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Engineering and Technology in India

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Program Quick Facts

General Description

This seminar investigates engineering, technology, business, and cultural aspects of India.

Upon completion of the program, students are able to demonstrate an improved understanding of the following: 

  1. major industries, companies, and technologies in each location, 
  2. key challenges and opportunities facing various sectors, and 
  3. ways in which businesses and agencies operate within the political, economic, and cultural contexts.

The weekly onsite program will generally include

Following pre-briefings by students, we will have visits to universities, research centers, industrial, government, and cultural sites. Debriefings will follow each visit.

Location

Delhi, Agra, Chennai, Sri City, Tirupati, and Mumbai.

Living and Travel Conditions

Students should understand that the conditions in certain locations can present difficulties and challenges not encountered here at Stanford University. Students should be prepared for a varying level of lodging, lack of amenities, new climate, new foods, and having less privacy and personal space than they are used to at the home campus.

  • Participants will be staying in hotels.
  • Travel will involve walking, hiking, and transit

Students who have concerns about the specific living and traveling conditions should consult with the Bing Overseas Studies Program before submitting their application.

Faculty

Mykel Kochenderfer is Associate Professor of Aeronautics and Astronautics and Assistant Professor, by courtesy, of Computer Science at Stanford University. Prior to joining the faculty in 2013, he was at MIT Lincoln Laboratory where he worked on airspace modeling and aircraft collision avoidance. He received his Ph.D. from the University of Edinburgh in 2006 in informatics. He received B.S. and M.S. degrees in computer science from Stanford University in 2003.

Prof. Kochenderfer is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods for the design of robust decision making systems. Research at SISL focuses on efficient computational methods for deriving optimal decision strategies from high-dimensional, probabilistic problem representations. Prof. Kochenderfer is also affiliated with the Stanford Artificial Intelligence Laboratory (SAIL), the Center for AI Safety, and the Center for Automotive Research at Stanford (CARS).

He authored Decision Making under Uncertainty: Theory and Application (2015), Algorithms for Optimization (2019), and Algorithms for Decision Making (2022). He has led Stanford overseas seminars to Scotland, Emirates, and Morocco, and served as faculty-in-residence at the Stanford in Oxford program.

Prerequisites and Expectations 

  • Three meetings during spring quarter.

Grading Basis

  • Letter grade only.