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Biography

Research

• Information Fusion and Complex Event Detection

• Efficient Computation of Social Network Metrics

• Optimal Resource Allocation for Spacial Analysis

• Causal Inference with Observational Data

• Social Network Analysis of Online Smoking Cessation Communities

• Stochastic Modeling of Hospital Readmission Process

Teaching

• IE 374: Systems Modeling and Optimization: Operations Research II

• IE 575: Stochastic Methods

• IE 411/511: Social Network Behavior Analysis

Teaching Interests

IE 374: Systems Modeling and Optimization: Operations Research II

Course Overview

Catalog Description     A companion of IE 373, this course discusses methods for probabilistic analysis in operations research, and in particular, focuses on quantifying uncertainty and analyzing risk. Topics include elementary stochastic proceeses, mathematical models of game theory, decision analysis, and queues (waiting lines). Course Overview     This course is motivated by the need for treating uncertinty in decision-making. Relying on probability theory concepts, it presents mathematical models as approximations for real-world systems where random events and decisions play key roles. For example, potential sources of uncertainty may include unknown future demand for a product, arrival of customers to a point of service, server reliability, device lifetimes, opponent's moves in a strategic game, the value of unexplored resource before it is purchased, currency exchange rates, weather, etc. The topics covered include Markov chains, Poisson process, Birth-and-Death processes, queueing theory, matrix games, decision analysis, value of information. These concepts will be useful for in-depth understanding of such problems as inventory control in supply chains, logistics, pricing and revenue management, portfolio optimization, system reliability, and traffic management. Course Objectives     Students completing this course will be able to understand: •    the concept of risk due to incomplete information inherest in a problem •    how to represent a system as a mathematical model, clearly stating assumptions, and recognizing limitations •    how to express uncertainty in the language of distributions and evaluate quantities of interest in expectation

Course Topics

1. Markov Chains Textbook Chapter 16 2. Queueing Theory Textbook Chapter 17 3. Game Theory Textbook Chapter 14 4. Decision Analysis Textbook Chapter 15

Staff

Instructor: Dr. Alexander Nikolaev, Ph.D. Assistant Professor Department of Industrial and Systems Engineering University at Buffalo (SUNY) 409 Bell Hall Buffalo, NY 14260-2050 U.S.A. Telephone: (716) 645-4710 FAX: (716) 645-3302 E-mail: anikolae@buffalo.edu Teaching Assistant: TBD

Basic Requirements

Algebra and Basic calculus Introductory Probability Theory General understanding of engineered objects

Required Work, Grading Policy, References

1.  Exam I    -  Markov Chains    25% 2.  Exam II    -  Queueing Theory    25% 3.  Exam III    -  Game Theory / Decision Analysis    20% 4.  Pop-Up Quizes    15% 5.  Homeworks    -  Weekly Assignments    15% Course Text [1]   Hillier and Lieberman, Introduction to Operations Research Ninth Edition.