Travelled to:1 × Korea
1 × Portugal
2 × Australia
2 × China
3 × USA
Collaborated with:S.Gong I.H.Witten Y.Li D.Xu W.M.Lively D.B.Simmons R.J.McNab C.Gutwin J.Chen W.Shang A.E.Hassan J.Lin J.Ouyang S.Lin J.Song Z.Hou Y.Wang P.Juang H.Oki M.Martonosi L.Peh D.Rubenstein J.Reese L.Li H.Darabi E.Osland M.S.Ozcan V.L.Baughman G.Edelman
Talks about:system (3) model (3) softwar (2) predict (2) experi (2) optim (2) earli (2) time (2) base (2) anesthesia (1)
Person: Yong Wang
DBLP: Wang:Yong
Contributed to:
2014
2012
2011
2010
2007
20062002
2002
1998
2019Wrote 10 papers:
- ASPLOS-2014-OuyangLSHWW #internet #named
- SDF: software-defined flash for web-scale internet storage systems (JO, SL, JS, ZH, YW, YW), pp. 471–484.
- CASE-2012-ReeseWLDOOBE #modelling
- Early warning system modeling for patient bispectral index prognosis in anesthesia and the operating room (JR, YW, LL, HD, EO, MSO, VLB, GE), pp. 297–302.
- ICEIS-v2-2011-LiW #enterprise #research #risk management
- Research on Integrated Management and Control System in Enterprises based on Project Risks (YL, YW), pp. 318–322.
- SEKE-2010-WangXLS #debugging #optimisation #probability
- A Stochastic Model for Optimizing the Patching Time of Software Bugs (YW, DX, WML, DBS), pp. 88–92.
- CIKM-2007-WangG #image #topic #word
- Translating topics to words for image annotation (YW, SG), pp. 995–998.
- ICPR-v3-2006-WangG #analysis #recognition
- Tensor Discriminant Analysis for View-based Object Recognition (YW, SG), pp. 33–36.
- ASPLOS-2002-JuangOWMPR #case study #design #energy #experience #trade-off
- Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet (PJ, HO, YW, MM, LSP, DR), pp. 96–107.
- ICML-2002-WangW #modelling #predict #probability
- Modeling for Optimal Probability Prediction (YW, IHW), pp. 650–657.
- SIGIR-1998-McNabWWG #predict #query
- Predicting Query Times (RJM, YW, IHW, CG), pp. 355–356.
- ASE-2019-ChenSHWL #behaviour #case study #experience #generative #testing #using
- An Experience Report of Generating Load Tests Using Log-Recovered Workloads at Varying Granularities of User Behaviour (JC, WS, AEH, YW, JL), pp. 669–681.