プロフィール詳細

プロジェクトを作成

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Dr. Andrew V.に依頼

United States

Ph.D. Operations Research Expert | Optimization, Predictive AI, Supply Chain & Revenue Management | 20+ Yrs Exp

プロフィール概要

Dr. Andrew Vakhutinsky is a Principal Operations Research Scientist and Applied AI Leader with over 20 years of experience designing and deploying large-scale mathematical optimization, statistical, and machine learning models for retail, supply chain logistics, revenue management, and telecommunications. He holds a Ph.D. in Operations Research from the University of Maryland and an M.S. in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology. Throughout his career at organizations such as Wayfair, Oracle Labs, Staples, Revionics, and Akamai, Dr. Vakhutinsky has spearheaded R&D projects that translate complex stochastic data into explainable, high-impact decision systems. Core Capabilities & Technical Expertise - Mathematical Programming & Optimization: Mixed-Integer Linear Programming (MILP), Stochastic Programming, Heuristics, Network Routing & Scheduling, and Inventory Allocation. - Advanced Solvers & Tools: GUROBI, CPLEX, FICO Xpress, Pyomo, OR-Tools, SCIP, and HiGHS. - AI, Machine Learning & Forecasting: Predictive modeling (PyTorch, TensorFlow, GCP Vertex AI), Quantile Regression, Causality/Econometric Analysis, and Discrete Choice Modeling. - Revenue Management & Retail Analytics: Dynamic pricing, markdown optimization, overbooking models, demand driver estimation, and space allocation. - Modern Generative AI Integration: Combining LLMs, AI coding agents (Cursor), and reasoning frameworks (Tree/Chain-of-Thought) with classical optimization models for explainable AI policies (e.g., OptiChat). Research, Patents & Publications - Patents: Inventor or co-inventor on 9+ granted and pending U.S. patents covering federated learning, overbooking algorithms, room assignment optimization, and differential pricing. - Publications: Published in top peer-reviewed journals and conferences including Manufacturing & Service Operations Management (MSOM), INFORMS Journal on Computing, IEEE, and Neural Networks.

専門分野

• ★ Algorithm Design
• ★ Predictive Modeling & Analytics
• ★ Decision Science
• ☆ Data Analysis
• ☆ Data Exploration
• ☆ Cluster Analysis
• ☆ Big Data Analytics
• ☆ Machine Learning
• ☆ Pattern Recognition
• ☆ Deep Learning
• ☆ Business Analytics
• ☆ Python
• ☆ SQL
• ☆ Java
• ☆ Tensorflow
• ☆ LaTex
• ☆ Scikit-learn

サービス

• Research
• Consulting
• Data & AI
• Product Development

Research Scientific and Technical Research

Consulting Operations Consulting, Scientific and Technical Consulting, Manufacturing Consulting

Data & AI Predictive Modeling, Statistical Analysis, Algorithm Design-Non ML, Algorithm Design-ML, Big Data Analytics

Product Development Formulation

職務経験

Senior Scientist

Wayfair

11月 2025 - 現在

Consulting Member of Technical Staff

Oracle

3月 2018 - 9月 2025

Sr. Scientist

Staples

1月 2016 - 2月 2017

Prinicipal Scientist

Revionics

1月 2013 - 12月 2016

Prinicipal Scientist

Oracle

1月 2010 - 12月 2012

Senior Software Engineer

Akamai Technologies

1月 2007 - 12月 2009

Lead Research Analyst

BAE Systems

1月 2003 - 12月 2006

学歴

Operations Research

University of Maryland, College Park

9月 1991 - 10月 1996

Physics and Applied Mathematics

Moscow Institute of Physics and Technology

9月 1982 - 5月 1988

認定資格

• Private Pilot. Instrument rating for single engine airplane

  Federal Aviation Administration

  6月 2005 - 現在

出版物

JOURNAL ARTICLE

Andrew Vakhutinsky, Övünç Yılmaz, Farbod Ekbatani, Zifeng Zhao, Ruxian Wang (2026). Data-Driven Pricing for Availability-Based Upgrades Under a Multiple Binary Choice Model with Copula . Manufacturing & Service Operations Management.