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1 - 10 of 11 results for: OIT

OIT 245: Optimization and Simulation Modeling

This course provides basic skills in quantitative modeling. The objective is to familiarize students with the main steps in an analytical approach to business decision making: constructing an abstract model for a relevant business problem, formulating it in a spreadsheet environment such as Microsoft Excel, and using the tools of optimization, Monte Carlo simulation and sensitivity analysis to generate and interpret recommendations. The class will be taught in a lab style, with short in-class exercises done in small teams, focusing on a variety of applications drawn from online advertising, healthcare, finance, supply chain management, revenue and yield optimization.
Terms: Aut | Units: 3

OIT 247: Optimization and Simulation Modeling - Accelerated

The course is aimed at students who already have a background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth, than in OIT 245.
Terms: Aut | Units: 3
Instructors: Bimpikis, K. (PI)

OIT 248: Optimization And Simulation Modeling - Advanced

This course constitutes an advanced option in the menu of classes satisfying the Management Perspectives requirement in Optimization and Simulation Modeling (OSM). The course is a superset of OIT 245 and OIT 247, starting with a very fast paced overview of basic concepts, and quickly diving into more advanced topics and software tools. By the end of the course, students should (1) leave with a good understanding of different types of optimization and simulation models and when they are useful; (2) be able to solve real-world models using up-to-date software; (3) when faced with a business problem, be able to identify what type of optimization model is appropriate, and how to set it up most efficiently; (4) be able to understand and discuss model outputs in a critical fashion. The class is taught in an interactive style, focusing on a variety of applications drawn from advertising, healthcare, finance, supply chain management, revenue management and pricing, scheduling, and risk manag more »
This course constitutes an advanced option in the menu of classes satisfying the Management Perspectives requirement in Optimization and Simulation Modeling (OSM). The course is a superset of OIT 245 and OIT 247, starting with a very fast paced overview of basic concepts, and quickly diving into more advanced topics and software tools. By the end of the course, students should (1) leave with a good understanding of different types of optimization and simulation models and when they are useful; (2) be able to solve real-world models using up-to-date software; (3) when faced with a business problem, be able to identify what type of optimization model is appropriate, and how to set it up most efficiently; (4) be able to understand and discuss model outputs in a critical fashion. The class is taught in an interactive style, focusing on a variety of applications drawn from advertising, healthcare, finance, supply chain management, revenue management and pricing, scheduling, and risk management. We will be using Python as the basic software, complemented with suitable packages for formulating and solving optimization models (e.g., the Gurobi software) and for conducting Monte Carlo simulation. Students should be comfortable using these software packages by the end of the class, but no specific prior experience with these packages is necessary. Some prior coding experience is helpful, but is not a strict requirement for the course.
Terms: Aut | Units: 3
Instructors: Whang, S. (PI)

OIT 611: The Drift Method: from Stochastic Networks to Machine Learning

Overview: This course is an introduction to the drift method in sequential decision-making and stochastic systems, a family of simple, yet surprisingly powerful, meta-algorithms that in each step the greedily and incrementally minimizes some potential function. Manifested in various forms, the drift method powers some of the most popular algorithmic paradigms in stochastic networks (MaxWeight, BackPressure), oneline learning, optimization and machine learning (SGD, Langevin dynamics, TD-learning). Using the Drift Method as a unifying theme, we will survey major developments in these areas and answer questions such as: What may explain the method¿s effectiveness? How can we rigorously evaluate its performance? We will develop rigorous probabilistic and optimization methodologies for answering these questions, such as Lyapunov functions and stability theory, state-space collapse, weak convergence and Stein¿s method. In terms of application topics, the course is roughly evenly divided bet more »
Overview: This course is an introduction to the drift method in sequential decision-making and stochastic systems, a family of simple, yet surprisingly powerful, meta-algorithms that in each step the greedily and incrementally minimizes some potential function. Manifested in various forms, the drift method powers some of the most popular algorithmic paradigms in stochastic networks (MaxWeight, BackPressure), oneline learning, optimization and machine learning (SGD, Langevin dynamics, TD-learning). Using the Drift Method as a unifying theme, we will survey major developments in these areas and answer questions such as: What may explain the method¿s effectiveness? How can we rigorously evaluate its performance? We will develop rigorous probabilistic and optimization methodologies for answering these questions, such as Lyapunov functions and stability theory, state-space collapse, weak convergence and Stein¿s method. In terms of application topics, the course is roughly evenly divided between stochastic queueing networks versus optimization + machine learning. Objective: For students to acquire fundamental methodologies that can be applied to tackling problems in dynamic decision-making, stochastic modeling and machine learning. Target Audience: Graduate students / advanced undergraduates with a solid grasp of probability and stochastic processes (Stat 310A / MS&E 321, or equivalent). Strong background and interests in queueing networks is highly recommend.
Terms: Aut | Units: 3
Instructors: Xu, K. (PI)

OIT 644: Research in Operations, Information and Technology

This year-long course takes a hands-on approach to learning about conducting research in Operations, Information and Technology. It will cover a broad spectrum of cutting-edge research in OIT from conceiving an idea to formulating a research problem, deriving results, and publication. The topical content will be customized to the specific interests of the enrolled students, but generally will be concerned with questions of operational interest.
Terms: Aut, Win, Spr | Units: 1 | Repeatable 12 times (up to 12 units total)

OIT 671: Operational, Economic, and Statistical Modeling in the COVID-19 Crisis

The COVID-19 crisis revealed many fundamental structural, cultural, and operational challenges in the world. Many of these challenges, for example managing patient care in a limited resource environment, were well-known before COVID-19 and the crisis simply highlighted the importance of developing effective strategies to handle them. Others, such as the design and adherence to non-pharmaceutical mitigation strategies like lock-downs, quickly appeared as countries took differing approaches to handling the pandemic. This course will discuss how operational, economic, and statistical modeling can be used to better understand different COVID-19 responses and strategies. This is a PhD seminar that will cover prior research that can shed light onto the COVID-19 crisis, current/ongoing research that directly addresses COVID-19 pressing issues, and will also explore new research directions in this space.The course will consist of a combination of lectures by the instructors, guest lectures by researchers from all over the world, and of students¿ presentations of their research projects. The course will be eclectic in terms of approaches, including tools from operations research, machine learning, statistics, econometrics, and microeconomics. The course will be co-taught via live virtual sessions by Prof. Carri Chan and Prof. Gabriel Weintraub and will be available to Business, Economics, Statistics, and Engineering PhD students from Columbia and Stanford University.
Terms: Aut | Units: 3

OIT 691: PhD Directed Reading (ACCT 691, FINANCE 691, GSBGEN 691, HRMGT 691, MGTECON 691, MKTG 691, OB 691, POLECON 691, STRAMGT 691)

This course is offered for students requiring specialized training in an area not covered by existing courses. To register, a student must obtain permission from the faculty member who is willing to supervise the reading.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

OIT 692: PhD Dissertation Research (ACCT 692, FINANCE 692, GSBGEN 692, HRMGT 692, MGTECON 692, MKTG 692, OB 692, POLECON 692, STRAMGT 692)

This course is elected as soon as a student is ready to begin research for the dissertation, usually shortly after admission to candidacy. To register, a student must obtain permission from the faculty member who is willing to supervise the research.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

OIT 698: Doctoral Practicum in Teaching

Doctoral Practicum in Teaching
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable 25 times (up to 50 units total)

OIT 699: Doctoral Practicum in Research

Doctoral Practicum in Research
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable 25 times (up to 50 units total)
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