PAST AND ONGOING WORK

Sergey works part-time as a senior applied research scientist at AI2, on the Semantic Scholar research team. He's worked on many different projects, including:

• A paper about gender bias in clinical trial recruitment published in JAMA Network Open, along with news coverage.
• A complete overhaul of the Semantic Scholar author disambiguation system, described in a published paper and a blog post. Also, see the open-sourced code & data
• Two published methods for high quality academic paper embeddings: Citeomatic (code) and SPECTER (code).
• Improving the Semantic Scholar search engine, described in a detailed blog post. Code is available as well.
• A blog post and paper about the association between posting your papers on ArXiV before review and subsequent citations.

The Healthy Birth, Growth, and Development (HBGD) program was launched in 2013 by the Bill & Melinda Gates Foundation.

The Knowledge Integration (Ki) initiative aims facilitates collaboration between researchers, quantitative experts, and policy makers in fields related to HBGD. The broad goal is to aggregate data from past longitudinal studies about pathways and risk factors that affect birth, growth, and neurocognitive development in order to better predict Ki outcomes.

Sergey works closely with Ki leadership - designing and overseeing data science contests; managing external collaborations with academic research labs and software companies; and modeling many diverse global health datasets (an example is described here).

This work is ongoing since February, 2015.

Actively Learn makes a reading tool that enables teachers to guide, monitor, and improve student learning. With our help, they wrote and were awarded an NSF SBIR grant to answer the key question: "How can we personalize reading instruction so as to increase comprehension & learning?" We are diving deep into the data with sophisticated machine learning tools, and bringing back testable hypotheses about what helps and hinders students.

This work is ongoing since April, 2014.

Jenny Dearborn, Chief Learning Officer and Senior Vice President at SAP, has written Data Driven, a "practical guide to increasing sales success, using the power of data analytics," and The Data Driven Leader (with David Swanson), "a clear, accessible guide to solving important leadership challenges through human resources-focused and other data analytics."

We helped her and her team come up with clear and compelling ways to communicate the deep mathematical models that are at the core of the book, as well as contributed to the plot and characterizations.

Seattle Against Slavery mobilizes the community in the fight against labor and sex trafficking through education, advocacy, and collaboration with local and national partners. We are proud to provide them with analytics and statistics services on a volunteer basis.

Long Tail NLP-Based Recommendations. Most e-commerce recommendation engines have difficulty highlighting less frequently bought products, which is an issue that compounds itself and ends up recommending the same popular products over and over. We developed a language-based model for RichRelevance that identifies good recommendations based on comparisons of the product descriptions and description metadata rather than purchase data. This evens the playing field between newer products and the old standbys, so the recommendations have more variety and are generally more applicable.

Bayesian A/B Testing. RichRelevance swears by their top-notch recommendations. But what's the right way to measure their efficacy? Sergey put together an intuitive, comprehensive Bayesian A/B testing system that works for any KPI, and can provide direct answers to key customer questions like "What is the probability that algorithm A has at least 5% lift over algorithm B?

Read all about this work in Sergey's three (archived) blog posts: [1], [2], and [3].

Bandits for Online Recommendations. The most important piece of RichRelevance's impressive big data pipeline is their core recommendation system. It serves thousands of recommendations every minute, and it has to learn quickly from new data. Working with their analytics team, Sergey engineered a modern bandit-based approach to online recommendations that learns from less data, adapts easily to any optimization metric, and does not compromise quality at production-scale.

Three (now archived) blog posts describe the results of our research: [1], [2], and [3].