How I Braved my CMU Graduate Life as a Human


Through this blog, I want to highlight my journey at my dream college — Carnegie Mellon University. This is an account of my academic voyage, experiments conducted and the results generated while maneuvering through project deadlines at CMU.

Preface 🖼️

In the last blog, I highlighted my journey into my dream college — Carnegie Mellon University. I talked about my motivation for getting into a college like CMU. Further, I shed some light on my profile, preparations, and selection process. If you want to know more about this process, you can read the previous blog post.



In order to build a cohesive flow, I will break down the blog into the four semesters I studied at CMU, and I shall leave out some trivial details to maintain the length of the blog post. Furthermore, I will cover some extracurricular activities, hackathons, and teaching assistantship experiences through the semesters. These four semesters being — Summer 2021, Fall 2021, Spring 2022, and Summer 2022. Also, I had to take 56 units (~5 courses) per semester according to my degree requirement.

Desk setup

Summer 2021🌻

During the summer of ’21, the world still suffered from the COVID-19 pandemic. Owing to this, none of the incoming grad students could travel to Pittsburgh to embark the course in person. Although disheartened, I decided to do a mini-semester from India (only 24 units or two credits). CMU also has a policy of skipping core courses to advanced level ones after proving a certain level of prowess in that subject. Since I exempted a few courses, I took Database Management Systems(95–703), Agile Methods (95–874), and Economic Analysis(95–710) for the summer semester.

  • Database Management Systems(95–703): My journey commenced with a deep dive into databases and their management. Although this course was conducted via Zoom, Prof. Raja Sooriamurthi enabled us to make the most out of the course. This course gave me the knowledge and skills necessary for efficient data storage and management. Understanding how to organize, retrieve, and manipulate data laid the groundwork for my future data science and analytics endeavors.
Dean’s List for 2022

Overall, the mini-semester was challenging but manageable. The most difficult thing to handle was the uncertainty due to the pandemic.

Some pointers that helped me through Summer 2021:

  • Attending classes through web conferencing was a new and different experience for me. I had to put in extra effort to concentrate through the entirety of the lecture.
  • I loved spending time with my family, knowing I had to move to Pittsburgh for the next semester.

Randy Pausch: The Last Lecture 🌟

In this blog, I would like to talk about two professors from Carnegie Mellon University — Randy Pausch and Tom Mitchell(covered later in the blog) whose work and methodology have greatly impacted me.

Randy Pausch was a remarkable computer science professor at Carnegie Mellon University whose life and work left an indelible mark on the world. However, it was not just his accomplishments as a researcher and educator that made him extraordinary, but the profound and enduring impact he had through his “Last Lecture.”

Randy PauschL: The Last Lecture

In 2007, Randy Pausch was diagnosed with pancreatic cancer, a disease with a grim prognosis. Facing a terminal diagnosis, he decided to deliver a lecture to his students and colleagues at Carnegie Mellon, sharing his wisdom, life lessons, and dreams. This lecture, titled “Really Achieving Your Childhood Dreams,” became known as “The Last Lecture.”

Randy Pausch’s Last Lecture encapsulates the spirit of resilience, optimism, and the pursuit of dreams that is often cultivated in an institution like CMU. His message deepened my appreciation for the university’s role in fostering personal and intellectual growth, instilling a sense of purpose, and nurturing a supportive community that values the pursuit of knowledge and the realization of one’s aspirations.

“You Just Have To Decide” — Randy Pausch

Fall 2021 🍂

I arrived in Pittsburgh on the 1st of August 2021, and the college was supposed to start on the 31st of August. I had a good 30 days to set up my apartment, routine, and prep for the classes. After four years, this would be my first time in the classroom; hence, I was justifiably nervous. Furthermore, I took some cumbersome courses to fulfill my core-course requirements and my specialization in Machine Learning and Information Retrieval. For the fall semester of 2021, I took Introduction to Machine Learning(10–601), Search Engines (11–472), Distributed Systems(95–702), Statistics(95–796), and Data Focussed Python(95–888).

  • Introduction to Machine Learning(10–601): As an undergrad student in India around 2017, I stumbled across some lecture videos on Machine Learning by Tom Mitchell. Four years later, as fate would have it, my introductory course on Machine Learning was taught by none other than Tom Mitchell. The first few days in the lecture hall were overwhelming for me. The professors I had only seen through my 15'’ laptop screen were now less than 10 feet away from me. The course design is great for both beginners and experts in the field. It covers the history of machine learning, dating back to Perceptron algorithms of 1943 to covering state-of-the-art models using Reinforcement Learning.
Intro to Machine Learning (10–601)
  • Search Engines (11–472): This course was a hands-on exploration of building search systems that seamlessly integrated machine learning with search engine theory, design, and implementation. We began by studying the statistical characteristics of text and how information needs and documents are represented, laying the groundwork for effective search engine design. We delved into various retrieval models, gaining insights into their strengths and weaknesses, pivotal to search engine functionality. The course also explored practical elements in commercial search engines, such as ranking, recommendation, and personalizing search results using ML.
  • Statistics(95–796): This course covered descriptive statistics, hypothesis testing, and regression analysis, providing a strong foundation. Hands-on practice with Minitab enhanced my ability to analyze real-world world data, setting the stage for more advanced analytical courses. It provided the statistical foundation upon which I could build my academic journey.
Project Demonstrations @ CMU

The fall 2021 semester was cumbersome. I spent the first few weeks getting back into a student's mindset. My situation was further aggravated by the two advanced courses (Introduction to Machine Learning and Search Engines) that I took this semester. I had to monotonically increase my effort as the assignment’s complexity increased each week.

