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Nick Haber

Principal Investigator

Nick is an Assistant Professor at the Stanford Graduate School of Education, and by courtesy, Computer Science. After receiving his PhD in mathematics on Partial Differential Equation theory, he worked as a postdoctoral fellow at Stanford in both the Wall Lab (working chiefly on the Autism Glass Project) and the NeuroAI Lab (on building curiosity within artificial intelligence, as well as cognitive models).


Logan Cross

Postdoctoral Fellow

Logan received a B.S. from University of Southern California (Neuroscience) and a Ph.D. from the California Institute of Technology (Computation and Neural Systems). During his graduate work, he worked with John O’Doherty and studied how the human brain makes decisions in complex domains by using cutting-edge methods in neuroimaging and machine learning. Logan also completed an internship at Google DeepMind, where he build deep reinforcement learning algorithms to solve a challenging a meta-learning benchmark. His research broadly examines how intelligence arises in humans and can be built in machines by combining approaches and insights from a variety of disciplines, including computational neuroscience, deep learning, and developmental psychology.


Isaac Kauvar

Postdoctoral Fellow

Isaac Kauvar received a B.S. (Engineering Physics), M.S. (Electrical Engineering) and PhD (Electrical Engineering) from Stanford. During his graduate work, jointly in the labs of Gordon Wetzstein and Karl Deisseroth, he developed tools for recording cortex-wide neural activity in mice, and he applied these tools to discover how circuits throughout the brain of behaving mammals function as a coupled system—and the consequences that can arise when they decouple. As a postdoc, he is excited to explore the phenomenon of curiosity. 


Xinjie Chen

Research Associate & Lecturer

Dr. Xinjie Chen is a Senior Research Associate and Lecturer at Stanford. Xinjie has been working as a core team member or research lead on several educational research projects at the Stanford Graduate School of Education, including the collaboration with multiple school districts in California. Her research interests are inter-disciplinary and include K-12 teaching and learning, educational psychology, second/foreign language acquisition, bi/multilingualism, and educational technology. Xinjie has published a series of empirical research articles in many prestigious international peer reviewed journals in the fields of education, language and psychology. Also, she has been invited to serve as editorial board member and peer reviewer for multiple top tier journals and conferences (e.g., AERA, AAAL). She is so excited to explore the intersection of education and artificial intelligence and understand the ways in which digital technology improve teaching and learning.


Chris Doyle

Research Scientist

Prior to his work in the lab, Chris was a co-founder of the technology startup Grove, where he designed an automation platform for financial advisors. Before his startup, Chris led an initiative at Malaria No More, sponsored by, to apply a new data source to identify counterfeit antimalarial medication. Chris started his career trading mortgage derivatives at Barclays and building predictive models of asset prices at a hedge fund. Chris received his undergraduate degree in Mathematics from the University of Virginia, where he was an Echols Scholar. In his research, Chris is working to build agents that are intrinsically motivated and can engage in social learning.


Samaher Radwan

Research Coordinator

Sama graduated from Emory University with a B.S. in Neuroscience and Behavioral Biology and a minor in Spanish, with a focus in linguistics. She completed her honors thesis exploring dissociable cognitive and neural systems for recognizing scenes and navigating through them. Using interdisciplinary approaches, she is interested in studying both the development of language and cognitive systems as well as their breakdown in typically developing children and patient populations, in order to create inclusive, accessible diagnostic and pedagogical tools.


Merve Cerit

PhD Student (Graduate School of Education)

After receiving her B.S. degrees in Computer Engineering and Industrial Engineering from Bogazici University, Turkey, Merve worked as a Data & AI Tech Specialist at Microsoft. She worked on several award-winning projects in the Middle East & Africa region and led their AI Female Talent Program. Her passion for learning science led her to pursue her M.A. in Education at Stanford as a Fulbright Scholar. She studied in the Learning, Design, and Technology (LDT) program at the GSE, focusing on social and emotional learning. She is a huge supporter of women in tech,  currently working as a Data Science Lead at an online vocational school for underserved women in Turkey. As a PhD student, she is excited to explore the intersection of affective computing and curiosity within artificial intelligence.


Wanjing Anya Ma

PhD Student (Graduate School of Education)

Anya is a Ph.D. student in the Learning Sciences and Technology Design program at Stanford, co-advised by Jason Yeatman and Nick Haber. She received her B.S. in Computer Science and Teaching Chemistry 7-12 from New York University and M.S.Ed in Learning Sciences and Technologies from the University of Pennsylvania. Anya’s previous research focused on science education and learning analytics under the guidance of Susan Kirch, Camillia Matuk, and Ryan Baker. After teaching middle school chemistry for two years in Brooklyn, NY, she became more interested in developing computational learning tools to support children with special needs. At Stanford, Anya is excited to explore adaptive reading assessments and interventions for children with varied reading abilities.


Frieda Rong

PhD Student (Computer Science)

Frieda Rong studied computer science and mathematics at the University of Waterloo before beginning her PhD in computer science at Stanford in 2020. She previously worked on autonomy at Uber ATG under the guidance of Prof. Raquel Urtasun and then-PhD student Shenlong Wang which culminated in a co-authored CVPR 2021 Best Paper Candidate on scalable photorealistic simulation for autonomous driving. She is interested in machine perception and communication, with an eye towards responsible development.


Fan-Yun Sun

PhD Student (Computer Science)

Fan-yun Sun is a CS PhD student working on generating simulations for training embodied agents. He believes in leveraging (3D) vision/graphics and large language models to improve the sample efficiency and generalization ability for reinforcement learning.For more information, visit his personal website.

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Eric Zelikman

PhD Student (Computer Science)

Eric Zelikman is a CS PhD student fascinated by how (and whether) algorithms learn meaningful representations and reason, with a particular interest in the way that we can apply inspiration from neuroscience and curiosity. Eric particularly cares about problems which require symbolic, compositional, and multi-step representations,  and hopes that machine learning can also teach us about non-machine learning.


Xi Jia Zhou

PhD Student (Graduate School of Education)

Xi Jia is a PhD student studying Developmental Psychology and Learning Sciences and Technology Design in the School of Education. She received her B.S degree in Cognitive Science and Computer Science from Minerva University. Before PhD, she studied social learning and causal learning in Psychology Department at Stanford. Currently, she is curious about curiosity and attached with attachment theory.


Raymond Zhang

Master's Student (Education Data Science)

Raymond Zhang received a Bachelor of Industrial Engineering degree from the University of Minnesota - Twin Cities. Prior to graduate school Raymond worked as an engineer in various functional areas at both 3M and His love for education developed when he ran the Speech and Debate program at a local Minnesota high school after work. Raymond is now pursuing the Master of Education Data Science at the Stanford Graduate School of Education with research interests in how computer vision can be used in educational settings and computational sociology.


Priscilla Zhao

Master's Student (Education Data Science)

Priscilla Zhao received her B.S. degrees in Pure Math and Communication & Media Studies from the University of Michigan, Ann Arbor. As an Education Data Science Master's student, her research mainly focuses on early childhood development, specifically supporting children’s cognitive, social & emotional, and language development using computer vision and NLP techniques.

People: Our Team
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