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.
Data Engineering with Python
Introduction to essential data engineering methods. Potential topics include Arrays, Linked Lists, Stacks and Queues, Trees and Graphs, Hash Tables, Search Algorithms and Efficiency, Relational databases, SQL, NoSQL, and Data Wrangling. Practice both theory and applications using Python programming.
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 11:40 a.m.-12:55 p.m.
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Instructor:
Uday Menon
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.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Christopher J Dolan
CULPA: 4
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.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Jay Sethuraman
CULPA: 10 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
Research Immersion in OR and Data Analyt
An overview of active research areas in Operations Research and Data Analytics, and an introduction to the essential components of research studies. This course helps students develop fundamental research skills, including paper reading, problem formulation, problem-solving, scientific writing, and research presentation. Classes are in seminar format, with students analyzing research papers, developing research projects, and presenting research findings.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Eric Balkanski
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:
Anish Agarwal
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:
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:
Yaren Kaya
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:
Fabrizio Lecci
Info -
Instructor:
Uday Menon
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:
Kaizheng Wang
CULPA: 1 h-index: 8 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
UNDERGRAD RESEARCH OR PROJECT
Independent work involving experiments, computer programming, analytical investigation, or engineering design.
- 1-3 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.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Daniel Bienstock
h-index: 38 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 10:10 a.m.-11:25 a.m.
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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.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Antonius B Dieker
CULPA: 10 h-index: 19 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 10:10 a.m.-11:25 a.m.
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Instructor:
Kaizheng Wang
CULPA: 1 h-index: 8 Info - 3 points
SUPPLY CHAIN ANALYTICS
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.
- TR 2:20 p.m.-3:50 p.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
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 1:10 p.m.-2:25 p.m.
- W 4:10 p.m.-5:25 p.m.
- R 10:10 a.m.-11:25 a.m.
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Instructor:
Harry West
CULPA: 1 h-index: 1 Info - 3 points
Policy for Privacy Technologies
Introduction to privacy technologies, their use in practice, and privacy regulations. Potential topics include anonymization, differential privacy, cryptography, secure multi-party computation, and legislation. Course material will be abased in real-world use cases of these tools.
- MW 5:40 p.m.-6:55 p.m.
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Instructor:
Rachel Cummings
h-index: 16 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.
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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.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Rachel Cummings
h-index: 16 Info - 3 points
Corporate Finance, Accounting & Investme
CorpFin, Account & Invest
Interpret financial statements, build cash flow models, value projects, value companies, and make Corporate Finance decisions. Additional topics include: cost of capital, dividend policy, debt policy, impact of taxes, Shareholder/Debtholder agency costs, dual-class shares, using option pricing theory to analyze management behavior, investment banking activities, including equity underwriting, syndicated lending, venture capital, private equity investing and private equity secondaries. Application of theory in real-world situations: analyzing financial activities of companies such as General Electric, Google, Snapchat, Spotify, and Tesla.
- R 4:10 p.m.-6:40 p.m.
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Instructor:
Rodney Sunada-Wong
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 4:10 p.m.-5:25 p.m.
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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 11:40 a.m.-12:55 p.m.
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Instructor:
Yuri Faenza
h-index: 11 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.
OPERATION RES IN PUBLIC POLICY
OR in Public Policy
Aims to give the student a broad overview of the role of Operations Research in public policy. The specific areas covered include voting theory, apportionment, deployment of emergency units, location of hazardous facilities, health care, organ allocation, management of natural resources, energy policy, and aviation security. Draws on a variety techniques such as linear and integer programming, statistical and probabilistic methods, decision analysis, risk analysis, and analysis and control of dynamic systems.
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Yaren Kaya
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.
- M 7:10 p.m.-9:40 p.m.
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Instructor:
Amit Arora
Info - 3 points
PROJECT MANAGEMENT
Project Mgmt of Complex System
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 9 a.m.-11:30 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 5:40 p.m.-6:55 p.m.
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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.
- 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
Performance, Objectives, & Results Using
Performance,Objectives&Re
OKR framework and different variations. Measurement techniques (A/B testing, validation, correlation, etc.) Identifying what to measure in product experience and business initiatives. Data-driven decision making.
- FSU 9 a.m.-5 p.m.
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Instructor:
Nicolas Chikhani
Info - 1.5 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.
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Instructor:
Krzysztof M Choromanski
Info - 3 points
Statistical Methods for Analytics
Stats Methods for Analyti
Focus on advanced statistical techniques for a career in data science or business analytics. Covers the use of writing probabilistic models for data-generating processes, using Bayesian Methods/MCMC to solve such problems. Emphasizes problem identification and general problem-solving tools. Special Topics: Survival Analysis, Missing Data, Robust Statistics, Sequential Analysis, Multiple Testing. Assignments are case-based.
- TR 1:10 p.m.-2:25 p.m.
