2009.09u20132012.07，中國(guó)林業(yè)科學(xué)研究院，森林經(jīng)理學(xué)， 博士，研究方向?yàn)榉蔷�性混合效應(yīng)模型算法及其應(yīng)用，師從著名林業(yè)科學(xué)家唐守正院士

2007.09u20132009.07，南京林業(yè)大學(xué)， 森林經(jīng)理學(xué)， 碩士，研究方向?yàn)閼?yīng)用數(shù)理統(tǒng)計(jì)

2003.09u20132007.07，山西農(nóng)業(yè)大學(xué)， 林學(xué)， 學(xué)士

2018.07-至今，中國(guó)林業(yè)科學(xué)研究院資源信息研究所研究員

2017.09-至今，中國(guó)林業(yè)科學(xué)研究院資源信息研究所研究室副主任

2014.11u20132018.06，中國(guó)林業(yè)科學(xué)研究院資源信息研究所副研究員

2016.03u20132017.03，美國(guó)賓夕法尼亞州立大學(xué)歐柏麗自然科學(xué)學(xué)院博士后

2012.07u20132014.10，中國(guó)林業(yè)科學(xué)研究院資源信息研究所助理研究員

工作至今，圍繞該領(lǐng)域主持包括國(guó)家自然基金在內(nèi)的項(xiàng)目22項(xiàng)，其中省部級(jí)以上8項(xiàng)。作為項(xiàng)目骨干參加省部級(jí)以上課題14項(xiàng)，參與其他項(xiàng)目9項(xiàng)。共發(fā)表學(xué)術(shù)論文80余篇，其中以第一作者或通訊作者發(fā)表的SCI收錄論文34篇，單篇最高影響因子11.67，累積影響因子113.23，中科院JCR分區(qū)一區(qū)11篇，二區(qū)15篇。包括1篇IEEE T NeurNet Lear，1篇Brief Bioinform，4篇Neural Networks，1篇IEEE T Image Process，1篇IEEE T Geosci Remote。副主編專著1部，登記軟件著作權(quán)14項(xiàng)。2012年、2014年、2016年曾3次獲第四屆和第五屆梁希青年論文獎(jiǎng)二等獎(jiǎng)、第六屆梁希青年論文獎(jiǎng)一等獎(jiǎng)。2018年獲梁希林業(yè)科學(xué)技術(shù)獎(jiǎng)三等獎(jiǎng)（排名第一）。作為主要骨干所開(kāi)發(fā)的生物統(tǒng)計(jì)和數(shù)據(jù)分析軟件（ForStat）已推廣到國(guó)內(nèi)外80余所高等院校和科研院所使用。國(guó)際林業(yè)期刊Forestry（二區(qū)，影響因子2.88）編委和林業(yè)遙感期刊Remote Sensing（二區(qū)，影響因子4.12）特約編輯。

[1]、中組部“萬(wàn)人計(jì)劃”青年拔尖人才項(xiàng)目、2019/01-2021/12，在研、主持

[2]、中國(guó)科協(xié)首屆“青年人才托舉工程”項(xiàng)目、2016/01-2018/12、已結(jié)題、主持。

[3]、國(guó)家自然科學(xué)基金面上項(xiàng)目，基于森林生物量的天然林立地質(zhì)量評(píng)價(jià)和生產(chǎn)力估計(jì)、2020/01-2023/12、在研、主持。

[4]、國(guó)家自然科學(xué)基金面上項(xiàng)目，含隨機(jī)效應(yīng)和度量誤差的生物量相容性方程系統(tǒng)研究、2016/01-2019/12、在研、主持。

[5]、國(guó)家自然科學(xué)基金面上項(xiàng)目子課題，三維樹(shù)干曲面的模擬與構(gòu)建、2015/01-2018/12、已結(jié)題、主持。

[6]、國(guó)家自然科學(xué)基金青年項(xiàng)目，林業(yè)中含度量誤差的非線性混合效應(yīng)模型研究、2014/01-2016/12、已結(jié)題、主持。

[7]、“十三五”國(guó)家重點(diǎn)研發(fā)計(jì)劃“陸地生態(tài)系統(tǒng)碳源匯監(jiān)測(cè)技術(shù)及指標(biāo)體系”子課題，新增林地區(qū)域的確定及其碳匯潛力評(píng)估、2017/01-2020/12、在研、主持

