Shubhankar P. Patankar

Hello! I am a Ph.D. candidate at the intersection of cognitive science and AI with Dani S. Bassett at the University of Pennsylvania, where I also earned a Master’s degree in robotics. Before Penn, I studied mechanical engineering and Russian at the University of California, Davis.

In my research, I am interested in translating insights from human behavior to the design of autonomous learning agents capable of lifelong and open-ended learning. Currently, I am working on generating synthetic training data for offline and offline-to-online robot learning in explicitly modular environments.

Some other projects I’ve worked on in the past include:

Adapting theories of human curiosity as reinforcement learning rewards for agents exploring graph-structured environments [1],
Evaluating how humans exhibit curiosity when exploring knowledge graphs [2], and
Understanding the controllability properties of structural brain networks [3].

More recently, I was a machine learning intern at Tesla, working on time-series and vision problems for the 4680 cell. I’ve also been an intern at General Motors, applying graph neural networks for predictive maintenance and anomaly detection problems in vehicle manufacturing.

Outside of science and engineering, some of my interests include history, playing Age of Empires II, and supporting Liverpool Football Club.

Selected Publications

2024

Mechanical prions: Self-assembling microstructures

Mathieu Ouellet, Dani S. Bassett, Lee C. Bassett, Kieran A. Murphy, and Shubhankar P. Patankar

arXiv, 2024

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Prions are misfolded proteins that transmit their structural arrangement to neighboring proteins. In biological systems, prion dynamics can produce a variety of complex functional outcomes. Yet, an understanding of prionic causes has been hampered by the fact that few computational models exist that allow for experimental design, hypothesis testing, and control. Here, we identify essential prionic properties and present a biologically inspired model of prions using simple mechanical structures capable of undergoing complex conformational change. We demonstrate the utility of our approach by designing a prototypical mechanical prion and validating its properties experimentally. Our work provides a design framework for harnessing and manipulating prionic properties in natural and artificial systems.
Architectural styles of curiosity in global Wikipedia mobile app readership

Dale Zhou, Shubhankar P. Patankar, David M. Lydon-Staley, Perry Zurn, Martin Gerlach, and Danielle S. Bassett

Science Advances, 2024

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Intrinsically motivated information seeking is an expression of curiosity believed to be central to human nature. However, most curiosity research relies on small, Western convenience samples. Here, we analyze a naturalistic population of 482,760 readers using Wikipedia’s mobile app in 14 languages from 50 countries or territories. By measuring the structure of knowledge networks constructed by readers weaving a thread through articles in Wikipedia, we replicate two styles of curiosity previously identified in laboratory studies: the nomadic “busybody” and the targeted “hunter.” Further, we find evidence for another style—the “dancer”—which was previously predicted by a historico-philosophical examination of texts over two millennia and is characterized by creative modes of knowledge production. We identify associations, globally, between the structure of knowledge networks and population-level indicators of spatial navigation, education, mood, well-being, and inequality. These results advance our understanding of Wikipedia’s global readership and demonstrate how cultural and geographical properties of the digital environment relate to different styles of curiosity. Wikipedia readers globally are driven by curiosity to seek information as nomadic busybodies, intent hunters, or leaping dancers.

2023

Intrinsically motivated graph exploration using network theories of human curiosity

Shubhankar P. Patankar, Mathieu Ouellet, Juan Cervino, Alejandro Ribeiro, Kieran A. Murphy, and Danielle Bassett

The Second Learning on Graphs Conference, Nov 2023

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Intrinsically motivated exploration has proven useful for reinforcement learning, even without additional extrinsic rewards. When the environment is naturally represented as a graph, how to guide exploration best remains an open question. In this work, we propose a novel approach for exploring graph-structured data motivated by two theories of human curiosity: the information gap theory and the compression progress theory. The theories view curiosity as an intrinsic motivation to optimize for topological features of subgraphs induced by nodes visited in the environment. We use these proposed features as rewards for graph neural-network-based reinforcement learning. On multiple classes of synthetically generated graphs, we find that trained agents generalize to longer exploratory walks and larger environments than are seen during training. Our method computes more efficiently than the greedy evaluation of the relevant topological properties. The proposed intrinsic motivations bear particular relevance for recommender systems. We demonstrate that next-node recommendations considering curiosity are more predictive of human choices than PageRank centrality in several real-world graph environments.