Lev Zadvinskiy

Philadelphia, Pennsylvania, United States Contact Info
463 followers 458 connections

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About

I am a recent MS Data Science graduate from University of Virginia with an unquenchable…

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Experience & Education

  • Morgan, Lewis & Bockius LLP

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Licenses & Certifications

Publications

  • Using abrupt change detection to categorize glucose variability of Type 1 diabetes patients

    IEEE

    According to the American Diabetes Association, Type 1 diabetes affects approximately three million Americans. This disease is characterized by an inability to regulate blood glucose, caused by diminished function of the pancreas to produce insulin. One current method of monitoring blood glucose is through continuous glucose monitoring, which measures patients' blood glucose levels at regular five-minute intervals via a skin patch sensor. Patients have an increased risk of mortality caused by…

    According to the American Diabetes Association, Type 1 diabetes affects approximately three million Americans. This disease is characterized by an inability to regulate blood glucose, caused by diminished function of the pancreas to produce insulin. One current method of monitoring blood glucose is through continuous glucose monitoring, which measures patients' blood glucose levels at regular five-minute intervals via a skin patch sensor. Patients have an increased risk of mortality caused by dipping into the hypoglycemic (<; 70 mg/dL) range, so careful accurate monitoring is critical to the well-being of Type 1 diabetes patients. Analysis of the continuous glucose monitoring signal can be challenging, as it is difficult to separate behavioral changes from purely physiological changes in the signal. In this study, individual patients' glucose variability was analyzed based on features created from the signal including glycemic state, risk index calculations, and blood glucose rate of change. Primarily the signal was analyzed for any abrupt changes in the variability following an inpatient glycemic trial designed to test the patient's physiological response to insulin. Results show that a known change-point caused by an inpatient study could be identified in 7 out of 15 patients. This is important as glucose variability is reflective of a patient's physiological state, and an understanding of changes in glucose variability can lead to increased ability to maintain optimal euglycemic levels.

    See publication

Courses

  • Actuarial Mathematics II

    MATH 442

  • Applied Time Series Analysis

    MATH 447

  • Data Mining

    SYS 6018

  • Data Visualization

    SARC 5400

  • Financial Statement Analysis

    FIN 403

  • Linear Models for Data Science

    STAT 6021

  • Machine Learning

    SYS 6016

  • Options and Futures Markets

    FIN 430

  • Regression Analysis

    MATH 344

  • Reinforcement Learning

    SYS 6582

  • Statistical Computing for Data Science

    STAT 6430

  • Stochastic Processes

    MATH 477

  • Stochastic Simulation

    MATH 448

Projects

  • Presidential Approval Ratings Based on Twitter

    -

    Created a framework for approval rating calculations based on sentiment analysis of word embeddings from Word2Vec model using Twitter data collected via AWS EC2 and RDS instances

  • Using abrupt change detection to categorize glucose variability of Type 1 diabetes patients (Capstone Project)

    -

    - Managed a team of data science students and lead client negotiations with the Center for Diabetes Technology, at University of Virginia Hospital
    - Applied Relative Unconstrained Least-Squares Importance Fitting algorithm for abrupt change detection in continuous glucose monitor data to boost glucose variability classification accuracy in patients with type I diabetes

    Other creators
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  • Florida Real Estate Pricing

    -

    Developed Naïve Bayes and Random Forest models to classify fairness of residential housing market in Florida based on Department of Revenue and Census data

Honors & Awards

  • Outstanding Performer Award 2013

    TD Bank

    An award given to the most valuable partner within service team at TD Bank.

Test Scores

  • SOA/CAS Exam FM/2

    Score: Pass

  • SOA/CAS Exam P/1

    Score: Pass

Languages

  • English

    Full professional proficiency

  • Russian

    Native or bilingual proficiency

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