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 1:30 p.m.-2:30 p.m.
• Instructor: Yi Zhang
  CULPA: 2 Info
• 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.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Yi Zhang
  CULPA: 2 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.
• 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.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Yi Zhang
  CULPA: 2 Info
• 4 points

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.
• 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.
• Instructor: Mingliu Chen
  h-index: 3 Info
• 3 points

UNDERGRAD RESEARCH OR PROJECT

Independent work involving experiments, computer programming, analytical investigation, or engineering design.

• 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
• 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
• 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
• Instructor: Henry Lam
  h-index: 17 Info
• Instructor: Uday Menon
  Info
• Instructor: Jay Sethuraman
  CULPA: 10 Info
• Instructor: Karl Sigman
  CULPA: 15 Info
• 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: David D Yao
  CULPA: 5 Info
• Instructor: Yi Zhang
  CULPA: 2 Info
• Instructor: Xunyu Zhou
  h-index: 54 Info
• Instructor: Kristen Maynor
  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.

• Instructor: Yi Zhang
  CULPA: 2 Info
• 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.

• MW 2:40 p.m.-3:55 p.m.
• 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.

• TR 10:10 a.m.-11:25 a.m.
• Instructor: Yuri Faenza
  h-index: 11 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.
• 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.

• TR 4:10 p.m.-5:25 p.m.
• Instructor: David D Yao
  CULPA: 5 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.

• M 10:20 a.m.-1:35 p.m.
• 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 7:30 p.m.-10 p.m.
• Instructor: Soulaymane Kachani
  CULPA: 12 Info
• 3 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.

• W 5:25 p.m.-6:40 p.m.
• R 10:10 a.m.-11:25 a.m.
• Instructor: Harry West
  CULPA: 1 h-index: 1 Info
• 3 points

SEM IN HUMAN FACTORS DESIGN

An elective for undergraduate students majoring in IE. An in-depth exploration of the application potential of human factor principles for the design of products and processes. Applications to industrial products, tools, layouts, workplaces, and computer displays. Consideration to environmental factors, training and documentation. Term project.

• M 4:10 p.m.-6:40 p.m.
• Instructor: Leon S Gold
  CULPA: 6 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.
• Instructor: Rachel Cummings
  h-index: 16 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.

• TR 1:10 p.m.-2:25 p.m.
• Instructor: Christopher J Dolan
  CULPA: 4 Info
• 3 points

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.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Eric Balkanski
  h-index: 13 Info
• 3 points

QUALITY CONTROL AND MANAGEMENT

Required for undergraduate students majoring in IE. Statistical methods for quality control and improvement: graphical methods, introduction to experimental design and reliability engineering and the relationships between quality and productivity. Contemporary methods used by manufacturing and service organizations in product and process design, production and delivery of products and services.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Van Anh Truong
  CULPA: 2 Info
• 3 points

TRANSPORTATION ANALYTICS & LOGISTICS

Transportation, primarily focused on the movement of people, and logistics, primarily focused on the movement of goods, are two of the most fundamental challenges to modern society. To address many problems in these areas, a wide array of mathematical models and analytics tools have been developed. This class will introduce many of the foundational tools used in transportation and logistics problems, relying on ideas from linear optimization, integer optimization, stochastic processes, statistics, and simulation. We will address problems such as optimizing the routes of cars and delivery trucks, positioning emergency vehicles, and controlling traffic behavior. Moreover, we will discuss modern issues such as bicycle sharing, on-demand car and delivery services, humanitarian logistics, and autonomous vehicles. Concepts will be reinforced with technical content as well as real-world data and examples.

• MW 5:40 p.m.-6:55 p.m.
• Instructor: Adam N Elmachtoub
  CULPA: 1 h-index: 13 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.

• M 7:10 p.m.-9:40 p.m.
• Instructor: Lynn Root
  Info
• 3 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.
• 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.

• T 7:10 p.m.-9:40 p.m.
• 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 9 a.m.-11:30 a.m.
• 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 2:40 p.m.-3:55 p.m.
• TR 5:40 p.m.-6:55 p.m.
• Instructor: Uday Menon
  Info
• 3 points

ANALYTICS IN PRACTICE

MSBA 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.
• Instructor: Hardeep Johar
  Info
• Instructor: Uday Menon
  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.
• 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.

• M 8:40 a.m.-11:10 a.m.
• Instructor: Hardeep Johar
  Info
• 3 points

TOPICS IN OPERATIONS RESEARCH

AI, Games, and Markets

This course focuses on core statistical techniques that are necessary to excel in a career in data science or business analytics. We start the course with a deeper dive into the theory of statistical inference, and introduce the Bayesian approach, which is common in industry. Next we introduce a common suite of testing methods, with a special emphasis on the general linear model. We conclude the course with several advanced topics that are highly useful in industry. Emphasis will be placed on applications and examples from industry.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Christian Kroer
  h-index: 16 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.

