2020). A multiscale dynamical model in a dry-mass coordinate for weather and climate modeling: Moist dynamics and its coupling to physics. Monthly Weather Review, v 148, n 7, p 2671-2699.

[37] Xu, L., Wang, A., Wang, C., Chen, Y., Chen, Y., Zhou, Z., Chen, X., Xing, J., Liu, K. and Huang, X.(*).(2020). Research on correction method of marine environment prediction based on machine learning. Marine Science Bulletin. 39(6):695-704.

[38] Huang, X.(*), Huang, X., Wang, D., Wu, Q., Li, Y., Zhang, S., Chen, Y., Wang, M., Gao, Y., Tang, Q. and Chen, Y.(2019). OpenArray v1. 0: a simple operator library for the decoupling of ocean modeling and parallel computing. Geoscientific Model Development, 12(11), pp.4729-4749. 

[39] Chen, D., Guo, J., Yao, D., Lin, Y., Zhao, C., Min, M., Xu, H., Liu, L., Huang, X., Chen, T. and Zhai, P.(2019). Mesoscale convective systems in the Asian monsoon region from Advanced Himawari Imager: Algorithms and preliminary results. Journal of Geophysical Research: Atmospheres, 124(4), pp.2210-2234.

[40] Xu, Y., Yu, L., Feng, D., Peng, D., Li, C., Huang, X., Lu, H. and Gong, P.(2019). Comparisons of three recent moderate resolution African land cover datasets: CGLS-LC100, ESA-S2-LC20, and FROM-GLC-Africa30. International Journal of Remote Sensing, 40(16), pp.6185-6202.

[41] Zhao, J., Yu, L., Xu, Y., Ren, H., Huang, X. and Gong, P.(2019). Exploring the addition of Landsat 8 thermal band in land-cover mapping. International Journal of Remote Sensing, 40(12), pp.4544-4559.

[42] Yu, C., Huang, X., Chen, H., Godfray, H.C.J., Wright, J.S., Hall, J.W., Gong, P., Ni, S., Qiao, S., Huang, G., Xiao, Y., Zhang, J., Feng, Z., Ju, X., Philippe C. Nils S., Dag H., Sun, Z., Yu, L., Cai, W., Fu, H., Huang, X., Zhang, C. and Liu, H.(2019). Managing nitrogen to restore water quality in China. Nature, 567(7749), pp.516-520.

[43] Chen, Y., Lu, Y., Zhao, P., Xu, F., Huang, X. and Huang, X.(*)(2019). Seasonal and interannual variations of sea temperature influenced by Galápagos Islands in eastern tropical Pacific Ocean. Journal of Geophysical Research: Oceans, 124(5), pp.3007-3020.

[44] Li, Y., Wang, W., Lu, H., Khem, S., Yang, K. and Huang, X.(2019). Evaluation of three satellite-based precipitation products over the lower mekong river basin using rain gauge observations and hydrological modeling. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), pp.2357-2373.

[45] Lin, Y., Huang, X. , Liang, Y. , Qin, Y. , Xu, S., Huang, W., Xu, F., Liu, L., Wang, Y., Peng, Y., Wang,L., Xue, W., Fu, H., Zhang, G., Wang, B., Li, R., Zhang, C., Lu, H., Yang, K., Luo, Y., Bai, Y., Song, Z., Wang, M., Zhao, W., Zhang, F., Xu, J., Zhao, X., Lu, C., Luo, Y., Chen, Y., Hu, Y., Tang, Q., Chen, D., Yang, G. and Gong, P. (2019). The community integrated Earth system model(CIESM)from tsinghua university and its plan for cmip6 experiments. Climate Change Research. 15(5): 545-550.

[46] Wu, Q., Ni, Y. and Huang X.(*)(2019). Regional Ocean Model Parallel Optimization in "Sunway TaihuLight". Journal of Computer Research and Development. 56(7):1556-1566.

[47] Huang, X.(*), Hu, C., Huang, X., Chu, Y., Tseng, Y.H., Zhang, G.J. and Lin, Y.(*) (2018). A long-term tropical mesoscale convective systems dataset based on a novel objective automatic tracking algorithm. Climate dynamics, 51(7), pp.3145-3159.

