About Me || Research || Industry || Publications || Talks || Videos || Program Committee || Workshops || Education
Before this, I have been a Machine Learning Scientist at Amazon for the Core Machine Learning Team that solves a range of problems partnering with various Amazon teams to drive customer Impact. I had particularly worked on problems from the Amazon Fashion Vertical. I am a customer obsessed 2x-Amazonian.
I had completed a PhD from IISc, where I performed most of my doctoral research at the Machine Learning Lab, CSA, IISc. I was advised by Dr. Chiranjib Bhattacharya. Prior to that, I was advised by Dr. Indrajit Bhattacharya. My research focus during PhD has been on Bayesian Non-parametric modeling, Probabilistic Graphical models and their applications. I am also interested in approaches for scaling inference algorithms and recently deep learning based techniques. So far, I have worked on probabilistic models for the e-commerce domain, systems domain, and Natural Language Processing.
Prior to joining PhD, I had worked in the industry in a variety of roles at Amazon.com(Seattle), Myntra.com(Bangalore) and InMobi(Bangalore). During my PhD, I have worked closely with NetApp(Bangalore) on designing machine learning models for mining block I/O traces and have also interned at Xerox Research Center(Bangalore), where I worked on modeling with Copulas.
I had also worked with Prof. Elaine Cohen (Geometric Design and Computation Group) in the Area of Geometric Modeling with B-Splines, during my Masters at University of Utah. My work involved finding novel algorithms for fitting B-Spline surfaces from noisy and incomplete point cloud data.
(Google Anita Borg Scholar APAC - 2014)
(GATE All India Rank 2 , Rank 4 in University)
-- Invited Panellist April 2019 @ Springboard Leap, Bangalore - "AI Across Application Domains"
-- Invited Talk Nov 2018 @ Amazon Blink Studio with WGSN Fashion Trend Forecasting, Delhi - ”AI Trends in Fashion"
-- Invited Talk Sep 2018 @ HasGeek AntHill Inside 2018, Bangalore - ”Product Size Recommendation in Fashion E-commerce"
-- Invited Talk July 2018 @ Robert Bosch Center for Cyber Physical Systems, Bangalore - ”Bayesian Models for Product Size Recommendation"
-- Invited Talk April 2018 @ Google AI/ML Workshop, Bangalore - ”Product Size Recommendation in Fashion E-commerce"
-- 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.
-- “Deep Learning for Natural Language Processing", Online Guest Workshop @Tata Consultancy Services (TCS), 2019, India.
-- “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).
-- AAAI 2018, CODS 2018, ICDM 2017, CODS 2017, Journal on Robotics and Autonomous Systems (Elsevier), Machine Learning for Big Data, Sadhana Journal (Springer)