Frontiers in Operations Research and Dat
Frontiers in OR&Data Anal
Introductory course for overview of modern approaches and ideas of operations research and data analytics. Through a series of interactive sessions, students engage in activities exploring OR topics with various faculty members from the IEOR department.
- F 1 p.m.-2 p.m.
- 1 points
FOUNDATIONS OF DATA SCIENCE
Designed to provide an introduction to data science for sophomore SEAS majors. Combines three perspectives: inferential thinking, computational thinking, and real-world applications. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? Teaches critical concepts and skills in computer programming, statistical inference, and machine learning, in conjunction with hands-on analysis of real-world datasets such as economic data, document collections, geographical data, and social networks. At least one project will address a problem relevant to New York City.
- TR 5:40 p.m.-6:55 p.m.
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Instructor:
Yi Zhang
CULPA: 2 Info -
Instructor:
Christopher J Dolan
CULPA: 4
Info
- 3 points
PRODUCTN-INVENTORY PLAN-CONTRL
For undergraduates only. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. Must be taken during (or before) the sixth semester. Inventory management and production planning. Continuous and periodic review models: optimal policies and heuristic solutions, deterministic and probabilistic demands. Material requirements planning. Aggregate planning of production, inventory, and work force. Multi-echelon integrated production-inventory systems. Production scheduling. Term project. Recitation section required.
- MW 8:40 a.m.-9:55 a.m.
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Instructor:
Ali Sadighian
CULPA: 3 h-index: 3 Info - 4 points
SIMULATION MODELING AND ANALYSIS
It is strongly advised that Stochastic modeling (IEOR E3106 or IEOR E4106) be taken before this course. This is an introductory course to simulation, a statistical sampling technique that uses the power of computers to study complex stochastic systems when analytical or numerical techniques do not suffice. The course focuses on discrete-event simulation, a general technique used to analyze a model over time and determine the relevant quantities of interest. Topics covered in the course include the generation of random numbers, sampling from given distributions, simulation of discrete-event systems, output analysis, variance reduction techniques, goodness of fit tests, and the selection of input distributions. The first half of the course is oriented toward the design and implementation of algorithms, while the second half is more theoretical in nature and relies heavily on material covered in prior probability courses. The teaching methodology consists of lectures, recitations, weekly homework, and both in-class and take-home exams. Homework almost always includes a programming component for which students are encouraged to work in teams.
ADVANCED OPTIMIZATION
For undergraduates only. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. This is a follow-up to IEOR E3608 and will cover advanced topics in optimization, including integer optimization, convex optimization, and optimization under uncertainty, with a strong focus on modeling, formulations, and applications.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Shipra Agrawal
h-index: 24 Info - 3 points
PROBABILITY FOR ENGINEERS
Introductory course to probability theory and does not assume any prior knowledge of subject. Teaches foundations required to use probability in applications, but course itself is theoretical in nature. Basic definitions and axioms of probability and notions of independence and conditional probability introduced. Focus on random variables, both continuous and discrete, and covers topics of expectation, variance, conditional distributions, conditional expectation and variance, and moment generating functions. Also Central Limit Theorem for sums of random variables. Consists of lectures, recitations, weekly homework, and in-class exams.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Daniel Lacker
CULPA: 4
h-index: 13
Info
- 3 points
UNDERGRAD RESEARCH OR PROJECT
Independent work involving experiments, computer programming, analytical investigation, or engineering design.