Some pointers that helped me through Fall 2021:

  • Stable friendships and relationships helped me gain the emotional and mental support required.
  • Started following a schedule to increase my productivity.
  • Asked for help whenever I felt vulnerable.
CMU Strangers Project

Spring 2022 ❄️

Spring semester starts in the month of January. By that time, I got into the academic rhythm and felt confident and comfortable with the complexity of the subject. However, the major issue was never the complexity but the barrage of deliverables at the end of each week. Since Spring 2022 was my last major semester, I decided to take more advanced courses to further my specialization and make the most of my time at CMU: Machine Learning with Large Datasets(10–605), Machine Learning in Production(17–645/11–695), Consumer Behavior(45-836), Data Science and Big Data(95–885), and Machine Learning in Practice(17–691).

  • Machine Learning with Large Datasets(10–605): Scaling up machine learning models to handle big data was a thrilling challenge. It provided insights into distributed computing, parallel processing, and efficient algorithms — an essential skill set in the age of data abundance. We tackled issues from data cleaning to scalable deep learning, exploring parallel and distributed computing for efficient model training. Through mathematical and practical insights, I gained a deep understanding of handling big data, optimizing computations, and achieving low-latency inference. Furthermore, model training competitions were organized for students to build the most efficient and lightweight model for the defined task.
Review Sessions @ CMU
  • Machine Learning in Production(17–645/11–695): Transitioning from theory to practical deployment was a significant milestone. This course immersed me in the complexities of integrating machine learning models into real-world applications, ensuring their scalability, robustness, and reliability. We had to develop a highly scalable, fault-tolerant ML recommendation pipeline that accounts for data quality and model drifts.
DSA Teaching Assistant
  • Consumer Behavior(45–836): Understanding the psychology behind consumer choices was fascinating and immensely valuable. It equipped me with the knowledge needed to design data-driven marketing strategies that resonate with consumer preferences.
  • Data Science and Big Data(95–885): This course was a comprehensive exploration of techniques and technologies for extracting actionable insights from vast datasets. We explored key themes, from exploratory data analysis to machine learning and big data technologies. Through hands-on experiences with tools like Jupyter Notebooks, Pandas, scikit-learn, and Spark, I gained practical skills in data science. This course provided a structured approach to understanding and harnessing the power of data for real-world applications. Once again, I had the pleasure of being taught by Prof. Raja Sooriamurthi, who made me fall in love with data science all over again.
Industry Project with cPacket

Some pointers that helped me through Spring 2022:

  • Being consistent helped me complete my assignments, independent projects, and perform well in the job interviews.
  • It is okay to have imposter syndrome while pursuing a master's degree since you are competing with the best minds in the world. However, one should not forget that it is also a medium to share ideas and foster innovations with the same minds.

Teaching Assistantship👨‍🔬

Data Structures and Algorithms (Spring 2021)

I had the honor of being a Teaching Assistant for Data Structures and Algorithms (95–771) for the Spring semester of 2021.

  1. Mentorship: Guiding students through complex data structures and algorithms, offering assistance through regular office hours and problem-solving sessions.
  2. Grading and Feedback: Responsible for evaluating assignments and exams, providing constructive feedback to help students improve their understanding and performance.
  3. Autograder: We created an autograder (Project Aurelia) to automate the assignment grading process for the students.
Project Aurelia: An auto-grading suite for Data Structures and Algorithms (95-771)

Data Management and Security (Summer 2022)

For Summer 2022, I was selected as a TA for Data Management and Security(95–568) under Prof. Raja Sooriamurthi.

  1. Assessment and Feedback: Evaluated assignments and projects, offering feedback to help students refine their data management and security skills.
  2. Review classes: Conducted review lectures for the classroom to help students evaluate the learnings from examinations.
TA for Data Management and Security (95–568)

Competitions and Extracurricular 🖥️

Pittsburgh Innovation Case Competition, 2021

Keith Block Competition, 2021

  • Finalists at Keith Block Competition, 2021(with Abhinaav Singh, Chirag Huria and Naman Arora)
  • Idea: Peeko (a platform for millennials to share their interests and build conversations around it)
Scourse: A smart course recommender for CMU students

Conclusion 🚀

My time at Carnegie Mellon University has been an incredible journey, filled with growth, challenges, and meaningful experiences.

From maneuvering through project deadlines to diving deep into diverse courses, each semester was a unique adventure. CMU’s faculty and community have been a wellspring of inspiration and support. Randy Pausch’s “Last Lecture” reminded me of the power of resilience and chasing dreams.


As a Teaching Assistant, I found fulfillment in mentoring fellow students and learning from their unique perspectives. Competitions like the Pittsburgh Innovation Case Competition and the Keith Block Competition expanded my horizons and honed my problem-solving skills.

Grateful for the opportunities, support, and friendships, I now embark on the next phase of life, carrying with me the knowledge, skills, and cherished memories from CMU. This journey has prepared me to face new challenges with determination and make a meaningful impact in the world.

Grad walk

Made with ❤️ and 🦙 by Akshay