[8]、“十三五”國(guó)家重點(diǎn)研發(fā)計(jì)劃“天然次生林生長(zhǎng)收獲預(yù)估及樹(shù)種更新模型構(gòu)建”子課題，新增林地區(qū)域的確定及其碳匯潛力評(píng)估、2017/01-2020/12、在研、主持。

[1]、Ye Q., Li D.,Fu L* (Corresponding author)., Zhang Z., Yang, W. 2019. Non-Peaked Discriminant Analysis for Data Representation. IEEE Transactions on Neural Networks and Learning Systems,DOI: 10.1109/TNNLS.2019.2944869.(IF=11.68)

[2]、Liu Q.,Fu L* (Corresponding author)., Wang G., Li S., Li Z., Chen E., Pang Y., Hu K. 2019. Improving Estimation of Forest Canopy Cover by Introducing Loss Ratio of Laser Pulses Using Airborne LiDAR. IEEE Transactions on Geoscience and Remote Sensing, DOI:10.1109/TGRS.2019.2938017. (IF=5.63)

[3]、Wang L., Wang B., Zhang Z* (Corresponding author)., Ye Q.,Fu L* (Corresponding author)., Liu G., Wang M., 2019. Robust auto-weighted projective low-rank and sparse recovery for visual representation. Neural Networks, 117: 201-215. (IF=5.79)

[4]、Zhao H.,Fu L* (Corresponding author)., Gao Z., Ye Q., Yang Z., Yang X. 2019. Flexible non-greedy discriminant subspace feature extraction. Neural Networks, 116: 166-177. (IF=5.79)

[5]、Wang C., Ye Q., Luo P., Ye N.,Fu L* (Corresponding author). 2019. Robust capped L1-norm twin support vector machine. Neural Networks, 114: 47-59. (IF=5.79)

[6]、Yang X., Yang H., Zhang F., Zhang L., Fan X., Ye Q.,Fu L* (Corresponding author). 2019. Piecewise Linear Regression Based on Plane Clustering. IEEE Access, 7: 29845u2013 29855. (IF=4.10)

[7]、Zhao H., Ye Q., Naiem M A.,Fu L. 2019. RobustL2,1-Norm Distance Enhanced Multi-Weight Vector Projection Support Vector Machine. IEEE Access, 7: 3275u2013 3286. (IF=4.10)

[8]、Zhang X., Chhin S.,Fu L., Lu L., Duan A., Zhang J. 2019. Climate-sensitive tree height-diameter allometry for Chinese fir in southern China. Forestry,92(2):167-176. (IF=2.88)

[9]、Wang M., Liu Q.,Fu L., Wang G., Zhang X. 2019. Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach. Remote Sensing, 11(9):1050. (IF=4.12)

[10]、Wang Q., Gao Z., Hu Z., Luo P., Duan G., Sharma R P., Song X.,Fu L* (Corresponding author). 2019. Comparing independent climate-sensitive models of aboveground biomass and diameter growth with their compatible simultaneous model system for three larch species in China. International Journal of Biomathematics, DOI:10.1142/S1793524519500530. (IF=0.89)

[11]、Fu L., Wang M., Wang Z., Song X., Tang S. 2019.Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first order conditional linearization and sequential quadratic programming. International Journal of Biomathematics, DOI:10.1142/ S1793524519500402. (IF=0.89)

[12]、Fu L., Jiang L., Ye M., Sun L., Tang S., Wu R. 2018. How trees allocate stem carbon for optimal growth: Insight from a game-theoretic model. Briefings in Bioinformatics, 19(4): 593-602. (IF=9.10)

[13]、Ye Q* (Corresponding author)., IEEE Member., Zhao H.,Fu L* (corresponding author)., Gao S. 2018. Underlying Connections Between Algorithms For Nongreedy LDA-L1. IEEE Transactions on Image Processing, 27(5): 2557-2559. (IF=6.79)

[14]、Ye Q., Zhao H., Gao S., Naiem M.,Fu L* (Corresponding author). 2018. Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis. Neural Networks, 105: 393-404. (IF=5.79)

