Anton Braverman joined the Operations group at Kellogg in 2017. He completed his PhD in Operations Research from Cornell University, and holds a Bachelor's degree in Mathematics and Statistics from the University of Toronto. Anton's research is focused on stochastic modelling and applied probability. Some application domains of interest include ridesharing services, as well as healthcare operations.

• PhD, 2017, Operations Research, Cornell University
• MS, 2015, Operations Research, Cornell University
• BS, 2012, Math and Statistics, University of Toronto

• Assistant Professor, Operations, Kellogg School of Management, Northwestern University

• 2017 Best Publication Award, The Applied Probability Society of INFORMS, 2016-2017

Queueing Networks: Models, Algorithms and Emerging Applications (OPNS-522-0)
This course aims to expose students to advanced methods in stochastic analysis and develop a toolbox of probabilistic analytical techniques. To focus the discussion, the course will be centered around queueing networks, which serve as building blocks in many modeling applications. Topics covered include fundamental queueing models, fluid and diffusion processes, limit theorems and approximations, and stochastic control. To discuss the algorithmic/computational elements of stochastic control, we will touch on approximate dynamic programming and explore how it is used in the control of queueing networks.

Stochastic Processes I (OPNS-516-1)
The course prepares the student with an understanding of Stochastic Processes. This course covers the following topics: Poisson Processes, discrete-time Markov chains, and continuous time Markov chains. It applies these concepts to queuing systems. Students are expected to have some background in probability.

Field Study (OPNS-498-0)
Field Studies include those opportunities outside of the regular curriculum in which a student is working with an outside company or non-profit organization to address a real-world business challenge for course credit under the oversight of a faculty member.

Decision Models & Prescriptive Analytics (OPNS-450-0)

The value of analytics and artificial intelligence (AI) in today's business landscape cannot be overstated. These tools have become integral to the decision-making process for many organizations across a variety of industries, including services, marketing, transportation, online platforms, and finance. AI systems often utilize a range of analytics techniques to make data-driven, evidence-based decisions. The analytics tools can be broadly classified to three types:

• Descriptive (What happened?): The interpretation of historical data to identify trends and patterns
• Predictive (What will happen?): The use of statistical and machine learning techniques to forecast future outcomes
• Prescriptive (What should be done?): The use of data-driven models to prescribe the best action plan.

Prescriptive analytics, in particular, plays a key role in the functionality of AI systems. This type of analytics involves the use of data-driven models to determine the best course of action in a given situation, based on data and analysis of past outcomes and trends. By utilizing prescriptive analytics, organizations can make informed, strategic decisions that optimize outcomes and drive business success. For example, an AI system might use prescriptive analytics to determine the best way to match drivers with riders, recommend the best portfolio of stocks, or to recommend the most effective marketing campaign for a new product. By leveraging the power of advanced analytics techniques, organizations can gain a competitive edge and make more informed, strategic decisions that can drive growth and success.

This course focuses on developing a holistic understanding of prescriptive analytics by introducing the basic principles and techniques of applied mathematical modeling for managerial decision-making. You will learn to use important analytic methods, such as spreadsheet modeling, optimization, and Monte Carlo simulation, to recognize their assumptions and limitations, and to employ them in decision-making. The emphasis will be on model formulation and interpretation of results, rather than on mathematical theory or coding. We will cover a wide range of prescriptive analytics models that are widely used in diverse industries and functional areas, including finance, operations, and marketing.

Operations Management (OPNS-430-0)
1Ys: This course is typically waived through the admissions process or the equivalent course Operations Management (Turbo) (OPNS-438A) was completed during the Summer term. MMMs: This course is equivalent to the MMM core course Designing and Managing Business Processes (OPNS-440) Operations management is the management of business processes--that is, the management of the recurring activities of a firm. This course aims to familiarize students with the problems and issues confronting operations managers, and to provide the language, concepts, insights and tools to deal with these issues to gain competitive advantage through operations. We examine how different business strategies require different business processes and how different operational capabilities allow and support different strategies to gain competitive advantage. A process view of operations is used to analyze different key operational dimensions such as capacity management, cycle time management, supply chain and logistics management, and quality management. Finally, we connect to recent developments such as lean or world-class manufacturing, just-in-time operations, time-based competition and business re-engineering.