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-present

• 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.

Decision Models & Prescriptive Analytics (OPNS-450-0)
Data-driven models and analysis are increasingly becoming the drivers of many managerial decisions in most domains including services, marketing, transportation, online platforms, and finance. Business analytics, the process of using quantitative methods to derive better insights from data and make informed decisions, is an essential skill in today's marketplace. At a high level, business analytics methods can be organized into three categories

• 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 an action plan for making decisions.

Forward-thinking organizations use a variety of analytics together to make smart decisions. 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 some important analytic methods (spreadsheet modeling, optimization, 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, not on mathematical theory. The emphasis is on models that are widely used in diverse industries and functional areas, including finance, operations, and marketing.

Operations Management (Turbo) (OPNS-438-5)
This course was formerly known as OPNS 438-A/OPNS 438-B
This accelerated course serves as an introduction to Operations Management. The course approaches the discipline from the perspective of the general manager, rather than from that of the operations specialist. The coverage is very selective: Students concentrate on a small list of powerful themes that have emerged recently as the central building blocks of world-class operations. The course also presents a sample of operations management tools and techniques that have proved extremely useful through the years. The topics discussed are equally relevant in the manufacturing and service sectors.

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.