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1 - 8 of 8 results for: OIT

OIT 385: Biodesign Innovation: Concept Development and Implementation

In this two-quarter course series ( OIT 384/5), multidisciplinary student teams from medicine, business, and engineering work together to identify real-world unmet healthcare needs, invent new health technologies to address them, and plan for their development and implementation into patient care. During the first quarter (winter 2018), students select and characterize an important unmet healthcare problem, validate it through primary interviews and secondary research, and then brainstorm and screen initial technology-based solutions. In the second quarter (spring 2018), teams screen their ideas, select a lead solution, and move it toward the market through prototyping, technical re-risking, strategies to address healthcare-specific requirements (regulation, reimbursement), and business planning. Final presentations in winter and spring are made to a panel of prominent health technology industry experts and investors. Class sessions include faculty-led instruction and case studies, coaching sessions by industry specialists, expert guest lecturers, and interactive team meetings. Enrollment is by application only, and students are expected to participate in both quarters of the course. Visit http://biodesign.stanford.edu/programs/stanford-courses/biodesign-innovation.html to access the application, examples of past projects, and student testimonials. More information about Stanford Biodesign, which has led to the creation of more than 40 venture-backed healthcare companies and has helped hundreds of students launch health technology careers, can be found at http://biodesign.stanford.edu/.
Units: 4 | Grading: GSB Student Option LTR/PF

OIT 604: Data, Learning, and Decision-Making

This aim of this course is to cover modern tools for data-driven decision making. Most decision making tasks involve uncertainty that is directly impacted by the amount and complexity of data at hand. Classical decision models rely on strong distributional assumptions about the uncertain events. But in recent years, and due to growing availability of rich data, there has been a rapid adoption of models from machine learning and statistics that provide more accurate and personalized picture of uncertainty which in turn lead to better decisions. The interplay between the multiple objectives of modeling the data, personalization, and decision optimization has created a number mathematical models that the course aims to cover. Examples of topics include graphical models and message-passing algorithms, high-dimensional and tensor models, and multi-armed bandits.
Units: 3 | Grading: GSB Letter Graded

OIT 664: Asymptotics in Operations Management

This course provides an overview of asymptotic models and methods used in various areas of operations management. It includes traditional heavy traffic asymptotics for queueing networks, the Halfin-Whitt regime, the supermarket model, inventory theory, revenue management, applications of measure-valued processes in queues, and applications of mean field equilibrium models in matching markets and auctions for ad exchanges. The lectures will focus on modeling and performance analysis, and not on convergence proofs. Prerequisites: Statistics 217 and 218, or consent of instructor; some prior exposure to stochastic models in general, and queueing theory in particular, is useful but not essential.
Units: 3 | Grading: GSB Letter Graded
Instructors: Wein, L. (PI)

OIT 691: PhD Directed Reading (ACCT 691, FINANCE 691, GSBGEN 691, HRMGT 691, MGTECON 691, MKTG 691, OB 691, POLECON 691, STRAMGT 691)

This course is offered for students requiring specialized training in an area not covered by existing courses. To register, a student must obtain permission from the faculty member who is willing to supervise the reading.
Units: 1-15 | Repeatable for credit | Grading: GSB Pass/Fail

OIT 692: PhD Dissertation Research (ACCT 692, FINANCE 692, GSBGEN 692, HRMGT 692, MGTECON 692, MKTG 692, OB 692, POLECON 692, STRAMGT 692)

This course is elected as soon as a student is ready to begin research for the dissertation, usually shortly after admission to candidacy. To register, a student must obtain permission from the faculty member who is willing to supervise the research.
Units: 1-15 | Repeatable for credit | Grading: GSB Pass/Fail

OIT 698: Doctoral Practicum in Teaching

Doctoral Practicum in Teaching
Units: 1 | Repeatable for credit | Grading: GSB Letter Graded

OIT 699: Doctoral Practicum in Research

Doctoral Practicum in Research
Units: 1 | Repeatable for credit | Grading: GSB Letter Graded

OIT 802: TGR Dissertation (ACCT 802, FINANCE 802, GSBGEN 802, HRMGT 802, MGTECON 802, MKTG 802, OB 802, POLECON 802, STRAMGT 802)

Units: 0 | Repeatable for credit | Grading: GSB Pass/Fail
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