The research track comprises of six invited talks, three oral sessions, and a poster session. There will be an additional session consisting of four talks, which are on topics of interest to the CoDS-COMAD community, coming from papers that are already-accepted in other top-tier venues.

Schedule - PDF | Interactive          Program Committee

Joydeep Ghosh, Professor of Electrical and Computer Engineering, The University of Texas at Austin

Saturday, January 5 , 2019, 10:00am - 10:30am

Joydeep Ghosh

Schlumberger Centennial Chair Professor of Electrical and Computer Engineering, The University of Texas at Austin

Abstract: Due to a variety of reasons including privacy, scalability, bandwidth restrictions and robustness, data is often aggregated or obfuscated in various ways before being released to the public. Is it possible to learn predictive models on aggregated data that can even come close in the predictive performance or parameter recovery possible if the full-resolution (non-aggregated) data were available? This is a challenging problem that requires significant algorithmic innovation since simple ways of imputing the missing data and then learning a model can fail dramatically. In this talk I will present new approaches that are actually able to obtain reasonable results from aggregated data in certain scenarios.
Bio: rofessor Joydeep Ghosh is currently the Schlumberger Centennial Chaired Professor at UT Austin. He has worked on a wide variety of data mining and machine learning problems, resulting in 400+ refereed publications (including 90+ full length archival journal papers), several successful industrial projects and 16 best paper awards. Service to the community includes chairing top data mining conferences (KDD'11, SDM'12, SDM'13 etc), giving keynote talks (ICHI'15, ICDM'13, MCS, ANNIE etc), and consulting with a wide range of companies, from startups to large corporations such as IBM. He is currently Chief Scientist of CognitiveScale, which was selected by the World Economic Forum in 2018 as one of the 100 emerging companies worldwide most likely to benefit humanity.

  • Paper ID
    Paper Title and Authors
  • 21
    Feature Subset Selection using Adaptive Differential Evolution: An Application to Banking

    Gutha Jaya Krishna and Vadlamani Ravi

  • 23
    DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging

    Senthil Mani, Anush Sankaran and Rahul Aralikatte

  • 110
    Space-time Prediction of High Resolution Raster Data: An Approach based on Spatio-temporal Bayesian Network (STBN)

    Monidipa Das and Soumya Ghosh

  • 26
    Where to Post: Routing Questions to Right Community in Community Question Answering Systems

    Nagendra Kumar, Srikanth G, Karthik Yadav Mudda, Gayam Trishal, Anand Konjengbam and Manish Singh

  • 86
    Towards designing an Automated Classification of Lymphoma Subtypes using Deep Neural Networks

    Rucha Tambe, Sarang Mahajan, Urmil Shah, Mohit Agrawal and Dr. Bhushan Garware

  • 67
    Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing

    Athindran Ramesh Kumar, Balaraman Ravindran and Anand Raghunathan

  • 88
    RepliSmart: A Smart Replication framework for optimal query throughput in read-heavy environments

    R K N Sai Krishna, Tekur Chandrasekhar and Phani Arnab

  • 60
    Monitoring the growth of polycystic ovary syndrome using Mono-modal image registration technique

    Suganya R, Vinodhini R and Rajaram S

  • 77
    HyPSo: Hybrid Partitioning for Big RDF Storage and Query Processing

    Tanvi Chawla, Girdhari Singh and Emmanuel S. Pilli