[48] Li, C., Lu, H., Yang, K., Han, M., Wright, J.S., Chen, Y., Yu, L., Xu, S., Huang, X. and Gong, W.(2018). The evaluation of SMAP enhanced soil moisture products using high-resolution model simulations and in-situ observations on the Tibetan Plateau. Remote Sensing, 10(4), p.535.

[49] Xu, S., Xu, Y., Xue, W., Shen, X., Zheng, F., Huang, X. and Yang, G.(2018). Taming the" Monster": Overcoming program optimization challenges on SW26010 through precise performance modeling. In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 763-773). IEEE.

[50] Huang, X., Yu, C., Fang, J., Huang, G., Ni, S., Hall, J., Zorn, C., Huang, X. and Zhang, W.(2018). A dynamic agricultural prediction system for large-scale drought assessment on the Sunway TaihuLight supercomputer. Computers and electronics in agriculture, 154, pp.400-410.

[51] Huang, X., Ni, S., Yu, C., Hall, J., Zorn, C. and Huang, X.(2018). Identifying precipitation uncertainty in crop modelling using Bayesian total error analysis. European Journal of Agronomy, 101, pp.248-258.

[52] Lv, M., Lu, H., Yang, K., Xu, Z., Lv, M. and Huang, X. (2018). Assessment of runoff components simulated by GLDAS against UNH–GRDC dataset at global and hemispheric scales. Water, 10(8), 969.

[53] Xu, Y., Yu, L., Peng, D., Cai, X., Cheng, Y., Zhao, J., Zhao, Y., Feng, D., Hackman, K., Huang, X. and Lu, H.(2018). Exploring the temporal density of Landsat observations for cropland mapping: experiments from Egypt, Ethiopia, and South Africa. International Journal of Remote Sensing, 39(21), pp.7328-7349.

[54] Wang, X., Gan, L., Xu, J., Yang, J., Xia, M., Fu, H., Huang, X. and Yang, G.(2018). PLZMA: a parallel data compression method for cloud computing. In International Conference on Algorithms and Architectures for Parallel Processing (pp. 504-518). Springer, Cham.

[55] Cui, W., Zhang, J., Schmidt, F., Cui, D., Huang, X., Li, T. and Tian, F.(2018). Simultaneous characterization of the atmospheres, surfaces, and exomoons of nearby rocky exoplanets. Earth and Planetary Physics, 2(3), pp.247-256.

[56] Tseng, Y. H.(*), Ding, R. and Huang, X.(*)(2017). The warm blob in the northeast pacific—the bridge leading to the 2015/16 El Niño. Environmental Research Letters, 12(5), 054019.

[57] Liu, F., Guo, J., Huang, X. and Lui, J. C. S. (2017). eBA: efficient bandwidth guarantee under traffic variability in datacenters. IEEE/ACM Transactions on Networking, 25(1), 506-519.

[58] Li, C., Lu, H., Yang, K., Wright, J. S., Yu, L., Chen, Y., Huang, X. and Xu, S. (2017). Evaluation of the common land model (CoLM) from the perspective of water and energy budget simulation: towards inclusion in CMIP6. Atmosphere, 8.

[59] Fu, H., Gan, L., Yang, C., Xue, W., Wang, L.,  Wang, X.,Huang,X. and Yang, G. (2017). Solving global shallow water equations on heterogeneous supercomputers. Plos One, 12(3), e0172583.

[60] Zhao, J., Tian, F., Ni, Y. and Huang, X. (2017). DR-induced escape of O and C from early MARS. Icarus, 284.

[61] Huang, X. (*), Tang, Q., Tseng, Y., Hu, Y., Baker, A. H., Bryan, F. O., Dennis, J., Fu, H., and Yang, G. (2016). P-CSI v1.0, an accelerated barotropic solver for the high-resolution ocean model component in the Community Earth System Model v2.0, Geosci. Model Dev., 9, 4209-4225, doi:10.5194/gmd-9-4209-2016, 2016.

[62] Huang, X. (*), Ni, Y., Chen, D., Liu, S., Fu, H. and Yang, G. (2016). Czip: a fast lossless compression algorithm for climate data. International Journal of Parallel Programming, 44(6), 1-20.