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Instructor:
Carmen Ng
Info -
Instructor:
Michael Robbins
Wikipedia Info -
Instructor:
Shipra Agrawal
h-index: 24 Info -
Instructor:
Eric Balkanski
h-index: 13 Info -
Instructor:
Daniel Bienstock
h-index: 38 Info -
Instructor:
Agostino Capponi
h-index: 19 Info -
Instructor:
Rachel Cummings
h-index: 16 Info -
Instructor:
Emanuel Derman
CULPA: 1 Wikipedia h-index: 30 Info -
Instructor:
Antonius B Dieker
CULPA: 10 h-index: 19 Info -
Instructor:
Christopher J Dolan
CULPA: 4
Info
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Instructor:
Adam N Elmachtoub
CULPA: 1 h-index: 13 Info -
Instructor:
Yuri Faenza
h-index: 11 Info -
Instructor:
Donald Goldfarb
CULPA: 1 Wikipedia h-index: 55 Info -
Instructor:
Vineet Goyal
h-index: 21 Info -
Instructor:
Ali Hirsa
h-index: 8 Info -
Instructor:
Garud Iyengar
CULPA: 6
h-index: 30
Info
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Instructor:
Hardeep Johar
Info -
Instructor:
Cedric Josz
CULPA: 2 h-index: 13 Info -
Instructor:
Soulaymane Kachani
CULPA: 12 Info -
Instructor:
Christian Kroer
h-index: 16 Info -
Instructor:
Daniel Lacker
CULPA: 4
h-index: 13
Info
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Instructor:
Henry Lam
h-index: 17 Info -
Instructor:
Uday Menon
Info -
Instructor:
Dylan Possamai
CULPA: 1 Info -
Instructor:
Jay Sethuraman
CULPA: 10 Info -
Instructor:
Karl Sigman
CULPA: 15
Info
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Instructor:
Clifford Stein
CULPA: 31 Wikipedia h-index: 53 Info -
Instructor:
Wenpin Tang
h-index: 6 Info -
Instructor:
Van Anh Truong
CULPA: 2 Info -
Instructor:
Kaizheng Wang
CULPA: 1 h-index: 8 Info -
Instructor:
Ward Whitt
CULPA: 9 Wikipedia h-index: 102 Info -
Instructor:
David D Yao
CULPA: 5
Info
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Instructor:
Yi Zhang
CULPA: 2 Info -
Instructor:
Xunyu Zhou
h-index: 54 Info - 1-3 points
FIELDWORK
Prerequisites: Obtained internship and approval from faculty advisor. Only for IEOR undergraduate students who need relevant work experience as part of their program of study. Final reports are required. This course may not be taken for pass/fail credit or audited.
- 1-1.5 points
OPTIMIZATION MODELS AND METHODS
A graduate course only for MS&E, IE, and OR students. This is also required for students in the Undergraduate Advanced Track. For students who have not studied linear programming. Some of the main methods used in IEOR applications involving deterministic models: linear programming, the simplex method, nonlinear, integer and dynamic programming.
- TR 4:10 p.m.-5:25 p.m.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Donald Goldfarb
CULPA: 1 Wikipedia h-index: 55 Info - 3 points
COMPUTATION DISCRETE OPT
Discrete optimization problems. Mathematical techniques and testing strengths and limits in practice on relevant applications. Transportation (travelling salesman and vehicle routing) and matching (online advertisement and school allocation) problems.
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Eric Balkanski
h-index: 13 Info - 3 points
STATISTICS & SIMULATION
Probability and simulation. Statistics building on knowledge in probability and simulation. Point and interval estimation, hypothesis testing, and regression. A specialized version of IEOR E4150 for MSE and MSBA students who are exempt from the first half of IEOR E4101. Must obtain waiver for E4101.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Mingliu Chen
h-index: 3 Info - 1.5 points
PROBABILITY STAT & SIMULATION
Basic probability theory, including independence and conditioning, discrete and continuous random variable, law of large numbers, central limit theorem, and stochastic simulation, basic statistics, including point and interval estimation, hypothesis testing, and regression; examples from business applications such as inventory management, medical treatments, and finance. A specialized version of IEOR E4150 for MSE and MSBA students.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Mingliu Chen
h-index: 3 Info - 3 points
STOCHASTIC MODELING FOR MSE
Introduction to stochastic processes and models, with emphasis on applications to engineering and management; random walks, gambler’s ruin problem, Markov chains in both discrete and continuous time, Poisson processes, renewal processes, stopping times, Wald’s equation, binomial lattice model for pricing risky assets, simple option pricing; simulation of simple stochastic processes, Brownian motion, and geometric Brownian motion. A specialized version of IEOR E4106 for MSE students.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Karl Sigman
CULPA: 15
Info
- 3 points
STOCHASTIC MODELS
Some of the main stochastic models used in engineering and operations research applications: discrete-time Markov chains, Poisson processes, birth and death processes and other continuous Markov chains, renewal reward processes. Applications: queueing, reliability, inventory, and finance.