[15]、Li T., Liu X., Li Z., Ma H* (Corresponding author)., Wan Y., Liu X.,Fu L* (corresponding author). 2018. Study on reproductive biology of rhododendron longipedicellatum: A newly discovered and special threatened plant surviving in Limestone Habitat in southeast Yunnan, China. Frontiers in plant science, doi:10.3389/fpls.2018.00033. (IF=4.11)

[16]、Yan He., Ye Q., Zhang T., Yu D., Yuan X., Xu Y.,Fu L. 2018. Least squares twin bounded support vector machines based on L1-norm distance metric for classification. Pattern Recognition, 74, 434-447. (IF=5.90)

[17]、Fu L., Liu Q., Wang G., Li Z., Chen E., Pang Y., Tang S., Song X., Wang G. 2018. Developing a system of compatible individual tree diameter and aboveground biomass prediction models using error-in-variable regression and airborne LiDAR data. Remote Sensing, 10(2), 325, doi:10.3390/ rs10020325. (IF=4.12)

[18]、Ya L*.,Fu L*., Affleck D L R., Nelson AS., Shen C., Wag M., Zheng J., Ye Q., Yang G. 2018. Additivity of nonlinear tree crown width models: Aggregated and disaggregated model structures using nonlinear simultaneous equations. Forest Ecology and Management, 427, 372-382. (IF=3.13)

[19]、Zhu G.,Fu L (Corresponding author). 2018. k-step adaptive cluster sampling with 6 Horvitzu2013Thompson estimator. International Journal of Biomathematics, 2. 11,doi: 10.1142/S1793524518500298. (IF=0.89)

[20]、Duan G., Gao Z., Wang Q.,Fu L (Corresponding author). 2018. Comparison of Different Heightu2013Diameter Modelling Techniques for Prediction of Site Productivity in Natural Uneven-Aged Pure Stands. Forests, 9, 63; doi:10.3390/f9020063. (IF=2.12)

[21]、Liu X., Ma H (Corresponding author)., Li T., Li Z., Wan Y., Liu X.,Fu L (Corresponding author). 2018. Development of novel EST-SSR markers for Phyllanthus emblica (Phyllanthaceae) and cross-amplification in two related species. Applications in Plant Sciences, 6(7): e1169. (IF=1.23)

[22]、Fu L., Ram P. S., Zhu G., Li H., Hong L., Guo H., Duan G., Shen C., Lei Y., Li Y., Lei X., Tang S. 2018. Comparing heightu2013age and heightu2013diameter modelling approaches for estimating site productivity of natural uneven-aged forests. Forestry, 91(4):419-433 (IF=2.88).

[23]、Zeng W.,Fu L., Xu Ming., Wang X., Chen Z., Yao, S. 2018. Developingindividual-tree-based models for estimating aboveground biomassof five key coniferous species in China. Journal of Forestry Research, 29(5):1251-1261. (IF=1.16)

[24]、Fu L., Sharma R. P., Wang G., Tang S. 2017. Modelling a system of nonlinear additive crown width models applying seemingly unrelated regression for Prince Rupprecht larch in northern China. Forest Ecology and Management, 386:71-80. (IF=3.13)

[25]、Fu L., Sharma R. P., Hao K., Tang S. 2017. A generalized interregional nonlinear mixed-effects crown width model for Prince Rupprecht larch in northern China. Forest Ecology and Management, 389, 364-373. (IF=3.13)

[26]、Fu L., Zhang H., Sharma R. P., Pang L., Wang G. 2017. A generalized nonlinear mixed-effects height to crown base model for Mongolianoak in northeast China. Forest Ecology and Management, 384, 34-43. (IF=3.13)

[27]、Fu L., Xiang W., Wang G., Hao K., Tang S. 2017. Additive crown width models comprising nonlinear simultaneous equations for Prince Rupprecht larch (Larix principis-rupprechtii) in northern China. Trees, 31(6):1959u20131971 (IF=1.80).

