Assistant Professor of Operations,

Information, and Decisions
Wharton School, University of Pennsylvania

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Classes I'm currently teaching at Penn

(both courses I took as an undergraduate!):

Information Strategy and Economics (OIDD469) (Spring)

This course is devoted to the study of the strategic use of information and the related role of information technology, and designed for students who want to manage and compete in technology-intensive businesses. The topics of the course vary year to year, but generally include current issues in selling digital products, intermediation and disintermediation, competing in online markets, emerging technologies, managing artificial intelligence and data science for business, and technology project management. Heavy emphasis is placed on utilizing information economics to analyze businesses in information-intensive industries. Technology skills are not required, although a background in information technology management, strategic management or managerial economics is helpful. The course is designed to complement OIDD 210, OIDD 215, OIDD 245, and OIDD 255X. (Course Syllabus)

Introduction to OIDD (OIDD101) (Spring)

OIDD 101 explores a variety of common quantitative modeling problems that arise frequently in business settings, and discusses how they can be formally modeled and solved with a combination of business insight and computer-based tools. The key topics covered include capacity management, service operations, inventory control, structured decision making, constrained optimization and simulation. This course teaches how to model complex business situations and how to master tools to improve business performance. The goal is to provide a set of foundational skills useful for future coursework at Wharton as well as providing an overview of problems and techniques that characterize disciplines that comprise Operations and Information Management. (Course Syllabus)

Courses I worked on at MIT as a student / post-doc:

MIT Sloan Analytics Lab (15.572) - Head Teaching Assistant, Mentor, and Guest Lecturer (Professors Erik Brynjolfsson and Sinan Aral), Fall 2015, 2016, 2017

Analytics Lab (A-Lab) is an action learning course offered to graduate students at MIT where students work with real organizations to solve problems in data analysis and decision-making. Students in the course partner with companies and apply cutting-edge techniques in machine learning, analytics, and causal inference to inform better business decisions. Selected "skills seminars" include data visualization, deep learning, and large dataset processing techniques.

The doctoral research seminar covers research in the economics of information technology. Selected topics include automation, crowdsourcing, information economics, information good pricing, and IT and productivity.