As I wrap up two incredible years as a Research Associate at Harvard Business School, I’ve had the chance to reflect on my journey, the knowledge I’ve soaked in, and the relationships I’ve built. Being an RA at HBS was not just about the daily tasks but about immersing myself in an ecosystem of innovation, learning, and constant curiosity. Here are some reflections from my experience.
From the outset, I felt like a sponge every day, absorbing as much as possible from all the opportunities I was surrounded by. I participated in conferences, seminars, and lectures. I registered for courses that I wouldn’t otherwise have had the opportunity to participate in. I formed relationships with faculty across departments, learned from various industry guests, and built friendships with classmates in both the PhD and MBA programs. These aspects of the experience will be memories I cherish forever. Being exposed to the sheer volume of new ideas, theories, and perspectives I was exposed to each day was both exhilarating and challenging. I worked with brilliant colleagues passionate about using research to impact the world. Collaborating with faculty and other thought leaders in business, technology, and data science provided me with a new perspective on how people can mold experiences from nascent technologies.
The Job
My experience may be different from that of other research associates. While I have been fortunate to participate in a handful of projects, a large part of my responsibility at HBS was establishing a strong foothold for the “Leading with Data Science” initiative at HBS. An incredible team of technology and operations management (TOM) faculty and I lead this effort by curating and launching the Data Science for Managers course. Our committed efforts have helped transform HBS’s MBA program to be recognized as a STEM degree. This role pushed me to test my limits in many facets. I took on many responsibilities to help the team present the course to 950 MBA students and showcase to the world how Havard is leading business education.
Beyond the responsibilities of developing course materials (business cases, data analysis exercises, assessments, etc.), I was the keystone supporting communication and commitments between departments across the university. The course we built was like none other in the first-year experience, and to bring a valuable educational experience to our students, many pieces needed to fit together behind the scenes. The new technologies we wanted to introduce to the business program needed contracts with the university, and the application of emerging NLP tools like ChatGPT brought about conversations about AI governance and policy requirements. This was particularly exciting because it allowed me to contribute to the pedagogy strategy while deliberating the institution’s broader technological and ethical framework.
The complexities of this class lie in the multitude of technologies running in the background. Building out courses at HBS required proficiency in various tools and technologies. Over the past two years, I became well-versed in:
- Technical infrastructure tools such as cPanel, Microsoft Azure, AWS Sagemaker, Google Colab, and Mersive Solstice
- Course and content management systems like Dropbox, Google Drive, Canvas, and Panopto
- Statistical and development environments like R Studio, Posit Cloud, Kaggle, and Shiny Apps
As we expanded the course’s technical content, we also ventured into emerging tools showcasing AI’s future, including ChatGPT, MindStudio, Akio, and H2O, among others. Testing these tools and considering their potential classroom applications was inspiring and educational.
AI Research
Parallel to my course development work, I conducted extensive research in AI and machine learning, focusing on their practical applications and ethical considerations. I read over 100 papers and articles covering a broad spectrum of AI/ML topics, from productivity gains enabled by generative AI to fostering trust in ML algorithms.
One of the central themes of my research was generative AI’s impact on productivity and performance. I had the privilege of designing experiments to explore how AI can augment human labor, and the results were fascinating. The data analysis revealed significant insights into the relationships between AI tools and human efficiency, contributing valuable findings to ongoing discussions in the AI research community.
Another area of my research revolved around trust in machine learning systems. Understanding how users perceive and interact with these systems is vital as ML becomes more embedded in decision-making processes. Through experiments and careful data analysis, I explored how transparency and explainability in algorithms affect trust, which has direct implications for how AI is adopted in business settings.
Expanding My Own Learning
Beyond my RA responsibilities, I took full advantage of the academic environment at HBS by enrolling in classes such as Linear Algebra and Empirical Research Methods. These courses allowed me to sharpen my quantitative skills and apply them to my research. For instance, understanding the mathematical foundations of machine learning proved incredibly valuable in both the research I conducted and the tools we employed for data analysis.
My time at HBS has been transformative, both in terms of personal and professional growth. I’ve learned to think critically about AI’s role in the future of business and education, built meaningful relationships, and contributed to cutting-edge research. These two years have solidified my passion for exploring the intersections of technology and business, and I’m excited to see where this journey takes me next.