July 27, 2026
Ahana Biswas, a fourth-year Ph.D. student in information science at the University of Pittsburgh School of Computing and Information (SCI) and researcher in the PICSO Lab, attended the Santa Fe Institute (SFI) Graduate Workshop in Computational Social Science from May 24 through June 5, 2026. A highly selective and prestigious program, the workshop brings together a small cohort of graduate students from leading institutions to collaborate on interdisciplinary research. Selected from a competitive pool of applicants, Biswas was the only University of Pittsburgh student chosen to participate this year.
“I applied because the workshop felt closely aligned with the direction my research is moving in,” said Biswas. Her research focuses on feedback loops, interpreting how platform incentives influence visibility and how trust in AI evolves through repeated interactions among users, systems, and the social environments in which they operate. Most recently, she analyzed TikTok content from the 2024 U.S. presidential election to examine how audience engagement amplifies toxic and partisan political content on short-video platforms.
“These are exactly the kinds of questions that benefit from a complex systems perspective,” Biswas noted.
According to Biswas, SFI has long played a major role in shaping the field of complex systems, and the workshop brings that tradition into conversation with computational social science. The two-week program immersed students in lectures, discussions, modeling exercises, and project development across topics including scaling laws, institutions, diversity and deliberation, networks, adaptive systems, social learning, and AI.
“That breadth is rare,” said Biswas. “Most academic spaces are organized around a specific method or topic, but SFI encourages people to ask what different systems have in common and which mechanisms generate complex social outcomes. It felt like an opportunity not only to develop my own work, but also to represent SCI in a space that is very important for computational social science.”
As AI systems become embedded within today's information ecosystem, Biswas's research on feedback loops has naturally expanded to explore human-AI interaction. While people once relied primarily on social media and traditional news sources for information, many now turn to AI to explain current events, summarize political issues, recommend what to believe, and support decision-making. The workshop further challenged and refined these ideas, exposing Biswas to new perspectives on the evolving relationship between humans and AI.
“The workshop helped me think more critically about these feedback loops,” Biswas stated. “It pushed me to ask not just “Does this AI signal increase trust?” but “What kind of trust does it produce over time, under what conditions, and with what system-level consequences?” That shift is important for my dissertation work.”
Biswas credits SCI's interdisciplinary environment with shaping her development as a researcher. Surrounded by peers studying networks, AI, human-centered computing, information behavior, and social systems, she learned to recognize connections across disciplines and approach research from multiple perspectives.
“That was incredibly helpful at SFI,” said Biswas. “The expectation is that students can move between disciplines, explain their work to people with different backgrounds, and think about the broader mechanisms behind social phenomena.”
Biswas encourages students to apply to competitive opportunities even when they feel like a reach. “Workshops and conferences like SFI are not only for people who already have fully finished projects. They are places where you can refine ideas, get critical feedback, and learn how other people think about similar problems. Even preparing an application can help you clarify what your work is really about.”
Learn more about Ahana’s research.