Sanjana Gautam

About

As a Senior Applied Scientist at Microsoft, I work at the intersection of AI, enterprise systems, and human-centered evaluation, helping shape the next generation of intelligent agents for finance and ERP workflows. My work focuses on designing and evaluating AI systems that operate in complex business environments, with particular emphasis on agentic workflows, human-AI collaboration, grounded reasoning, trustworthiness, and workflow-level evaluation for enterprise finance scenarios.

Previously as a Bullard Research Fellow at the University of Texas at Austin’s iSchool (opens in new tab), I explore the ethical and human-centered design of AI systems, with a particular focus on explainability, trust, and human-AI collaboration. My work integrates human-computer interaction (HCI), AI ethics, and behavioral science to advance AI systems that not only enhance human capabilities but also promote responsible and community-driven innovation.

My research aligns closely with the mission of Good Systems (opens in new tab), where I examine the unintended consequences of AI deployment, ensuring that AI systems are designed for societal benefit while mitigating potential risks. In addition, I am also engaged with the Cosmic AI Institute, where I explore how trustworthy, interpretable AI methods can support scientific discovery in astronomy. I am particularly interested in AI’s role in broadening access to scientific knowledge through open-source tools, data sharing, and educational initiatives.

I bring a strong interdisciplinary background that blends machine learning, human-computer interaction, and human behavioural dynamics. I completed my PhD at Penn State’s College of Information Sciences and Technology (opens in new tab) under the supervision of Dr. Mary Beth Rosson (opens in new tab). I was also an active member of the Innovation and Collaboration Lab(https://cil.ist.psu.edu/) led by Dr. Jack M. Carroll (opens in new tab) and Dr. Rosson. I have  collaborated with Dr. Matthew Lease (opens in new tab) in my fellowship work. Today, I continue to pursue these themes through the lens of enterprise AI, focusing on how intelligent systems can responsibly augment human work at scale.