Shanahan Foundation Fellows

Yiliu Wang

Yiliu Wang joined the Allen Institute in 2023 as a Shanahan Foundation Fellow. At the Allen Institute, she addresses fundamental questions in neuroscience by developing advanced mathematical models that bridge AI and brain research. Her current work focuses on studying neuronal populations as interconnected cell types, investigating their spatial organization, firing patterns, and interactions across different brain states—including loss of consciousness, behavioral tasks, and disease onset.

Yiliu holds a Master’s degree in Mathematics and Statistics from the University of Oxford and a PhD in Statistics from the London School of Economics (LSE), where she was supervised by Prof. Milan Vojnovic. During her doctoral research, she worked on inference, optimization, approximation, and set selection problems in relational learning, a subfield of high-dimensional statistics. She finds neuronal datasets an ideal testing ground for relational learning methods and a source of inspiration for advancing them.

Iris Stone

Iris Stone joined the Allen Institute and the University of Washington as a Shanahan Foundation Fellow in October 2023. She received her Ph.D. in Quantitative and Computational Neuroscience and a graduate certificate in Statistics and Machine Learning from Princeton University. During her graduate studies, she worked with Prof. Jonathan Pillow and Prof. Ilana Witten to develop latent variable models for characterizing the dynamic structure underlying complex behaviors in mice, such as decision-making and exploration. Her past experience also includes an internship at a biotech startup focused on using cerebral organoids to develop treatments for neurological diseases, as well as a B.S. in Physics from George Mason University, where she researched the use of organic and nanomaterials in biomedical applications. Iris is enthusiastic about working on open, team-oriented science and supporting the Allen Institute’s mission of unlocking treatments and cures for human healt

Denis Turcu

Denis Turcu joined the Allen Institute and the University of Washington as a Shanahan Family Foundation Fellow in September 2024. He received his Ph.D. in Neuroscience from Columbia University, working with Prof. Larry Abbott and Prof. Nathaniel Sawtell. His graduate studies focused on computational models of active electro-sensing, a complex foraging behavior of weakly electric fish. He developed and combined a physics model of the behavior, an input-output model of electroreceptors based on local field potential data, and artificial neural network models to investigate how weakly electric fish extract behaviorally relevant information from electrosensory stimuli. He also investigated how decision making could be supported by biologically plausible circuits, such as recurrent and highly sparse networks similar to connectivity in the neocortex, where decision likely take place.

As a Shanahan Fellow, Denis is interested in how external synaptic modulators affect plasticity and what is the role of various neuromodulators in learning. He is also interested in the role of sensory feedback in producing motor activity that is robust to perturbations. Denis is enthusiastic about contributing to the Allen Institute’s mission of open, team science and collaborating with the neuroscience community at the University of Washington, by seeking computational principles that enable neural circuits to function remarkably well.

Maria Tikhanovskaya

Maria Tikhanovskaya joined the Allen Institute and the University of Washington as a Shanahan Fellow in January 2025. Maria earned her Ph.D. in Physics from Harvard University in 2024, where she worked with Professor Subir Sachdev on developing theoretical frameworks to better understand the complex phase diagram of high-temperature superconductors. During her Ph.D., she also worked at Google Research as a student researcher, applying large language models to problems in physics and quantum chemistry. As a Shanahan Fellow, Maria is eager to leverage her expertise in physics and modern AI to contribute to advances in computational neuroscience.

Shuchen Wu

Shuchen Wu joined the Allen Institute and the University of Washington as a Shanahan Foundation Fellow in February 2025. Before the fellowship, Shuchen conducted research at the Explainable Machine Learning Lab at Helmholtz Munich, developing data-driven methods to interpret neural activity in large language models. Prior to that, Shuchen completed a PhD at the Max Planck Institute for Biological Cybernetics, investigating sequence chunking—how primitive sequences form reusable chunks, facilitating the acquisition of complex sequence representations, mentored by Eric Schulz, Peter Dayan, and Felix Wichmann. Shuchen holds a bachelor’s degree from the University of Rochester and a master’s degree in Neural Systems and Computation from the University of Zurich & ETH Zurich.

Tim Kim

Tim Kim joined the Allen Institute and the University of Washington as a Shanahan Foundation Fellow in December 2024. He received his PhD in Neuroscience from Princeton University, where he worked with Carlos Brody and Jonathan Pillow. During his PhD, Tim developed unsupervised methods for discovering interpretable latent dynamics in high-dimensional neural data. Before that, he completed his undergraduate studies at the University of Pennsylvania, where he worked with Joshua Gold. As a Shanahan Fellow, Tim is analyzing new neural population datasets at the Allen Institute, and developing data-driven methods to bridge different levels of description, from individual cell types to interactions between multiple brain regions and behavior.