## MS&E 391: Doctoral Research Seminar in Energy-Environmental Systems Modeling and Analysis

Restricted to PhD students, or by consent of instructor. Doctoral research seminar covering current topics in energy and environmental modeling and analysis. Current emphasis on approaches to incorporation of uncertainty and technology dynamics into complex systems models. May be repeated for credit.

Terms: Aut
| Units: 1-3
| Repeatable
for credit

Instructors:
Weyant, J. (PI)

## MS&E 394: Advanced Methods in Modeling for Climate and Energy Policy

Design and application of computational models and techniques for assessing climate and energy policy, and for predicting the impacts of climate change. Topics include 1) best practices in research design, model design and selection; 2) types of models available, taxonomy, core concepts, and limitations; 3) interpreting and presenting model results; and 4) advanced topics and recent literature, e.g. representing uncertainty, technological change, distributional change, and cross-sectoral climate impacts. Prerequisites: MS&E 241, MS&E 211, or equivalents.

Terms: Spr
| Units: 3

Instructors:
Weyant, J. (PI)

## MS&E 408: Directed Reading and Research

Directed reading and research on a subject of mutual interest to student and faculty member. Available to undergraduate, master, and doctoral students. Student must clarify deliverables, units, and grading basis with faculty member before applicable deadlines. Prerequisite: consent of instructor

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit

Instructors:
Ashlagi, I. (PI)
;
Bambos, N. (PI)
;
Basse, G. (PI)
...
more instructors for MS&E 408 »

Instructors:
Ashlagi, I. (PI)
;
Bambos, N. (PI)
;
Basse, G. (PI)
;
Blanchet, J. (PI)
;
Brandeau, M. (PI)
;
Byers, T. (PI)
;
Eesley, C. (PI)
;
Eisenhardt, K. (PI)
;
Giesecke, K. (PI)
;
Glynn, P. (PI)
;
Goel, A. (PI)
;
Goel, S. (PI)
;
Hinds, P. (PI)
;
Johari, R. (PI)
;
Katila, R. (PI)
;
Lo, I. (PI)
;
Pate-Cornell, E. (PI)
;
Pelger, M. (PI)
;
Saberi, A. (PI)
;
Seelig, T. (PI)
;
Shachter, R. (PI)
;
Sidford, A. (PI)
;
Sutton, R. (PI)
;
Sweeney, J. (PI)
;
Tse, E. (PI)
;
Ugander, J. (PI)
;
Valentine, M. (PI)
;
Van Roy, B. (PI)
;
Weyant, J. (PI)
;
Ye, Y. (PI)

## MS&E 448: Big Financial Data and Algorithmic Trading

Project course emphasizing the connection between data, models, and reality. Vast amounts of high volume, high frequency observations of financial quotes, orders and transactions are now available, and poses a unique set of challenges. This type of data will be used as the empirical basis for modeling and testing various ideas within the umbrella of algorithmic trading and quantitative modeling related to the dynamics and micro-structure of financial markets. Due to the fact that it is near impossible to perform experiments in finance, there is a need for empirical inference and intuition, any model should also be justified in terms of plausibility that goes beyond pure econometric and data mining approaches. Introductory lectures, followed by real-world type projects to get a hands-on experience with realistic challenges and hone skills needed in the work place. Work in groups on selected projects that will entail obtaining and cleaning the raw data and becoming familiar with techniqu
more »

Project course emphasizing the connection between data, models, and reality. Vast amounts of high volume, high frequency observations of financial quotes, orders and transactions are now available, and poses a unique set of challenges. This type of data will be used as the empirical basis for modeling and testing various ideas within the umbrella of algorithmic trading and quantitative modeling related to the dynamics and micro-structure of financial markets. Due to the fact that it is near impossible to perform experiments in finance, there is a need for empirical inference and intuition, any model should also be justified in terms of plausibility that goes beyond pure econometric and data mining approaches. Introductory lectures, followed by real-world type projects to get a hands-on experience with realistic challenges and hone skills needed in the work place. Work in groups on selected projects that will entail obtaining and cleaning the raw data and becoming familiar with techniques and challenges in handling big data sets. Develop a framework for modeling and testing (in computer languages such as Python, C++ , Matlab and R) and prepare presentations to present to the class. Example projects include optimal order execution, developing a market making algorithm, design of an intra-day trading strategy, and modeling the dynamics of the bid and ask. Prerequisites: MS&E 211, 245A, 245B, or equivalents, some exposure to statistics and programming. Enrollment limited. Admission by application; details at first class.

Terms: Spr
| Units: 3

Instructors:
Borland, L. (PI)
;
Horel, E. (TA)

## MS&E 449: Buy-Side Investing

In-class lectures and guest speakers who work in the Buy-Side to explore the synergies amongst the various players¿ roles, risk appetites, and investment time and return horizons. We aim to see the forest and the different species of trees growing in the forest known as the Buy-Side, so as to develop a perspective as financial engineers for how the ecosystem functions, what risks it digests, how it generates capital at what rate and amount for the Sell-Side, and how impacts in the real economy are reflected - or should be reflected - in the culture and risk models adopted by the Buy-Side participants.

Last offered: Winter 2020

## MS&E 454: Decision Analysis Seminar

Current research and related topics presented by doctoral students and invited speakers. May be repeated for credit. Prerequisite: 252.

Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit

Instructors:
Shachter, R. (PI)

## MS&E 463: Healthcare Systems Design (PEDS 463)

Students work on projects to analyze and design various aspects of healthcare delivery including hospital patient flow, clinical risk prediction, physician networks, clinical outcomes, reimbursement incentives, and community health. Students work in small teams under the supervision of the course instructor and partners at the Lucille Packard Children's Hospital, the Stanford Hospital, and other regional healthcare providers. Prerequisite: 263 and a mandatory meeting during the preceding Winter quarter to choose projects.

Terms: Spr
| Units: 3-4

Instructors:
Scheinker, D. (PI)

## MS&E 472: Entrepreneurial Thought Leaders' Seminar

Learn about entrepreneurship, innovation, culture, startups and strategy from a diverse lineup of accomplished leaders and entrepreneurs in venture capital, technology, education, philanthropy and more. Open to all Stanford students. Required weekly assignment. May be repeated for credit.

Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit

## MS&E 489: d.Leadership: Leading Disruptive Innovation (ME 368)

d.Leadership is a course that teaches the coaching and leadership skills needed to drive good design process in groups. d.leaders will work on real projects driving design projects within organizations and gain real world skills as they experiment with their leadership style. Take this course if you are inspired by past design classes and want skills to lead design projects beyond Stanford. Preference given to students who have taken other Design Group or d.school classes. Admission by application. See
dschool.stanford.edu/classes for more information

Last offered: Winter 2020

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