- MW 11:50 a.m.-1:05 p.m.
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Instructor:
Karl Sigman
CULPA: 15
Info
- 3 points
SUPPLY CHAIN ANALYTICS
MS IEOR students only. Supply chain management, model design of a supply chain network, inventories, stock systems, commonly used inventory models, supply contracts, value of information and information sharing, risk pooling, design for postponement, managing product variety, information technology and supply chain management; international and environmental issues. Note: replaced IEOR E4000 beginning in fall 2018.
- W 8:30 a.m.-11:30 a.m.
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Instructor:
Awi Federgruen
Wikipedia Info - 3 points
OPERATIONS CONSULTING
Aims to develop and harness the modeling, analytical, and managerial skills of engineering students and apply them to improve the operations of both service and manufacturing firms. Structured as a hands-on laboratory in which students "learn by doing" on real-world consulting projects (October to May). The student teams focus on identifying, modeling, and testing (and sometimes implementing) operational improvements and innovations with high potential to enhance the profitability and/or achieve sustainable competitive advantage for their sponsor companies. The course is targeted toward students planning careers in technical consulting (including operations consulting) and management consulting, or pursuing positions as business analysts in operations, logistics, supply chain and revenue management functions, positions in general management, and future entrepreneurs.
- R 7:10 p.m.-9:40 p.m.
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Instructor:
Soulaymane Kachani
CULPA: 12 Info - 3 points
INTRO-PROBABILITY & STATISTICS
Covers the following topics: fundamentals of probability theory and statistical inference used in engineering and applied science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference: pint and confidence interval estimation, hypothesis tests, linear regression. For IEOR graduate students.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Christopher J Dolan
CULPA: 4
Info
- 3 points
INTRO-PROBABILITY & STATISTICS
EXEMPTION:INTRO PROB/STAT
Covers the following topics: fundamentals of probability theory and statistical inference used in engineering and applied science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference: pint and confidence interval estimation, hypothesis tests, linear regression. For IEOR graduate students.
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Instructor:
Laura Radev
Info - 0 points
Think Bigger
Innovative solutions to complex problems that are both novel and useful. Focuses on The Think Bigger Innovation Method, uses decision-making theory, cognitive science, and industry practice to facilitate creativity and innovation. Designed to foster new ideas during the beginning of the semester that will then function as the seeds for entrepreneurially minded. Culminates in a final project with presentation of formal and polished pitch of an innovative idea in front of a distinguished panel of successful minds from across the city.
- W 8:30 a.m.-11:45 a.m.
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Instructor:
Sheena Iyengar
Wikipedia Info - 3 points
MSIEOR Quantitative Bootcamp
Zero-credit course. Primer on quantitative and mathematical concepts. Required for all incoming MSOR and MSIE students.
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Instructor:
Laura Radev
Info - 0 points
HUMAN-CENTERED DESIGN AND INNOVATION
HUMAN-CENTERED DESIGN & I
Open to SEAS graduate and advanced undergraduate students, Business School, and GSAPP. Students from other schools may apply. Fast-paced introduction to human-centered design. Students learn the vocabulary of design methods, understanding of design process. Small group projects to create prototypes. Design of simple product, more complex systems of products and services, and design of business.
- F 10:10 a.m.-12:40 p.m.
- T 1:10 p.m.-3:40 p.m.
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Instructor:
Harry West
CULPA: 1 h-index: 1 Info - 3 points
ANALYSIS OF ALGORITHMS I
Prerequisites: COMS W3134, COMS W3136, or COMS W3137, and COMS W3203. Introduction to the design and analysis of efficient algorithms. Topics include models of computation, efficient sorting and searching, algorithms for algebraic problems, graph algorithms, dynamic programming, probabilistic methods, approximation algorithms, and NP-completeness.
