IBM Research Computer Science Blog  

IBM computer scientists have been at the forefront of scientific and technological innovation across a broad range of research areas.  They have made pioneering contributions in artificial intelligence, high-speed processor design, computer architecture, natural language processing, programming languages, optimizing compilers, operating systems, storage systems, computer-supported cooperative work, databases, speech recognition, integer programming, and service-oriented architectures, to name a few.

 



IBM Researchers at KDD 2014    (up to IBM Research Computer Science Blog)

IBM Researchers will be presenting more than a dozen papers at the 2014 ACM SIGKDD Conference on Knowledge Discovery and Data Mining coming up 24-27 August 2014 in New York City. The talks include:

  • Targeting Direct Cash Transfers to the Extremely Poor - Brian Abelson, Enigma; Kush R Varshney, IBM Thomas J. Watson Research Center; Joy Sun, GiveDirectly; 
  • FoodSIS: A Text Mining System to Improve the State of Food Safety in Singapore - Kiran Kate, IBM Research; Sneha Chaudhari, Carnegie Mellon University, Pittsburgh, USA; Andy Prapanca, IBM Research; Jayant Kalagnanam, IBM Research;
  • Novel Geospatial Interpolation Analytics for General Meteorological Measurements - Bingsheng Wang, Virginia Tech; Jinjun Xiong, IBM Thomas J. Watson Research Center; 
  • Predicting Student Risks Through Longitudinal Analysis - Ashay Tamhane, IBM Research India; Shajith Ikbal, IBM Research India; Bikram Sengupta, IBM Research India; Mayuri Duggirala, Tata Research Development & Design Centre; James Appleton, Gwinnett County Public Schools;
  • Predicting Employee Expertise for Talent Management in the Enterprise - Kush R Varshney, IBM Thomas J. Watson Research Center; Vijil Chenthamarakshan, IBM Thomas J. Watson Research Center; Scott W Fancher, IBM Corporate Headquarters; Jun Wang, IBM Thomas J. Watson Research Center; Dongping Fang, IBM Thomas J. Watson Research Center; Aleksandra Mojsilovic, IBM Thomas J. Watson Research Center;
  • Automated Hypothesis Generation Based On Mining Scientific Literature - Scott Spangler, IBM Research; Angela D Wilkins, Baylor College of Medicine; Benjamin J Bachman, Baylor College of Medicine; Meena Nagarajan, IBM Research; Tajhal Dayaram, Baylor College of Medicine; Peter Haas, IBM Research; Sam Regenbogen, Baylor College of Medicine; Curtis R Pickering, The University of Texas MD Anderson Cancer Center; Austin Comer, The University of Texas MD Anderson Cancer Center; Jeffrey N Myers, The University of Texas MD Anderson Cancer Center; Ioana Stanoi, IBM Research; Linda Kato, IBM Research; Ana Lelescu, IBM Research; Jacques J Labrie, IBM Research; Neha Parikh, Baylor College of Medicine; Andreas Martin Lisewski, Baylor College of Medicine; Lawrence Donehower, Baylor College of Medicine; Ying Chen, IBM Research; Olivier Lichtarge, Baylor College of Medicine;
  • Unsupervised Learning of Disease Progression Models - Xiang Wang, IBM Research; David Sontag, New York University; Fei Wang, IBM Research;
  • Learning with Dual Heterogeneity: A Nonparametric Bayes Model - Hongxia Yang, IBM T.J. Watson Research Center; Jingrui He, School of Computing, Informatics, Decision Systems Engineering, Arizona State University;
  • FEMA: Flexible Evolutionary Multi-faceted Analysis for Dynamic Behavioral Pattern Discovery - Meng Jiang, Tsinghua University; Peng Cui, Tsinghua University; Fei Wang, IBM Watson Research Center; Xinran Xu, Tsinghua University; Wenwu Zhu, Tsinghua University; Shiqiang Yang, Tsinghua University;
  • Class-Distribution Regularized Consensus Maximization for Alleviating Overfitting in Model Combination - Sihong Xie, University of Illinois at Chicago; Jing Gao, University at Buffalo; Wei Fan, Huawei Noah's Ark Lab; Deepak Turaga, IBM T.J Watson Research; Philip S Yu, University of Illinois at Chicago;
  • The Setwise Stream Classification Problem - Charu C. Aggarwal, IBM T. J. Watson Research Center;
  • From Micro to Macro: Data Driven Phenotyping by Densification of Longitudinal Electronic Medical Records -  Jiayu Zhou, Arizona State University; Fei Wang, IBM T.J. Watson Research Center; Jianying Hu, IBM T.J. Watson Research Center; Jieping Ye, Arizona State University;
  • Clinical Risk Prediction with Multilinear Sparse Logistic Regression - Fei Wang, IBM T. J. Watson Research Center; Ping Zhang, IBM T. J. Watson Research Center; Buyue Qian, IBM T. J. Watson Research Center; Xiang Wang, IBM T. J. Watson Research Center; Ian Davidson, IBM T. J. Watson Research Center;
  • A Bayesian Framework for Estimating Properties of Network Diffusions - Varun R Embar, IBM India Research Lab; Rama Kumar Pasumarthi, IBM India Research Lab; Indrajit Bhattacharya, IBM India Research Lab;
  • Analyzing Expert Behaviors in Collaborative Networks - Huan Sun, University of California, Santa Barbara; Mudhakar Srivatsa, IBM T.J. Watson Research Center; Shulong Tan, University of California, Santa Barbara; Yang Li, University of California, Santa Barbara; Lance M Kaplan, U.S. Army Research Lab; Shu Tao, IBM T.J. Watson Research Center; Xifeng Yan, University of California, Santa Barbara;
  • Improved Testing of Low Rank Matrices - Yi Li, Max-Planck Institute for Informatics; Zhengyu Wang, Tsinghua University; David P Woodruff, IBM Almaden Research Center;
  • Quantifying Herding Effects in Crowd Wisdom - Ting Wang, IBM Research; Dashun Wang, IBM Research; Fei Wang, IBM Research;

 

 

 

 

 

 

posted by Brent Hailpern on Mon, 18 Aug 2014 13:36:59 -0400