[63] Baker, A. H., Hu, Y., Hammerling, D. M., Tseng, Y.-H., Xu, H., Huang, X., Bryan, F. O., and Yang, G.(2016). Evaluating statistical consistency in the ocean model component of the Community Earth System Model (pyCECT v2.0), Geosci. Model Dev., 9, 2391-2406, doi:10.5194/gmd-9-2391-2016.

[64] Qiao, F., Zhao, W., Yin, X., Huang, X., Liu, X., Shu, Q., Wang, G., Song, Z., Li, X., Liu, H., Yang, G. and Yuan, Y.(2016). A highly effective global surface wave numerical simulation with ultra-high resolution. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2016). IEEE Press. November 13-18, Salt Lake City, USA.

[65] Li T., Tian, F., Wang, Y., Wan, W. and Huang, X.(2016). Distinguishing a Hypothetical Abiotic Planet-Moon System from a Single Inhabited Planet. The Astrophysical Journal Letters. 817, L15.

[66] Fu, H., Liao, J., Xue, W., Wang, L., Chen, D., Gu, L., Xu J., Ding, N., Wang, X., He, C., Xu, S., Liang, Y., Fang, J., Xu, Y., Zheng, W., Xu, J., Zheng Z., Wei, W., Ji, X., Zhang, H., Cheng, B., Li, K., Huang, X., Chen, W., and Yang, G. (2016). Refactoring and optimizing the community atmosphere model (CAM) on the sunway taihulight supercomputer. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2016). IEEE Press, November 13-18, Salt Lake City, USA.

[67] Fu, H., Liao, J., Yang, J., Wang, L., Song, Z., and Huang, X., Yang, C., Xue, W., Liu, F., Qiao, F., Zhao, W., Yin, X., Hou, C., Zhang, C., Ge, W., Zhang, J., Wang, Y., Zhou, C. and Yang, G. (2016). The sunway taihulight supercomputer: system and applications. Science China Information Sciences, 59(7), 1-16.

[68] Wang, X., Wang, G. and Huang X. (2016). The application of scientific big data in the research of global climate change. Climate Change Research, 12(4), 313-321.

[69] Xu, S., Huang, X. (*), Oey, L. Y., Xu, F., Fu, H., Zhang, Y. and Yang, G. (2015). Pom.gpu-v1.0: a gpu-based princeton ocean model. Geoscientific Model Development, 8(9), 2815-2827.

[70] Hu, Y., Huang, X. (*), Baker, A.H., Tseng, Y.H., Bryan, F.O., Dennis, J.M. and Yang, G. (2015). Improving the scalability of the ocean barotropic solver in the community earth system model. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2015). IEEE Press, Austin, TX, USA.

[71] Liu, S., Huang, X., Fu, H., Yang, G. and Song, Z. (2015). Data reduction analysis for climate data sets. International Journal of Parallel Programming, 43(3), 508-527.

[72] Gan, L., Fu, H., Luk, W., Yang, C., Xue, W., Huang, X., Zhang, Y. and Yang, G.(2015). Solving the global atmospheric equations through heterogeneous reconfigurable platforms. ACM Transactions on Reconfigurable Technology & Systems, 8(2), 1-16.

[73] Fu, H., Wang, Y., Um, E. S., Fang, J., Wei, T., Huang, X. and Yang, G. (2015). A parallel finite-element time-domain method for transient electromagnetic simulation. Geophysics, 80(4), E213-E224.

[74] Huang, X. (*), Wang, W., Fu, H., Yang, G., Wang, B. and Zhang, C. (2014). A fast input/output library for high-resolution climate models. Geoscientific Model Development, 7(1), 93-103.

[75] Ruan, H., Huang, X., Fu, H. and  Yang, G. (2014). A fully pipelined probability density function engine for gaussian copula model. Tsinghua Science and Technology, 19(2), 194-202.

[76] Xu, S., Huang, X. (*), Zhang, Y., Hu, Y. and Yang, G. (2014). A customized GPU acceleration of the princeton ocean model. In Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors (ASAP 2014). IEEE press. Zurich, Switzerland.

