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Current and Past Ph.D. Students

Wesley Koo, Thesis: “Business Groups and Entrepreneurship”, Anticipated Date of Graduation: 2018
Daniel Armanios (Carnegie Mellon University) (co-chair), Thesis: “Mixed Institutions and Entrepreneurship: Evidence from China”, (Winner, Best Dissertation Award, Technology and Innovation Management Division of the Academy of Management). Date of Graduation: 2015.
Jamber Li, Thesis: “Culture and Entrepreneurship in China”, Date of Graduation: 2017, National University of Singapore
Michael Leatherbee, Thesis: “Informal Institutions and Entrepreneurship in Chile”, Date of Graduation: 2015, Universidad Catholica
Robert Eberhart, Thesis: “Institutions, Japan and Entrepreneurship”, Date of Graduation: 2013, Santa Clara U.
Henning Piezunka (INSEAD) (committee member)
Rory McDonald (HBS) (committee member)
Sam Garg (HKUST) (committee member)
Emily Cox-Pahnke (Univ. of Washington - Seattle) (committee member)
Willow Wu, Anticipated Date of Graduation 2019

MS&E 372 Doctoral Course - Empirical and Theoretical Perspectives on Entrepreneurship
I've created a spreadsheet of entrepreneurship literature organized by doctoral course syllabus. It is available for download here. Along the rows are individual papers listed on syllabi. Along the columns are the different doctoral course syllabi from different professors and universities. The sheet is sorted so that the most frequently cited (core) papers are at the top.
BPS Dissertation Consortium presentations
Sarah Kaplan - What is a strategy dissertation?
Getting Started, Selecting a Topic
Getting Startup on your Dissertation - Idea to Proposal
Forming and Managing Your Committee
Research Design, Data Collection and Analysis
Martin Ganco on Research Design
Sarah Kaplan - Qualitative Dissertations
Jasjit Singh - Finishing Up
Structuring Successful Research Collaborations
Teams, Resources and Support
Academic Job Market
Abhishek Nagaraj at UC-Berkeley has a great resource on PhD applications.
E145 Technology Entrepreneurship (Undergraduate course in the Engineering School) - Watch a video summary.
How do you create a successful start-up? What is entrepreneurial leadership in a large firm? What are the differences between an idea and true opportunity? How does an entrepreneur form a team and gather the resources necessary to create a great enterprise? Mentor-guided project focused on developing students' startup ideas, immersion in nuances of innovation and early stage entrepreneurship, case studies, research on the entrepreneurial process, and the opportunity to network with Silicon Valley's top entrepreneurs and venture capitalists. For undergraduates of all majors who seek to understand the formation and growth of high-impact start-ups in areas such as information, energy, medical and consumer technologies. No prerequisites. Limited enrollment.

My MOOC on NovoEd and iTunesU
Eesley, C. (2016, October 3). Online mentorship and teamwork best practices. Entrepreneurship & Innovation Exchange. Retrieved February 8, 2017, from (Winner, Schulze Award, 2017).
My Presentations

MS&E 272: Entrepreneurship Without Borders

How do you create a start-up outside of the U.S.? What are the unique issues involved in creating a successful startup in emerging economies such as China or India? What is entrepreneurial leadership in a venture that spans country borders? Is Silicon Valley-style entrepreneurship possible in other places? How does an entrepreneur act differently when creating a company in a less-developed institutional environment? Learn through forming teams, a mentor-guided startup project focused on developing students' startups in international markets, case studies, research on the international aspects of the entrepreneurial process, and networking with top entrepreneurs and venture capitalists who work across borders. For graduate students only, with a preference for engineering and science majors who seek to understand the formation of high-impact start-ups in emerging economy contexts.

MS&E 379: Applied Analytics: Social Data Analysis

This course offers an applied introduction to good empirical research and causal inference for social scientists and others analyzing social data. This course is designed to provide an introduction to some of the most commonly used quantitative techniques for causal inference in social data including: survey design and inference, regression and propensity score matching, instrumental variables, differences-in-differences, regression discontinuity designs, standard errors, and the analysis of big data. Applications: organizations, entrepreneurship, public policy, innovation, economics, online education, visual representations, communication, critique and design of figures, graphs. Students will work in groups and individually to design and carry out a small research project based on the use of analytics, large data sets, or other digital innovations related to business or other organizations. The course is aimed at PhD students, but is open by permission to Master’s students and to students in other Stanford programs with relevant coursework or experience in analytics and statistics. Students become acquainted with a variety of approaches to research design, and are helped to develop their own research projects.

Discovering Data presentation on non-archival methods at WCRS 2016

Teaching Online Notes
Eesley, C. (2016, October 3). Online mentorship and teamwork best practices. Entrepreneurship & Innovation Exchange. Retrieved February 8, 2017, from (Winner, Schulze Publication Award, 2017).
Bloomberg article - Behold: A Virtual Course Without Online Ed's Huge Dropout Rate
MOOC That Spawned Three Startups
Team and mentorship best practices - White paper here.
Why I did the first entrepreneurship MOOC
New Online Platform - CreativityPost