Introducing Stanford HAI’s New Graduate Student Fellows
What does “human-centered AI” mean? How do different disciplines think about the definition of “human”?
Stanford HAI welcomes 16 graduate students from across Stanford schools to work together to answer these questions as part of the institute’s graduate student fellowship program, now in its second year. Throughout the fellowship, students will share perspectives from their individual disciplines, and learn from HAI faculty. By the end of the program, the students will have worked collaboratively to develop a point of view on how fields such as education, medicine, law, and others approach human-centered design and application of artificial intelligence.
“This is a way for our Stanford students to broaden their understanding of the societal implications of artificial intelligence, and to think about what it means to keep the focus on humans, drawing from a diverse set of expertise and viewpoints” says Hariharan Subramonyam, Stanford HAI Faculty Junior Fellow and one of the program’s faculty directors.
This year’s fellows are:
Surin Ahn (Electrical Engineering): Surin’s research applies the tools and principles of information theory to various statistical inference and machine learning problems.
Martino Banchio (Business): Martino is interested in the interaction of artificial intelligence systems with economics agents, and the design of incentives and economic environments to improve human-AI interactions.
Jonah Cader (Business): Jonah is interested in understanding how to both foster top-notch AI research ecosystems within the academy and how to accelerate the commercialization of their products from lab to market.
Wai Tong Chung (Mechanical Engineering): Wai Tong’s research improves scientific understanding of rocket propulsion and novel energy systems with state-of-the-art machine learning, high-performance computing, and predictive modeling techniques.
Brendan Fereday (Education): Brendan studies how text-based machine learning can further our understanding of how higher order constructs like purpose impact more immediate processes like moral decision making and emotion regulation.
Haiwen Gui (Medicine): Haiwen is interested in developing techniques to further enhance the understanding of explainability studies on multi-data format fusion models.
Saurabh Khanna (Education): Saurabh studies algorithmic fairness and the diversity of information on the internet in an increasingly digitized world.
Radhika Koul (Comparative Literature): Apart from studying the relationship between philosophy and literature in early modern France and England and medieval Kashmir, Radhika is beginning research this year on the impact of visual narratives on the mind, and the role that AI imbricated in social media might play therein.
Yingdan Lu (Communication): Yingdan studies political communication and information manipulation in the digital age. Her current research employs computational methods on massive multimodal datasets to study social media propaganda, misinformation and disinformation, and the presence of digital inequality around the world and on diverse platforms.
Ying Sheng (Computer Science): Ying’s research interests are broad including big data algorithms, property testing and game theory. Ying has focused on Automated Reasoning and Formal Verification and has contributed to the advancement of Satisfiability Modulo Theories (SMT) solvers. Most recently, she has been thinking about the combination of symbolic reasoning and deep learning.
Jacob Silberg (Statistics): Jake's research focuses on two areas: 1) applying computer vision and generative modeling techniques to satellite imagery and considering how counterfactually-generated imagery can be used for causal inference, and 2) improving performance on high-dimensional classification problems with biomedical datasets.
Rayne Sullivan (Law): Rayne’s research focuses on the nexus between climate science, artificial intelligence, and traditional knowledge systems, specifically focusing on empowering frontline communities in developing sustainable climate solutions.
Alberto Tono (Civil and Environmental Engineering): Alberto is studying how humans and artificial intelligence can co-design sustainable buildings within a global ethical framework.
Sang Truong (Computer Science): Sang studies probabilistic machine learning for decision making with structured data.
Betty Xiong (Biomedical Informatics): Betty is interested in applying artificial intelligence to healthcare problems, with a particular interest in biomedical natural language processing.
Xi Jia Zhou (Education): Xi Jia is a doctoral student studying curiosity and attachment in human and artificial agents. She is interested in developing cognitive and developmental theories, building computational models, and creating technological tools to understand curiosity and attachment.
Learn more about Stanford HAI fellowship opportunities.