Instructor: Dr. Robert Plummer

My first programming course at Stanford, and what an eye-opener it was. I had taken three quarters of Visual Basic at The Hill School and thought I had some programming background. I was wrong. CS 106A showed me what programming truly means - the layers of complexity, the depth of logical thinking required.

The first two assignments were manageable, but the third program marked a dramatic escalation. Suddenly, I was writing 500 to 1,000 lines of complex code. Everything I had learned in those three quarters at prep school was covered in the first two weeks at Stanford. After that, you were expected to swim on your own.

The course taught me something beyond programming: the importance of trusting your own thinking and logic. You attend the lectures, but then you're left to solve problems independently. You must develop confidence in your own decisions. It was demanding, but there was nothing quite like the satisfaction of watching my program finally run correctly.

Section Leader: Dr. Stuart Finkel

This course was another profound eye-opener, but in an entirely different way. Throughout my education in Thailand, studying literature meant learning advanced vocabulary and memorizing plot summaries - who did what, when, and how. We never analyzed texts critically or considered alternative interpretations. IHUM 19 shattered that approach entirely.

The structure was intimidating: large lectures with over 60 students, followed by small discussion sections of about 10 people. Those discussions counted for 30% of the grade, and at 19 years old, I was paralyzed with anxiety. It wasn't just that I didn't know what to say - I was terrified to speak English in front of my peers. Would they understand me? Would they find my accent funny? If I could return now with the confidence and knowledge I've gained, I know this course would be far more enjoyable.

We read Leo Tolstoy and various Russian and Western literary classics - the titles have faded from memory, but the struggle hasn't. Each week brought 50 to 80 pages of dense reading. There was no Wikipedia then to provide context, no AI to help digest the material. I tried CliffsNotes, but they barely helped. I would sit in the library or my dorm room, attempting to read, only to doze off within 15 minutes. I'd sit there for hours, fighting through each line, comprehending very little.

Writing the essays was even more challenging. I believed that writing meant producing perfect grammar and selecting impressive vocabulary. I didn't understand that writing is fundamentally about thinking - about expressing your perspective on a topic. I was so afraid of grammatical errors, so worried about not using sophisticated enough words, that I couldn't focus on what I actually wanted to say. It was one of the most difficult periods of my academic life.

Instructor: Doron Levy

This course was actually enjoyable, partly because I studied alongside many of my dormmates from Paloma. We'd work through problem sets together in the common room, and that shared struggle built lasting friendships.

Every freshman taking MATH 51 in fall quarter had already passed AP Calculus BC in high school. And this being Stanford, all my classmates were remarkably fluent in mathematics. This was a stark contrast to my high school in Thailand, where perhaps 10% of students were truly comfortable with math. Here, everyone could handle single-variable differentiation and integration with ease.

Stanford grades on a curve, so you're essentially thrown into a battle ring with other highly competitive students. Math had always come easily to me back home, but MATH 51 demanded real effort. The structure included lectures, weekly sections with TAs, homework exercises, and MATLAB problems - we'd have to run computations, print out the resulting shapes and graphs, and submit everything together.

Although Professor Doron Levy appears on my transcript, I actually attended lectures by a different professor - an Indian gentleman whose name escapes me now. People said he was a better lecturer, and most of my dormmates went to his class, so I followed. For sections, though, I attended both - my designated section under Professor Levy and the other professor's section as well. I wanted to absorb every bit of material possible, something I never felt compelled to do in Thailand.

This quarter taught me a humbling lesson about mathematics. There was also MATH 51H - the honors version - designed for students who truly excelled at math: International Math Olympiad participants, those with exceptionally strong foundations. In Thailand, I had been confident in my mathematical abilities. At Stanford, I realized math was not my strong suit at all. Other students operated on an entirely different level. My approach to math had always been practice and muscle memory - repetition until problems became familiar. But real mathematics, I discovered, requires abstract thinking and the ability to apply logic many steps ahead. That was a profound realization.

In the end, I finished with a solid A in both MATH 51 and CS 106A. As for my IHUM grade... well, let's not discuss that.

Instructor: Jennifer Ly

This was the course required to become an RCC - Residential Computer Consultant. We learned about networking, troubleshooting, and how to help people solve their computer problems. The class met once a week in the evening.

My RCC for freshman year was Rob Boyle, who later became a product manager at Facebook. Watching how he helped residents with their tech issues gave me a glimpse of what the role entailed. I would eventually become an RCC myself during my senior year - but that's a story for later.

Unfortunately, I took this course by myself, without any friends in the class. It made the experience less enjoyable, and I didn't make any new connections through it either. The material was useful, but the social aspect - which I've come to realize is so important - was missing entirely.

Frankly, I don't remember anything about this course. It's one of those small seminars that has completely faded from memory. I know I took it - it's right there on my transcript - but what we discussed, who taught it, what I learned... all gone. Sometimes courses leave their mark, and sometimes they simply pass through without leaving a trace.

