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Open to MD, graduate, and undergraduate students. Assessing the global burden of disease, its distribution among and within countries, its causes, and appropriate interventions requires rigorous quantitative approaches. This course develops skills in these areas by critically examining questions like: How do we know who is sick and where? How are risk factors incorporated into our projections of future disease trends? How do we combine mortality and morbidity in a meaningful way? What works for improving health efficiently? Workshops build familiarity with relevant data and their analysis. Prerequisite: coursework in statistics, biostatistics, quantitative epidemiology, econometrics, or equivalent.

This course teaches skills in disease control epidemiology. Students will engage in in-depth interdisciplinary study of disease detection and control strategies from a "systems science" perspective, which addresses classical public health dilemmas, such as how to allocate limited resources, investigate disease outbreaks, and analyze common problems at the intersection of social policy and public health. Lectures and problem sets will focus on developing quantitative skills essential to public health practice, emphasizing the use of common mathematical techniques for disease control. Readings will complement the lectures and problem sets by offering critical perspectives from the sociology of public health. In-depth case studies from non-governmental organizations, departments of public health, and international agencies will drive the course. Human Biology 154 courses can be taken separately or as a series.

Clinical epidemiological methods relevant to human research in cancer will be the focus. The concepts of risk; case control, cohort, and cross-sectional studies; clinical trials; bias; confounding; interaction; screening; and causal inference will be introduced and applied. Social, political, economic, and ethical controversies surrounding cancer screening, prevention, and research will be considered. Human Biology 154 courses can be taken separately or as a series

Approaches to the study of conflict and cooperation in world affairs. Applications to war, terrorism, trade policy, the environment, and world poverty. Debates about the ethics of war and the global distribution of wealth.

Introduction to Scientific Computing Numerical computation for mathematical, computational, physical sciences and engineering: error analysis, floating-point arithmetic, nonlinear equations, numerical solution of systems of algebraic equations, banded matrices, least squares, unconstrained optimization, polynomial interpolation, numerical differentiation and integration, numerical solution of ordinary differential equations, truncation error, numerical stability for time dependent problems and stiffness. Implementation of numerical methods in MATLAB programming assignments. Prerequisites: MATH 51, 52, 53; prior programming experience (MATLAB or other language at level of CS 106A or higher).nGraduate students should take it for 3 units and undergraduate students should take it for 4 units.

Unique physical and chemical properties of organic materials and their uses.The relationship between structure and physical properties, and techniques to determine chemical structure and molecular ordering. Examples include liquid crystals, dendrimers, carbon nanotubes, hydrogels, and biopolymers such as lipids, protein, and DNA. Prerequisite: Thermodynamics and ENGR 50 or equivalent. Undergraduates register for 190 for 4 units; graduates register for 210 for 3 units.

Analytical techniques such as linear and integer programming, Monte Carlo simulation, forecasting, decision analysis, and Markov chains in the environment of the spreadsheet. Probability management. Materials include spreadsheet add-ins for implementing these and other techniques. Emphasis is on building intuition through interactive modeling, and extending the applicability of this type of analysis through integration with existing business data structures.

Concepts and tools for the analysis of problems under uncertainty, focusing on focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite: CME 100 or MATH 51.

How to make good decisions in a complex, dynamic, and uncertain world. People often make decisions that on close examination they regard as wrong. Decision analysis uses a structured conversation based on actional thought to obtain clarity of action in a wide variety of domains. Topics: distinctions, possibilities and probabilities, relevance, value of information and experimentation, relevance and decision diagrams, risk attitude. Students seeking to fulfill the Writing in the Major requirement should register for MS&E 152W.