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Instructor:
Christopher J Dolan
CULPA: 4
Info
- 3 points
TOPICS IN OPERATIONS RESEARCH
FINANCE & STRUCTURING FOR DS
The last decade of 20 th century witnessed a rapid convergence of three C’s: Communications, Computers, and Consumer Electronics. This convergence has given us the Internet, smart phones, and an abundance of data with Data Science playing a major role in analyzing these data and providing predictive analytics that lead to actionable items in many fields and businesses. Finance is a field with a large amount of information and data that can utilize the skills of Data Scientists, however, to be effective in this field a data scientist, in addition to analytic knowledge, should also be knowledgeable of the working, instruments, and conventions of financial markets that range from Foreign Exchange to Equities, Bonds, Commodities, Cryptocurrencies and host of other asset classes. The objective of this course is to provide Data Science students with a working knowledge of major areas of finance that could help them in finding a position in the Financial Industry. The wide range of topics covered in this course besides expanding the range of positions where students could be a fit, it gives them more flexibility in their job search. The course will also be of value to them in managing their own finances in the future.
- R 1:30 p.m.-3:40 p.m.
- 3 points
TOPICS IN OPERATIONS RESEARCH
Comput. Model Supplychain
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:
Uday Menon
Info - 3 points
TOPICS IN OR
DEEP LEARNING FOR NLP
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 7 p.m.-9:30 p.m.
- 3 points
TOPICS IN OR
Analytics Engineering
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.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
Fabrizio Lecci
Info - 3 points
TOPICS IN OPERATION RESEARCH
Forecasting realworld app
A project-based course in Forecasting, predicting a time series into the future, to prepare students for real-world applications including articulating the business case, value creation, problem statement, and the iterative development of solutions including building a data pipeline, exploration, modeling, and visualizations. The course will use Statistical methods, Machine Learning, and Deep Learning with Transformer-based methods to predict a time series. It will use nuggets of signal processing to augment Machine Learning models to characterize and filter orders of dynamics in the time series data.
- T 7 p.m.-9:30 p.m.
- 3 points
TOPICS IN OR
MACHINE LEARNING IN PRACT
In this course, you'll leverage student engagement data to create a photo and text recommendation app similar to Instagram/Twitter. This app will utilize AI-generated photos and text and require you to recommend a feed from over 500,000 pieces of AI generated content. We'll explore various techniques to achieve this, including, but not limited to: Candidate Generation (Collaborative filtering, Trending, Cold start, N-tower neural network models, Cross-attention teachers, Distillation, Transfer learning, Random graph walking, Reverse indexes, LLMs as embedding), Filtering (Small online models, Caching, Deduplication, Policy), Prediction/Bidding (User logged activity based prediction (time-series), Multi-gate mixture of experts (MMOE), Regularization, Offline/Online evaluation (NDCG, p@k, r@k), Boosted Trees, Value Based Bidding), Ranking (Re-ranking, Ordering, Diversity, Enrich/Metadata/Personalization, Value Functions), Misc (Data Privacy and AI Ethics, Creator Based Models, Declared, Explicit and implicit topics, Explore/Exploit, Interpret/Understand/Context/Intention).
These concepts are applicable to various recommendation systems, from e-commerce to travel to social media to financial modeling. The instructor's experience at Uber Eats, Facebook, Instagram, and Google will provide valuable insights into real-world use cases.
- T 6 p.m.-8:30 p.m.
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Instructor:
Gary Kazantsev
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 4:10 p.m.-5:25 p.m.
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Instructor:
Christopher A Perez
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.
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.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Xunyu Zhou
h-index: 54 Info - 3 points
MONTE CARLO SIMULATION METHODS
Monte Carlo Simulation
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.
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Instructor:
Ali Hirsa
h-index: 8 Info - 3 points
FE CONTINUOUS TIME MODELS
FE: CONTINUOUS TIME MODEL
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 2:40 p.m.-3:55 p.m.
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Instructor:
Xunyu Zhou
h-index: 54 Info - 3 points
STATISTICAL ANALYSIS AND TIME SERIES
STATISTICAL ANALYSIS & TIME SE
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 10:10 a.m.-11:25 a.m.
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Instructor:
Agostino Capponi
h-index: 19 Info - 3 points
INTRO-IMPLIED VOLATILITY SMILE
BEYOND BLACK-SCHOLES: IMPLIED
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.
- T 7:10 p.m.-9:40 p.m.
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Instructor:
Amal Moussa
h-index: 7 Info - 3 points
TOPICS IN QUANT FINANCE
App Analytic Data2Decison
Applied Analytics focuses on practical skills that make a Data Analyst autonomous and effective in a modern business organization. From querying and transforming data with SQL, to defining and visualizing metrics, to measuring impact of products and processes via experiments, we will go over tools and techniques that are needed to convert raw data into usable insights for business decisions and subsequent statistical analysis. By the end of the course, students will be able to apply these techniques to real-world datasets, and they will have the necessary knowledge to continue learning about other topics in Analytics and Statistics and fully leverage them in greater autonomy.
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Fabrizio Lecci
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.
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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.
- R 7:10 p.m.-9:40 p.m.
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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.
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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.-9 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.
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Instructor:
Anish Agarwal
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:
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 - 1-3 points