Dmitriy Selivanov

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Key skill and competencies: • Solid background in math and statistics. • Strong computer science fundamentals - algorithms and data structures. • Good problem solving and 'hacker' skills - successful performed in Kaggle competitions and data analysis hackathons. • Strong knowledge of modern machine learning techniques – regression, tree ensembles(boosting, bagging), svm, etc. Technology and frameworks: • Cluster computing - Apache Spark. • Extensive experience with R (C/C++ code for resolving bottlenecks + parallel computing) for data exploration, machine learning, visualization. • SQL (PostrgresSQL, MSSQL). • NoSQL (MongoDB, TokuMX). Contributing to development of R drive

alternating-least-squares svd recommender-system big data matrix-factorization minhash lsh lshr

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Dmitriy Selivanov

Modern Recommendation for Advanced Practitioners part2

Modern Recommendation for Advanced Practitioners

Recent Trends in Personalization: A Netflix Perspective

Neighbor methods vs matrix factorization - case studies of real-life recommendations (Gravity LSRS2015 RECSYS 2015)

Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorial

Parallelize R Code Using Apache Spark

FlinkML: Large Scale Machine Learning with Apache Flink

Fast ALS-Based Matrix Factorization for Recommender Systems

Steffen Rendle, Research Scientist, Google at MLconf SF

Winning Kaggle 101: Introduction to Stacking

Distributed Coordinate Descent for Logistic Regression with Regularization

Building a real time, solr-powered recommendation engine

Enabling Python to be a Better Big Data Citizen

word2vec, LDA, and introducing a new hybrid algorithm: lda2vec

Linear models for data science

SparkR + Zeppelin

Mining of massive datasets using locality sensitive hashing (LSH)

LSH

Feature Importance Analysis with XGBoost in Tax audit

Introducing DataFrames in Spark for Large Scale Data Science