• W 7:10 p.m.-9:40 p.m.
• Instructor: Krzysztof M Choromanski
  Info
• 3 points

TOPICS IN OPERATIONS RESEARCH

Distribution Systems

We introduce various queueing models, including queueing networks, some classic and some new, with applications to a variety of industries such as telecommunications, computer science, inventory systems, health care, and insurance risk, among others. The basic mathematical tools for analyzing the models and solving for quantities of interest involve stochastic processes such as Markov chains, but also other tools from operations research such as optimization and stochastic simulation. This course is aimed at MS and undergraduate students who have already had a basic course in stochastic modeling such as IEOR 3106/4106.

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Van Anh Truong
  CULPA: 2 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.

• 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.

• Instructor: Nicolas Chikhani
  Info
• 1.5 points

TOPICS IN OR

FIN & STRUCTURING FOR DS

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.

• F 1:30 p.m.-3:40 p.m.
• 3 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 11:40 a.m.-12:55 p.m.
• Instructor: Shipra Agrawal
  h-index: 24 Info
• 3 points

TOPICS IN OPERATIONS RESEARCH

Value Invest,Modrn Apprch

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.

• W 5:40 p.m.-8:10 p.m.
• Instructor: Khosrow Dehnad
  CULPA: 2 Info
• 1.5 points

TOPICS IN OPERATION RESEARCH

Causal Inference

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 4:10 p.m.-5:25 p.m.
• Instructor: Christopher J Dolan
  CULPA: 4 Info
• 3 points

TOPICS IN OR

MACHINE LEARNING IN PRACT

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 6 p.m.-8:30 p.m.
• Instructor: Gary Kazantsev
  Info
• 1.5 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.
• 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.
• 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.

• MW 10:10 a.m.-11:25 a.m.
• MW 11:40 a.m.-12:55 p.m.
• MW 4:10 p.m.-5:25 p.m.
• MW 2:40 p.m.-3:55 p.m.
• TR 1:10 p.m.-2:25 p.m.
• 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 11:40 a.m.-12:55 p.m.
• Instructor: Xunyu Zhou
  h-index: 54 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 8:40 a.m.-9:55 a.m.
• Instructor: Ali Hirsa
  h-index: 8 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.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Xunyu Zhou
  h-index: 54 Info
• 3 points

STATISTICAL ANALYSIS AND TIME SERIES

STAT ANALYSIS & TIME SERI

This graduate course is only for M.S. Program in Financial Engineering students. Empirical analysis of asset prices: heavy tails, test of the predictability of stock returns. Financial time series: ARMA, stochastic volatility, and GARCH models. Regression models: linear regression and test of CAPM, non-linear regression and fitting of term structures.

• MW 11:40 a.m.-12:55 p.m.
• Instructor: Agostino Capponi
  h-index: 19 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.

• M 6 p.m.-8:30 p.m.
• Instructor: Siddhartha Ghosh Dastidar
  CULPA: 2 Info
• 3 points

INTRO-IMPLIED VOLATILITY SMILE

During the past 15 years the behavior of market options prices have shown systematic deviations from the classic Black-Scholes model. Examines the empirical behavior of implied volatilities, in particular the volatility smile that now characterizes most markets, the mathematics and intuition behind new models that can account for the smile, and their consequences for hedging and valuation.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Emanuel Derman
  CULPA: 1 Wikipedia h-index: 30 Info
• 3 points

TOPICS IN QUANT FINANCE

AI Applications in Finance

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.

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Ali Hirsa
  h-index: 8 Info
• 3 points

COMPUT METHODS IN FINANCE

MS IEOR students only. Application of various computational methods/techniques in quantitative/computational finance. Transform techniques: fast Fourier transform for data de-noising and pricing, finite difference methods for partial differential equations (PDE), partial integro-differential equations (PIDE), Monte-Carlo simulation techniques in finance, and calibration techniques, filtering and parameter estimation techniques. Computational platform will be C++/Java/Python/Matlab/R.

• T 7:10 p.m.-9:40 p.m.
• Instructor: Alireza Javaheri
  Info
• 3 points

ALGORITHMIC TRADING

Prerequisite(s): IEOR E4700. Large and amorphous collection of subjects ranging from the study of market microstructure, to the analysis of optimal trading strategies, to the development of computerized, high-frequency trading strategies. Analysis of these subjects, the scientific and practical issues they involve, and the extensive body of academic literature they have spawned. Attempt to understand and uncover the economic and financial mechanisms that drive and ultimately relate them.

• W 7:10 p.m.-9:40 p.m.
• Instructor: Sebastien Donadio
  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

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.

• 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
• 1-3 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.

• 1-3 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.

• 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
• 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
• 1-3 points