Alexander Ulanov
Staff Machine Learning Engineer at Hewlett Packard Enterprise
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolAlexander Ulanov is a Staff Machine Learning Engineer in Palo Alto with 12+ years delivering end-to-end ML systems, specializing in recommender systems and ads retrieval at Hewlett Packard Enterprise. He combines applied research and production engineering—designing retrieval, ranking, and semantic-signal pipelines while driving privacy-aware signal engineering for major placements. A PhD in mathematical modeling, he brings deep expertise in graphical models, anomaly detection, and large-scale time-series and text analytics developed across HP Labs and industry projects. Alexander is an active Apache Spark committer who improved MLlib (MLP classifier, multi-label metrics, chi-squared feature selection) and benchmarked deep learning performance on JVM, a contribution that bridges research and scalable open-source engineering. Notably, he has shipped zero-to-one semantic ranking features and practical modeling paradigms that capture more signals at lower cost, demonstrating an emphasis on measurable product impact.
11 years of coding experience
15 years of employment as a software developer
Technische Universität DresdenTechnical University of MunichMaster's degree, Control theory, Master's degree, Control theory at Peter the Great St.Petersburg Polytechnic UniversityUniversity of HamburgDoctor of Philosophy (PhD), Mathematics and Computer Science, Doctor of Philosophy (PhD), Mathematics and Computer Science at St.Petersburg Institute for Informatics and Automation of Russian Academy of Sciences
English, Russian, German
Github Skills (9)
scala10
big-data10
machine-learning10
apache-spark10
feature-selection10
ml10
java7
javas7
sql4
Programming languages (3)
Github contributions (5)
apache/spark
Jul 2015 - Jul 2015
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
ML Engineer & Backend Developer
ML Engineer & Backend DeveloperContributions:1 commit, 15 PRs, 155 comments in 1 day
Contributions summary:Alexander primarily contributed to the Apache Spark MLlib module, focusing on implementing and improving machine learning functionalities. They developed multi-label evaluation metrics and implemented chi-squared feature selection. Additionally, they refactored the Multilayer Perceptron (MLP) classifier, enhancing its extensibility, optimizing memory usage, and introducing testing for gradient and loss functions. Furthermore, they made a simplification in the Pregel GraphX code.
analyticspythondata-processingsqlapache
avulanov/scalability
Apr 2016 - Oct 2016
Contributions:20 commits, 22 pushes, 5 branches in 6 months
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