Faculty Advisor: Adjunct Faculty, Carnegie Mellon School of Computer Science

Research Practicum Introduction

Humans and machines have different capabilities. Steve Jobs, decades ago, waxed eloquent on the amplification of human ability by referring to the efficiency of man with a bicycle: https://www.youtube.com/watch?v=0lvMgMrNDlg&feature=emb_logo(relevant excerpt starts at 5:20). 

With the increasing prevalence of Artificial Intelligence (AI), the debate is shifting to how machines can complement people, scaling human performance and expertise as well as reducing risk. Frameworks for and best practices in teaming are just starting to evolve in different milieus. Data, mores, task goals, and component capabilities may dictate the overall team architecture. This is a fertile ground for learning and experimenting in a contemporary sandbox. Bias-free implementations with attention to ethical considerations are a focus area. 

In this project, the Faculty Advisor will provide motivating scenarios along with pathways for exploration. Participants will get a taste for data analysis and concepts in AI. This innovative practicum will lay a foundation for critical, data-oriented thinking and problem solving in a technologically advancing world.

Students will also learn general and subject-specific research and academic writing methods used in universities and scholarly publications. Students will focus on individual topics and generate their own work products upon completion of the program.

Project Topics

• Human-Machine teaming for Health: How can wellness and clinical care be improved by combining expert clinicians and tools?

• Human-Machine teaming in the Wealth milieu: Predicting stock prices, analyzing economic data

• Human-Machine teaming in the Wisdom milieu: How to disseminate reliable information in a world with over-flooded information? 

• Human-Machine teaming for prediction: How to design surveys and polling to inform results of future events (in sports, politics, etc.) 

• Human-Machine teaming for social responsibility: How to reduce bias via teaming? How to achieve social justice?

Program Detail

• Cohort Size: 3-5 students

• Duration: 12 weeks

• Workload: Around 4-5 hours per week (including class time and homework time)

• Target Students: 9-12th grade students interested in Computer Science, Artificial Intelligence, Data Analytics, FinTech, Healthcare Technology, or Machine Learning. Coding, algorithm or data analytics experience is a plus.

• Schedule: TBD. Meetings will take place for around one hour per week, with a weekly meeting day and time to be determined a few weeks prior to the class start date.