Upper School Intersession 2025

You have probably heard about the importance of fair pay - but what does that mean and who decides what is fair? In this class you'll learn about how companies get market information about pay rates, how they set a pay philosophy, and how they then apply these both to determining the right amount and type of pay to offer a candidate for a job. We'll start by understanding job descriptions, job titles, job levels, and some basic laws about pay. We'll then learn about salary, bonus, and equity compensation. We'll talk about negotiation approaches and how to determine what is most important for you, based on your personal priorities. Finally we'll practice all of these learnings with a few sample pay negotiations.

What do self-driving cars, Alexa, iPhone's face recognition technology, and ChatGPT have in common? They are driven by modern advances in artificial intelligence. Whether you’re interested in law, healthcare, art, or economics, AI is poised to transform every discipline and industry in the future. AI is already all around us today, and by the end of the program, students will understand the underlying concepts and motivations behind technology such as computer vision, natural language processing, and neural networks.
 
In this course, we will explore the foundations of machine learning and explore different applications of machine learning models. In the first half of the course, students learn AI’s core technologies including applications, foundational concepts, and programming tools through live lectures and coding labs. Students will not only learn about different types of machine learning models but also apply those models to real data sets. In the second half of the course, students will complete an instructor-led group project applying AI to the discipline of their choice (e.g., music, healthcare, astrophysics, finance, etc.), utilizing the programming skills they developed in the first half.

Please read more here!

Course Structure

Machine Learning Talks
Learn about machine learning algorithms and techniques in a uniquely interactive, engaging format, before you apply that knowledge in live coding labs.

Hands-On Python Coding
Develop valuable skills in Python, machine learning, and artificial intelligence in our hands-on coding labs, using cutting-edge research to solve real-world problems like breast cancer diagnosis, building self-driving cars, and more.

Project-Based Learning
In our AI for Social Good project, students will be able to apply their newly acquired talents in a collaborative, challenging environment, applying AI to a domain they’re passionate about (e.g., music, healthcare, astrophysics, finance, etc.). Students can use these projects in their résumés and college applications.

"Nine to five is how to survive, I ain't trying to survive, I'm trying to live it to the limit and love it a lot.” - Jay Z

In the 23 years since Jay-Z first rapped this line, he has released 14 albums, founded a clothing company, served as president of a record label, taken charge of a media streaming service, and is raising three children. This is creative hustle at its peak - the unmarked process outside of institutions, beyond survival, and where our best innovation lives. There is creative hustle living inside us all -- many of us en route to our own peak without even knowing it. This experience explores how to build confidence and effective mindsets, develop industry-relevant skills, and increase access to valuable networks through the stories of a wide array of diverse hustlers, with enough space between the lines for you to see your own path.

Throw a rock in Silicon Valley and you’ll probably hit a few startups, and at least some of them will claim to be AI (including my own!). But where do startups sprout from? Is there a specific pattern they follow in their journey, or are they all unique flowers? How should you evaluate startups as you consider interning at them, joining them in a few years, or heck, starting your own?

This talk will focus on a specific stage in the life of startups – germination. We will talk about the different ways startups get seeded, grow green shoots (which might take many years!) and start showing signs of life. We will share some key observations from this stage that often correlate with startup success.

What do Theranos, WeWork, Uber, FTX, Boeing, and Wells Fargo all have in common?  All of these were high profile and high performing companies that became embroiled in scandal, after some previously hidden information became publicly known - and some of these companies survived and others did not.  How does a company that employs hundreds or thousands of well-intentioned people end up breaking laws or behaving unethically?  Can you picture yourself working at a company like this and if you came across something that looked potentially unethical, how would you respond?  

In this session we'll discuss a few case studies of companies that were viewed in high regard before they came undone.  We’ll seek to understand what elements of the company culture, leadership, and other factors led to the circumstances.  We will learn about which companies were able to recover and which could not.  We will then review some key principles and frameworks of business and leadership ethics, then put our thinking to the test with a simulation of an ethical dilemma.

Get ready to dive into the real story of denim! In Denim Insider: From Fabric to Fashion to the Sales Floor, you’ll explore how your favorite jeans make their way from raw fabric to the racks of every go-to store. From design sketches to manufacturing, and choosing the right silhouettes to the marketing secrets behind making you want to buy, this session reveals the hidden steps in denim’s journey to becoming a style staple.
Led by a former fashion merchant and buyer who’s been in the thick of it, you’ll learn how brands create what’s in style and discover the tricks of the trade that make denim a billion-dollar industry. By the end, you’ll see your jeans in a whole new way!

Suppose you need to match one million students to one thousand colleges. Students have preferences over colleges, and colleges have preferences over their applicants. Is there a "good" way to reconcile all these preferences on both sides -- and what does "good" even mean in this context? We are going to discuss the basics of matching theory, a research area at the intersection of Economics and Computer Science that develops tools for answering such questions. We will also discuss applications of this theory: matching young doctors to hospitals, students to schools, and cadets to army branches.

Matching theory is one of the key parts of the broader discipline of the design of economic mechanisms, and if time permits, we will briefly touch on some other topics in this discipline.

Pandemics! Geopolitics! Economics! Bad behavior! These are turbulent times and businesses and other organizations are encountering lots of new challenges and even full-fledged disasters. When it all goes wrong, how should organizations think about communicating? What are the rules of the road? The common mistakes? Which audiences matter? What do you do when the New York Times calls or social media explodes? We'll talk about all of this and explore some scenarios.

In this dynamic workshop, participants will explore the art of startup pitching. Students will discuss what makes a great pitch, brainstorm innovative ideas, and learn to structure compelling presentations in small teams. The session culminates in a pitch competition where 3-4 teams present to expert judges, including Wayee and her associates. Teams will receive valuable feedback, refining their pitching skills and understanding key investor considerations. The workshop concludes with the selection of a winning team, celebrating creativity, teamwork, and effective communication. This hands-on experience provides students with practical skills in startup ideation, collaboration, and presentation – essential for aspiring entrepreneurs and innovators.

This session is an introduction to low latency trading. It covers varies of technologies we can leverage to trade financial products at single digit microsecond level.

No prerequisite financial market knowledge required.

Shein seemingly came out of nowhere during COVID, becoming the #1 most downloaded shopping app in 61 countries (including the U.S.) and growing from less than $1B to over $20B in sales in the U.S. in only a few years.   

- How did Shein achieve such a meteoric rise? 
- How can Shein get you such fashionable looks for so cheap? How do they make money?
- Can they sustain this success even with regulatory and ethical pressure?

Through the lens of Shein, learn about how different types of fashion retailers design and source products and get them from overseas to your closet - and how they make money! We will also discuss regulatory and ethical issues in fashion, including labor and environmental practices. 

If you own anything from Shein bring it to class so we can use some real-life examples!

It takes an average of $1.3 billion and more than 10 years to bring a new medicine to the point of applying for FDA approval. After pursuing this long and often tortuous path, the prospects are risky, and the FDA faces a binary choice: Grant marketing authorization, or reject the application?

What does the drug development process entail, and why is it so long, expensive, and complex? What is FDA's role in protecting American consumers and the public? How are the concepts of "benefit" and "risk" used to provide a framework for these decisions?

In this interactive session, you will learn the basics of the drug development process, including the role of the FDA in deciding whether to allow access to new drugs. Students will work in groups to evaluate whether a new drug should be "approved", and, if so, under what conditions.