- TR 4:10 p.m.-5:25 p.m.
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Instructor:
Clifford Stein
CULPA: 31 Wikipedia h-index: 53 Info - 3 points
MSE Quantitative Bootcamp
Zero-credit course. Primer on quantitative and mathematical concepts. Required for all incoming MSOR and MSIE students.
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Instructor:
Laura Radev
Info - 0 points
QUANTITATIVE CORPORATE FINANCE
Required for students in the Undergraduate Advanced Track. Key measures and analytical tools to assess the financial performance of a firm and perform the economic evaluation of industrial projects. Deterministic mathematical programming models for capital budgeting. Concepts in utility theory, game theory and real options analysis.
- F 10:10 a.m.-12:40 p.m.
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Instructor:
David Derosa
CULPA: 4 h-index: 9 Info - 3 points
SIMULATION
Generation of random numbers from given distributions; variance reduction; statistical output analysis; introduction to simulation languages; application to financial, telecommunications, computer, and production systems. Graduate students must register for 3 points. Undergraduate students must register for 4 points. Note: Students who have taken IEOR E4703 Monte Carlo simulation may not register for this course for credit. Recitation section required.
PRODUCTION SCHEDULING
Required for undergraduate students majoring in IE and OR. Job shop scheduling: parallel machines, machines in series; arbitrary job shops. Algorithms, complexity, and worst-case analysis. Effects of randomness: machine breakdowns, random processing time. Term project.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Clifford Stein
CULPA: 31 Wikipedia h-index: 53 Info - 3 points
TRANSPORTATION ANALYTICS & LOGISTICS
Introduces quantitative techniques and state-of-the-art practice of operations research relevant to the design and both the tactical and strategic management of logistical and transportation systems. Discusses a wide variety of passenger and freight systems, including air, urban and highway traffic, rail, and maritime systems. Explores the practice of revenue management and dynamic pricing. Through case studies, analyzes successes and failures in third-party logistics, postal, truck and rail pickup and delivery systems. Investigates large-scale integrated logistics and transportation systems and studies the underlying principles governing transportation planning, investment and operations.
- MW 5:40 p.m.-6:55 p.m.
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Instructor:
Adam N Elmachtoub
CULPA: 1 h-index: 13 Info - 3 points
APPLICATIONS PROGRAMMNG FOR FE
We will take a hands-on approach to developing computer applications for Financial Engineering. Special focus will be placed on high-performance numerical applications that interact with a graphical interface. In the course of developing such applications we will learn how to create DLLs, how to integrate VBA with C/C++ programs, and how to write multithreaded programs. Examples of problems settings that we consider include simulation of stock price evolution, tracking, evaluation and optimization of a stock portfolio; optimal trade execution. In the course of developing these applications, we review topics of interest to OR:FE in a holistic fashion.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Daniel Bienstock
h-index: 38 Info - 3 points
FOUNDATIONS OF DATA SCIENCE
Designed to provide an introduction to data science for sophomore SEAS majors. Combines three perspectives: inferential thinking, computational thinking, and real-world applications. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? Teaches critical concepts and skills in computer programming, statistical inference, and machine learning, in conjunction with hands-on analysis of real-world datasets such as economic data, document collections, geographical data, and social networks.
- TR 5:40 p.m.-6:55 p.m.
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Instructor:
Christopher J Dolan
CULPA: 4
Info
- 3 points
TOOLS FOR ANALYTICS
MS IEOR students only. Introduction programming in Python, tools with the programmer's ecosystem. Python, Data Analysis tools in Python (NumPy, pandas, bokeh), GIT, Bash, SQL, VIM, Linux/Debia, SSH.
- W 7:10 p.m.-9:40 p.m.
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Instructor:
Paul Bulkley-Logston
Info - 3 points
TOOLS FOR ANALYTICS
EXEMPTION:TOOLS ANALYTICS
MS IEOR students only. Introduction programming in Python, tools with the programmer's ecosystem. Python, Data Analysis tools in Python (NumPy, pandas, bokeh), GIT, Bash, SQL, VIM, Linux/Debia, SSH.