The sequel to CS106A where we switched to C++. Learning about recursion, data structures like linked lists, trees, and graphs. The assignments were challenging but incredibly rewarding.

Continuing the math sequence with multiple integrals, line integrals, and vector calculus. The visualization of 3D surfaces was mind-bending at first.

Second quarter of the humanities sequence. Deep discussions about philosophy, literature, and the human condition.

Required writing course that helped me articulate technical concepts clearly. The skills learned here proved invaluable throughout my career.

Diving deep into how computers actually work - assembly language, memory management, and systems programming. This course changed how I think about software.

Solving differential equations and understanding their applications in physics and engineering. The mathematical foundations for many CS applications.

Final quarter of IHUM. Wrapping up the year-long journey through human thought and expression.

Learning about circuits, transistors, and basic electronics. Building physical circuits and understanding the hardware side of computing.

Logic, proofs, set theory, and the mathematical foundations that underpin computer science. Learning to think rigorously about algorithms and computation.

Probability theory and its applications in CS. From randomized algorithms to machine learning foundations - this course opened my eyes to the probabilistic nature of computing.

Continuing the physics sequence with E&M. Understanding electromagnetic waves and their relationship to computing and communications.

Introduction to microeconomics. Understanding markets, incentives, and decision-making - concepts that later proved valuable in tech entrepreneurship.

Operating systems, concurrency, networking, and distributed systems. Building complex systems from the ground up and understanding how the internet works.

The core algorithms course - divide and conquer, dynamic programming, graph algorithms. Learning to analyze efficiency and prove correctness of algorithms.

Counting techniques, graph theory, and combinatorial structures. Essential mathematics for algorithm analysis and discrete optimization.

Second required writing course focusing on research and argumentation. Learned to write compelling technical proposals and documentation.

Building an operating system from scratch - threads, virtual memory, file systems. One of the most challenging and rewarding courses in the curriculum.

Relational databases, SQL, query optimization, and database design. Understanding how to store and retrieve data efficiently at scale.

Digital logic design, combinational and sequential circuits. Building hardware components and understanding the foundation of computer architecture.

Exploring the nature of scientific knowledge and reasoning. Great complement to the technical curriculum - learning to think critically about what we know.

Building full-stack web applications from scratch. HTML, CSS, JavaScript, and server-side programming. This course sparked my interest in web technologies.

Introduction to AI - search algorithms, game playing, machine learning basics. Fascinating to see how machines can exhibit intelligent behavior.

User-centered design, prototyping, and usability testing. Learning that great software is not just about code but about the people who use it.

Understanding human behavior and cognition. Valuable insights that complemented HCI studies and helped me think about user experience.

Building a compiler from lexical analysis to code generation. Understanding how programming languages are implemented and optimized.

The famous Andrew Ng machine learning course. Supervised learning, neural networks, SVMs - this course was ahead of its time and hugely influential.

Cryptography, network security, and software vulnerabilities. Learning to think like both an attacker and a defender.

Understanding how organizations work, leadership, and management principles. Essential knowledge for anyone aspiring to lead tech teams.

TCP/IP, routing, network protocols. Understanding the infrastructure that powers the internet. Building networked applications from the ground up.

Teaching computers to understand human language. Parsing, machine translation, and information extraction. A fascinating intersection of linguistics and CS.

Rendering, ray tracing, and 3D graphics programming. Creating visual worlds with code - both technically challenging and artistically rewarding.

Digital audio, synthesis, and music programming. A creative outlet that showed me how CS can be applied to the arts.

Advanced compiler techniques - dataflow analysis, loop optimization, and program transformation. Going deeper into how to make programs run faster.

Advanced database concepts - transaction processing, query optimization, distributed databases. Understanding the internals of database engines.

Deep learning for computer vision. Image classification, object detection, and neural network architectures. A glimpse into the future of AI.

Exploring the societal impact of technology. Privacy, intellectual property, and the ethical responsibilities of computer scientists.

Cutting-edge networking research - software-defined networking, network measurement, and internet architecture. Seeing where the field was heading.

Building AI systems that can play any game given only the rules. A fascinating challenge in reasoning and search.

Algorithms for big data - MapReduce, recommendation systems, and large-scale machine learning. Essential for the data-driven world we live in.

Learning to build startups - from idea to product to market. Guest speakers from successful Silicon Valley companies. Inspiring and practical.

Research-level OS topics - virtualization, file systems, and distributed systems. Reading and discussing cutting-edge research papers.

Understanding social networks, viral phenomena, and graph algorithms. Analyzing the structure of online communities and information flow.

Capstone project working with a real client on a substantial software system. Putting four years of learning into practice in a team setting.

A philosophical exploration of happiness and meaning. A fitting course to wrap up the undergraduate experience and think about what comes next.