In May 2025, I had the incredible opportunity to participate in the Summer Institute in Computational Social Science (SICSS) at Carnegie Mellon University. SICSS_CMU 2025 brought together an inspiring group of interdisciplinary researchers from around the world to learn, share, and collaborate on cutting-edge computational methods applied to social science questions.

The program was intense and deeply rewarding. Throughout the institute, we worked hands-on with advanced computational tools and techniques, including text analysis, network analysis, topic modeling, and the development of Shiny Dashboards for data visualization. What made the experience even more valuable was the chance to apply these methods to real-world social science questions in collaborative projects with other participants.

One of the most memorable aspects of SICSS was the focus on ethical and responsible research. We didn’t just learn about technical methods—we also discussed the importance of transparency, accountability, and the responsible use of large-scale data. These conversations challenged me to think more critically about my own research practices and the broader impact of data-driven communication strategies.

Beyond the workshops and lectures, SICSS_CMU was an amazing networking experience. I met scholars and practitioners from diverse disciplines and cultural backgrounds, each bringing unique perspectives to the challenges and opportunities of computational social science. These connections have already led to ongoing discussions about potential collaborations and ideas for future research.

Participating in SICSS_CMU has had a lasting impact on my approach to research. It reinforced my commitment to interdisciplinary collaboration and my belief in using computational methods not just for innovation, but also for social good and ethical inquiry. I’m deeply grateful to the organizers, instructors, and fellow participants for making it such a transformative experience.

If you’re a fellow researcher considering SICSS or interested in computational social science, I highly recommend it. Please feel free to reach out if you’d like to know more about my experience or discuss these topics further!