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Instructor:
Laura Radev
Info - 0 points
Python for Analytics
Zero-credit course. Primer on Python for analytics concepts. Required for MSBA students.
- 0 points
HEALTHCARE OPERATIONS MGT
Prerequisite(s): for senior undergraduate Engineering students: IEOR E3608, E3658, and E4307; for Engineering graduate students (M.S. or Ph.D.): Probability and statistics at the level of IEOR E4150, and deterministic models at the level of IEOR E4004; for healthcare management students: P8529 Analytical methods for health services management. Develops modeling, analytical, and managerial skills of engineering and health care management students. Enables students to master an array of fundamental operations management tools adapted to the management of health care systems. Through real-world business cases, students learn to identify, model, and analyze operational improvements and innovations in a range of health care contexts.
- TR 8:40 a.m.-9:55 a.m.
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Instructor:
Van Anh Truong
CULPA: 2 Info - 3 points
PROJECT MANAGEMENT
Management of complex projects and the tools that are available to assist managers with such projects. Topics include project selection, project teams and organizational issues, project monitoring and control, project risk management, project resource management, and managing multiple projects.
- W 7:10 p.m.-9:40 p.m.
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Instructor:
Moshe Rosenwein
Info - 3 points
Industry Projects in Analytics & Operati
Industry Projs in Analyti
Teams of students work on real-world projects in analytics. Focus on three aspects of analytics: identifying client analytical requirements; assembling, cleaning and organizing data; identifying and implementing analytical techniques (e.g., statistics and/or machine learning); and delivering results in a client-friendly format. Each project has a defined goal and pre-identified data to analyze in one semester. Client facing class. Class requires 10 hours of time per week and possible client visits on Fridays.
- M 8:40 a.m.-9:55 a.m.
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Instructor:
Michael Robbins
Wikipedia Info - 3 points
DATA ANALYTICS
IEOR students only; priority to MSBA students. Survey tools available in Python for getting, cleaning, and analyzing data. Obtain data from files (csv, html, json, xml) and databases (Mysql, PostgreSQL, NoSQL), cover the rudiments of data cleaning, and examine data analysis, machine learning, and data visualization packages (NumPy, pandas, Scikit-lern, bokeh) available in Python. Brief overview of natural language processing, network analysis, and big data tools available in Python. Contains a group project component that will require students to gather, store, and analyze a data set of their choosing.
- TR 1:10 p.m.-2:25 p.m.
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Instructor:
Uday Menon
Info - 3 points
ANALYTICS IN PRACTICE
MSOR students only. Groups of students will work on real world projects in analytics, focusing on three aspects: identifying client analytical requirements; assembling, cleaning, and organizing data; identifying and implementing analytical techniques (statistics, OR, machine learning); and delivering results in a client-friendly format. Each project has a well-defined goal, comes with sources of data preidentified, and has been structured so that it can be completed in one semester. Client-facing class with numerous on-site client visits; students should keep Fridays clear for this purpose.
- R 7:10 p.m.-9:40 p.m.
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Instructor:
Hardeep Johar
Info - 3 points
MACHINE LEARNING FE & OPR
MS IEOR students only. Introduction to machine learning, practical use of ML algorithms and applications to financial engineering and operations. Supervised learning: regression, classification, resampling methods, regularization, support vector machines (SVMs), and deep learning. Unsupervised learning: dimensionality reduction, matrix decomposition, and clustering algorithms.
- F 10:10 a.m.-12:40 p.m.
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Instructor:
Christian Kroer
h-index: 16 Info - 3 points
ANALYTICS ON THE CLOUD
To introduce students to programming issues around working with clouds for data analytics. Class will learn how to work with infrastructure of cloud platforms, and discussion about distributed computing, focus of course is on programming. Topics covered include MapReduce, parallelism, rewriting of algorithms (statistical, OR, and machine learning) for the cloud, and basics of porting applications so that they run on the cloud.