[77] Zhang, C., Huang, X. (*), Hu, Y., Xu, S., Fu, H. and Yang, G. (2014). CFIO2: Overlapping Communications and I/O with Computations Using RDMA Technology. In Proceedings of the Network and Parallel Computing. Springer Berlin Heidelberg. Ilan, September 18-20, Taiwan, China.

[78] Guo, J., Liu, F., Huang, X., Lui, J., Hu, M., Gao, Q. and Jin, H. (2014). On efficient bandwidth allocation for traffic variability in datacenters. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM 2014). IEEE Press. Toronto, Canada. April 27-May 2, Toronto, Canada.

[79] Xu, S., Huang, X. (*), Zhang, Y., Hu, Y., Fu, H. and Yang, G. (2014). Porting the Princeton Ocean Model to GPUs. In Proceedings of the International Conference for Algorithms and Architectures for Parallel Processing (ICA3PP 2014). Springer International Publishing. August 24-27, Dalian, China. 

[80] Geng, Y., Huang, X. and Yang, G. (2014). SwiftArray: Accelerating queries on multidimensional arrays. Tsinghua Science and Technology, 19(5), 521-530. 

[81] Geng, Y., Huang, X. (*) and Yang, G. (2014). Adaptive Indexing for Distributed Array Processing. In Big Data. In Proceedings of the IEEE International Congress on Big Data (BigData 2014). IEEE. , June 27-July 2, Anchorage, Alaska, USA. 

[82] Liu, S., Huang, X. (*), Ni, Y., Fu, H. and Yang, G. (2014). A high performance compression method for climate data. In Proceedings of the IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA 2014). IEEE Press. August 26-28, Milan, Italy. 

[83] You, Y., Fu, H., Song, S. L., Dehnavi, M. M., Gan, L., Huang, X. and Yang, G. (2014). Evaluating multi-core and many-core architectures through accelerating the three-dimensional Lax–Wendroff correction stencil. International Journal of High Performance Computing Applications, 28(3), 301-318. 

[84] Fu, H., Gan, L., Clapp, R. G., Ruan, H., Pell, O., Mencer, O., Flynn M., Huang, X.  and Yang, G. (2014). Scaling Reverse Time Migration Performance through Reconfigurable Dataflow Engines. IEEE Micro, 34(1), 30-40. 

[85] Hu, Y., Huang, X., Wang, X., Fu, H., Xu, S., Ruan, H., Xue, W., and Yang, G. (2013). A scalable barotropic mode solver for the parallel ocean program. In Proceedings of the International Conference on Euro-Par Parallel Processing (Euro-Par 2013). Springer Berlin Heidelberg. August 26-30, Aachen, Germany.

[86] Ruan, H., Huang, X. (*), Fu, H., Yang, G., Luk, W., Racaniere, S., Pell, O. and Han, W. (2013). An FPGA-Based Data Flow Engine for Gaussian Copula Model. In Proceedings of the International Symposium on the Field-Programmable Custom Computing Machines (FCCM 2013). IEEE Press. April 28-30, Seattle, Washington, USA.

[87] Liu, S., Huang, X. (*), Fu, H. and Yang, G. (2013). Understanding data characteristics and access patterns in a cloud storage system. In Proceedings of the IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2013). IEEE Press. May 13-16, Delft, The Netherlands.

[88] Huang, X. (*), Pan, T., Ruan, H., Fu, H. and Yang, G. (2013). Parallel Buffer Generation Algorithm for GIS. Journal of Geology & Geosciences, 2(115), 2.

[89] Zhu, M., Huang, X. (*), Liu, S., Fu, H., Fang, Q. and Yang, G. (2013). Optimize Multidimensional Arrays Queries with Heterogeneous Replica Method. In Proceedings of the International Conference on Networking, Architecture and Storage (NAS 2013). IEEE Press. July 17-19, Xi’An, China. 

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[91] Geng, Y., Huang, X. (*), Zhu, M., Ruan, H. and Yang, G. (2013). SciHive: Array-based query processing with HiveQL. In Proceedings of the IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2013). IEEE Press. July 16-18, Melbourne, Australia. 

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