Lavanya Sita Tekumalla

    Industry: Over 7 years - Amazon, InMobi, Myntra  

    Masters: Computer Graphics   ( 2002 - 2005 )
    School of Computing, University of Utah  

    Ph.D: Machine Learning   ( 2011 - 2016 )
    Dept. of Computer Science and Automation(CSA)
    Indian Institute of Science(IISc), Bangalore


    LinkedIn:     linkedin.com/in/lavanyats

Publications


    -- Bayesian Models For Product Size Recommendation, WWW 2018, Vivek Sembium, Rajeev Rastogi, Lavanya Tekumalla, Atul Saroop.

    -- "Vine copulas for mixed data : multi-view clustering for mixed data beyond meta-Gaussian dependencies", Lavanya Sita Tekumalla, Vaibhav Rajan, Chiranjib Bhattacharyya, Machine Learning Journal, 2017, 10.1007/s10994-016-5624-2. (pdf)

    -- Interpretable Semantic Textual Similarity with ILP based Multiple Chunk Aligner (iMATCH), Lavanya Sita Tekumalla, Sharmistha Jat, Accepted at NAACL 2016 workshop on Semantic Textual Similarity (SemEval), San Diego, 2016.(pdf)

    -- Copula-HDP-HMM: Non-parametric models with Copulas for I/O Efficient Bulk Cache Preloading, Lavanya Sita Tekumalla, Chiranjib Bhattacharyya, Accepted at SIAM Data Mining conference (SDM), Miami, 2016 (pdf).

    -- Mining Block I/O Traces for Cache Preloading with Sparse Temporal Non-parametric Mixture of Multivariate Poisson, Lavanya Sita Tekumalla, Chiranjib Bhattacharyya, Proceedings of SIAM Data Mining conference (SDM), Vancouver, 2015 (pdf) (Honorable mention at Xerox Research India Colloquium 2016)

    -- "Multilevel Nonparametric Admixture Modeling with Nested Hierarchical Dirichlet Process", Lavanya Tekumalla*, Priyanka Agarwal*, and Indrajit Bhattacharya. (* equal contribution authors), arXiv:1508.06446, 2015 (pdf)

    -- "Nested Hierarchical Dirichlet Process for Nonparametric Entity-Topic Analysis", Priyanka Agrawal*, Lavanya Tekumalla*, and Indrajit Bhattacharya. European Conference of Machine Learning (ECML) 2013, Prague. (* equal contribution authors) (pdf)

    --"Smoothing Space Curves with the MLS Projection", Lavanya Sita Tekumalla, Elaine Cohen, Proceedings of Symposium on Geometric Modeling, Vizualization and Graphics, Salt Lake City, July 2005. (Short Paper) (pdf).

    --"Reverse Engineering Point Clouds to Fit Tensor Product B-Spline Surfaces by Blending Local Fits, arXiv:1411.5993", 2005 (pdf)

    --"Hole-Filling Algorithm for Triangular Meshes"(46 citations), Lavanya Sita Tekumalla, Elaine Cohen, Technical Report, School of Computing, University of Utah, Dec 2004, UUCS-04-019. (pdf)

Talks



    -- Invited Talk Feb 2018 - Keynote Speaker @ Developer Weekend Bangalore, “Is Machine Learning the Future”

    -- Invited Talk Nov 2017 @ Grace Hopper India, Bangalore, ‘Machine Learning At Amazon’', Lavanya Sita Tekumalla, Rajeev Rastogi.

    -- Invited Talk Feb 2017 @ Invited talk at Conference on Data Science (CODS 2017), Bangalore: Copula-HDP-HMM: Bayesian models for Bulk Cache Preloading

    -- Invited Talk Oct 2016 @ Core Machine Learning Team, Amazon Development Center India, "Modeling Multivariate Count Data for Bulk Cache Preloading".

    -- Invited Talk May 2015 @ Facebook AI Research, Palo Alto, USA: 'Statistical Models for Bulk Cache Preloading', Lavanya Sita Tekumalla

    -- Invited Talk April 2015 @ NetApp University Day, Bangalore, India: 'Mining Block I/O traces for Bulk Cache Preloading', Lavanya Sita Tekumalla

    -- Invited Talk 2014 @ Xerox Research Labs India, Bangalore, India: "Nested Hierarchical Dirichlet Process for Nonparametric Entity-Topic Analysis", Lavanya Tekumalla

    -- Talk : "Reverse Engineering Point Clouds to Trimmed NURBS" , presented as a talk at the conference 'Mathematical Methods for Curves and Surfaces', Oslo, July 2004.

Workshops Organized



    -- “Introduction to Predictive Analytics with R and python”, Workshop @GraceHopperConference, GHC 2016, India.

    -- Organized the CSA Undergraduate Summer School 2014.

    -- Workshop on Probabilistic Graphical Models @ Amazon India (Gurukul Series).



Reviewer/Committe member



    -- ICDM 2017, CODS 2017, Journal on Robotics and Autonomous Systems (Elsevier), Machine Learning for Big Data, Sadhana Journal (Springer)

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