- TR 4:10 p.m.-5:25 p.m.
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Instructor:
Hardeep Johar
Info - 3 points
DATA MINING
Course covers major statistical learning methods for data mining under both supervised and unsupervised settings. Topics covered include linear regression and classification, model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. Students learn about principles underlying each method, how to determine which methods are most suited to applied settings, concepts behind model fitting and parameter tuning, and how to apply methods in practice and assess their performance. Emphasizes roles of statistical modeling and optimization in data mining.
- M 7:10 p.m.-9:40 p.m.
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Instructor:
Krzysztof M Choromanski
Info - 3 points
LAUNCH YOUR STARTUP: TECH
Tools and knowledge to develop a comprehensive new venture that is scalable, repeatable, and capital efficient. Covers customer discovery, market sizing, pricing, competition, distribution, funding, developing a minimal viable product, and other facets of creating new ventures. A company blueprint and final investor pitch are deliverables.
- R 2 p.m.-5:15 p.m.
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Instructor:
Owen E Davis
Info - 3 points
TOPICS IN OPERATIONS RESEARCH
VISUALIZATION & STORYTELLING W
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit.
- T 7:10 p.m.-9:40 p.m.
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Instructor:
Michelle Glaser
Info - 1.5 points
TOPICS IN OR
PEFORMANCE OBJECTIVES & RESUL
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit.
- T 7:10 p.m.-9:40 p.m.
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Instructor:
Nicolas Chikhani
Info - 1.5 points
TOPICS IN OPERATIONS RESEARCH
Reinforcement Lrning:Thry&Prac
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Shipra Agrawal
h-index: 24 Info - 3 points
MSBA Quantitative Bootcamp
Primer on quantitative and mathematical concepts. Required for all incoming MSBA students.
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Instructor:
Laura Radev
Info - 0 points
DYNAMIC PRICING/REVENUE MGMT
Focus on capacity allocation, dynamic pricing and revenue management. Perishable and/or limited product and pricing implications. Applications to various industries including service, airlines, hotel, resource rentals, etc.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Vineet Goyal
h-index: 21 Info - 3 points
ASSET ALLOCATION
Models for pricing and hedging equity, fixed-income, credit-derivative securities, standard tools for hedging and risk management, models and theoretical foundations for pricing equity options (standard European, American equity options, Asian options), standard Black-Scholes model (with multiasset extension), asset allocation, portfolio optimization, investments over longtime horizons, and pricing of fixed-income derivatives (Ho-Lee, Black-Derman-Toy, Heath-Jarrow-Morton interest rate model).
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Yanwei Jia
Info - 3 points
BUSINESS ANALYTICS
Prepares students to gather, describe, and analyze data, using advanced statistical tools to support operations, risk management, and response to disruptions. Analysis is done by targeting economic and financial decisions in complex systems that involve multiple partners. Topics include probability, statistics, hypothesis testing, experimentation, and forecasting.
- M 10:10 a.m.-12:40 p.m.
- M 1:10 p.m.-3:40 p.m.
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Charles R Guetta
Info - 3 points
INTRO TO FINANCIAL ENGINEERING
Prerequisite(s): IEOR E4106 or E3106. Required for undergraduate students majoring in OR:FE. Introduction to investment and financial instruments via portfolio theory and derivative securities, using basic operations research/engineering methodology. Portfolio theory, arbitrage; Markowitz model, market equilibrium, and the capital asset pricing model. General models for asset price fluctuations in discrete and continuous time. Elementary introduction to Brownian motion and geometric Brownian motion. Option theory; Black-Scholes equation and call option formula. Computational methods such as Monte Carlo simulation.