[28]、Fu L., Ram P. S., Zhu G., Li H., Hong L., Guo H., Duan G., Shen C., Lei Y., Li Y., Lei X., Tang S. 2017. A Basal Area Increment-Based Approach of Site Productivity Evaluation for Multi-Aged and Mixed Forests. Forests, 8, 119; doi:10.3390/f8040119. (IF=2.12)

[29]、Fu L., Lei X., Hu Z., Zeng W., Tang S., Marshall P., Cao L., Song X., Yu L., Liang J. 2017. Integrating regional climate change into allometric equations for estimating tree aboveground biomass of Masson pine in China. Annals of forest science, 74:42,1-15. (IF(5 years)=2.63)

[30]、Fu L., Sun W., Wang G. 2017. A climate-sensitive aboveground biomass model for three larch species in northeastern and northern China. Trees, 31(2): 557-573. (IF=1.80)

[31]、Fu L., Zeng W., Tang S. 2017. Individual tree biomass models to estimate forest biomass for large spatial regions developed using four pine species in China. Forest Science, 63(1): 42-50. (IF=1.06)

[32]、Fu, L.,Lei, X., Zhu, G., Li, H., Hong, L., Guo, H., Duan, G., Shen, C., Lei, Y., Li, Y., Tang, S. 2017. Dominant heightu2013diameter models for estimating forest site productivity in natural uneven-aged pure stands. Forest Science, accepted. (SCI, IF=1.78, 三區(qū)).

[33]、Hu Z., Liu S., Liu X.,Fu L., Wang J., Liu K., Huang X., Zhang Y., He F. 2016. Soil respiration and its environmental response varies by day/night and by growing /dormant season in a subalpine forest. Scientific report, 6:37864. (IF=4.01)

[34]、Cao L., Coops N. C., Innes J. L., Sheppard S.R.J.,Fu L., Ruan H., She G. 2016. Estimation of forest biomass dynamics in subtropical forests usingmulti-temporal airborne LiDAR data. Remote Sensing of Environment, 178: 158-171. (IF=8.22)

[35]、Fu L., Lei Y., Wang G., Bi H., Tang S., Song X. 2016. Comparison of seemingly unrelated regressions with error-invariable models for developing a system of nonlinear additive biomass equations. Trees, 30(3): 839-857. (IF=1.80)

[36]、Pang L., Ma Y., Sharma R.P., Shawn R., Song X.,Fu L(Corresponding author). 2016. Developing an improved parameter estimation method for the segmented taper equation through combination of constrained two-dimensional optimum seeking and least square regression. Forests, 7: 194. (IF=2.12)

[37]、Fu L., Zhang H., Lu J., Zang H., Lou M., Wang G. 2015. Multilevel Nonlinear Mixed-Effect Crown Ratio Models for Individual Trees of Mongolian Oak (Quercus mongolica) in Northeast China. PLoS ONE, 10(8): e0133294. (IF=2.78)

[38]、Diao J., Lei X., Wang J., Lu J., Guo H.,Fu, L., Shen C., Ma W., Shen J. 2014. Quantifying the variability of internode allometry within and between trees for Pinus tabulaeformis Carr. using a multilevel nonlinear mixed-effect model. Forests, 5, 2825-2845. (IF=2.12)

[39]、FuL., Lei Y.,SharmaR. P., TangS. 2014. Parameter estimation of nonlinear mixed- effects models using first-order conditional linearization and the EM algorithm. Journal of applied statistics, 40(2): 252-265. (IF=0.77)

[40]、Fu L., Tang S., Sharma R. P., Zhang H., Liu Y., Lei Y., Wang H. 2014. Developing, testing and application of rodent population dynamics and capture models based on an adjusted leslie matrix-based population. International Journal of Biomathematics, 7(2): 1-15. (IF=0.89)

[41]、Fu L., Wang M., Lei Y., Tang S. 2014. Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm. Computational Statistics & Data Analysis, 69: 173-183. (IF=1.32)

[42]、Fu L., Zeng W., Zhang H., Wang G., Lei Y., Tang S. 2014. Generic linear mixed-effects individual-tree biomass models for Pinus massoniana Lamb. in southern China. Southern Forests, 76(1): 47-56. (IF=0.90)

[43]、符利勇，雷淵才，曾偉生，幾種相容性生物量模型及估計(jì)方法的比較，林業(yè)科學(xué)，2014，（06）：42-54.

