This course is designed to lay the foundations of good empirical research in the social sciences. It does not deal with specific techniques per se, but rather with the assumptions and the logic underlying social research. Students become acquainted with a variety of approaches to research design, and are helped to develop their own research projects and to evaluate the products of empirical research.

A large proportion of contemporary research on organizations, strategy and management relies on quantitative research methods. This course is designed to provide an introduction to some of the most commonly used quantitative techniques, including logit/probit models, count models, event history models, and pooled cross-section techniques.

Double affine Hecke algebras (DAHA), also called Cherednik algebras, and their representations appear in many contexts: integrable systems (Calogero-Moser and Ruijsenaars models), algebraic geometry (Hilbert schemes), orthogonal polynomials, Lie theory, quantum groups, etc. In this course we will review the basic theory of DAHA and their representations, emphasizing their connections with other subjects and open problems.

Immune cells protect our bodies from both self-derived threats and exogenous pathogens, while keeping peace with normal cells and non-harmful commensal microbiota. They have various mechanisms to perform these tasks, a capacity that is essential for maintaining homeostasis. However, these same mechanisms can backfire, resulting in severe disorders such as immunodeficiency, chronic inflammation, allergy, degenerative diseases, and cancer. This course discusses the connections between normal physiology and disease by examining the developmental relationship between innate and adaptive immune cells as well as the functions and malfunctions of immune cells. The course familiarizes students with both basic biological principles (such as cell death and immune cell signaling) and clinical applications (such as immune checkpoint blockade). More generally, students learn to identify relevant primary research literature, critically evaluate experimental data, and reach their own conclusions based on primary data.
This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

This seminar focuses on downtowns in U.S. cities from the late nineteenth century to the late twentieth century. Emphasis will be placed on downtown as an idea, place, and cluster of interests; on the changing character of downtown; and on recent efforts to rebuild it. Subjects to be considered will include subways, skyscrapers, highways, urban renewal, and retail centers. The focus will be on readings, discussions, and individual research projects.

This course focuses on the origins, functions, and implications of downtown management organizations (DMOs), such as business improvement districts, in a variety of national contexts including the United States, Canada, South Africa, and the United Kingdom. It critically examines how a range of urban theories provide a rationale for the establishment and design of DMOs; the evolution and transnational transfer of DMO policy; and the spatial and political externalities associated with the local proliferation of DMOs. Particular emphasis is given to the role of DMOs in securing public space.

The aim of this course is to highlight some technical aspects of the classical tradition in architecture that have so far received only sporadic attention. It is well known that quantification has always been an essential component of classical design: proportional systems in particular have been keenly investigated. But the actual technical tools whereby quantitative precision was conceived, represented, transmitted, and implemented in pre-modern architecture remain mostly unexplored. By showing that a dialectical relationship between architectural theory and data-processing technologies was as crucial in the past as it is today, this course hopes to promote a more historically aware understanding of the current computer-induced transformations in architectural design.

This class examines the relationship between a number of mind-altering substances and cultural processes. We look at the relationship between drugs and such phenomena as poverty, religion, technology, inter-generational conflict, colonialism, and global capitalism. We read about the physiological and psychological effects of these substances – ranging from alcohol to LSD, cocaine and ecstasy – and ask why different societies prohibit and sanction different drugs. We examine the use of mind-altering substances in a number of “traditional” societies, and follow the development of a global trade in such substances as sugar, coffee, tea, nicotine, cocaine, and marijuana concurrent with the evolution of global capitalism. We look at the use of LSD as a mind-control substance by the CIA and as a mind-altering substance in the 1960’s counter-culture, and we look at the rise of Prozac® and Viagra® as popular, if controversial, pharmaceutical products in recent years. Finally, we evaluate America’s current drug laws.

This class is a multidisciplinary introduction to pharmacology, neurotransmitters, drug mechanisms, and brain diseases from addiction to schizophrenia.
From Abilify® to Zyrtec®, the world is full of fascinating drugs. If you are poisoned by sarin nerve gas, you may be able to save your life by huffing some BZ nerve gas. This class will explain that chemical curiosity, along with a host of other interesting tidbits of pharmacology. The structure of the class interleaves basic concepts with specific examples and entertaining tangents, so it is not loaded with boring abstract theory. In the first class you will learn what a neurotransmitter is, and you will immediately apply that knowledge when we discuss the mechanism of caffeine. The class is highly multidisciplinary, including topics such as patent law, medical ethics, history, and the physics of crack pipes.