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Emanuel Derman
CULPA: 1 Wikipedia h-index: 30 Info - 3 points
STOCHASTIC MODELS FOR FIN ENG
This graduate course is only for M.S. Program in Financial Engineering students, offered during the summer session. Review of elements of probability theory, Poisson processes, exponential distribution, renewal theory, Wald’s equation. Introduction to discrete-time Markov chains and applications to queueing theory, inventory models, branching processes.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
David D Yao
CULPA: 5
Info
- 3 points
MONTE CARLO SIMULATION METHODS
This graduate course is only for M.S. Program in Financial Engineering students. Multivariate random number generation, bootstrapping, Monte Carlo simulation, efficiency improvement techniques. Simulation output analysis, Markov-chain Monte Carlo. Applications to financial engineering. Introduction to financial engineering simulation software and exposure to modeling with real financial data. Note: Students who have taken IEOR E4404 Simulation may not register for this course for credit.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Ali Hirsa
h-index: 8 Info - 3 points
FOUNDATIONS FR FINANCIAL ENGIN
This graduate course is only for M.S. Program in Financial Engineering students, offered during the summer session. Discrete-time models of equity, bond, credit, and foreign-exchange markets. Introduction to derivative markets. Pricing and hedging of derivative securities. Complete and incomplete markets. Introduction to portfolio optimization and the capital asset pricing model.
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Wenpin Tang
h-index: 6 Info - 3 points
FE CONTINUOUS TIME MODELS
This graduate course is only for MS program in FE students. Modeling, analysis, and computation of derivative securities. Applications of stochastic calculus and stochastic differential equations. Numerical techniques: finite-difference, binomial method, and Monte Carlo.
- TR 5:40 p.m.-6:55 p.m.
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Instructor:
Xunyu Zhou
h-index: 54 Info - 3 points
GLOBAL CAPITAL MARKETS
An introduction to capital markets and investments providing an overview of financial markets and tools for asset valuation. Topics covered include the pricing of fixed-income securities (treasury markets, interest rate swaps futures, etc.), discussions on topics in credit, foreign exchange, sovereign ad securitized markets—private equity and hedge funds, etc.
- T 7:10 p.m.-9:40 p.m.
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Instructor:
Siddhartha Ghosh Dastidar
CULPA: 2 Info - 3 points
TOPICS IN QUANTATIVE FINANCE
ALTERNATIVE INVESTMENTS
Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets.
- M 7:10 p.m.-9:40 p.m.
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Instructor:
Khosrow Dehnad
CULPA: 2 Info - 1.5 points
TOPICS IN QUANTITATIVE FINANCE
Networks:Form,Contgn,Epidemics
Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
Agostino Capponi
h-index: 19 Info - 3 points
Programming for Financial Engineering
Covers C++ programming language, applications, and features for financial engineering, and quantitative finance applications. Note: restricted to IEOR MS FE students only.
- W 7:10 p.m.-9:40 p.m.
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Instructor:
Sebastien Donadio
Info - 3 points
Deep Learning for OR and FE
Selected topics of interest in area of quantitative finance. Some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets. Note: open to IEOR students only.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Ali Hirsa
h-index: 8 Info - 3 points
Applied Financial Risk Management
Applied Financial Risk Mg
Introduces risk management principles, practical implementation and applications, standard market, liquidity, and credit risk measurement techniques, and their drawbacks and limitations. Note: restricted to IEOR students only.
- TR 1:10 p.m.-2:25 p.m.
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Instructor:
Allan M Malz
CULPA: 1 Info - 3 points
Financial Engineering Practitioners Semi
FE Seminar Series
Degree requirement for all MSFE first-year students. Topics in Financial Engineering. Past seminar topics include Evolving Financial Intermediation, Measuring and Using Trading Algorithms Effectively, Path-Dependent Volatility, Artificial Intelligence and Data Science in modern financial decision making, Risk-Based Performance Attribution, and Financial Machine Learning. Meets select Monday evenings.
- M 7 p.m.-8:30 p.m.
- 0 points
MSFE Quantitative and Computational Boot
MSFE Quant/Comp Bootcamp
Primer on quantitative and mathematical concepts. Required of all incoming MSFE students.
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Instructor:
Laura Radev
Info - 0 points
MASTERS RESEARCH OR PROJECT
Prerequisite(s): Approval by a faculty member who agrees to supervise the work. Independent work involving experiments, computer programming, analytical investigation, or engineering design.