[44]、符利勇，雷淵才，孫偉，唐守正，曾偉生，不同林分起源的相容性生物量模型構(gòu)建，生態(tài)學(xué)報(bào)，2014，（06）：1461-1470.

[45]、Fu L., Sun H., Sharma R. P., Lei Y., Zhang H., Tang S. 2013. Nonlinear mixed-effects crown width models for individual trees of Chinese fir (Cunninghamia lanceolata) in south-central China. Forest Ecology and Management, 302: 210-220. (IF=3.13)

[46]、符利勇，孫華，基于混合效應(yīng)模型的杉木單木冠幅預(yù)測(cè)模型，林業(yè)科學(xué)，2013，（08）：65-74.

[47]、符利勇，張會(huì)儒，李春明，唐守正，非線性混合效應(yīng)模型參數(shù)估計(jì)方法分析，林業(yè)科學(xué)，2013，（01）：114-119.

[48]、Fu,L.Y.,Zeng,W.S.,Tang,S.Z.,Sharma,RP.,andLi,HK.2012.Using Linear Mixed Model and Dummy Variable Model Approaches to Construct Compatible Single-Tree Biomass Equations at Different Scales—A Case Study for Masson Pine in Southern China. Journal of Forest Science ,58(3):101-115.

[49]、符利勇，李永慈，李春明，唐守正，利用2種非線性混合效應(yīng)模型(2水平)對(duì)杉木林胸徑生長(zhǎng)量的分析，林業(yè)科學(xué)，2012，（05）：36-43 .

[50]、符利勇, 張會(huì)儒, 唐守正. 基于非線性混合模型的杉木林優(yōu)勢(shì)木平均高，林業(yè)科學(xué)，2012，48(7)： 66-71.

[51]、符利勇, 唐守正, 劉應(yīng)安. 關(guān)帝山天然次生針葉林林隙徑高比， 生態(tài)學(xué)報(bào)，2011，31(5)：1260-1268.

[52]、符利勇, 曾偉生, 唐守正. 利用混合模型分析地域?qū)��?guó)內(nèi)馬尾松生物量的影響，生態(tài)學(xué)報(bào)，2011，31(19)：5797-5808.

[1]、唐守正，李勇，符利勇，生物數(shù)學(xué)模型的統(tǒng)計(jì)學(xué)基礎(chǔ)，高等教育出版社，310頁(yè)，2015

[1]、第四批國(guó)家“萬(wàn)人計(jì)劃”青年拔尖人才(2019)，中組部

[2]、首屆國(guó)家林業(yè)和草原科技創(chuàng)新青年拔尖人才(2019)，國(guó)家林業(yè)和草原局

[3]、第十四屆中國(guó)林業(yè)青年科技獎(jiǎng)(2017)，國(guó)家林業(yè)局(排名：1/1)

[4]、中國(guó)科協(xié)首屆“青年人才托舉工程”入選者(2016)，中國(guó)科協(xié)

[5]、第四屆“中國(guó)林科院杰出青年” ，中國(guó)林業(yè)科學(xué)研究院(2014)

[6]、第九屆梁希林業(yè)科學(xué)技術(shù)獎(jiǎng)三等獎(jiǎng)(2018)，國(guó)家林業(yè)局. (排名：1/10)

[7]、第六屆梁希青年論文獎(jiǎng)一等獎(jiǎng)(2016)，國(guó)家林業(yè)局科學(xué)技術(shù)委員會(huì)、中國(guó)林學(xué)會(huì). (排名：1/1)

[8] 、第五屆梁希青年論文獎(jiǎng)二等獎(jiǎng)(2014)，國(guó)家林業(yè)局科學(xué)技術(shù)委員會(huì)、中國(guó)林學(xué)會(huì). (排名：1/1)

[9] 、第四屆梁希青年論文獎(jiǎng)二等獎(jiǎng)(2012)，國(guó)家林業(yè)局科學(xué)技術(shù)委員會(huì)、中國(guó)林學(xué)會(huì). (排名：1/1)

[10]、中國(guó)林學(xué)會(huì)青年科技優(yōu)秀論文獎(jiǎng)(2012)，中國(guó)林學(xué)會(huì)(排名：1/1)

[11]、2017年第十四屆林業(yè)青年科技獎(jiǎng)