Hi, I'm Aarav. I'm a computational neuroscience and machine learning researcher interested in how biological neural systems generate intelligent behavior, and how those principles can inform new AI systems.
I'm currently a researcher at Johns Hopkins University in the Dynamical Intelligence Group studying the computational theory of predictive grid cells in the medial entorhinal cortex (MEC), and a computational neuroscientist at Eon Systems PBC working on embodied Drosophila brain models, with our goal being to one day replicate human consciousness. I'm also a Member of Technical Staff at Humanis, where I construct probabilistic world models of complex physical systems using neurosymbolic AI. Previously, I was a summer researcher at Harvard University's Kempner Institute training RNN agents for odor plume tracking, and a student research assistant at the UC Davis Center for Neuroscience.
I'm a junior at Tompkins High School in Katy, Texas, where I'm ranked 5th out of 800+ students with a 4.0 GPA. I'm also a USACO Gold competitor, a Science Olympiad state medalist, and the founder of my school's AI Club and Engineering Club.
Two papers accepted to ICML 2026 workshops — MapShift (RLxF) and Can AI Scientists Discover Neural Mechanisms? (GenBio).
Eon's embodied Drosophila brain emulation — the first multi-behavior embodied brain model — reached 100M+ views across X, LinkedIn, YouTube, and more, and drew a reaction from Elon Musk. Read the story →
I'm passionate about computational neuroscience, connectomics, embodied neural models, and deep reinforcement learning. My work focuses on understanding how biological neural circuits give rise to behavior, using both data-driven simulations and biologically inspired AI agents.
ICML 2026 RLxF Workshop: Reinforcement Learning from World Feedback, 2026 — Accepted workshop paper; 40% acceptance rate
An executable benchmark for controlled post-intervention evaluation of embodied world models. MapShift separates stale map reuse, belief update, and post-change planning across matched environment interventions.
ICML 2026 GenBio Workshop: Generative and Agentic AI for Biology, 2026 — Accepted workshop paper
A pilot benchmark for agentic biological discovery in a digital fly, casting mechanism discovery as a budgeted hypothesis-experiment-update loop with held-out counterfactual predictions.
Research Square Preprint, 2026
A theory of communication-constrained recurrent credit assignment on cortical graphs, deriving graph-spectral lower bounds for low-bandwidth diffuse and cell-type-specific feedback.
NeurIPS AI for Science Workshop, 2025
We use deep RL to train biologically inspired RNN agents to navigate to odor sources, comparing walking and flying modes. Walking agents develop fine-scale orientation strategies and compact neural representations, while flying agents use broad sweeping turns with higher-dimensional dynamics.
Society for Neuroscience (SfN), 2025 — Poster
A connectome-constrained Drosophila brain model that accurately predicts neural activity and controls behavior in a virtual environment. I contributed to the brain embodiment component of the project at Eon Systems, whose embodied fly emulation later reached 100M+ views online.
Off the screen, I play the violin.
A dedicated watcher — Death Note is a forever favorite.
And I make a serious bowl of matcha.
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