Vishal Gupta - Teaching
- 15.S60 Software Tools for Operations Researchers (PhD and MBA Elective), IAP 2013-2014
Instructor (2013), Curriculum Development (2014)
Primary role in course proposal, curriculum development and lecturing.
Lecture Slides on CVX, CVXOPT
The Big Data revolution has placed added emphasis on computational techniques in
Operations Research. Large-scale optimization, data analysis and visualization are
now commonplace among researchers and practitioners alike.
This course is a multi-session workshop focusing on software tools specific to the
practice of Operations Research. While other courses focus upon theory and background,
this course focuses on the mechanics of using software to apply specific methodologies.
Each module consists of an interactive workshop where participants learn a specific
software tool. Topics include
large-scale data analysis with R
interactive visualizations through D3.js,
linear programming interfaces through Python,
call-back mechanisms for cut generation and heuristics in CPLEX/GUROBI,
optimization on the cloud, and
CVX and CVXOPT for convex optimization.
Examples on GitHub
- 15.S05 Risk Management (Executive MBA Elective), Spring 2012, Spring 2013
Curriculum development. Advised students in small groups on term projects.
With Dimitris Bertsimas and Retsef Levi
This elective course is tailored to Sloan's Executive MBA program. Leveraging
students own varied experiences in industry, this short course aims to expose students
to several core analytical models and tools to identify, think about, analyze and manage risk.
In particular, we shall discuss how to apply these concepts and tools in various
real-life settings and application domains (e.g., data rich environments, financial
markets, supply chains, natural disasters, energy).
- 15.060 Data, Models, and Decisions (MBA Core), Fall 2011
Led weekly recitations. Assisted in designing exams.
With Georgia Perakis
This course is designed to introduce first-year MBA students to the
fundamental quantitative techniques of using data to make informed management decisions.
In particular, the course focuses on various ways of modeling, or thinking
structurally about, decision problems in order to enhance decision-making skills.
Topics include decision analysis, probability, random variables, statistical estimation,
regression, simulation, linear optimization, as well as nonlinear and discrete optimization.
Management cases are used extensively to illustrate the practical use of modeling tools to
improve the management practice.
- 6.251J/15.081 Introduction to Mathematical Programming (PhD Core), Fall 2010
Lectured selected topics. Led weekly recitations. Assisted in designing problem sets and exams.
With Vincent Blondel
This course is an introduction to linear optimization and its extensions
emphasizing the underlying mathematical structures, geometrical ideas,
algorithms and solutions of practical problems. The topics covered include:
formulations, the geometry of linear optimization, duality theory, the simplex method,
sensitivity analysis, large scale optimization, interior point methods,
network flows, discrete optimization formulations and algorithms.