This class is a multidisciplinary introduction to pharmacology, neurotransmitters, drug mechanisms, and brain diseases from addiction to schizophrenia.
From Abilify® to Zyrtec®, the world is full of fascinating drugs. If you are poisoned by sarin nerve gas, you may be able to save your life by huffing some BZ nerve gas. This class will explain that chemical curiosity, along with a host of other interesting tidbits of pharmacology. The structure of the class interleaves basic concepts with specific examples and entertaining tangents, so it is not loaded with boring abstract theory. In the first class you will learn what a neurotransmitter is, and you will immediately apply that knowledge when we discuss the mechanism of caffeine. The class is highly multidisciplinary, including topics such as patent law, medical ethics, history, and the physics of crack pipes.

The first two weeks of this course are an overview of performing improvisation with introductory and advanced exercises in the techniques of improvisation. The final four weeks focus on applying these concepts in business situations to practice and mastering these improvisation tools in leadership learning.

The unifying theme of this course is best captured by the title of our main reference book: “Recursive Methods in Economic Dynamics”. We start by covering deterministic and stochastic dynamic optimization using dynamic programming analysis. We then study the properties of the resulting dynamic systems. Finally, we will go over a recursive method for repeated games that has proven useful in contract theory and macroeconomics. We shall stress applications and examples of all these techniques throughout the course.

This course focuses on dynamic optimization methods, both in discrete and in continuous time. We approach these problems from a dynamic programming and optimal control perspective. We also study the dynamic systems that come from the solutions to these problems. The course will illustrate how these techniques are useful in various applications, drawing on many economic examples. However, the focus will remain on gaining a general command of the tools so that they can be applied later in other classes.

The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.

The course addresses dynamic systems, i.e., systems that evolve with time. Typically these systems have inputs and outputs; it is of interest to understand how the input affects the output (or, vice-versa, what inputs should be given to generate a desired output). In particular, we will concentrate on systems that can be modeled by Ordinary Differential Equations (ODEs), and that satisfy certain linearity and time-invariance conditions.
We will analyze the response of these systems to inputs and initial conditions. It is of particular interest to analyze systems obtained as interconnections (e.g., feedback) of two or more other systems. We will learn how to design (control) systems that ensure desirable properties (e.g., stability, performance) of the interconnection with a given dynamic system.

This course covers the fundamentals of Newtonian mechanics, including kinematics, motion relative to accelerated reference frames, work and energy, impulse and momentum, 2D and 3D rigid body dynamics. The course pays special attention to applications in aerospace engineering including introductory topics in orbital mechanics, flight dynamics, inertial navigation and attitude dynamics. By the end of the semester, students should be able to construct idealized (particle and rigid body) dynamical models and predict model response to applied forces using Newtonian mechanics.

This course reviews momentum and energy principles, and then covers the following topics: Hamilton’s principle and Lagrange’s equations; three-dimensional kinematics and dynamics of rigid bodies; steady motions and small deviations therefrom, gyroscopic effects, and causes of instability; free and forced vibrations of lumped-parameter and continuous systems; nonlinear oscillations and the phase plane; nonholonomic systems; and an introduction to wave propagation in continuous systems.
This course was originally developed by Professor T. Akylas.

Introduction to the dynamics and vibrations of lumped-parameter models of mechanical systems. Kinematics. Force-momentum formulation for systems of particles and rigid bodies in planar motion. Work-energy concepts. Virtual displacements and virtual work. Lagrange’s equations for systems of particles and rigid bodies in planar motion. Linearization of equations of motion. Linear stability analysis of mechanical systems. Free and forced vibration of linear multi-degree of freedom models of mechanical systems; matrix eigenvalue problems. Introduction to numerical methods and MATLAB® to solve dynamics and vibrations problems.

This class is an introduction to the dynamics and vibrations of lumped-parameter models of mechanical systems. Topics include kinematics; force-momentum formulation for systems of particles and rigid bodies in planar motion; work-energy concepts; virtual displacements and virtual work; Lagrange’s equations for systems of particles and rigid bodies in planar motion; linearization of equations of motion; linear stability analysis of mechanical systems; free and forced vibration of linear multi-degree of freedom models of mechanical systems; and matrix eigenvalue problems. The class includes an introduction to numerical methods and using MATLAB® to solve dynamics and vibrations problems.
This version of the class stresses kinematics and builds around a strict but powerful approach to kinematic formulation which is different from the approach presented in Spring 2007. Our notation was adapted from that of Professor Kane of Stanford University.

Upon successful completion of this course, students will be able to:

Create lumped parameter models (expressed as ODEs) of simple dynamic systems in the electrical and mechanical energy domains
Make quantitative estimates of model parameters from experimental measurements
Obtain the time-domain response of linear systems to initial conditions and/or common forcing functions (specifically; impulse, step and ramp input) by both analytical and computational methods
Obtain the frequency-domain response of linear systems to sinusoidal inputs
Compensate the transient response of dynamic systems using feedback techniques
Design, implement and test an active control system to achieve a desired performance measure

Mastery of these topics will be assessed via homework, quizzes/exams, and lab assignments.